2025-04-16 08:00:01
LMQL (arxiv) is a tool to steer language model generation. The language interface nicely stitches together a set of features such as:
Here’s how it works:
1. String manipulation: [WORDS] denotes a hole that needs language model to fill; {WORDS} denotes a variable that exists in the scope context. For instance:
Write a summary of {name}, the singer:
{{
"name": "[STRING_VALUE]",
"age": [INT_VALUE],
"top_songs": [[
"[STRING_VALUE]",
"[STRING_VALUE]"
]]
}}
Given the context {'name': 'Bruno Mars'}, the variable name is replaced with Bruno Mars to get the initial prompt Write a summary of {name}, the singer: \{\{ "name": ", the generation spits out a few tokens, followed by a quote ", LMQL detects the quote and stops the generation, appends ", "age": , and continues.
2. String constraints using masking: constraints, options all use the simple idea that we could limit the available tokens models can choose from. In openai API, this is done by setting the logit_bias parameter, with the format of {"50256": -100, ...}.
3. Tool use: This is similiar to variable substitution, with the ability to call a function to get the variable’s value. However it is different from the commonly used ‘tool use’ where models can choose among a set of tools. Although in theory, there’s nothing that stops LMQL to switch to structured output to choose a tool and run it, and switch back to the previous generation context.
I suspect there are two reasons: The real world use cases are largely satisfied by structured/json output; instruction following has improved a lot. Combining these two gives us a nice alternative to LMQL using simply string interpolation using context from json output.
2025-04-16 08:00:01
Cancer Detection (The Derby Mill Series ep 09)
I mean, people really care. I think people care about their skin a lot. It’s not a coincidence that dermatology shows up in an episode of Seinfeld. I don’t know if you guys remember this episode of Seinfeld where George is trying to show this guy the mole on his back at a party. I think it is, not sure where it is. That’s amazing. Dermatology is the one place in medicine where there’s enough demand on the consumer side where I think people that and anything involving kids will be very happy to pay.
Welcome to the Derby Mill series, intrepid pioneers of the next economy, featuring discussions with entrepreneurs at the forefront of deploying machine intelligence and brainstorming sessions about where the technology may go at the limit. I’m Jay Agrawal, co-founder of Intrepid Growth Partners, and I’m here with my three collaborators, all of whom are senior advisors at Intrepid: Neve, Gavin, Rich Sutton, and Sendhil Mullinathan. This episode, we explore cancer detection. We’re here with the leadership team of Skin Analytics, a UK-based firm using AI to automate the diagnosis of serious skin conditions, starting with skin cancer. Neil is the founder and CEO, and Jack is the AI director. All right, let’s start the show.
Neil, we’ll begin with you just giving a quick synopsis. Everyone’s read the summary, but if you can just describe also for the listeners what is the essence of the business and, in particular, the key prediction that your system makes. Thank you very much. So Skin Analytics has built a series of artificial intelligence algorithms that we regulate as medical devices that are able to make autonomous decisions about potential skin cancers. We can take an image of a skin lesion and then we can predict whether or not that patient has skin cancer, a pre-malignant type of lesion that we could treat, or a benign one that doesn’t need to be taking healthcare resources to resolve.
The idea behind the business is that we can use this artificial intelligence to effectively replicate many of the decisions that a dermatologist would, which is aiming to address the significant shortfall of specialist dermatologists that we have around the world for patients with potential skin cancers. Thanks, Neil. Can you describe? Yours is the first product of its type to make it through the regulatory process in the UK with the NHS, the National Health Service. Can you describe what’s involved in getting through the regulatory process? In other words, is it all about prediction accuracy, or what are the key elements of that process?
Yeah, we’re very proud of being able to be the first company to launch an autonomous skin cancer pathway anywhere on the planet, and we did that first in the UK. The journey to get out to the point where the device could be used autonomously started first with a version of the algorithm that was supervised by clinicians that had appropriate safety nets and eventually second reads to evaluate that the technology was as good as we thought it was in making those decisions and working in the messy reality of medicine, where there’s a significant amount of gray. So that took us a number of years from launching commercially to be able to get to the point where the system was proven enough to work autonomously.
To get through the regulatory process, there are I guess three foundational elements that we see. Firstly, the technology has to be good. You have to figure out how to make the thing work. I think you guys have spent a lot of time in your careers and in this podcast series talking about how to translate the potential that the mass has brought us into something that can actually work for patients and be reliable enough to work for patients. That’s a non-trivial part of our business that we spend a lot of time on, and Jack can talk more about that.
The second foundational element is really bringing the clinical evidence to support that the technology can do what you say it can do. That starts out with observational clinical studies and then moves into prospective clinical studies, and increasingly you raise the bar in terms of what you’re asking the AI to do and how you evaluate whether it can do that.
The final piece of the puzzle is making sure that you have the appropriate systems in place to run a company that is making decisions that affect patients’ lives. From our point of view, we’re making decisions around cancer, and delayed diagnosis can lead to death, so there’s a very high risk of our product not working in the right way. This means that as we build out our product, we have to make sure that we have appropriate quality management systems for every possible aspect of our business, from building the technology to how we do post-market surveillance to how we interact with patients and capture any issues that need to be resolved.
We’ve had to build these systems into the very fabric of our business, and then we’ve had to be audited by about three separate bodies, probably a sum total of about 15 times now. We’ve had people come in and audit that we meet the standards and continue to do that ongoing. So taken together, the technical, the clinical evidence, and then the quality assurance part of our business are what allowed us to get through that regulatory hurdle and get the product into the market.
Okay, thanks for that. Neil, I’m going to ask one more question, and then I will circle through Neve, Sendhil, and Rich if they have any clarifying questions. Then we will shift gears and talk about where this might go at the limit. But my question for you is, I think you started this project a decade ago or maybe even longer. I remember we had a conference that Rich was at in 2015. At that time, when people were trying to describe what would be a valuable, useful application of image recognition, one of the first things that people talked about was reading medical images. So my question is, what took so long?
What took so long? That, you know, even as of today, you’re the only one that’s made it through this regulatory process. So why is it so hard? In other words, were there elements to training the model to get past the inaccuracy threshold? What was it that’s so hard? Yes, I started the business in 2012. So we’ve been at it for a number of years now. To answer the question, what’s so hard from a technology point of view, when we started the business, we were using classical machine learning approaches. We were trying to meet dermatologists and clinicians, asking them to explain to us what it was that identified something as skin cancer to them. It was frustrating to me as someone with a maths background that the answer was, you know, it just looks wrong. Something looks funny. It’s like, I can’t codify that. How do I measure it?
We spent a lot of time, and there were some metrics. People gave us the number of colors, the symmetry of the shape. We basically codified about a hundred different things that we were measuring, and then we were trying to optimize the weightings for those. It just didn’t work. The technology wasn’t sufficient to be able to make the decisions. Then we moved into the world of using deep learning, and that very quickly changed the capability of the systems.
But we had so much to learn about it that we didn’t know. Back then, I think the idea was you just needed a ridiculous amount of data. You put it in, and everything was great at the end. We saw that wasn’t true. We thought we did a great job; we had an algorithm that worked, and then we did a test and we told everyone in the test that had cancer, and that just clearly wasn’t true. We realized that we had made a massive mistake here in terms of overfitting, and all the sorts of things that are well known now that we were on the forefront of, falling into the traps for.
It took us a while to understand how to design a system to operate around those problems. As a business, we have been very slow and methodical about making sure we do that in a safety-critical system where failure means an impact on patients’ life. We wanted to be very sure that we knew how to do that. I think the other challenge is that while you are a hundred percent right in my opinion that healthcare is one of the best places to start applying this technology, not a lot of healthcare is digitized.
It’s true that if you go into a hospital and you’re taking a radiology image, that’s digitized. In dermatology, there was not a lot that was digitized. It meant that the data that had been collected was in no way representative of what the patient population actually looked like. For example, some of the earliest datasets we got were from dermatologists who just collected it out of interest. The way they collected it was they would say, “Oh, that’s weird. I want to get a record of that patient. Do you mind if I keep this and take an image?” As you can imagine, if that’s what you’re training a system with, it’s not going to work very well.
It took us a while. We had to go out and start digitizing a lot of the data ourselves. We obviously weren’t the only ones, but we had to get a hold of enough of this data that is indicative or more illustrative of the patient population. That took some time to do. On top of that, you have to generate the clinical evidence. Then from a company our size, you had to figure out how to operate with 200 standard operating procedures that govern how you do everything to make sure that when you build something, it works for patients. This just takes time.
I think we’ve done it pretty quickly in the grand scheme of things, even though it might feel like it’s taken a really long time for that dream in 2015 to become a reality in 2025. Excellent, Neil. Thank you. Niamh, any clarifying questions?
Yeah. Thanks, AJ. Kudos to the team. I think I have a quick one about where the patients drop off after your referral. Let me prefix that a little bit around or broaden the question to the importance of the end-to-end platform versus just the image. As I think you alluded to at the start, where you’ve been very clever is around creating a digital pathway for the entry point of the patient, enabling them to opt in and say you can be seen quicker via AI or wait and see a doctor.
Then they upload the patient history. But thereafter, when you’ve kind of done your binary decision of yes or no, we think this is benign or malignant, what you’d really want to do is follow through that end-to-end. You know, start reviewing the treatment that’s being prescribed, how effective that is, and loop it back in. Ideally, go with a patient on the journey in cases, any future discovery and capture that re-entry back in so you can course-correct.
How do you currently get that longitudinal data, and where does it drop off? Is it just that passing on to the dermatologist, or how far do you follow through? I think you’ve hit on one of the key aspects of us as a business. One of the very early decisions that we made that in retrospect I think has proven to be the right decision, but at the time was considered to be a bit of a mistake, quite frankly, was that when we first started doing this, everyone was talking about appifying the world.
You know, everything was going to be an app. Everything was going to sit on your iPhone. You didn’t need anything; it was all going to be your phone doing everything. We refused to deploy our technology in a way that wasn’t embedded into a healthcare system. The reason being was exactly what you just outlined: telling a patient they have something is the end of our role in the pathway, but it’s not the end of the patient’s role. From a patient perspective, the utility of that decision is exceptionally low.
The utility for them comes after they get the treatment. While we don’t want to be a treatment provider, we do want to sit within a system that solves that problem and really unlocks the value of being able to do that at scale. For us, that meant deploying within the UK’s National Health Service or with partners that were set up to take the output of the artificial intelligence and then do something with it that actually meant something to the patient, which is, you know, biopsy, treatment, or discharge.
That was a really critical element of our business. Just one other point: you’re right to say that right now the vast majority of the use of our system is sort of a referral, non-referral, or a red light-green light. That’s not how the algorithm necessarily works. It’s how people are willing to use it in the short term as we build up more and more confidence.
There is a lot more power in the algorithm to identify pre-malignant lesions that can be treated in different ways, things that could be followed up over time, and then things that could be directly listed for biopsy, which we’re increasingly seeing an appetite to do. This allows you to really optimize your use of healthcare resources from the moment the AI has made a decision. So we are definitely pushing very, very hard into the space where we’ve built up enough trust in the AI that we can start to really radically optimize the existing healthcare system.
That’s not where we’re trying to get to, and we’re going to talk more about that later. But right now, our focus has been on optimizing the healthcare system and pathways that exist, making sure that patients get that treatment faster. Excellent. I confess it was somewhat a leading question in terms of wanting you to capture that follow-up. So that’s terrific. Thank you. I guess your company is all about getting the classification right. I mean, there are all these other steps, but it sounds like the major use now is you’re saying yes, no, referring or not referring.
So the question is, how much of the company is about getting the classification right? Our underlying AI models are trained on lesion classification. We have our data that comes through our pathways, and we have a very high accuracy on the labels we have for our data. All the malignant lesions are labeled by pathology. I think that’s a really interesting thing in itself because we train the input to our models on a dermoscopic image, just an image of the skin before a biopsy has been done or anything like this.
We then try to map that onto a lesion classification, which is confirmed using histology. That’s information that’s very difficult to know with a high degree of accuracy at the time the image is captured and can only be found later after a biopsy is performed. I think I wasn’t clear. I was just, you know, there are many aspects which you’re doing the interaction with the patients, there’s the data gathering, and you make a classification.
I want to know how important to your business is getting that classification right. Could be that it’s relatively straightforward, and that’s kind of done, and you’re dealing with all the other things, or it could be that getting the classification accuracy, increasing it by a percentage or two is really critical to your business. Yes, so the classification is critical to our business, and we spend a lot of time fighting to get even small incremental improvements that we can either assign to improve diagnostic accuracy or sensitivity or to the discharge rate of specificity or the ability to route patients to the appropriate care pathway.
But we fight for that tooth and nail because each little incremental improvement unlocks a lot of downstream utility for the healthcare system. Because while we don’t do the downstream treatment, if I can tell you that a patient that’s referred in the UK on an urgent skin cancer pathway can instead be sent to a treatment option where they could have a topical cream applied, that doesn’t eat up a dermatologist’s time. That is a significant cost and utility benefit for the UK’s healthcare system, and that will be true in every healthcare system.
Just to build on that very slightly, while we talk about the classification having low utility to the patient, the utility for the patient comes on the treatment option. Within the healthcare system, the blockage is not on the treatment. That can be done by so many different people that you can train up. We don’t have a problem there. We have a problem at the classification step where you need specialist clinicians, where you train for 15 years and have to pay them relatively large salaries in the global scheme of things to make those decisions. That constraint is how we’ve designed our entire healthcare system. Primary and secondary care exists entirely to deal with that scarcity problem that we’re able to address by being good at the classification.
Do you have to come into a clinic to gather the data, or can a person do it in their home? We can do it in many different settings. So we do some of our pathways where patients collect the data at home, and in some of our pathways, they come into a clinic. There is a lot of flexibility in this data acquisition. Right now, we use, as Jack mentioned, a dermoscopic image, which is a lens that is relatively simple. It basically has cross-polarized light and a couple of filters in it and magnifies the image a little bit, so it’s a very simple bit of additional technology that we use on that can attach to a smartphone. There are a variety of different ways you can solve that problem.
That’s what we do right now in our product roadmap. We’re getting some really great results on the research side of things at the moment. We’re removing that dermoscopic attachment as a requirement, so we’re able to maintain the diagnostic accuracy without the dermoscopic attachment. What we lose is some of the ability to discharge patients, so you’ll have an over-referral rate that is higher, but you can pick that up later with a dermoscopic image at a location or however you wanted to deal with it. The direction of travel is to eventually use the sensors that are available or in the hands of everyone at home and be able to really streamline the way we do this.
Sendhil, any other clarification questions on your side? Yes, I think I want to pick up on something you said, Neil, about integrating into the health system rather than going direct to consumer. What were some of the unexpected things, positive or negative, that you encountered in the process of trying to work yourself into an existing decision system?
One of the things that surprised me when we made that decision is that when I started out, there were many reasons why it was the right decision. But my very first hypothesis was that to get something that was trusted by patients, we first needed to be trusted by doctors. Certainly, you’ve seen this change over the last 10 years, but in the beginning when I started the business, doctors were still held up as the place that you got any answer to do with your health. I thought that if we didn’t have clinicians believing that the technology worked, we’d never get patients to believe the technology worked.
I actually don’t think that’s true. I think patients are a lot more open to believing the technology works than clinicians are. If that had been the primary driver for going into the healthcare system as the reason that made the most sense, then we would have got that wrong and made the wrong decision. In actual fact, as I explained it to you, Niamh, I personally and everyone in the company feels very strongly that our job is not to make a classification decision. It is to get the patient the outcome they need.
The classification decision is really a critical step in that from our point of view. From the patient’s point of view, what matters is getting that diagnostic decision or that outcome that they need, and that is the real driver that decision has led to us being able to fulfill. But that realization came later; it was not something that I started the company with. That was what came out of talking within the healthcare system and talking to patients early on, within the first couple of years, but it wasn’t the first proposition or reason. All our listeners will be able to imagine what you’ve described, Neil, in terms of a patient either at home or coming into the hospital or clinic and getting a picture taken and having an AI classify a skin lesion or a mole as malignant or benign or pre-malignant.
That classification then leading to a set of actions inside the healthcare system. So that’s what it looks like today. In a previous episode of this podcast, we talked about it being in the geological exploration industry and their version of a biopsy is to drill a hole into the ground to take out a core sample. When we started talking about what this industry looks like at the limit, I know both Niamh and Sendhil have thought a lot about the healthcare industry at the limit. You know, in that, in the mining industry or geological exploration, they made reference to a lot of other data that could be collected at much cheaper.
Like the dust or the chips off the drill, and satellite imagery and other things, even before they make the expensive drilling decision. If I think of the drilling decision as analogous to the biopsy, maybe I’ll start with just our group and then Neil come to you.
Niamh, any comments on where you think this goes at the limit with this type of high-fidelity classification capability? Yeah, it’s a really interesting question, and it almost questions the gold standard itself in that if I understand correctly, skin analytics now outperforms dermatologists.
In the same way as Rich said on the mining one, the algo will see thousands or hundreds of thousands more data than an individual expert will in a lifetime. And so that almost kind of questions by what bar are we assessing it against in terms of other data.
This is something again around alluding back to the question I asked at the start of how far and how much patient history they capture in their pathway, and how much they get to see after the fact. I always wonder if it can kind of flip your question, AJ, whether skin analytics can almost be a biomarker or a predictor for other broader issues.
So, for example, on the way in, they see that this patient smoked all their life. They have a higher number of moles on their skin, the normal, albeit the majority of them are benign. However, five years after they came to us, they were diagnosed with stage three lung cancer.
I wonder if there’s looking at the patient in the round, whether there are patterns that can always broaden the utility and additional benefit to the patient of skin analytics. Other data, I think, I’m not sure anything springs to mind on the demo bit, just because it’s so specific to the photo.
So, I’m going to turn to my wiser colleagues to see if they have better ideas than I do on this. But, before Neil responds, let’s go around.
Sendhil, you thought a lot about what this, like what healthcare looks like at the limit. What’s your starting perspective from dermatology, and where do you think this all goes?
Even just sitting at dermatology, I guess I have two thoughts that I might kind of go back to. The first thought is I totally understand why you deployed in the health system, and that makes complete sense.
But I think there’s one advantage to deploying in an app, which is that you will get many more people taking photos, possibly much earlier; people who might never choose to come in. In some sense, there’s a huge selection bias in the dataset of who shows up.
It’s not a problem for what you’re doing now, definitely not a problem. But it does seem like an opportunity to expand the scale and scope of people taking these photos. As an example, you can imagine that you catch some things much, much earlier.
Now, I know you guys are sensitive to the overuse fear, and that’s very real. The predictors trained on the sample that has chosen to come in might lead to a lot of false positives. So there are issues to be dealt with, but those strike me as statistical issues that can be worked on over time.
When you look at the scale of the opportunity, I think the scale of the opportunity is for the entire population and the ability to catch early. That’s one thought.
The other thought that strikes me is that by virtue of building a device, if I understand how Derm operates, it’s basically a device. Just to verify my understanding, it’s a camera that captures images that are run through a deep learning algorithm of some kind.
But if that’s the case, the nice thing is you have a form factor of an object that is now in the system. For you to add additional sensors that are not optical sensors, or at least they may be optical, but they need not be just a traditional camera, will allow you to start collecting a wide variety of data quite easily.
It’s worth noting that so far, everything we’ve done in dermatology, not everything, but the majority has been rate-limited by the human eye. There’s no reason that has to be the sensor we use.
There’s a ton of sensors that we can imagine using that have nothing to do with what the human eye could possibly interpret. I think one thing I like about what we’ve done is by now having it as a device, you can easily upgrade that device to include new sensors.
Putting aside the approvals, you have the easiest thing solved. It’s not a new device you need people to start using; they’re just getting version two of a device they’re already using. So that strikes me as two pathways to kind of think about: one is changing the scope to which you’re applying, and the other is changing the input modalities that you’re taking.
It seems like you’ve set yourself up to do both of those pretty well. Sendhil, two comments. First of all, just as you say, the device is just the device that people carry around already, which is their iPhone or whatever, and the lens is an add-on.
They connect this special lens, and as Neil described on their product roadmap, the next version won’t require the lens. So then it’ll literally just be your iPhone, but there might be good reasons to have that add-on device for the reasons you say.
I know, Sendhil, that you have been thinking hard about transforming the whole healthcare system to be focused much more on preventative. When you describe the two pathways, the do-it-yourself at home pathway is motivated by your overall view of the healthcare system being much more heavily weighted towards preventative.
Is there anything beyond what you’ve described of how you think taking this technology into the home changes the flow? If you look at what’s happening with prenatal stuff and you look at ultrasounds, the rate-limiting step is how rarely an expecting mother comes in to have an ultrasound.
If you can have ultrasounds taken at home, which now we can, you can have ultrasounds literally taken every day. There’s no rate-limiting step at that point. Now with dermatology, I would have thought that some of the expansion of a lot of this stuff does, well, I don’t know.
Now this is me; I don’t know enough about the skin cancer end of things. But let me point out that there are a bunch of other dermatological conditions that are much more prevalent, like psoriasis, for example—extremely prevalent and we don’t really understand much about it.
It’s not life-threatening, but it is very painful for people who go through bouts of psoriasis. One of the problems with things like psoriasis is measurement; you’re not going to go to the dermatologist every day or every week.
So, I think there’s probably a lot of skin conditions where the ability to measure at very high frequency and low cost, which people who have these conditions will be very happy to do, is going to radically open up whole new avenues of what we can understand and treat better—to really get a sense of what all is happening.
Psoriasis is a good example. If you want to go to things that we view as maybe cosmetic, I don’t know, but many people are very concerned about things like wrinkles and a bunch of other issues, but we just don’t have data on how wrinkles form.
We don’t know any of that stuff. I think there’s going to be this opening up in dermatology that seems ideal for these kinds of understandings about a whole set of skin conditions we never really understood before.
So to me, that’s the enormous appeal of suddenly being able to think of it as not just preventive, but giving us a window into whole categories of conditions that people are living with where we don’t really know much about it all.
And Sendhil, when you think about the economics, in other words, the business model for the company right now, they sell the service access to the platform to the health system. In the case where it’s an at-home service that people are accessing just using their iPhones or handheld device, have you thought about what the economic model is that makes that work?
I think part of it will depend on the conditions. For example, with cancer, there’s more interest in capturing the dollars saved when skin cancer is caught early. So, there are many reimbursement mechanisms where we are trying to incentivize providers to catch if it’s caught early.
For example, there are these capitation payment systems where health insurers pay a fixed amount per person. In that world, you can see why health systems would take everyone that comes in for a regular checkup and say, “Hey, look, we’ve gotten this app for you.
You should just take a photo whenever you want. We might text you periodically. If you see anything, just send us the photo.” The incentives are there for those types of things.
I also wouldn’t overlook the fact that dermatological stuff has an enormous direct-to-consumer market. People really care about their skin a lot.
It’s not a coincidence that dermatology shows up in an episode of Seinfeld. I don’t know if you remember that episode of Seinfeld where George is trying to show this guy the mole on his back at a party.
He calls it a pimple-popping MD. A lot of people would even be very happy if you said, “I’ll charge you to tell you if there’s anything you need to be worried about.” Dermatology is the one place in medicine where there’s enough demand on the consumer side that I think people would be very happy to pay.
The amount of money people pay for certain skincare products is just wild. Even if you capture one percent of that, that’s a lot of money.
The last question before I go to Rich is in a number of the prior episodes, when we talk about taking the industry to the limit, we end up shifting from supervised learning to reinforcement learning, or systems that learn from experience.
The phrase you coined, I can’t remember who, but I think you brought it up several episodes ago, was to make the system RL-able. Do you have instincts in the healthcare industry, and we can use dermatology as our setting, on how you would make this RL-able for the system to learn and improve on its own?
I would find it interesting to think a little bit about going to a drug manufacturer that provides some topical creams, like cortisol or something that targets psoriasis. Look at that whole field; we do one clinical trial, and then we say to people here, “Apply this as needed, up to twice a day.”
That makes no sense. You could easily imagine working with a company like that to say we’re going to try to work out an RL system where we take photos of your psoriasis regularly. We give you suggestions on whether to apply or not apply the cream, and we’re just going to try it out together.
Most people think, “The doctor says as needed,” and I think people are like, “I guess I shouldn’t do it unless it’s absolutely needed.” This leads to the question of what do we do when people are itchy?
These dermatological topical conditions are very common, and they can be debilitating. So, I could imagine something simple like that, where it’s one condition you could RL the entire pipeline and truly use this to change treatment patterns in a way that is actually where we’d learn a lot about what treatment patterns are working.
We have a chance through photographs, in a way we rarely do with other conditions in medicine. We can actually look at both photographs and some modest amount of self-reports about how itchy people are to actually start creating a loop that gets things going.
Rich, that was a setup for you in terms of how you see what this could look like at the limit in terms of how it would be very different than it is today.
First, I want to say something: I’m a dermatology patient, and I went in and had the big thing. I had skin cancer, melanoma, metastatic melanoma. I almost died—it was 20 years ago, but it was a big deal.
Now, I want to continue with the idea of patient flow. Just as the video described, you start with a patient who’s kind of worried about their mole and wants to bring it in and have it looked at, which probably wouldn’t have helped me at all.
My mole was on my back, and we just took it off. What would be another way of handling patients? One is to say, well, people are worried about their skin or they want their skin to be healthy.
You could ask people to sign up in a pilot program where you say, “Oh, we’re looking at different ways of diagnosing skin health.” If you can volunteer to come in, you don’t have a mole, but your body has moles all over the place.
If you could come in or rely on your bed and have someone take pictures of your skin, then you’d enter the system and have sequences. You might take a picture of your whole back, and then maybe you’d have close-ups on certain moles or send that in.
The system would prompt you: “Oh, could you give us a close-up of this particular mole that was on your back? It might be suspicious, so we need to look at it more carefully.”
You could proceed like that. Just think about what this would do for the machine learning aspect. Machine learning is very sensitive to the distribution of data that you send it. If you send a lot of positives and a lot of negatives, you get different results.
If you could bring in people who aren’t specifically worried about their moles, you could see their population of moles. That would create a very different distribution with different hits and misses.
It would lean towards the direction of preventative, focusing on overall health. Obviously, you’ll get more referrals, which you’re worried about. But we’re talking about the limit, and maybe that kind of thing would be really desirable.
It would engage a broader portion of the population, and it would be more interactive. What do you think about those ideas?
Okay, let me turn it back to Neil and Jack, and you can react or comment to anything you’ve heard.
There’s so much that you guys covered. This is so great. I wish we had this conversation five or 10 years ago.
I think there’s a lot to unpack here. If I summarize it in two broad categories, the first one, which Neve, you touched on, and Sendhil, you also touched on a bit, is that we’ve designed, as I mentioned earlier, a healthcare system that has a primary care function, a secondary care function, and in some cases, a tertiary care function.
We’ve done that because of the scarcity of the resources. If you suddenly have an abundance of a resource, you can make the whole pathway better. That’s what we focus on doing to start with, but you can also rip it all up and start again, reimagining a model where you’re collecting data on a regular basis.
One thing we know for absolute certain is that patients are highly motivated to give data if they think they’re going to get something back. We don’t have the resource constraint in terms of expert ability to analyze and interpret this data.
That’s what artificial intelligence brings us. There’s no rate-limiting factor on how much data we can get, how much granular information we can gather, or how we can start identifying earlier staged diseases. The sky really is the limit on this.
There’s so much missing data right now that we can start to collect. If you redesign the way the system works, we can now, for the first time, do it in a way that reduces costs to the health system overall rather than drives them up.
Going back to the founding mission of our business, we have two parts to it. One is we want to drive down the cost of care for skin cancer, and our ambition now is more broadly in dermatology.
The second is we want to dramatically increase access. We want to find cancers earlier. We want people to be able to check themselves. We want the barrier to seeking help to be much lower.
When you look at skin cancer and the delay between a patient being worried about something and actually getting treatment, the survival rates drop as the cancer spreads and progresses. What stops people more than anything else is not necessarily a diagnostic delay, which is a problem we really have now and is driving our business today. But it’s that seeking help delay, which people aren’t doing that we want to tackle next. That is a hundred percent where we want to go because that is when it starts getting really exciting in terms of making a real difference on a global scale.
So there’s one side of things, which is we can redesign the healthcare system now, now with the technology that changes everything about how we design care delivery. Then the second part, which I really loved and that we’ve started to do a lot of thinking about now is we are looking at using a very, very, very narrow band of the information that exists about our skin by using optical sensors and only optical sensors.
And we’ve had to do that because as humans and dermatologists, that’s what we have. We are now no longer bound by that. We can add all sorts of additional sensors, low-cost sensors, which matter to us because we want this to be something that happens outside of a hospital. But we can add all sorts of sensors to this to improve the information that we have to make decisions.
And we shouldn’t just be limited to what we’ve thought of as dermatology, which is psoriasis, eczema, skin cancer, inflammatory skin diseases, all of those sorts of things, which are a huge problem in their own right. But we strongly believe, as some of our advisors and dermatologists help us think through this, that there is a lot more information encoded in our skin. There’s hypertension information. There’s liver disease information.
There is undoubtedly, and I’m going out on a limb here ahead of the science, which we want to prove. But I would argue that it would be remarkable if the only information that was available on our largest organ, which is visible to us in every part of our body, is if we’re going to have an inflammatory skin disease or a skin cancer.
I just don’t believe that that’s ultimately going to end up being true. And I think what we have as an opportunity is using dermatology as it exists now, redesigning the way the system works, but adding additional sensors and getting the downstream outcome data, which we focus very heavily on to start to try and tease out what some of those relationships might be.
And then try and build in solutions in the longer term, which really opens up how we think about a new level of zero care. If you like, instead of primary care, we have something that sits in front of that, that technology is driving.
And Neil, before, sorry, AJ, just before you move on, can I double click? Cause I realize that we all accidentally became somewhat politicians and none of us actually answered AJ’s question as to what ancillary data could be used.
So just on the additional sensors beyond kind of visual, I saw online and I’m super intrigued by this. And I think it would be interesting for the audience. You mentioned things like infrared, but also audio as mechanisms for detection. Can you unpack what some of the other sensors of data that AJ kind of asked us about you could use as proxies for diagnosis?
Jack, did you want to jump in there? You’ve given this a lot more thought than I have at this moment.
Yeah. So, I mean, obviously there’s a huge amount of information in images. It being a very high bandwidth source of data, very high dimensional. And as Neil said, the image capture hardware that we use is focused on capturing data to present back to humans. Whereas what we’re actually doing is using the machine to analyze the data.
Going beyond the visible light spectrum, I think is a huge thing. The data that the type of image capture we use, we use something called a Dermatoscope, which uses polarized lighting. There are also options to use non-polarized, polarized and non-polarized. Depth information possibly could be something that’s useful.
The interesting thing is that we work in image recognition, computer vision. There are ways to embed sound in a way that you can also drop that in. If you convert an audio clip into a spectrogram, it actually sits inside the same type of model architectures as images. So all of these modalities sit next to each other very comfortably.
So anything that could be captured as an image or an audio signal, there’s a huge number of things. I think what we’re really interested in also is very high-dimensional, high bandwidth sources of information. So that’s why things like images are a lot more interesting to us than, say, a patient questionnaire or a patient survey. A bunch of tick boxes has nowhere near as much signal in it as an image.
And I guess what we’re really doing at the core is pattern recognition. We’re industrializing, finding these patterns inside data. There’s huge amounts of complexity locked inside our data sets. And what we want to do is harness that, learn some kind of internal data representation, and then use it to do something useful, whether that’s diagnosing skin conditions, skin cancer, or something beyond that entirely.
Can I share a little anecdote just to illustrate Jack’s point? One of the things early on in the business that we identified when we were doing all of our research and reading all the research papers was that there are algorithms to predict the risk of cancer that are based on patients’ medical records, or a questionnaire the patient answers, family history of skin cancer, there’s a bunch of them.
And they reported quite high sensitivities for finding cancer and reasonable specificities. And so we thought, okay, naturally, we’re going to collect this data, we’re going to put it in with the images and it’s going to make everything better. And it just didn’t. It made everything worse. And it made it worse because most people find it very, very difficult to answer some of the critical questions.
So has this changed? I had a lesion that I got checked out by a dermatologist and they were saying, you know, it’s interesting. Is it new? And I said, I’ve never seen it before in my life. I was getting really nervous and he was planning to biopsy it on the back of me never seeing it and it appearing in the last three months. And then I saw a photo of me 10 years earlier. And then there it is on my face.
And I just hadn’t noticed it. I had to call them up and say, I don’t think we need the biopsy anymore. I think that when you lose the fidelity of that data very quickly, you make a mess out of how these systems work. And so we want to try and stick to things that we can be really objective about rather than introducing much subjectivity in it.
And we already deal with subjectivity in terms of histopathology outcomes. The discordance between pathologists is relatively high. And so we want to try and minimize the amount of noise that we have in our data and outcomes.
One of the interesting things in the area we work, I guess, is we’re kind of at the intersection of medicine and computer science. So we have this world of grayness meeting this world of ones and zeros, and trying to navigate that is a really interesting problem. It’s something you mentioned, Neil.
I think, Ajay, the way you described it, you were getting us to think about other modalities. But I think, Neil, you reminded us of something else, too, which is even just sticking with your modality. Thinking of the skin as an early indicator of many diseases. It’s worth noting that amongst the things that were classical medicine, like you go back hundreds of years across many cultures, the things that people would look at are skin, teeth, and urine, basically.
And so it’s arguably because that was the easiest thing to look at. But I actually think it’s because there was genuine diagnostic potential. You’ve all had this experience of looking at someone you know really well and just from something on their face, being like, I think you’re falling sick. The fact that there’s a lot of signal in that object, even with just images strikes me as something very promising.
Okay. We’re almost out of time. We’ll just do a quick round, Robin, to wrap it up. Niamh and then Rich and then Sandal. Any final comments in terms of thinking, you know, Neil and Jack have, you know, they’re building an incredible business. And this is one actually intrepid as an investor in their business and very happy to be so springboarding from where they are today to our in the limit.
Any other comments in terms of for listeners that might shift their sort of the Overton window in their mind of what healthcare can be with machine intelligence, you know, in the types of things that we’ve spoken about? In other words, much more diagnostic power from, for example, images of the skin than we’re used to taking it upstream so that people are collecting data at home, perhaps in the interactive way that Rich described in his characterization.
Making the process eligible as we go. So, Niamh, we’ll start with you, then Rich and then Sandal, and then we’ll wrap it up.
Yeah, absolutely. And I think the challenge always with this is there’s a possibility space meeting reality, both in terms of the reimbursement mechanism that Sandal outlined, but also you’ve got that trade-off between early detection being absolutely key for survival rates and cancer. But overdiagnosis and referrals can quickly outstrip the dermatologist and overwhelm the system.
So how would you, you know, that’s a fine balance to tread. But I do think the timing now back to your point is why has it taken a decade to get here? A lot of it, especially in the US, is around that reimbursement mechanism as a central unpacked earlier of getting rewarded for preventative care and reducing speed downstream.
I think now that that’s there, the chaps can indeed expand beyond just cancer to dermatology as a whole, beyond just psoriasis. But early indicators pan disease states provided that they do get that longitudinal data and get to see where the patient goes over time. I think that will be the difficult part of the business model.
But if you think about it, jaundice, the first indicator is always your skin goes a bit yellow or with smoking, your skin goes a bit gray or the elasticity degrades. Bruising is an early indicator of a lot of autoimmune mechanisms as well. It’s just how do they embed themselves both in the healthcare system to get that quality data and trust factor from the clinicians to actually utilize what they state, but also to Sandal’s point now start to leverage the trust and the quality that they’ve garnered over the past decade of hard work to then springboard into that direct-to-consumer in a way that I think a lot of the other healthcare apps failed because they were a bit too over-promise under deliver and there was kind of a low adherence rate.
So, yeah, I’m super excited. Even if you want to really go beyond the extremes, I’m sure you can then start to use your device for routine care, such as eye tests. A lot of it when you go is just reading a chart and examining the back of the eye through reflection, probably even dental. Like, do you need fillings or not? I’m sure you can take a photo and start to look at some of those vision and care bits.
So as the camera on our devices becomes better and better and more of a commodity and the traps and the company really leverages everything they’ve done today, I think the possibility space is huge.
Thanks, Niamh. And when you bring up the reimbursement part of this, we have to imagine that in the limit, not only are we advancing the technology, but we can, you know, if Doge has taught us anything, it’s we don’t have to have our mind. We don’t have to be constrained by the system.
That’s right. We don’t have to be constrained by the system. And so somebody that were to take the type of world that Sendhil has in mind of healthcare much more weighted towards prevention, that the whole reimbursement system you could imagine is redesigned in order to accommodate that.
And I guess in some sense, when the reimbursement system is redesigned, that can be aligned with a reward system that lends itself to being RL-able. Rich, any comments on your side to wrap up?
I think we might want to dwell a moment longer on the advantage of making it very interactive and having a higher cadence of interaction rather than I go in once and then I wait a few months to get something else.
If we can increase the cadence of interaction, there are many more possibilities, I think, for being appealing to the patient. The patient goes in and right away gets a result and maybe they do something else. I don’t know about you guys, but I think, well, I could go to the doctor, but if I go to the doctor, you know, and get asked for this or that, it might take me months before I get to the next step.
We could have our system with a higher cadence of interaction. It could be more effective in lots of ways and more RL-able. And, Rich, that world is likely much more plausible when it’s coupled with what Niamh had said, which is it’s not no longer just about skin cancer. It’s all these things because then there’ll be many more benefits to the regular interaction, because it’s not only detecting cancer.
Sandal, the last word is yours. So, let me actually finish us off where you started us, Ajay, which is about, you know, I guess you’re gently trolling them by saying what took so long. And I think that one thing I’ll observe is that we’re in this accelerated phase of these curves right now.
For me, if I was running your company, which I’m sure nobody on earth would want me to do, but if I was running your company, the thing I’d be mainly, or if I was funding your company, I think the main thing I would be trying to do is to figure out how aggressive, can you be even more aggressive in a growth pattern where there are a lot of opportunities that are lateral to what you’re doing right now?
How aggressively can you seek out all of these other opportunities? Because it does seem like we’re in this period where there’s real value in sort of spreading out, if that makes sense. You’ve clearly had for the earlier phase the right strategy, like careful accumulation, laying the groundwork, doing it right.
That strikes me as exactly the right thing to do. But we appear to be entering a phase where there is actually genuine value in hitting many targets, trying many things going in this way, not in an irresponsible way, but in a way that would seem unfamiliar to an organization that has been kind of very carefully accumulating in a good way.
And that’s what I would be introspecting on is how do we kind of expand the scale and scope of the next five years in a way that will feel almost bewildering to the care that you put into the previous 10 years.
That’s a great point. And, Neil, I’ll give you 30 seconds to react to that. In other words, do you understand what Sandal is saying, and does it make sense to you?
Yeah, absolutely. And again, you guys are really insightful in terms of I spent a lot of time thinking about these things and you’ve just come to a pretty dry on a little bit of information and hitting that out of the park. So literally this week we have the whole of our management team sitting off-site and talking about exactly this.
We have built our company about making sure that we can do what we say we can do. We are a trusted and competent part of the healthcare system that has slowly and methodically built the foundations to play that role. That is no trivial act.
But the next act for the business is figuring out how we leverage all of that power that we’ve created to actually deliver on the potential of the business. We’re not delivering on the potential of the business if we’re being used as a red light, green light within the healthcare system, taking a bit of pressure off the dermatology teams.
And that’s not our ambition. So we’re sitting here as a group thinking about how we break off that mentality that has been so valuable to us. We don’t want to give it up. We don’t want to just suddenly go, oh, we don’t care about clinical outcomes anymore, let’s just go to town.
We also know that we can’t act like we did for the years that it took us to build these foundations. I think that is going to be very, very difficult for us to do, but it’s something that we are a hundred percent focused on. Part of it is having conversations like these and exposing the team to the conversations like these, where they can see the potential that we have and realize that the world’s not going to stand still while we try to execute on this.
Our job is to execute on it better than others and faster than others. We’re certainly up for the challenge.
Neil and Jack from Skin Analytics, thank you very much. Wonderful to have you here, especially when we’re talking about something like healthcare, which, you know, some of the industries apply to some people more than others, but this one applies to everybody. Thank you, Niamh, Rich, and Sandhal, as always.
Rich, I want to thank you for sharing your personal connection to this case. I think even though nobody commented on afterwards, it reminded everybody how important this work is. Thank you for sharing that. Everybody, we will see you soon in the next episode. Thanks very much.
Thank you. Thank you. And that’s our show for today. Thanks to Neil and Jack from Skin Analytics, as well as my colleagues, Rich, Sendhil, and Niamh. Follow us on the Intrepid Substack at insights.intrepidgp.com and subscribe on your favorite platforms, including YouTube, Spotify, Apple Podcasts, and more. Thanks everyone for listening.
The views, opinions, and information expressed in this podcast are those of the hosts and guests and do not necessarily reflect the official policy or position of Intrepid Growth Partners. This content is for informational purposes only and should not be considered as financial investment or legal advice.
This is an experimental rewrite
Neil: I mean, people really care. I think people care about their skin a lot. It’s not a coincidence that dermatology comes up in popular culture, like the episode of Seinfeld where George is trying to show a mole on his back at a party. That’s amazing! Dermatology is the one area in medicine where consumer demand is so high that people, especially when it involves kids, are willing to pay for services.
Jay: Welcome to the Derby Mill series, intrepid pioneers of the next economy! We feature discussions with entrepreneurs at the forefront of machine intelligence and brainstorm ideas about where this technology may go in the future. I’m Jay Agrawal, co-founder of Intrepid Growth Partners, and I’m joined by my collaborators: Neve, Gavin, Rich Sutton, and Sendhil Mullinathan, all senior advisors at Intrepid. In this episode, we focus on cancer detection. We’re here with the leadership team of Skin Analytics, a UK-based firm utilizing AI to automate the diagnosis of serious skin conditions, beginning with skin cancer. Neil is the founder and CEO, and Jack serves as the AI director. All right, let’s start the show.
Jay: Neil, let’s kick things off with you. Could you provide a brief summary of your business for our listeners? While everyone has read the overview, please highlight what is at the core of your business and the key predictions that your system makes.
Neil: Thank you! Skin Analytics has developed a series of artificial intelligence algorithms that we regulate as medical devices. These algorithms can autonomously make decisions regarding potential skin cancers. By capturing an image of a skin lesion, we can predict whether the patient has skin cancer, a pre-malignant lesion that requires treatment, or a benign lesion that shouldn’t drain healthcare resources.
Neil: The main idea behind our business is to utilize artificial intelligence to replicate many decisions that a dermatologist would make. This approach aims to address the significant shortage of specialist dermatologists worldwide, particularly for patients with potential skin cancers.
Jay: Thanks, Neil. Your product is the first of its kind to clear the regulatory process in the UK through the NHS (National Health Service). Can you explain what it takes to navigate the regulatory landscape? Is it solely about prediction accuracy, or are there other key factors?
Neil: We’re very proud to be the first company to launch an autonomous skin cancer pathway globally, and we started this journey in the UK. The road to deploying our device autonomously began with a supervised version of the algorithm. We implemented necessary safeguards and initiated second reads to ensure the technology was performing well and could adapt to the messy reality of medical practice, which often involves many gray areas. This journey took us several years from our commercial launch to proving that our system could work independently.
Neil: To navigate the regulatory process, we identify three foundational elements. First, the technology must be effective; you need to figure out how to make it work reliably. I know you all have spent considerable time discussing how to translate new technological potential into something that benefits patients. This is a non-trivial aspect of our business that requires significant effort, and Jack can elaborate further on that.
Neil: The second critical component is providing clinical evidence that supports our technology’s capabilities. This begins with observational clinical studies and moves into prospective studies, where you increasingly challenge the AI and evaluate its performance.
Neil: Finally, it’s essential to have the right systems in place to run a company making decisions that impact patients’ lives. For us, making decisions related to cancer is particularly sensitive—delayed diagnosis can be fatal. This high-risk aspect compels us to ensure robust quality management systems throughout our business, from product development to post-market surveillance and patient interactions.
Neil: We have integrated these systems into the very fabric of our business and have been audited by about three separate bodies, totaling around 15 audits to date. These efforts ensure we meet regulatory standards and continue to uphold them. Together, the technological effectiveness, clinical evidence, and quality assurance are what allowed us to hurdle the regulatory requirements and enter the market.
Jay: Thanks for that insight, Neil. I have one more question for you before I switch to Neve, Sendhil, and Rich for any clarifying inquiries. Then we can discuss the future implications of this technology. My question is this: I believe you began this project a decade ago or maybe even longer. I recall a conference in 2015, where people were discussing valuable applications of image recognition, particularly in reading medical images. What took so long for you to bring this to fruition?
Neil: What took so long? I’ve been working on this since 2012, so it has indeed been a lengthy journey. To address why it has been challenging from a technological standpoint: when we initiated the business, we focused on classical machine learning approaches. We engaged with dermatologists and clinicians to understand how they identify skin cancer. It was frustrating as someone with a math background to hear responses like, “It just looks wrong.” How do I translate that into measurable criteria?
Neil: We spent a significant amount of time collecting metrics. Dermatologists provided data on features like color and symmetry, leading us to quantify about a hundred different measurements, which we then attempted to optimize. Unfortunately, this approach didn’t yield effective outcomes; the technology wasn’t advanced enough to support decision-making. We subsequently transitioned to deep learning, which significantly transformed our system’s capabilities.
Neil: However, we realized we had a steep learning curve ahead of us. Initially, the belief was that we only needed vast amounts of data for everything to work seamlessly. We discovered that was far from the truth. We thought we had a solid algorithm, but after testing, we found it inaccurately classified all test samples as having cancer. We recognized the substantial overfitting issue and encountered many common pitfalls in AI that are well-documented today.
Neil: It took time for us to devise a system that could function effectively within those constraints. As a business, we have approached our development cautiously because we understand that failing in a safety-critical system can impact patients’ lives. We aimed to ensure we navigated this process correctly. Another challenge we’ve faced is that, while healthcare is one of the best places to apply this technology, much of it isn’t yet digitized.
Neil: For instance, while radiology images might be digitized, dermatology historically hasn’t been. Consequently, the data collected was often not representative of the patient population. Early datasets were gathered by dermatologists from unusual cases that piqued their interest—which is not ideal for training algorithms. We had to take the initiative to digitize much of the data ourselves, a process that took time. Alongside that, we had to establish clinical evidence and find a way to operate with over 200 standard operating procedures governing our processes to ensure patient safety. This is all time-consuming.
Neil: However, in the grand scheme of things, I believe we’ve made significant progress relatively quickly—especially considering the gap from the vision in 2015 to our reality in 2025.
Jay: Excellent, Neil. Thank you! Niamh, do you have any clarifying questions?
Niamh: Yes, thanks, Jay. Kudos to the team! I have a quick question regarding patient drop-off rates after a referral. Let me broaden this by asking about the importance of an end-to-end platform compared with just the image. As you hinted earlier, you’ve been astute in creating a digital pathway for patients, allowing them to choose quicker access via AI or wait for a doctor’s appointment.
Niamh: After you’ve made a binary decision of benign or malignant, it seems essential to follow the end-to-end process: reviewing prescribed treatments, evaluating effectiveness, and ensuring any discoveries are looped back into the system. How do you currently collect that longitudinal data, and at what point does the process tend to drop off? Is it simply at the dermatologist referral, or do you track it further?
Neil: You’ve pinpointed a critical aspect of our business. One early decision we made, which seemed risky at the time but ultimately proved wise, was that as everyone else focused on “appifying” everything, we opted against deploying our technology in ways that didn’t integrate with existing healthcare systems.
Neil: We understood that informing a patient about a diagnosis is only part of the process; the real utility for patients arises after they receive treatment. While we don’t provide treatment ourselves, we want to embed into systems that can solve that problem, creating genuine value at scale. Therefore, we focused on collaborations within the UK’s National Health Service (NHS) or with partners who could utilize our AI’s outputs effectively, whether it be for a biopsy, treatment, or discharge.
Neil: It was crucial for us to deploy within these established healthcare frameworks. You’re correct to observe that, for now, our system primarily focuses on the referral or non-referral process—a preliminary function. However, that doesn’t truly capture the full depth of the algorithm’s capabilities. The algorithm can identify pre-malignant lesions suitable for various treatment options or flag cases that should proceed directly to biopsy, something we’re seeing an increasing appetite for. This approach enables better optimization of healthcare resources from the moment the AI makes a decision.
Neil: We’re pushing hard to build enough confidence in the AI to optimize existing healthcare pathways. Our current goal is to enhance these systems to ensure patients receive timely treatment.
Niamh: Great insights! It seems like your company’s focus is about getting the classification right. While other steps are crucial, the primary immediate utility appears to be the yes/no—referring patients or not.
Niamh: How significant is proper classification to your overall operations? Is refining classification relatively straightforward, or is it more critical than that? Do small percentage improvements make a considerable difference for your business?
Neil: Understanding the classification process is indeed vital. We invest substantial time striving for even the slightest improvements in diagnostic accuracy, sensitivity, and the ability to route patients to appropriate care pathways.
Neil: Each incremental gain in classification translates into considerable downstream benefits for the healthcare system. For instance, if we can suggest that a UK patient on an urgent skin cancer pathway could instead be directed towards a less resource-intensive treatment, like topical cream, it would save valuable time for dermatologists.
Neil: This efficiency is not just a cost-saving measure for the UK’s healthcare system but applies universally across healthcare systems. Interestingly, while we emphasize that the initial classification has limited direct utility for patients, real utility comes from the relevant treatment options.
Neil: The bottleneck isn’t in providing treatments—many practitioners can be trained for that. The constraints exist in the classification step that necessitates the expertise of specially trained clinicians, who invest many years in training and command higher salaries in the global context. This scarcity forms the backbone of our healthcare system, which primary and secondary care tries to address.
Jay: Are you able to gather data in the clinic, or can patients do this from home?
Neil: We can operate in various settings. Some of our pathways allow patients to collect data at home, while others necessitate a clinic visit. There’s a lot of flexibility in how we acquire data. Currently, we utilize a dermoscopic image—a simple lens that employs cross-polarized light and filters to magnify the image slightly. It can attach to a smartphone, allowing for a variety of solutions.
Neil: Moving forward, our product roadmap indicates impressive results on the research front. We’re working towards eliminating the dermoscopic attachment as a requirement while maintaining diagnostic accuracy. What we might lose initially is some capacity for discharging patients, leading to a higher over-referral rate. Still, this can be rectified later with dereoscopy or other approaches. Ultimately, we aim to leverage everyday sensors that are widely available at home to streamline our processes.
Jay: Sendhil, do you have any additional clarifying questions?
Sendhil: Yes, Neil, I’d like to revisit your integration of systems within healthcare rather than pursuing direct-to-consumer approaches. What unexpected challenges or benefits have you encountered while working to incorporate into existing decision-making frameworks?
Neil: One surprising realization was that, while I believed we needed to gain the trust of clinicians for patients to subsequently trust the technology, patients seemed far more open than I anticipated. At the outset, I thought if clinicians didn’t believe in the technology’s efficacy, patients wouldn’t either.
Neil: However, my experience suggests that patients are more amenable to AI solutions than clinicians. If we’d leveraged this belief in trusting clinicians as our core motivation for engaging with healthcare systems, we may have made a misstep in our approach. Ultimately, our true focus is not merely to classify but to ensure patients receive effective outcomes.
Neil: The classification step is crucial, but the patient’s ultimate need is for a diagnostic decision and the following outcomes—that realization emerged through early conversations with healthcare professionals and patients.
Jay: All the listeners can visualize the process you’ve described, Neil, where patients, whether at home or in a clinic, have a photo taken of a skin lesion or mole for AI classification as malignant, benign, or pre-malignant.
Jay: This directly leads into subsequent actions within the healthcare system. In a prior episode, we compared this to the geological exploration industry, where drilling a hole for a core sample represents a kind of biopsy. As we discuss the trajectory of this domain, I know both Niamh and Sendhil have contemplated the future of the healthcare industry.
Jay: Niamh, do you have any perspectives on where you envision this technology leading in the future, particularly regarding high-fidelity classification capabilities?
Niamh: That’s an intriguing question, and it raises issues about the gold standard itself. If I understand correctly, Skin Analytics now outperforms dermatologists. In the same vein as Rich referenced mining, the algorithm can process thousands or even hundreds of thousands of data points that an individual expert may never encounter in their lifetime. This potentially raises questions about the benchmarks we’re using to evaluate performance and consider other data sources.
Niamh: This also ties back to my earlier inquiry about patient history collection. Could Skin Analytics serve as a biomarker or predictor for broader health issues? For instance, if a patient who has smoked their whole life exhibits a higher mole count, are there patterns indicative of future health risks, such as lung cancer?
Niamh: I’m curious if there’s a way to leverage the insights gained through your platform to broaden its utility for additional patient benefits.
Sendhil: Before Neil responds, I’d like to share my perspectives.
Sendhil: With regard to dermatology, I have two initial thoughts. Firstly, I understand your choice to integrate within the healthcare system, but deploying your solution via an app could engage a broader audience. It might allow earlier photo captures from individuals who may never seek medical assistance, thus helping to mitigate selection bias.
Sendhil: I recognize that this isn’t an immediate issue for your current operations, but expanding the pool of individuals taking photos could significantly enhance early detection efforts. I’m aware there are valid concerns regarding overuse, leading to false positives, but those are statistical challenges that can be addressed over time.
Sendhil: Given the vast potential for population-informed data, I think the opportunity lies in early intervention.
Sendhil: Additionally, as you’ve developed the device, if I understand correctly, it functions as a camera capturing images, which are processed through a deep learning algorithm. This device’s versatility allows for adding additional sensors beyond traditional optical capabilities.
Neil: It’s important to note that, up until now, most of what we’ve achieved in dermatology has been limited by the capabilities of the human eye. There’s no reason we must rely solely on this sensor.
There are numerous sensors available that surpass anything interpretative by the human eye. One aspect I appreciate about our current development is that, by utilizing a device, we can easily enhance it to support new sensors.
Setting aside the regulatory approvals, we’ve effectively simplified one aspect: it’s not a completely new device that people need to adapt to; rather, they are just getting an upgraded version of something they’re already familiar with. Thus, we can consider two pathways: one involves expanding the scope of our applications, and the other focuses on introducing new input modalities.
It seems we’ve positioned ourselves well to explore both avenues.
Sendhil: Just to clarify, the device you’re referring to is essentially the smartphone that users already carry, right? The lens serves as an add-on.
Neil: Correct! People currently connect this specialized lens to their phones. As Neil mentioned regarding our product roadmap, the future version might not even require that lens, making it truly just their smartphone. However, there could still be reasons to keep the add-on for various advantages.
Jay: Sendhil, I know you’ve been contemplating the transformation of the entire healthcare system, pushing for a stronger focus on prevention. With the concept of those two pathways you’ve outlined, do you think introducing this technology for home use would alter the data collection process? Looking at practices in prenatal care and ultrasound procedures, typically, the limiting factor is how infrequently an expectant mother visits for screenings.
If at-home ultrasounds become commonplace, then frequency won’t be a restriction. I’d like to explore how this concept might apply to dermatology. While I’m not an expert on skin cancer, I recognize that many dermatological conditions, like psoriasis, are incredibly common yet not well understood.
While these conditions may not be life-threatening, they substantially impact quality of life. With some conditions, continual monitoring is vital, and it’s impractical to visit the dermatologist frequently. I believe that harnessing the ability to measure skin conditions frequently and affordably would greatly enhance our understanding and treatment of many dermatological issues.
Jay: Exactly! Moreover, some conditions, like wrinkles, also lack significant data on their formation processes. The potential for expanding dermatology’s scope is immense, especially for understanding conditions previously overlooked.
As you mentioned, improving preventative care gives us opportunities to deepen our knowledge of a wide range of skin conditions that haven’t been thoroughly studied.
Neil: When discussing the economic models for our company, it’s essential to recognize that our service is currently marketed primarily to health systems. However, if we shift towards an at-home service accessible through smartphones or handheld devices, have you thought about how that could work economically?
Sendhil: Absolutely, and I think the economic framework will vary by condition. For skin cancer, the prevailing interest lies in the financial implications of early detection. For example, several reimbursement models currently incentivize healthcare providers to catch skin cancer early.
Consider capitation systems where insurers pay a fixed amount per individual. In such scenarios, health systems might encourage everyone coming in for regular checkups to use our app, recommending that they take photos whenever they wish.
Jay: That’s an excellent perspective, Sendhil. Dermatology has a robust direct-to-consumer market. People genuinely care about their skin.
Neil: Exactly! It’s no accident that dermatology is a significant topic in pop culture—like that Seinfeld episode where George is trying to show off a mole. The demand is so high that plenty of people would happily pay for an assessment and guidance.
Sendhil: Before I pose my next question, I’d like to touch upon the need for transitioning the industry toward supervised learning to more dynamic systems that can learn from experience.
Considering the dermatology context, have you thought about how to enable this system to evolve autonomously? For instance, collaborations with drug manufacturers could help develop a reinforcement learning (RL) model where patients are guided through treatment plans based on ongoing data about their skin conditions.
Neil: Working alongside companies that produce topical treatments might indeed lead to an innovative system. Monitoring conditions such as psoriasis could involve regularly capturing photos and providing real-time recommendations to patients on how to manage their treatments effectively.
Rich: That’s a compelling proposal. As a dermatology patient who had a serious experience with melanoma, I think about patient flow. Many people may be worried about their skin health or want aesthetic treatments.
What if we established a program encouraging individuals to sign up for a pilot where they could voluntarily contribute data about their skin health?
Rich: Imagine having people who aren’t necessarily concerned about their moles submit images of their skin for assessment. This approach could significantly diversify our dataset, as machine learning is sensitive to the distribution of inputs.
The larger and more varied the dataset, the more accurate our models could become, allowing us to shift focus toward preventative care. While this might increase referrals, engaging people broadly could fundamentally alter how we approach dermatological health.
Neil: Indeed, that’s a great point! If we rethink the system design, we have the opportunity to gather copious data. Patients are often willing to share their information if they believe they will receive useful insights in return.
With this technology, we can break down resource constraints and drastically improve how we deliver care while reducing costs. Our founding mission is twofold: to reduce skin cancer care costs and increase access while drawing attention to the importance of early detection.
Neil: We know that a significant delay exists between when a patient first notices a concerning issue and when they seek treatment. This delay can significantly affect survival rates, especially for skin cancer. Hence, tackling these delays is our next frontier. Neil: We’ve had to do this because, as humans and dermatologists, we’re limited by what we know. However, that’s changing. We now have the ability to utilize low-cost additional sensors, which is critical since we want this technology to be used outside of hospitals. By adding various sensors, we can improve the information we gather to make better decisions.
We shouldn’t confine ourselves to traditional dermatology categories like psoriasis, eczema, skin cancer, and inflammatory skin diseases, which are already significant issues on their own. Our advisors and dermatologists believe there’s much more information available in our skin. For example, we may find indicators of hypertension or liver disease.
I’d argue that it would be surprising if the only relevant information from our largest organ was limited to inflammatory skin diseases or skin cancer. It just doesn’t seem plausible. Our opportunity lies in redefining the dermatological system and incorporating additional sensors to gather downstream data, which we can analyze to uncover various health relationships. This approach could pave the way for what we might call zero-care, an advanced form of care driven by technology.
Neil: Before I continue, AJ, I want to dive deeper because I realize we haven’t fully addressed your question about what ancillary data could be used. When it comes to additional sensors beyond visual ones, you’ve mentioned infrared and audio for detection. Can you elaborate on other data types that could serve as proxies for diagnosis?
Jack: Sure, there’s an enormous amount of information in images; they act as a high-bandwidth source of data. As Neil mentioned, the image capture tools we utilize, like the Dermatoscope, gather data to be analyzed by machines rather than just presented to humans.
Going beyond the visible light spectrum is promising. We have options like polarized and non-polarized lighting for image capture, and we could use depth information too. Interestingly, in our field of image recognition and computer vision, there are ways to incorporate audio. For instance, if we convert an audio clip into a spectrogram, it fits well within the same model frameworks as images.
The real value lies in the high-dimensional, high-bandwidth sources of information. While patient questionnaires or surveys yield limited data, images are far more informative. At our core, we engage in pattern recognition, industrializing the process to identify complex patterns within our datasets. This knowledge enables us to diagnose skin conditions, cancer, or even broader categories.
Neil: Let me share a quick story to illustrate Jack’s point. Early on, we realized that algorithms predicting cancer risk based on patients’ medical records and questionnaires had high sensitivity and reasonable specificity. We thought merging this data with images would enhance accuracy, but instead, it worsened our outcomes.
The issue is that many people struggle to accurately answer critical questions. For instance, I had a lesion that a dermatologist wanted to biopsy because I claimed I hadn’t noticed it before. However, I later found a photo from ten years prior showing the same lesion on my face. This highlighted how unreliable subjective data can be, making it hard for these systems to function properly.
We want to focus on objective data rather than introducing unnecessary subjectivity. We already navigate subjectivity with histopathology, where pathologist discordance is high. Minimizing noise in our data is vital for reliable outcomes.
Jack: It’s interesting that the area we work in intersects medicine and computer science. We’re navigating the gray areas between the two worlds. Also, considering skin as an early indicator of various diseases, it’s worth recalling that traditional medical observations focused on skin, teeth, and urine. This is likely because these are easy to observe and may genuinely hold diagnostic potential.
We’ve all had that moment of noticing something about a friend’s appearance and realizing they might not be well. This points to the diagnostic capacity within those observations, even just through images.
Neil: We’re almost out of time, so let’s do a quick round for final thoughts. Niamh, then Rich, then Sandal. Any closing comments for our listeners that might broaden their perspective on what healthcare could achieve with machine intelligence?
Niamh: Absolutely! The challenge lies in the balance between possibility and reality. There’s a fine line between the need for early detection, which is crucial for survival rates in cancer, and the risk of overdiagnosis, which can overwhelm the system.
Now, with the reimbursement mechanisms evolving, we have a chance to expand beyond just skin cancer to a broader scope of dermatology. Capturing longitudinal data over time will be key to this business model.
Rich: I think enhancing patient interaction frequency could yield significant advantages. If we increase the cadence of interactions, patients could receive results immediately, potentially leading to better outcomes.
Many times, patients hesitate to visit a doctor due to long wait times for follow-ups. A system that allows for more rapid interactions could be much more appealing.
Sandal: To wrap up, I’ll echo your earlier thoughts. We’re in a phase of accelerating opportunities. If I were managing your company, my focus would be on seeking these lateral growth opportunities aggressively.
You’ve laid a strong foundation, but now it’s time to explore various avenues for growth.
Neil: Absolutely, and it’s essential for us to leverage the power we’ve built in the last decade to deliver on our business’s potential. We don’t want to become just another option in the healthcare system; we aim to do much more.
Our challenge lies in executing at a speed that exceeds expectations while remaining true to our mission and values. Conversations like these are crucial for encouraging our team to recognize the urgency of our objectives. We’re ready for the challenge.
2025-04-06 08:00:01
大家好，我是汉阳，欢迎大家收听本期的山有虎。我的嘉宾是山有虎的另外一位制作人重轻。重轻前一段翻译了一本书，叫《创意行为存在及答案》。我俩就是想借着他这本书出发的机会来聊一聊关于创作这件事。
我对创作本身有很多问题，也是想问重轻的。正好借着这个机会聊一聊。咱俩先界定一个事吧，就是什么是创作。因为我说一下为什么我问这个问题，就是很多时候我们在说创作的时候，大家脑中的那个图景是不一样的。
有人觉得我每天需要写一篇稿子，这叫创作。有人觉得我要周更我的短视频，或者一天更两次我的小红书，一天发五条吹，这叫创作。有人可能觉得说我花五年拍了一组摄影作品，这叫创作。我觉得咱们先界定一下吧，咱俩今天这个语境中聊创作的时候是在聊什么。
对，这个书它说的这个创作，其实更多就是广义的，就是将你的个人的意图表达出来的这个过程，而且它得是相当的有机的。它显然不是在强调它的形式，比如说作为一个画家是一种什么样的体验，它不是关于当画家这个事，显然不是这个，对吧。
它显然也不是关于它结果的，就是说怎么做出报款，或者是做出什么样的效果，才叫创作。它完全不是关于这个。它有点像是一个临床心理咨询师在谈论的东西，就是它是一个关于人的那个心理过程。比如说如果写作作为一种创作的话，它是一个就是struggle to find words，对吧？就是你在非常非常困难的在脑海中搜索词，把它们给排在一起。它说的是这个过程，它更多的就是关于这个process。
那创作和艺术的关系呢？因为这本书里面作者大量的用创作和艺术这两词来形容他聊的这件事。因为很多人觉得艺术是只有我画画，我做个雕塑，这叫艺术。它不是关于那个模样的，或者关于一个社会声望的，不是关于社会对这个事的看法的，或者是关于一个体制化的。就是说，哦，所以写书是正经的创作，然后拍vlog就不是，是关于社会对这个事的看法，跟这个没有任何关系。
就是在这个书的语境里面，我说的是那个内在的机制。这个机制是关于你自己的intention怎样被表达出来的这么一个往外掏的过程。这个过程只要足够的有机，足够的密度它就是。相应的什么就不是呢？就是比如说你就是想优化一下你的SEO，这个我觉得就不是。你也不能说它完全没意图，但你也很难说是创作。你想要通过修改你的标题提高15%的阅读量，我就觉得就太稀薄了，这可能就不算是。
或者说它因为我这么描述它，你感觉到它是关于它的结果的，或者关于它的效果的，这就不算，是这样。对，因为我自己有一本特别喜欢的书，叫《没有人是艺术家，也没有人不是艺术家》。我挺喜欢这名的，我觉得主要就是我看这本书的时候，有一个很大的感觉就是在这个语境下创作和艺术的确就是你说的那个行为，就是我有个东西想表达出来，我要费劲的把它弄出来，这个过程是我们今天想聊的创作。
对对对，我对这本书特别有感触。因为我之前帮朋友们开过一个写作课，就是不是我教人写作，而是说我帮你一起写出一篇文章出来。你可以教人写作，但我真的没有教，因为我就是在我陪你把它写出来，我作为一个陪伴者。你当我们给你把它弄出来，然后一共30个人报名，只有一个人写出来东西。
然后我那一次就意识到一个事，就是说一个人想写作写不好，跟会不会写一点关系没有，它关于的是一个人会不会创作。因为大部分人不是不会写，是因为他没体验过创作是怎么回事。就如何从零到一把自己的脑中的东西、思想的东西弄出来，这个过程是很多人其实没体验过的。
所以我觉得这本书，其实很多聊的时候就是这种感觉。当一个人想从零到一把一个东西弄出来的时候，他会面对什么样的挑战，他会有什么样的顾虑，然后他会有什么问题需要解决。对，当然了，就是他这个书，你要实际看这个书的话，你会感觉到他的那个段位会高一些。他就属于是那个，一个体能教练要解决的问题，都是解决的国家队的那个一线队员的问题。就是属于是艺术家或者是创作者世界里面，属于他本身已经很厉害的人， 但是他们依然无可避免的要陷入的那些困境的问题。
而不是那种就是什么，我突发奇想就想写篇文章，我不知道该写啥，他不太是关于这个。但是他里面有非常多的那个完全可以普世到我们生活中的很多事了。因为他这人是一个应该是现在活着的最重要的音乐制作人，可能过去30年里面，可能能跟他比的就没几个。所以他是从一个音乐制作人的角度，去推而广直，把他的经验泛化到所有的创作里面，是这样一过程。
所以你对这本书这么感兴趣的原因，也是因为他在说那是你自己的身上的困难。对，他就肯定有明显的普遍性，任何一个长期从事创作工作的人，我不相信他读了这本书以后没有感触，或者是他觉得这说的跟自己没关系，是绝对不可能的。当我刚开始读这本书，就感觉贼怪，这书里面充实着让宇宙穿过你，然后你要和宇宙取得连接，就这种特别神仙灵的话。
但读完之后，我就意识到，这书跟神仙灵一点关系都没有。我就感觉那感觉特微妙。你翻译的时候，你没这感觉吗？有，但是他那就是他的模样，就是他的模样和他实际要说的东西的问题，他有一个好像有一个分叉似的，有一个discrepancy。但是其实没事，就是因为你读起来只是他看上去这些字眼说起来好像是，比如说他一上来，他就给你讲了一观念，说这个我们呢，其实不是个体，我们是这个整个宇宙是一大生命体，我们是里边的一细胞吗？
对，所以这是宇宙在表达他自己，只是通过你而已。所以你其实就连器官都不是，你就是你里边的一个小颗粒。他就讲这个，然后你就会觉得这个味儿要点冲，是吧？对，特别冲。但他不是，就是如果你把这书读完了以后，你就知道他在说什么。就是他的点不在于告诉你这世界就是这样的，这只是他为了解决我刚才说的那些，就是永恒的创作者的困境的时候，必须要经过的一些通路，或者说是最好的认知和调整自己的状态的方式。他完全他也不是骗自己，他不是教你怎么哄诱你自己，也不是，但他的点显然不是停在这，说这个世界是怎么样，我告诉你啊，这个世界是一个这样的，他不是。
这只是他的这个论述的开头，所以那个文字的模样和他实际想说的东西，他确实有一个差别。那模样是完全不重要的。那你要是看不完，那你只是翻两页，那你可能就觉得他是一个就大师再过两天要来北京开课，那我只能说很遗憾了。对，因为读完这本书，就一直到他完全不是在说这个事。就我在读这本书的时候，包括回想我自己的创作过程，有个非常明显的感受就是，我们在说创作的时候，一方面，你要有一个非常强的主体性，就是你的自我， 就你有想表达的东西，对吧？
你要是没有想表达的东西，你可以不表达。但另外一方面，当你真正想表达出来一个东西的时候，你会明确的意识到，就那事跟你没关系。 我觉得这个是那个书里面我读完之后的那个感受。就他把这个事点得特别明白，就是你一方面，这本书里说你要重视自我，但他一方面，这本书又告诉你要协调自我。就这个事听着特别，你怎么既要又要呢？但实际上每个创作的人可能都能理解那话什么意思。你是需要有一个自我去理解有些东西你想做，但另外一方面，你真做出来之后，那事跟你没关系。
对，因为他对这个自我是有明确定义的，他有很多很多的那个analogy，比如说贯穿整本书的，他有一个叫容器和滤器的概念。容器就是你容纳了某种能量，或者是一种文化世界里的的一种势头。滤器就是说，同样的东西，经由别人的手出来和经由你出来，它就是不一样的。那个明显不一样的那个机制不就是你吗？对，那不是你是啥呢？但是你不是穿过东西，而是东西穿过你。所以那个他作为一个filter，他作为一个过滤器，他无可避免的沾染了你的痕迹，所以你是一个过滤过程。
他是这样的，他在说这个的时候呢，他还是希望你能够聚焦于这个过滤出来的东西的样子和过滤之前的样子。他让你注意这个，注意这两端，而不是注意这个过滤器。不是关于这个东西的。因为咱俩今天聊过很多次，就是你特别讨厌一个事，就找选题这个事。为啥呢？就没人逼你说话，是有人拿枪顶着你头吗？还是怎么着的？你没有什么想说，你就别说呗，谁逼你了？对吧？我觉得你要是没有那个冲动和迫切性的话，我就不知道，那创作这个玩意是干嘛，它是某种健身吗，还是什么？就是你想要增长自己的肌肉，一种类似于肌肉的在精神世界的对应物吗？还是什么？我其实我不太明白。
因为我在这方面的洁癖是相当严重的。我连定期创作我都接受不了。就是说如果一个人他说他每周一要说五千字，我都觉得就是陈切做不到。更不用说是这个什么没话找话说，我觉得这、你为啥觉得你做不到呢？因为有很多，我觉得你也可能会认可的，就比较优秀的创作者，他们的确可以做到定期更新。不是这本身没任何问题，我只说我做不到。怎么说呢？就是我还是想说东西，而不是想说。想说东西和想说是完全不一样的。我自己有很明确的感觉。
因为经常有人问我，问我就是说你怎么着选题的？我的选题跨度不贼大吗？然后我自己品出来，就一围上，我没有任何一次选题找的，都是撞上的。有一事在你前面，你感觉没法不写的，或者你没法不给它做细节目，然后你才能做。但这个事，你的确你必须撞到那个事上。你要是一年不撞到，那你就一年没有东西做。我觉得这也是OK的，但在这个里面，我觉得这就是那个我看这本书的时候觉得那个特别微妙、特别有意思的点。
就是他和身心灵之间的区别在哪？身心灵他的问题是在于他们把创作当成了一种方法，就是我在创作，这是一个治自己病的事。但实际上我他妈不管我又没有病，我都不要把东西做出来。 他是关于我感觉好。但是创作的过程，按照我，咱刚才讲，如果你是一个宇宙是一个整体，然后他在表达他自己，经过自己身体里的器官跟粒子的话，那这个器官跟粒子本身是没有意志的。不是关于让自己的身体的肉舒服，对吧？
所以你看他说这些玩意，你乍一看，好像是在这块跟你粉饰太平一样的去勾勒一个让你很舒服的状态，但实际是，他是在帮你卸包袱。他帮你卸的那个认知的包袱非常非常多，其中的一种就是关于你能不能够放下对自己的那个attachment。对，因为我身边有很多朋友，他们对于创作都有一种特别强的执念。就是他做东西，我觉得是好的，但他从来没有东西做出来。就是总是感觉我这还能更好，他还能跟现在不一样，我还有东西没做到。
我之前就有这毛病，对吧？就我之前拍东北那照片的时候，我总想的是，哦，还有这地方没去呢，我去了这项目就好了，我都从来去四年了，那项目我也没做出来。所以我感觉那个就是一个对于自己的一个强烈的过度的在乎。有没有可能没有那个所谓的你想象中的完美的存在？我觉得很多种吧， 就是在乎自己是一种， 就容易导致同样症状的原因不一定是一个。也有的人就是，他就是他陷入了一种fetishism。就是他就是好像铺一块地毯，他不能有褶，他就强迫症了， 就一直在这铺，一直在这铺，然后把那个地毯都铺凸了，但他还觉得不够平，他就一直在这，妈子年复一日复一日，年复一年的吗？
很多人是这样，这个书里就有，就是关于这个的，对吧？我甚至想把那张打出来贴自己墙上。所以我觉得人会陷入到对于各种各样事情的那个执迷里面，但是就是，我觉得任何东西都有可能生发出好的东西，但是对于你创作来说，你总是要有一个取舍，你不能完全的放任。就像我也特别喜欢说的一个事，就是hifi人群和音乐人的人群几乎是没有重合的。如果你认识这两个圈子的话，发烧音响爱好者和音乐家，不是音乐人没有重合，就这两个圈层各是各的。就是那个问题就是，首先我说的是一事实层面的，我不是在讲道理，就是他不应该重合。
不是，我说是实啊，它就是不重合。就是音乐人没有在乎那个的，然后在乎那个的人，他不是那么在乎的演奏和欣赏音乐本身。就如果你仔细去分辨的话，如果你在那个音响里面去听那个声音的毛刺的话，听那个钟高屁，说那个蔡琴唱那歌的时候嗓子有一个， 有一个嗡嗡的一个共鸣的一个嗡嗡的声音，你就在找那声音，然后你就说这套三十万的这个什么胆激和功放出来的就这个声音就会大一些，你就能听见，在另外一个里边你就听不着， 然后你就觉得那特别冷漠，那声音， 如果你是在分辨这个的话，那音乐就跟你没关系。
不是，但是这也可以成为一个兴趣爱好，一个游戏嘛。你看那个周鸿一不是在直播的时候就经常晒自己就是什么成千万，上亿的那个设备去分辨这个嘛，它也可以成为一个玩物，一个hobby，这都没问题，就跟Finders一样，它可以成为一个人的兴趣爱好。 但是它就是，它是non musical的，它是跟音乐性本身没有关系。所以如果你打定主主意你是一个做音乐的人的话，你不太可能陷入那个，或者说如果你发现自己你察觉到你自己陷入那个的话，那你就得有意识的去调整一下。
而你比如说，你比如你是一摄影爱好者，或者一摄影师，你不可能无止境的放纵你自己，对于那个什么颜色色调，什么暖不暖，或者是什么这个粉色，色彩16比特还是14比特，浓郁或者什么之类的，那个obsession对吧？就当然这个obsession可以成为你的一种strength，但是它不能，不能把你停在这，不能把你困在这。其实很多时候是这样，它不一定跟自我有关，它有可能是跟这个fetishism有关系，还可能是关于一些它的里程碑。
你看它这个书里面还讲了一些那种，就是一个人做了一个特别厉害的一东西，然后你也很难说它是在乎它自己，但是它就是觉得有一座大山，就是就好像一个人25岁唱了一歌，或者是写了一个特牛逼的小说，然后它这辈子就是活在这个阴影里面，你也很难说它是为了它自己，但它就是过不去，它就是过不去，怎么也不行。你能想象吧？就是那个人他就觉得说他一动笔，写一个字，他觉得这是一坨屎，这根本就不值得写，就没有办法超过他之前的那个明显了不起的东西，然后那个玩意就把他给压死了，这也是一类。
就有非常非常多类。你看这书，就是他在探讨，因为这个人做了一辈子，他就是他接触的艺术家的这些病，他都整挺全的，就挺有意思，全科大夫。对，但主要是到底你全都懂，那你为什么买了这么多器材之后就不拍呢？因为主要你每次买器材，如果你只买跟我说颜色润什么的，我都接受，但你每次给我的理由都是你可以拿这个拍什么，但你到现在你也没拍的东西。对啊，所以我特别懂这个，但就是摄影对于我来说，就是这么一个非常腐朽的一门爱好。对于我来说就是这样。
那你为什么要和自己说你想拿他拍什么呢？你可以完全跟自己说就这器材就是好，那不行吗？就是我身边一朋友他不也说吗，就是什么哈苏莱卡，就整一户兜子，然后就永远只拍自己工位上的那个一盆花，同一盆花。 而且这甚至是good practice if you think about it。你怎么检验一个镜头。是不是好呢？你应该拿他拍一样东西，对不对？因为控制变量嘛。所以他没完没了，他年复一年的拍他工位上的同一盆花是非常对的。对啊，就如果他玩的就是这个的话，他甚至他连那盆花他都不应该拍。
他应该去买那个测试板，测试板，然后就挂在家墙上，然后把他家那幅画给摘下来，就挂一次测试板，然后买一巨贵的那个三脚架，然后每天就往这三脚架上放不同的相机，然后装卸不同的镜头，是不是就极致了？但是他还不愿意这么做，因为他不愿意原谅自己，他不甘心，你知道吧？他还老想骗自己，说不行不行，我还是得搞点创作。不是，你别把他跟你比， 你俩并不太一样。就是你会在工作室里面搭那个俯拍的架子，为了说有到一日拍视频的时候能拍底下那堆，就手上手里冻的东西。
然后你会想的说，为了某一个特定场景，你买这个镜头就为了拍这人的那画面。对，就你每次买东西的时候，你脑中其实是有那个一个特别明确的创作场景。对，像我这个就是所谓的那个对磨刀的这个事的执迷，就卡在这了。磨时间长了，我就变成了一个喜欢磨刀的人。就擅长磨刀的人，你知道以前小龙叔有个博主，每天到河边拿石头磨铁锄，就看那磨，磨成针。后来磨到两千多天，那玩意儿被水冲走了，然后又花了好几时间找回来。
要不你看这个事吧，那也是创作，我觉得对。但是他那是行为艺术，行为艺术也是艺术，没毛病。我觉得你这是行为艺术。对，我为什么对这个问题，我问你这么多遍，我还是在问他，就是因为那里面在我看来很明显的，让我感觉到矛盾的地方。如果你是一个完全沉迷到这套东西的人，那我完全觉得所有事合理的。 但你能做创作，就为什么在有些时候，这些你对于一些东西的沉迷不会限制你，为什么呢？你得承认，这个事本身是有乐趣的。
就别说我这个了，就我刚才说那发声音响，在里边听那个蔡琴的那个嗓子汪汪的那声他也有乐趣，你能说他没乐趣吗？当然有，就是听一个所有人，包括他自己，根本都通过不了双盲测测试的同一张唱片的50个版本。他就硬要说里边哪一张黑胶，或者哪个remaster比别的要厚重一点点，他也有乐趣。他就是分辨区别，本身是一个可以玩的游戏。而且他可以给你提供足够多的乐趣，因为你分辨的越多，你就越觉得每一样东西都有存在意义。
就跟喝咖啡的人会觉得那个100种产地有100种的味道。同一个产地的每一批豆子有不同的味道。同一个产地，同一批豆子，放的时间长了，是哄哄哄都有不同的味道，都不一样。完了不同的烘焙方法都不一样，这就是一个矩阵，一个复杂的多维度的这么一矩阵。然后他就在里面穿梭分辨其中的区别，这本身就是一个自我满足、自我实现的，它就是一个自足的东西，self rewarding的东西。
但在喝咖啡这个事里面，你的最终目的是喝，你的确喝了。但是在器材这个目的里面，你的最终目的是拍，但你没有拍。对，我没有拍，就是因为我也在分辨那个器材的区别。对，就是分辨完了就完了。但为什么在有些创作里面，器材没有拦着你，是因为，比如录音器材太容易到顶了吗？你从来没有因为录音的问题，在不在场里面困住过，因为我刚认识你的时候，有一个事让我特震惊，就我去你家， 当时你的上演工作室，然后在我录那节目，然后你跟我说你不降噪。
我说你用啥麦克，就随便用，就我以为你店音乐节目，你得老他妈专业了，就劳动了，然后就给我整这整那。我觉得我得第一次体验这种高端的设备，后来发现，就是你用的设备跟我一样。对，然后你还没我在乎，你甚至连防防罩都不掏，以至于那期节目的徐管的那个那部分被我剪掉了，因为徐管全是喷音，喷到麦克上， 那跟我没关系，那只是纯徐管自己拿一个麦克风，这忽远忽近的，然后就还拉过，这事里面你完全不在乎这些。
就这俩差别在那，就为什么在摄影这个里面，因为我对你比较了解。你要是说你是一个完全没有拍摄机会的人，那你就算了。就你是有大把的，你想拍的东西就在你前面，但你在这，你就始终没突破那个线去真的拍的。但在做不在场这事里面，你就真突破去了，你就在做的。而且你没有受限于任何的，你会困扰你的东西， 为啥这俩事会有这个区别，感觉可能也困扰。
这不是好久没做了吗？但你不是因为要买个好麦，因为他对，他就是明显不是。不是， 就是你说我有没有试着去拍东西，我也有，但那个感觉就是，我拿着一个全服务单反和我自己觉得搭配的特好的一个镜头，走到街头，或者是在土耳其的什么在卫城的废墟里面，我也比伙比伙，然后我看了我就删了。就是那种， 那种难以忍受的厌恶和自我嫌弃和那种就是disgust，就很恶心，因为就感觉不到自己在做一个有意义的选择。
感觉自己就是在瞎整，然后就是这么多年始终没有突破这个。就好像是创作的第一关，我始终没有觉得摁下快门的这个过程，虽然我也比伙半天，我也就找角度，找构图，然后去决定那个曝光，但我始终没有觉得我在做一个有意义的选择。就是有意义的选择，我没有觉得我在实际的做一个事，这个事可以这样，也可以那样。但是出于一个不足够有分量的原因，我选择了这样，在千万种样子里面，我选择了这一个样。
我就始终没有突破这个，所以我就觉得我在瞎扯淡。就是我连把那个卡拿回来，在电脑上删，我都做不到。我就就现场删了，然后有的是出门一天，然后我那卡里是空的，就是我都无法忍受它在我的的卡里面存在。所以就是我就是没有办法突破那个第一层障碍，然后开始去做这个事。就是这个事，你没有办法。对对对对，那显然在，就是在做播客上，我肯定就早早就突破了，或者说我就没有被那困住过，根本就。
所以你又想过为啥，就是为什么这个事会把你困住，但做播客就没困住你，我不知道，因为我也没有突破那个障碍的过程，我就是从来没有困住过我，所以我就，我没有这么来对比过，不知道你要是在YouTube天天看什么Jerald Undone的那个评测，然后看什么相机入魔的公众号，然后你会觉得，这玩意儿就是一球赛，实际上你会去踢球吗？你不会，但是你每天看那个体坛周报，你就劲劲的，完了去虎铺，去跟别人吵，他就已经足够了，够了。
你还想咋的，我连球都不看，我就看看几锦。我是连几锦都不看，我就想知道，今天我该跟着网友一起，该阴阳谁，就已经对足够的， 就是今天消费社会普遍的一状态，每一个事的每一个层面上，他都想要困住你。就这个世界，他就是暖洋洋的，他就是想要让自己成为一个足够舒服的温柔乡，能把你留住。他有足够的激励形成这个样子，每一个事的每一个层面，不管多肤浅，他都在做这个事。相机的厂商就是靠这个，难道靠摄影师吗？你告诉我，佳能这公司是靠摄影师样的吗？
你在跟我开玩笑吗？摄影师是个屁啊，佳能没靠你。那索尼只靠你，还不是因为你，我就早去买尼康，对吧？所以没吧，这就是消费社会的一个普遍状态。你也很难说是置身事外，因为创作本身它就不是一个特别普遍的东西。人不是无可避免的要做一些创作，我觉得就并不是。我觉得恰恰就是这个点，就我觉得这本书里其实在聊的就是这个事。就是有些时刻，你会遇到一些无可避免的，你觉得你必须要创作的事。
你知道吗？就在安身这么久，你问我的所有器材问题里，只有一个是我觉得是真弄的问题，就是你跟我抱怨说ACCR的rolling shutter。但是太严重了，导致你拍建模照片的时候，它那个股东效应太明显，这么多年。你不能说唯一一个，但照着我印象中，唯一一个真正的问题，就是那个的确影响你干活了。你那个东西干不出来活， 但如果你顺着这个去想，就是为什么那个是个真问题的，就是因为你在干一件真正的事。
你在拍一个建筑的时候，你绝对不会觉得说我拍这个没意义。而且建模是一个， 就是至少在当时，它是个施工的过程。对，就是你在尽量的在有限时间里面，尽上尽美的拍到，它的每一个角度，每一个凹凸，对吧？所以在这个过程中它也符合我刚刚描述的那个逻辑，就是你建模的话，按下这块门，你可以或免于自我审查，因为这一下就只是在扫描的， 一步准，就是扫描仪划过的一条线而已。 这条线不涉及表达，也不涉及创作， 你只是在干一个活，就是就跟种一个地一样，就跟开拖拉机一样，就踩油门一样。
你不会觉得我这个油门踩的， 有没有在表达， 有没有在完美的实现你心中的图景？不是，它就是一个需要踩的东西，所以它就豁免于那个自我审查和自我批判，它就完全不涉及这个事。你就可以踏实实的去做，它像是一个特别普通的劳动过程。但你觉得建模这个事本身它是一个创作吗？对，它做一个整体式。就从你起心动念说我想要拍一个东西，到你得到这个模型的过程中，这个是有的。而且就是你在拍摄它的时候，你的想漏，然后你选择的那个，你的笔墨更多的花在什么地方，包括你的，你选择的时间，然后你赋予它的这个意义，这个是， 就是它足够 constitute as a creative process。
但是这是施工的那个过程中，你按一千下快门的每一下单独拎出来，它不是，或者它不需要是，它不需要被作为艺术创作的动作来检验，它还是一个新模的东西。所以我感觉这个事就特有意思，就是创作无论如何都是对于你自我的表达，你有想表达的东西。 但是你真正想创作出来一件事，你的所有困难都是在于如何你不要让那个自我去限制你自己。我觉得整本书其实说到最后就是突破自我， 就别在乎自我的存在。
但你没有自我，你没法创作。所以我理解中，比如说像我，我这也买一堆相机，对吧，这你都知道我买这些东西。那如果我回看我做出了什么，什么没做出来，就是在于我做出来的东西里面，我一直到有个事贼，他们重要，我想的那些瞎事都不是那么重要，然后我就把那个最终目标做了，我就做出来些东西。但是我但凡开始觉得次要目标是特别重要的时候， 就是我自己的那些喜欢的东西，我自己那些想法特别重要的时候，我就会沉迷到那些东西里头。
比如说，妈买六四五，能放十六张照片，这样的话交片钱会省点拍项目的时候。或者说，马七这个镜头确实好，拍出来效果确实棒。或者说这个十六比特就是比十四比特调的多，万一有着一种打成两米成三米大的大小的时候，这个颜色能撑得住。我如果用数码就撑不住。或者画幅再大一点就是不一样，转换完了以后，就是比那个不转换的那个，全幅的镜头拍同样的东西，哪怕同样的景别都不一样。
对，就我发现后来我一知道，这本书它做的作用是拔刺。我不能代表别人说，那只要我知道，我脑中一堆刺，就那堆刺在影响我做创作，这堆刺在限制我自己。它像是我的思想刚硬，比如说我就长时间就觉得说，我想要搞创作，我就得拿胶片去拍，我就不能数码，因为数码色彩不行。然后以及胶片慢慢拍，就是我可以花更多时间去想。我最近就在想，我为什么会有这个想法，就是我谁给我强加这个想法？
就是假设今天把我过去拍的所有作品全部换成数码拍的，我的表达就不存在了吗？显然是存在的，那为什么我会被这个事限制住呢？我就特好奇，有时候我也在想，就是为什么这些刺会刺到脑袋里面。我觉得每个创作者脑中都有一堆刺。对，但它不是唯一的答案，不同的人困住的东西不一样。我被这事困住了，你困住的那个东西，可听起来更像是就是因为你拍那个东西没有那么大的分量。
然后呢，就是你心里对自己这个创作，就是我是个正经的创作过程，我这动作是很创作的。当我创作的时候，我真的是在创作， 你知道我不是在瞎瞎整，那个从瞎整到创作，它有一个那个自我审查，你得过一个线，那个线勒着你的时候，你就更倾向于去抓一些救命稻草来帮你把你的脚垫高，为了能过那个线。而这个垫高的就是你踩住的东西，那些东西是特别容易的。
就是当你觉得我真的对我的镜头特别尽心尽力了，我对我相机的选择特别的用心了，我对于我测光，我买了最贵的那个什么测光的设备，或者是我闪光灯同步，我整明白了怎么怎么样的，你在捅过这些事的时候，你有一种很安心的感觉。但这些东西它不是那个真正substantial的那个，你的问题听起来像是这样，但我的问题就跟你这个完全不一样。你这个我也具备，但我有另外一套问题。
我的那个过度自我批评的这个事，他在比如拍照片这个事上，就是一个求老，在做播客的时候，它是我的生产力，它是我的动力，因为我就是在over criticizing一个东西。所以over criticize并不是个问题，它是我的力量。但是在拍照的时候，它就是一个纯粹的坑叠的东西，它是在消灭我的，或者是在压制我的东西。所以我到今天都没突破这个。但这个东西是同一个东西，它就是我。
我的脑子里的比较发达的一块肌肉，它就是会发力，它一发力，在摄影上就会摁住我。但是在评论一个事的时候，在谈论一个事的时候，它就会托起我，它其实是同一块肌肉。所以我这个问题是另外一套问题，就是只要你评论的事不是你自己的事，你都可以非常好地发挥这个技能。但一旦这东西变成你自己的事之后，你就开始批评你自己了，你就没法弄了，就是关不掉它。我觉得就是人各种各样，我觉得人群里有很多像我这样的人，就是他等不及，等不及要评价，等不及要批判，它马上就要。
就你下笔写第一个字的时候，你就开始说，这不行，这差太远了，实际上你着啥急呢？对吧？但这不是一个你想通了就好使的事。对，就想通一个事很容易，想开一个事很难。因此别强求，但是就是在真正重要的事上，这就跟健身或者任何你改变你自己的迫切性一样，你也不能逃避，你不能说完全躺平说，哎呀，我就是这样的人，那你就这样，那你就你就啥也别追求了。那这书帮你解决啥问题了你觉得？ 你在consciously说说这个事。北京让我遭罪，所以我喜欢北京。这个不是，这还有任何值得谈论的，那个和辩析的。
对，是。这不就是未遭罪而遭罪。这啥呀，这是，这不用说了吧，这不用说。对，所以我觉得，我觉得痛苦它就是作为一种stimulant，它就作为一种刺激物。它就像所有的刺激物一样，它就是会刺激到你。
所以如果它在发挥正向的作用，那它就是好的。但是它能在你的意识层面发挥作用吗？这我非常非常怀疑。你说不行，我没有感觉，我得遭点罪，这个我真的非常非常的怀疑。
但如果I don’t know，如果对你来说work的话，那你就应该这么做。但我确实很难想象这样。不是，那你要说俄罗斯的经典文学，那痛苦对于他们那就是源源不绝的力量的根源。但你能说，说这托斯托的耶夫斯基，他每天就是想想这世界还有什么罪可以遭，他那不可能是这样啊。我感觉这更像是一个，就是事后的事。
你事后你做文学评论的人，他会这么来说，但他更多的像是一种文化的显露。他跟他的整个这国家的历史，这民族，跟他民族性什么，包括他个体他都有关系。然后事后你可以这么说，但你很难说他作为一种方法，或者是作为给自己扎一枕，我就这种是非常非常糟糕的想法。就还是觉得自己是一种特定的机器，要有特定的按钮来吹个自己。甚至我觉得吹个自己都也没啥。
但是你把这个作为一个特别，就像我在挑选更好的镜头一样的。今天我把我该吹个我自己的东西全做了一遍，然后并且对这个事心满意足。这我觉得这就是，那肯定是没任何用的。它就不能是一个特别conscious的过程。
我觉得这本书里比较有意思的一点是，他说了一堆规则。我觉得对于我和你来说肯定都挺实在的。但是今天当一个人想创作的时候，他会遇到一堆特别奇怪的规则，然后他会进入到我们的脑中。就比如说像我们摄影，如果去参与一下任何艺术圈的讨论，他都会说你这个题材信不信，你这个东西有没有人拍过。哎呀，就这个上世纪90年代比较火，现在拍有点老，有点旧了。这本书里完全没提任何跟这种事有关的话，但当一个人想创作的时候，他在网上如果搜怎么做一个东西，他大部分时候学到的其实是刚才我说这套话。
对，这点其实挺有意思的。就是哪些东西其实是创作真正你该注意的事，哪些东西你根本就他妈不用在乎的事。今天其实分的挺模糊的。对的，还是那个艺术和艺术评论混杂了。那你到底是在做一个样子呢，还是在做一个东西呢？或者你做东西是为了啥？是为了让它看起来像一个样子？因为其实如果你的使命就是要像一个样子，它也可以成为一种艺术，某种艺术，肯定不会太了不起，但它也可以成为一种艺术。
但是大多数人连做样子他也没有真正的权利以赴。他只是一种特别mindless的，就是习了糊涂的觉得该怎么弄。他也不想这咋就该这么弄。就比如说拍这个所谓的梦盒，那他就是觉得淡了吧唧的，发白的那个褪色的，那个颜色用特别长焦然后去调一个，一看就像八九十年代的，那模样的房顶，它就是好看。
你可以说这个玩意不高雅，但你不能说这不是创作，因为他非常非常的那个，就是用各种方式，然后满世界的找这个玩意，然后去各种方式去接近。他心里有一个痒痒，他就使劲去挠。可能确实不是特高明，但你也不能说他不是创作。
但大部分人就像咱们原来聊如何正确的曝光一样。你他妈你连这个都没想明，就是这个。这个就是一个特别基础的东西。就是当你问怎么曝光最正确，就是在那你抓着那老师，你说你还是没告诉我怎么曝光最正确，我怎么读那个数，我怎么设置那个，然后看他的曝光值显示成多少，然后怎么用斑马线才是最正确。
就这个问题体现出来的那种盲目，这个才我觉得才是拦住了99%的人。因为他甚至没有在学一个样子。因为我刚才说那个梦盒，他也在，他在接近一个样子，对吧？那个也很难。你现在去窗外，你随便拍你能拍出那么梦的吗？你也很难对吧？那你也在追求一个事，但我刚才说的那个甚至没有在追求一个事，他只是在学一个样子。
他就是觉得这帮摄影师就他妈缺德，他们知道咋曝光他不告诉我。但问题是，你为什么要要一个特定的曝光的方式呢？因为曝光是你拍照片的一部分。你把那屋里拍的发黑，你把那屋里拍的发黑，窗外拍的正好和你把外面抱的完全白，然后把屋里的那个人的脸或者人的轮廓拍得很清楚，你到底想拍啥？
你不是你心里有没有明确你想拍啥？你要有的话，这还叫问题吗？这不叫问题啊？就是因为你啥也没有，你只是你只是有一种特别不假思索，觉得就是拍照，就是有一种正确的方法。我还是就不够勤劳，就是没有cover全。等我全学到了以后，我摁，我就像机器一样的每摁一张就可以放到世界中国上赚点钱。
对，就是拍照是关于拍照的方法，还是关于你想拍那个东西的。对啊，很多人就在这里面，他就迷惑了。他不就又回到了那个，那说明你还是没啥想拍的。你没有足够具体的想追求的东西。你要有的话，这些问题就没了。这些问题就被你那个你具体的那个要追求的东西给消解掉了。
那你当然要那么报，那不是废话吗？你不那么报，你就拍不到了。但只有当你心里空空如也的时候，只有当你把你的创作的目标定为，老子要拍一张妥当的照片，老子要拍一张像是在七九八能看到的照片，那就是无敌糟糕的创作目标，对不对？他不是目标。
对啊，但是很多人的心态就是这样。他甚至连这个都没想。然后你仔细追问他的时候，他会很害臊的对自己承认，其实我心里就只是想这样。我只是想让别人觉得我拍的这个照片好看。我想要，我想艺术，就是非常非常糟糕的目标。我就我这目标，就是我有的目标。
我当年刚开始写东西的时候想要是我要写成一个像什么什么杂志的特兽那样的文章，或者说我做播客的时候想的是，我也想做一期像不在场那样的节目。但Tunstall这么多年过来，我自己喜欢的，我自己做的那些东西，没有哪个是以这个目标作为驱动的。因为他听起来就很差劲。
你想做一些像不在场那样的节目是啥样啊？不在场啥样啊？like你有试图拆过吗？因为他不就是一个人就是说一个事吗？他也没什么特了不起，就是有没有具体的东西。我告诉你我干过啥这个事，首先听着你所有关于创作的访谈，然后不断的重复听同一期节目，就在看你是怎么把一个事切入进去的。就是把你当成一种语法在品，就觉得就这语法我没学会，我学会了我也能说这话。
我绝对不知我干过这个事，但我反正属于病比较重的。就我那段真的在干这个事。我现在完全从这出来了，但那个的确困住过我一段。我会觉得我节目做的不满意的原因是因为我都是那种特别肤浅的对谈。
然后对，就是语法并不是一个不能追求的东西，对吧？或者是说话的那个口吻和那个就是那个韵律的感觉，并不是一个不能追求的东西。就还是那样，梦盒都可以被追求，那这有啥不能被追求的呢？但是其实你连这个也没追求。就你还是就是你说这能被称为懒惰吗？好像也不好说，感觉也不是懒惰。
就是一直没有真正的去tackle，就是短兵相接的去confront你到底在追求什么。这个问题，你就始终站在了门外，然后允许自己就饶了自己一下。你就饶了自己，你就说，哎你就是想做这个纽约，我写文章，我就想写的像纽约课。我就想写的像纽约课一样，完了就停在这了。然后你每次想试图，那我到底该咋写的时候呢？你就又退缩了。
你不想仔细想，你还是想，哎我想写啥，我就想写的那个，那个时代杂志翻开的时候的那样。你知道我刚刚最傻逼的事是啥吗？你有你的创作问题，我想有我的陷阱。就我，我当时就是听了一个故事说纽约时报让一个人去写那个西闭市村，然后给那人放了一年还是两年假，挺长时间前行的故事。然后那人花了两年调查去写，结果我就想，我写的不好，就没有花这么多时间。
也就是一个事，真的。我真的会想，包括我当年拍照的时候，我就想说我特别喜欢team的照片。我就问他，你是拿哪个相机拍的，啥角断，你拿什么软件调色，你拿开不车版联机的时候，你用开不车版调色。你要再到lighting room里调，你哪部分调色是放lighting room里的，哪部分其实调到ps里的。我问老多这些问题了，我问了他两三年这些问题。我也觉得team挺好的，到现在没烦我。
当年问这么多这种问题。对，就是我说过无数遍的那个话，就是咱们的一个，就是一个永恒的struggle就是这个。当你没有什么足够重要的事让你抓狂的时候，你就会把那些不重要的事提到那个重要的事的位置去抓狂。就是因为你还是，因为你空空如也就还是那个问题。但是你刚才说的，那个其实在那个书里也有。
对啊。有啊，我所以为什么说这书好用，你知道他也治我病。他不只治你病，他还治挺广泛的一个创造的病。对，就是症状贼多了。对，78个呢，总有一个符合你的症状，而每一个里边也有好多，就是，确实努力过想去做任何媒介的创作的人都能在这个他讲述这个里面感受到一种慈悲。
对，就是他真是，就是在度大家。他就是，你看的时候你会觉得，这他妈太他妈真实了，就不像我这样的去struggle过的人，就是一个在任何意义上都不创作的人看这个书，就会觉得是一个纯矫情。实际上，这话不应该在一个推荐书的节目里说。就这书不适合大部分人，大部分人的问题根本没跟这书没关系。
但你要是觉得有关系，我觉得你聊到这，你肯定已经知道你就是有关系的人，你就可以买来读一读。因为我觉得这书，我读的时候当然我今天读还好，但如果他让我早两天读，我会觉得我感受会更明显。就他给我吃了一个定心丸，就他告诉我哪些问题是重要的，哪些问题是不重要的。
我不知道你有没有这个体验。就我不是因为没有一个自己固定的基地，我要到处求爷爷高奶的投稿，然后想办法做出一些东西道找地方去发。在这过程中，我会遇到很多挑战。会有人问你， 你这个信息增量是啥，然后你这个事有没有什么独家的视角，然后你这个东西大众关不关心。然后我的全部所有都放到那些事里面。
包括你要发微信的话，你最多能放几张图，然后多少字，字多了就不行。然后我的非常多的精力花在了解决这些挑战上面。就我要把我的文章说到多少字以内，然后要保证有哪些信息是完全全新的，然后如果不够的话要再采访什么人。这真是苦海无边。但我今天，这些千千万万的心魔，中间那个成为更好自己，或者是成为那个proper artist就是成为那个被社会认可和允许的人，只是其中比较流行的一款，它甚至不是唯一。
它完全不是唯一。就是千千万万，你刚才说的里边就已经有五六种了。实际上，就是比如说最流行的这款就是，我其实并不想做什么艺术，我就是想成为一个艺术家。我为什么想成为一个艺术家呢？因为我想获得当艺术家的感觉。就活成了小红书，因为小红书就是关于这个的，关于这世界万事万物的感觉的。
对，就是你在小红书上写怎么当艺术家不会火，但是说当艺术家扮展什么感觉，会火。那肯定的，因为that’s all that matters。人活着就是关于每一个事的感觉的，对吧？这是一个比较主流的叙事。但是你看你刚，你刚说里面也有另外一种是啥呢？就是说，你说这个人为什么写文写文字特别想要去模仿他，就是他想要的那个报纸杂志，或者是那个什么知名专栏的口吻，他想要成为那个特定的样子。
这是一类对吧？就然后他就分辨，他就拆不清楚了。他就觉得那个，比如说你拍纪录片，就觉得这NHK拍，这个就特悲悯，特人文，特好，哎呀就对平凡人的那种体恤，哎呀妈呀简直的，哎呀我去，我就想要这个。 但这就还是停留在一个贼边缘的一个事上，想要那感觉，不行了。
因为这个时候你就发现哦原来你想关怀的那个平凡人也就是你的一耳环一项链，因为你就是想要一个我特别关怀平凡人的那感觉。 所以你需要很多很多的平凡人来那个testify，你可真词，就是你可真悲悯，太能够悲悯了，哎呦我，你就悲悯疯了简直是。
对，就是这也是一种，对吧？还有一种，就是你就是你刚里面还夹杂着第三种，就是其实你没有仔细在分辨的这些，但这些东西其实是完全不一样的叙事。这些叙事都在绑架着我们。第三种就是传奇的创作故事。传奇的创作故事，这其实是一个特别典型的，就是不在场应该去聊的的东西。就是关于那个自发性，或者是关于那个无敌的， 就是那种就是鱼公一山一般的努力和付出和对于自己的手艺的奉献，来获得一个真正杰出的作品的这种叙事的迷恋。
这同样的没有任何意义上比刚才说的那几样是高明的。就是说，哦，原来一说什么样的特稿最好的，我两年时间放假，什么都不干，每天就在那待着，采访了无数的人，哇，就这才，这个描述里边，作为一个人的目标，一个人的写作的目标或者一个写作的追求里面，它包含啥了？它包含什么？它想写的东西了？没有。
是一样的呀，还是在迷恋一个传奇故事。因为一个好东西的创作，那得老痛苦了，那个得，你写一万字你得笔记，得做五百万字，你做两百万字，你这不可能好的，不是，这跟那个就是35毫米 1.4比1.2，那个就是但是画质要好一点，紫边要少，这有啥区别吗？就是比后者要更高明，我都觉得是完全一样的。就这些叙事在绑架我。
对，但有的时候当我真做出一个东西来的时候，我其实在这边会混淆一个东西。我到今天其实都没有完全达到这个答案。就是说假如我今天写了一个东西，我就写出了两万字来，然后我想发到一个地方里面，这个地方他用一个非常合理的理由跟我说，读了读不完，今天大家没这个耐心，你就得把它变成八千字。我觉得那创作中还有一堆这样的挑战。
就这只是其中一个，有一堆这样的东西，比如有人会跟你说，B站视频你要是20分钟以上看的人弯播率少，你就得让它变成短的，或者有人会说，说你这个照片在手机上传播的时候，你就不能拍这种横屏的，大家不好看，你就得拍竖屏的。这也是一类限制。
对，这类限制会稍微健康一点。不，但这是另外一回事， 就是你和你的媒介本身的冲突，就是你和你的出版发行的那个管道的冲突。这是另外一回事，这不是你心魔。这就是你要做的事。但是有的人会，这也他们能卡住人，对吧？有的人就觉得不行，我删不下去，或者是有的人他能删，他就是觉得这太辜负我了。然后他就是会很着迷，或者是他就特别发出这个往下剪的这个事，就这个书是关于删减这个部分。
他讲特别多，因为他做的众多的那个artist里面，很多人就是都是在testify这个事。就是Rick Rubin做我的唱片，就那个Connier West的那个ESUS那张专辑，就是他接受采访的时候他就说这个Rick Rubin不是一个producer,不是个制作人。他是一个reducer，是一个减少人。他把Connier给他的海量的材料，就是框框框下砍，砍得就是血肉模糊，就剩一些特别基本的骨架子的东西。
最后把那张专辑给做出来了，康叶自己，他下不了手，下不了刀的东西。他觉得这也好，那也好，这也舍不得，那个也舍不得，各有各的那个什么，但是他就是帮助你，往下砍。这个过程，它也是一个过程。然后他就讲了，其中一个他的这个心法，我觉得就是他是从反其道而行之的去帮助你过这坎。
他就告诉你，就说也没有什么是真的创作。他就是说，你创作也是编辑。就是这个世界上的所有的创作者只有一个角色，就是编辑。你可能觉得你做那个是真创作，然后把那两万字删到八千字的时候是编辑吧，对吧？因为编辑是没有创作性可言的，它不是一个creative process。但实际完全不是，因为你的创作本身也是基于就是你自己的知识和经验以及你所在的文化圈层的所有的东西的基础上的。
你其实就是在做编辑。你觉得你在你在做一个悬空的，纯洁的创作吗？其实也没有吧？所以他其中有一章就是讲，编辑创作本身，他就是一个不断curate的过程。他在帮助你卸下这个，把那个圣洁的创作和那个低贱的，为了什么庸俗的大众和那个严格的arbitrary的那个发行管道，为了削足势力而做的牺牲的差别给抹平。
他就说这都是一个创作的过程。他其实很多时候也是你心里的坎。对，因为我我自己还比较幸运，因为我合作编辑，我觉得都特好。所以说我非常明确能意识到他改的就是比我的改的版本好。就每次我会想，能不能不改。然后再看他改完的版本的时候，他用另外一个眼睛做了一次创作。
那让我的东西变得更好了，但这个里面，有时候我也在分辨，就是为什么有些时候的编辑我会觉得是，是你说的这种，但有些时候的编辑是我不太接受的。我现在意识到其实这里面的差别就是在于，这是究竟在干嘛，是你想把这个文章变得更好，或者这个照片变得更好，而是想把它编辑成一个国家地理的样子。
但今天很多时候你就是在现实世界中，因为你其实就主要是给自己做东西嘛，像我们这种需要给外面供稿的，经常遇到一个事是，就是这个你的合作的那边的人，他想把你变成一个样子。但你，你做这个东西不是为了变成那个样子。我拍张照片不是为了变成国家地理的样子。你要想变成国家地理的样子，你找国家地理的摄影师来拍。
对，你干嘛找我来拍？那这个时候其实就会有一个，有一个毛作风冲突。然后你就很难解释，就他说，那国家地理大家都喜欢，他就好，你这个就是太太亚了，太小众了，然后你那么整一整那么拍一拍，那没办法，那你要真是为了上国家地理那你就得这样。
对，但是就是怎么说呢？因为如果你就说他像一个样子，他还是好。这是一个特别反制的说法。不值一搏了，就是那种。我跟你说，那国家地理还是牛逼， 就是就停留在这了，然后也不去分辨他牛逼在哪，对吧？很多特白痴的文章就是因为作者付出了过大的努力，为了让自己的文风看起来像一个砖栏，或者是像一个杂志一样，给你整一个什么卡拉玛祖的流浪汉，为什么这么这么多，是个谜。
对，这种书里面还有一点我觉得它提的挺有道理的，就是你要找一些能帮助你做创作的朋友。就很多人会觉得创作是个纯私人的事。就是就我自己只要熬的足够多，我就总能熬出来。但是让我自己的体验正好相反。就是我要没有我身边这些朋友，我可能很难做出好东西。因为有一个人，他可以大胆的批评你，然后你全心全意的接受这人的批评，你会觉得说，我特别想知道他怎么来，这今天怎么骂我。
这是一个挺特别重要的事。对，而非常多的人他没做出来的东西不是他能力不行，其实他可能都没有，甚至没有那么多心魔，就是因为他在他的创作过程中找不到任何一个听众或者读者，或者观众来能对他的东西做反馈。然后他只能自己跟自己反馈，然后就哦进去了。
从另一个角度来看，反馈也是众多的那个外界刺刺激的那个feedback loop中的一种。所以他这书里面有一个章节的名字我忘了，好像是打破成规，好像是。他就是给你提供了七八种，就是他就是只是为了举例子给你讲，他在录音棚里边干过的，关于这个反馈循环是怎么去调整。你比如说他说有的歌手就属于特别那个恐人的，就他这屋里一个人不能有。他才能放松，不然的话他就会，就是会特别僵，特别神经质，歌也没法唱，弹吉他也弹得就是越弹越难受。
这样的话，他就会把那屋全清空，然后就是该在的人都不在，把机器就给摁上，然后把工作室全清出去。但是有另外一些人，他就人来风。他就是可能就是他年轻时候经验或者反正不知道啥原因，反正是有的人他就是喜欢在一个特别嘈杂的或者是特别不受控的环境，就是有一些随机的人，甚至不是来欣赏他的人在这儿，他就会特别好。他就是，他有一种冲动想要取悦别人，或者想要赢得别人的注意力。
当没有什么注意力可让他赢得的时候，就不知道该干啥了。你看这也是一个对于那个反馈循环的调整。就是再比如说，他就他举过一个例子，他就是觉得这个吉他手弹得过于使劲，他就是这个没有那种强弱分别的那种细腻情感。然后他跟他说，他发现他跟他说了也不行，那个人说好我再试试。他就他再试一遍还是不行，我再试一遍还是不行。
然后你知道，他有干了一啥方法吗？他就他就给那个人的耳机音量调贼那么大，就他反送，调过于大，过于大之后就震耳欲聋，然后他发现，我就是弹的稍微使劲一点就呲了头。这个时候他就会被迫的降低自己的力量，然后他就发现这样就不好听了。然后就是有点像是用设备就是反馈的这个回路来倒逼他轻一点来找那个音符和音符之间力量和力量之间的那个轻和重之间的差别。
就所以这个反馈，外界的反馈循环是对你有特别大的作用的。这里边不应该有任何纯粹的截然的独断的看法，就是只有一种行为是真正的创作。其他东西都是只是一种干扰，或者是一个真正的创作者是不会被什么外部环境所干扰，这我觉得就全扯淡。所有东西都在发挥作用。如果有一种方法能够帮助到你，你不要害羞，或者吝啬于去求助于他。
你只是不要把它当成拐棍。你只是不要把它当成那个，就是我还是差一镜头，就是咱前辈说的那个问题。你不要走到那一步就可以。我见到大部分人里面，如果是困到这个问题里面的一般不是困到你那，是困到他总感觉这都没做完，我不能给人看。
那这就是又是就专门有张杰就聊这个。对，你先说，比如说我们经常举那个例子，就我在拍摄这么多年，他一直没有作品出来。然后我那天跟他聊，他就跟我在说，他自己觉得自己的问题在那，就是他说他自己心中他总觉得他自己的第一个作品就应该是史蒂芬小尔的作品。他现在没达到史蒂芬小尔的那个水平，他就不应该把这个东西拿出来，他就是， 他说他就困到这。
但那是什么呢？能允许我第一位的问一声，那到底是什么呢？就是那史蒂芬小尔的水平到底是什么水平呢？就是还是那个问题，你没法回答，然后你就跟我说，那艺术这往上太玄妙，那可是不可说的。那问题就是我也没有逼你说，但是你自己有没有把它figure out你自己是清楚的。
就是这样的过程，这个是一个，就是这个是咱们跟前面已经讨论的一类。另外一类就是他觉得容不得错误。就是那个关于金山金美的这个问题，完美主义的问题，完美主义的问题就是说，他有一种观念就是，他认为金山金美是一个绝对改进的过程，错误少一个他就是好。就绝对好。他有一种这样的观念，这样的观念在很多时候是一个纯粹负担，会让你去陷入一个无止境的那个自我折磨。
而且那只会让那种作品变得更差。因为他这个书里面很多是那种真言式写作，他那个就是写那种短诗，他其中有一首诗就是就三句，反正是说，我忘了，反正就说有五个错误的时候，这个作品可能才做了一半。但他当他有八个错误的时候，这个作品就达到了最好。
对，是有这种事，我对这种印象特别深。对吧，就还是那个问题。你是不是就是不经意间又投降于，或者是那个陷入到了特别严格的一种叙事里面，往来去想说，我还是没改利索，我还是没有调到尽善尽美，这又是另外一种。
说着这个说，就那天我俩还聊了一个事，我觉得特别有意思。就是说到即时摄影，然后因为他不喜欢即时摄影，然后我俩有本分歧非常大的书，就是杨颜康拍的神品的人，就在架子上那本。他花了十年时间拍那个，一个天主教村子里面的这些事，然后他觉得这个不算是现在他心中的艺术。
就是因为那个事，你在拍的是别人，那是一个事，就你别人来拍，可能拍的不样，那他也能拍出来。所以就这个事不是没有体现不出来作者自己的意图。就现代摄影和现代艺术就得是，就把我自己体现贼明白。那个事，我拍别人砸了，我即时砸了，我觉得这个是今天，尤其在摄影圈这个创作中，我觉得特别大的一个问题。
就是觉得艺术是一条线，从前往后走，就跟进化的术似的。总会有越来越好到今天，我们创造到这一步了，瞬即就不行了，我们必须走下一个时代的新东西，然后会有一个更新的东西取代现在的。那那个时候的人都要拍，那模式的东西。
我觉得这也是一个特别糟糕的事。就是我们总把创作当成是， 就当成进化论，就有一类创作，就是比老子那种好。你用上AI了，就比不用AI强。然后你今天拍的是这种，自己不景的这种现代创作，就是比传统中拍即时强。然后罗伯特卡帕那种，你拍的不过好，这边不过近就是不行。就是因为即时已经被淘汰了，或者说我们就得拍彩色，因为黑白已经被人拍完了。
就这还是刚才说那套东西，但它是一个针扎在很多人脑袋里了。 对，但问题是， 就还是那艺术和艺术评论的问题，或者艺术和艺术史的问题。你在写艺术史吗？当你按下快门的时候，你是人类吗？因为艺术史的主语是人类。就是人类已经进入到了，不管是按照MOMA的叙事，还是按照大都会的叙事，反正人类肯定是怎么着了，反正，但你是人类吗？你是那个人类共同体吗？你肯定不是吧？
对，所以你为什么要考虑这个问题，这就是有点疯狂了，在我看来。但在我们摄影圈里，就是你有没有，你为什么要answer to这个主主语的问题，对吧？所以我不知道，我觉得好的艺术家，不需要均等的原谅别人。好的艺术家不需要成为超级open minded的艺术评论家。我觉得你就是，如果一个人说你这样想太狭隘，就是，问题是他太狭隘了，我是不认同的。
我觉得艺术家不需要一定不狭隘，因为他不是艺术评论家，他只是在做他的。如果这个东西没有干扰到你，你就是一个贼狭隘的一个乡村，挤着手，然后你觉得那个摇滚乐跟跟中金鼠全傻逼，你就觉得那个班舟，速度180的兰草是好的，有啥问题吗？我觉得没有任何问题，狭隘并不是这个事的问题。你也不是人类，关键是，那个东西有没有妨碍到你？
如果要妨碍到你，那可就是另外一个问题了。就我觉得狭隘不是问题，你不需要特别那个端水端贼品。我呢，我跟你说，这个世界所有的文艺的瑰宝我全部都理解，我全部都接受，各有各的好，萝卜青菜各有所爱。我觉得这跟创作屌毛关系没有，这是关于艺术欣赏的一个事。
但是这个东西为什么这种观念会有一种倾向去妨碍你？就是你创作的，这个是你可以就在里面品的。就还是那个，当你在做减法，就是你开除，我跟你说，即时摄影那不是真摄影，什么什么摄影那不是真摄影。比如那个街头摄影的人说，那个不是自发的，就不是真摄影，就是不是决定性瞬间，被我完全在完全钉点，屌毛都不受控的情况下，就是纯粹的spontaneously的把握住的，那才是唯一的真摄影。
他把别的东西全开除的那个过程中，他有没有限制到你，然后有没有成为一种教条。这个教条有没有让你寸步不能向前，把你卡在这，这才是那个问题，而不是说你狭隘，你不需要不狭隘。但你得往前走。说到这，就完全另外相反的一个坐标准上的问题。
就是说创作是不是必须做到绝对客观，对吧？就经常很多人创作的时候想的是，我要没有偏见，我对这个主题我要完全的open minded的来理解这个事。 这跟那传奇故事一样，这就是另外一种传奇故事，就是我跟你说，什么是最牛逼的艺术，我告诉你，就是最开放的，最开放的就是完全的好，这不也是一种特别点的叙事。
因为这俩叙事都在左右的困住人。比如我这个叙事里面很多，包括我有时候也在那。然后一些朋友他做一个东西的时候，他第一想法是说，我现在想做这个东西，是不是我对他了解不过多有所偏见呢？我说得多看看呀，然后整半天，到最后，你也偏见，也不一定消除了，然后你最终啥也没做出来。
比如说像我之前，忘了写哪个文章了，我当时的第一想法是，我觉得我了解的角度是偏。我想要先大量收集资料，把不同角度全了解一遍，然后做出一个东西来。但你这么弄，其实你最终做不出任何东西来，因为你在试图写，它是一百科。就是他又变成一拐棍了，就是我写不出来，我跟你说，还是没弄全。我还是不够勤劳，因为勤劳是很容易的。
你就可以一直整。你要说咱这个事， 就差事， 就差在不够勤劳上，那可简单了。你就接着整呗，你就再整个十年呗。你就一遍一遍的再去做调研呗，然后你就可以一直跟自己说这个。因为如果这个就是咱们的真问题，咱就卡这了，那我们该做的事是一个很容易的事，而不是一个困难的事。
那我就继续勤动不着呗。就是反正你就是还是要注意，它是不是一个take the easy way out的一个cop out的一个过程，我觉得。就这个书里面也有专门的章姐就提示你这个事。就是你会，因为一个事容易，而那个就是特别一厢情愿的把那个事给提到更高的位置。因为它容易，然后就开始骗自己，就说，还是这个没做到位，我再多做。
对，我对我的事之前有一段我写东北的东西，我当时，你知道我做啥事吗？就是我把孔福子上面从1900年到1945年中间所有的关于东北书都买了。就出关于这段历史的东北的书都买了，我把那个当成我创作的过程。到最后，我那文章也没写出来。是啊，就是买书是容易的。对，因为买书是容易的，我还觉得我勤劳查了呢。
能把书查全，我也不容易，谁查了，我天下这么多人，除了我一般，这没人查吧。对，我距离那个接引，上天伟大的那个创意，穿过我身体的这个事上，我绝对是排的最靠前了。那别人都得排我后面，因为我，我买了这么多书，别人都没买。
对，然后你要最讽刺是啥吗？就孔福子他一个发货做贼差，导致我一次买太多书了，有几本我根本就没给我发过来，我都对不上号，就哪一本我究竟没收到，我都没对上号，最后，然后又感觉自己挺愚蠢的。是，但你起码不是那个纯收书爱好者，就是跟这挑品项在矫情这个事的人，你要是限入那个的话，你就到我这level了，我但有些事里是这个情况，对吧？
书显然不是，是因为我写东西。 我毕竟肯定是有文字就行。对，但我在相机里面显然是你这个，对吧。就是买了一堆镜头，觉得这个镜头比那个镜头强。算了，不说这块。就是我们聊太多这问题了。
那我觉得最后这段，咱聊点正向的事，可以。就是为啥有的时候，我们会突然间觉得有东西我要做呢？然后最后我们把它做出来了。就是我们真正做出来的，我们自己觉得好的那些东西，它有什么特殊的能让我们觉得就我们最终这些都被我们做出来了。你有想过这个事吗？
那种重要性感受，对我来说是特别，就是势大力尘的。就不用你找它，是它找你，给你一拳打晕。你还用想怎么对待他吗？就它是这么一种非常巨大、overwhelming的东西。就对我来说，它要是那个力道不够大的话，你也不甘心。
我同意你说的。就是因为很多人批评我的时候说，我会开一堆坑，然后也没填完。但首先我不是每一天，可能又只有做出来的东西。对，但这里面很明显的区别，就是我觉得我是撞到一个东西，撞我的筋是大不大。有人撞我的筋是特别大，我这东西我一定要给它做了。但有些东西就创我一下，我会引起我的兴趣。我是个很容易被引起兴趣的人，这是我的问题。这不叫问题无所谓，但我会，那这个话我可能就停这了。就是因为他对我兴趣没那么大，但有些东西对你特别让你觉得这已经不是兴趣的问题了， 就我必须把这事做了的事，我最终就会做出来。
但我现在觉得就是很多，当然不是说一个人必须有这东西， 但是很多人就试图搞创作或者是做东西的时候，他可能也是没有那个，没撞到过这种东西上。首先我在这个事上，我不支持那个无边无际的open mindedness， 就我不支持说我们必须要时刻保持一个正无穷大的开放心态，说任何好东西都是好的。我们应该允许任何好的东西在我们的手里面去做，因为我又不觉得这是一个有任何实际指导意义的东西。我觉得人觉得特定的东西是好的，或者是好的东西在你心里就是有一种特定的样子，这个是无可避免的。
我举个例子，比如说咱们在美国的车上聊， 就是说美国的牛奶为啥不好喝。完了你就是最后咱聊了一路，然后你当时你就觉得这绝对是一篇好文章，对不对？对，然后那到底为什么你会觉得这绝对是一篇好文章，但你确实你没时间写，所以就搁那了。 但是如果你写的话，就是它绝对是一篇好文章，你心里是有一个特定的样子，这个算狭隘吗？我觉得不是。
我替你总结一下，你觉得那个东西绝对是一篇好文章，它符合了这样一种特点。我具体就不记得了，就是当时咱们在讨论的那玩意，就是对我们先说的是美国没有巴士奶，买的都是常温奶。对，没有短保奶。对，然后后来一想不合理，就美国咋能没有巴士奶呢？
后来我们发现实际上，按照美国的定义里面，美国的大部分奶都是巴士奶，只不过就是它是低温和高温杀菌的区别。对，只不过在我们这，就是那个高温杀菌，我们就把它当巴士奶了。但是在美国，它依然还算巴士奶下面的这个品类。对，它不往里区分了，它也没有更细的preference，因为人没有办法说这个是更低温，它就更新鲜，那个奶味更醇厚，因为它不区分，就是它的那个在食品监管的这个里面，它不区分，它怎么能让这个preference出来呢？
对，这是第一个，这是第一个，然后这事引起我兴趣了，就是因为那很明显有个好喝呀，你为了挣钱你为了卖更高价，你也应该把那好喝都做出来，因为好喝是绝对的，它好喝就比不好喝要好喝。我操，因为它是绝对，这不是萝卜青菜各有所爱的问题，对，那个有奶味的就比没奶味要好喝，那咋的，这就觉得这他们怎么能呢？对，怎么能呢，然后就查嘛，然后我的一个推论是说，我觉得美国太分散了，对，没有冷链。对，所以它的那个，它没有办法用足够集约化的方式把那个成本压下来，就是你三天保时期的东西，你做出来你送不到人嘴里，它就过期了，所以它不可能做，这是我们的第二层的推论吧，就是刚才第一层，这个是第二层的推论。
对，然后又发现这个也不对，就是它是有这个能力的，或者说是比它更分散的国家，人家也有短保奶，也有奶味特别醇厚奶。对，你就不能这么说。对，完了就再往下推，完又推，这就不展开了，对吧？对，我们当时在市场聊了两小时这事，推了有三四层出来，最后就不说了，反正就是最后是一个稍微有点，mildly racist的一个结论，就不说了。
因为我之所以花这么多时间推论这个事，就是我不想承认那个结论。我觉得那结论是不对的，但我， 我找了各种各样的方法都没有办法，没有办法撼动它，没有办法取消它的解释力，那个在所有解释力中就是最强的。好，所以我来说，我替你总结，但不一定对啊，就是我的感觉就是你，当时就是你在拍大腿说，哇塞，就没时间写，这样有时间写，这他们文章绝对是一好文章，它符合了你一种特定的preference，一种特定的aesthetic。就对于你来说，这个美学就是，它是层层递进的，每一层都好像是可以停在这，而且每一层都明显有很多人停在这。当我们讨论一个问题的时候，明显就是有25%的人就停在第一层了，有18%的人不知足，它去到了第二层，但就是你最后找到了可能有四层，也许有第五层，但你没找到。但每一层都不是假的，它只是不够究竟，没有完全的把这个事给解释掉。
它还是留有一个那你咋解释不通，那人俄罗斯农村也有很好呢，你咋你那个产业集约化的那逻辑你就解释不了这个，好像也有道理，并不是说被颠覆的那一层就是伪逻辑，也不是对吧。但它确实有一个层层的推进越来越深的过程。而且你回头来一看，它这一层一层，就是剥洋葱剥出来的那个东西，它又在互相解释和互相的连接，最后它连成一片，它完全不是一个关于牛奶的问题，它是关于一个社会的问题，关于一个经济的问题。这图景已经完全不需要，就根本你都忘了，咱们是在讨论奶了。这就是你的美学， 就是这个是你的一个特定的preference，文章可以有各种各样的样子，这是其中一种特定的样子。你觉得这样的文章就是一定是好文章，就我替你总结的，我不知道对不对。
对，就是因为我自己非常知道我自己的毛病，就是我想事特别快，就是你跟我说一声，马上就有一个反应。因为你是一个，你觉得你自己就有这毛病，就是容易停在一个置得已满的第一层。对，我非常知道我有特别大的这个毛病，所以我完全知道我给的所有结论，尤其我马上的及时反应，只能把人唬住，但那不一定是对的，还是大概率不是对的。所以我对我自己写文章我特别警惕这个事，就是我这个结论是不是给的太容易了。对，所以这种好里面，它掺杂着一部分，就是一种松一口气的感觉， 就不是成为更好自己，而是说我没有掉入我自己惯常喜欢掉进的那个陷阱里，我没满足。
对，就是我自己拿个绳子把自己勒住了，然后就我多走了，我找到那我才能把那个绳子松开，我没有不全力以赴，我确实是就是竭尽了我能找到的东西和我能推进的逻辑，我都推到头了，是这个意思。对，那这里面还有一些别的，就是你觉得就是你读别人的文章是这样的，是你也觉得很满足，所以就是这文章有一种特定的好的样子。对，还有另外一个就是，我对于问题本身特别看重，我觉得问题意识是创作的一半的东西在那个里头。对吧，就是你这个问题意识究竟是不是一个对的问题意识，但是你这个算是不知道，你要探究完成你发现是错的，那你就可以不写这文章了。我觉得大部分时候问题都比它答案价值大。
对，就你能提出一个问题已经很不容易了，所以有些时候我在想，就咱们刚才聊的说你撞到一个东西你准备写它，那你撞到啥呀，那就是问题。你只能撞到问题上面，你不能撞到一个事实上面。你撞到一个巨大的事实，你只把这个事实告诉所有人，那这，我觉得它可以算是读家新闻，但它不算是创作，对吧？那创作，至少在我这，它唯一的起因就是你撞到一个特别大的问题。所以我会特别着迷于那些特别，我感觉这咋是个问题的问题。包括就你咋说奶的那个事，我为什么对这问题就是在后座就是狂查这事，就是因为我觉得不是问题。因为就是咱没去那个好店买好奶呀，那买好奶就有好奶喝了，这不就这么简单吗？咱就全国人满了， 有什么好奶可以喝，但常伟泽他俩不是喝了足够多吗？对，所以人就不乐意吗？他俩不同意这个事，对，你不能这么说，你不能说我们样本不够。
对，因为他俩真的去找了所有能买到的店里的所有牛奶，对，都去喝了一遍，然后觉得奶爆喝。对啊，然后我当时就显得你像个小丑，因为这里边不求善解， 就是你了。对，因为本来你像是那个更理科中的那个人，哎呀，那是因为你们样本不够， 就好像你是更理性的那个人。但人家是更究竟的，就更求真的人，所以把你整尴尬了。对，然后我就感觉那这里面你不可能，就肯定是没买到好奶，咱就那时候你那倦这就上来了，就是你就想研究没事这个事，然后你发现你错了。我觉得这个是对我来说特别吸引我那个问题，就是我发现我他妈想错了。我讲的一个特别简单，我觉得我讲贼多，贼理科中，其实我怎么，我是那小丑，或者说你没有权力以赴。
就是别人戳穿你了。对，就是那个权力以赴，它也可以停留为一种类似小数的，就是它也可以停留为一种模样。你看我说话那个斩钉截铁的那个样子，你看我那个特别思想开放的，你们这个样本不够多，你们不能下这个断言， 这显得多理性。而且我在否认一个相片，但也只是个样子，因为人家真的买了，经不起你考验。而且你没有经得起别人考验，所以就显得那个小丑竟是我自己，就是那个感觉。
对，所以有的时候写这种深度文章有点像是自我救赎的过程，就是挽回的过程。对，包括像是前一段写那个绣带那个两篇文章，一个题目的一个美国绣带的文章，其实我不能说那是我写的，因为我觉得我就是把你们说的话集合到一起了。那它毫无疑问是你写的，是我写的，但是你们是很，我的养料嘛。但为什么我会在乎那个事，就是因为我得到的所有结论都太直接了。
就每个人在跟我说题目的时候，在美国有什么火的时候，每个人在跟我说美国绣带的问题的时候，都把这事当成一个既定事实，就等于像是一加一等于二一样。因为，所以绣带出了什么什么问题，然后就这样了。我对所有特别顺的事都挺恐惧的，因为我从我自己的经历我觉得大部分事不可能那么顺。对，所以一个人一旦对我一个很重要的问题给我一个特别顺的回答的时候，都会让我激发起我的那个问题意识。尤其我作为创业者，我们经常要讲一套，可是讲太多遍，讲特别多遍的时候，你就特别斩定截铁。我觉得open mindedness是一个创业者的敌人，因为你要是特open minded，你啥干不了，你就paralyze，你就是瘫痪了。
因为这你也不懂，那你也没有穷尽，这你也不究竟，那你做啥呀？你没法做决定了，对，你知道吗？你就卡住，你说，你点个菜都没法点，因为你老觉得你没有把每一个菜都研究明白了。对，你总觉得你不做一个事是因为心上不够。对，这个对于创业来说的确是敌人。但是对于创作来说也是敌人，他是有时候是朋友是敌人。有时候是朋友是敌人，这要在我这他是敌人。对，就是因为，我觉得挺有意思， 就是因为你在回看每个人，这也是这书里写的一个点，就是传奇故事。对，就是因为你看，我说我的这套，但是你在另外两个节目里面聊你的那套东西的时候完全不一样，对吧？所以你说假设有人特喜欢你的节目，然后对着你那个学就不如之前的我，但事实证明到最后几年之后，我真正做出来的东西完全是有一套另外一套的东西，模式做出来的，对吧？我的方法就是在车上给你们问傻逼问题，然后通过你们骂我把它都寄到我的那个nose里面，然后再看他们节省文章。
对，我觉得对我来说很好使这种。但是有的时候那个不好的东西，它也是很前进的，就是它也是非常非常真诚的，非常sincere的。就是说一个艺术爱好者，但是他就是，他就是卡在这。 这事就是让他成不了艺术家，他就是你不能说他虚伪，或者说他不真诚，他非常非常的虔诚。他就是说，我就是想成为写出那个最妙的文章的那个作者，因为那种感觉真好。就是我一边看一篇美文，我就在想如果这文章是我写的该多好。但是他就是过不去，他就停留在这，我听了一个播客，这播客真好。然后他就始终过不去，他就老是想，要那个，就是我要是能做出这样的播客的话，我要是成为一个这样播客的作者的话，该多好啊。然后可能很久很久他也过不去。
那你不能说这个东西是不真诚的，或者是，这特别真诚啊，这其实完全、完全是真诚的。而且他是，怎么说呢，他甚至是美好的，因为你确实在向往一个美好的东西。对，但这是不行的，这你连门都没进去，你甚至没有开始。这甚至在防止你开始做真正的创作的第一步，因为你没有从那个模样穿过去。那你还停留在小王树帖子的那个阶段，因为你只想要做一个好东西的体验。这是我感觉就是现在特别支配性的一个，特别主流的一个敌人的一种最流行的东西。但我实话说我挺悲观的。就是咱俩昨天晚上吃饭的时候说的事，就是这本书，你拿出来，我觉得99%情况下面它不会当成你心中的那个对于一个创作者的救赎和一个致命的药，而只不过会被变成又一个，就是跟所有东西一样，就是变成，哎呀，我创作已遇到这个问题了，这本书写的真好，然后把它当成一种对于自己心灵的安慰，然后最后还是什么都没做。
就舒服一下，对，就是它会停留在一个，就即使是这样的诊断和药方的东西，你依然能够在这个里面停留在那个样子上，啥样子呢？就是我原来真的艺术家也有我这个苦恼，那说明我的苦恼是真正艺术家的苦恼，然后呢，艺术家面对这样的苦恼，它会怎么开导自己呢？它会这么开导自己，所以它开导的那个方法我也学会了。完了就完了。
然后就沾沾自喜，哎呦，你看，我的困扰和我的努力也都像真正的艺术家一样。完了就停留在这了，因为它还是，你还是想当个艺术家。对，就是谢谢我被安慰到。就是还是， 要拿全世界的底层人来装点悲悯的自己，就还是，就还是停留在自己。所以我知道挺悲观的，我知道我有非常大的偏见。就我觉得所有这些书其实最后到最后都没有用。就不管是这本书，还是我挺喜欢的李大人写那本，我让XX把那本书给我朋友背下来。哪本？就李大有个那个透口秀工作手册。
那书和这本书应该一起读，就是一个是思想上的，一个是实操上的。因为你把这些事，你要看上一个你要干完的工作，你要把这事真的做了，而不是沉迷在那个前置过程中。但我觉得都没有用。我对整个创作最大的偏见就是我认为，当一个人想要创作，想要做东西的时候，只有一个原因，就他真的遇到了一个他必须不得不做的事。那时候一切技巧都保实，一切东西都没有用。可能好使，技巧可能好使，但都没有用了。就因为那个事已经太重要了，以至于他做的时候完全忽略了这些事。
然后最终他做完之后发现，哦原来我也能做出这样的东西，然后再看这本书的时候，他会知道，我当时也遇到这些问题了。但是如果一个人没撞到那个，如此让他重力这么大，如此把他拽进去的东西，多少本这样的书都一点又没有。对，千万人拒网瘾。就是，所以那这玩意有的时候是命，或者说有的时候它就是你没那缘分。可能一个比较积极的心态是你要调整好自己去等那个玩意。手术带兔，就是你确实要把这个篮子冲上，这样的话，他掉下来的时候他能落进篮子里，或者让他崽脑上别崩飞了。
就是别崩别地儿。你可能确实有一些准备工作可以做，其实这个书里面很多的叙述都是有一个这个basic idea。对，就是那个任何实际好的东西，你都没有办法直接要。卧槽，要能直接要，他不是艺术了。对啊，所以你怎么办呢，你只能营造那环境，你只能把自己调整成那个，就是，就是你双音机调台。你要调到那个正确的频率，完就等着， 就等着，或者是，所谓的那个围绕闪电，他有就他有一张，就是围绕闪电。那闪电你能要着吗？要不着，要不着怎么办，把自己调整成一个类似避雷震，或者是，就是你维护一空间，然后你别气馁，你就伺候好你自己的这个空间，然后等那闪电来。
这世界上没有比这个更积极的了。这就是你能做的最多的了，对，你不能指导闪电，你只能等着闪电。对对对，你指导的闪电，那不是闪电，那是闪电的模型。闪电能，要是能见门的和你能比，这时候有一个观点我特别同意。就是说创作就像种种子，就是你浇了一堆水， 你最希望他发芽的一般的都没发芽。你看这这也是跟那个什么一样，它就是有点那个，就是泛神论那种感觉，就是或者说自然神论的感觉，就是它也在帮你卸包袱。
就这东西特别好，你乍一听，你感觉特庸俗，神心灵就整这套， 那实际完全不是。它在帮你卸包袱，因为如果思想是一个种子，如果一个creative idea是一个种子的话，它就不全是由得你的。它不是被你拔出来的，它得自己长。它要是自己长的话，你就只能围绕着它。就比如说保持它好的温度和湿度，然后观察它，你不能完全的去操纵它。它还是在帮你卸包袱。在这里面有条特别微妙的线，就是你看表面就感觉这书就是神心灵，但你深入进去，你知道这有一条非常，就看着特别简单，但是却牢不可破的线在那中间，环衡着。
就是这本书不是关于让你自己舒服的，它全部都是想告诉你如何把东西做出来。对，而不是让你做不出来的时候让安慰自己，让自己舒服，甚至是因为自己舒服，就你不需要做任何东西。你只要在创作就一样。就像咱俩有一次嘲笑一个事， 就那个宣传说让我们一起来阅读，阅读是关于读什么书的，而不是阅读这件事的，对吧？但它就是让我们今天下午一起焚香，然后打手蝶，然后一起阅读。对，当阅读变成不及乌动词的时候，那你也别读算了。对。所以这个书不是关于那个东西的，是关于那个线底下的。
对，所以我觉得，它不是关于你是一个什么样的人，或者关于成为个好自己，你的生活图景是啥样的。问最后一个问题，关于这本书的，说了说也可以，不是关于这本书的。就是我是觉得，就是所谓的创作的金线是存在的，就是这个不是说什么，相对主义说有这有这的好，那有这的好，我觉得不是。我觉得就是有好的创作和不好的创作，好的作品和不好的作品。而我觉得咱们对于好的东西的评价其实还是比较一致的。
所以我比较好奇就是，你什么时候会发自真心的感觉这人做这个东西真好，或者我自己做这个挺好的。就你什么东西你来说是好呢，在创作上，这不是关于过程的，过程咱已经聊，就是纯实结果出发了。是啊。我不知道，就是我这么说吧，我没有预见过我需要分辨它的时候，我这辈子还没有遇到过一个。我做了一个东西，比如说一半成品，或者基本做完了，然后我有点含糊了。我相信很多人都有，我确实没有。
就是所谓啥含糊，就是哎，怎么这个，够不够好呢。我没有预见过，我这辈子没有预见过，要么就是明显不够好，就是说，完全不行，要么就是，甚至就是连剪辑都可以不用剪，就是音色都不用调，你就发就行了，就是他就是，就就可以了， 就是要不然就是照片一张不能带回家，要不然就是直接上传Fireside 发，对， 大概就这感觉吧。
就所以那个，我确实没有在这个事上困住我，就是它要么就完全不行，要么就是明显性。所以我，我很难去回答这个问题，但是我有一些，我有一些描述性的东西，就是比如说对于具体的产出。比如说一篇文章，就是就像你刚才那样，就是我替你总结的。就是你有一个，你有一个你的美学，就是你的那个filter。你的那个过滤器会允许通过的东西。就是你比如你刚才说那是层层地像剥洋葱一样的，层层地进不断的那个上升，然后并且不推翻原来的那样，并且这个多个层次汇合成了一个超出最初你问题scope的一个更大的图景。
这就是一个能被允许你的过滤器通过的东西，对吧？那这过滤器就是你，那没问题，我觉得人都应该有一个这个。不是人应该有一个这个，人必然都有一个这个。这个它不可以是，它不可能是全通的，也不应该把任何东西我都允许它通过，作为一种，真open mind，作为一种自我的那个要求。这是纯傻逼，我觉得。就是就还是那个，就是要求自己不能有自己，也是一种特别自己的东西。我也有一套关于我，我就不展开说了，但是就是我不觉得这是个问题。
就是你想要的东西有一个确定的样子，这不寒碜。对，你只要不要让它阻塞你的那个河流的流动就行。你不用展开说，你能不能简单说一下，就你那个过滤器里面，你觉得会有什么呢？就是它要走一段比较远的路，其实咱在那个节目里不是说过吗？就是它要走一段比较远的路，然后人们回看的时候会感到惊讶。就不管是你自己，还是听节目的人，还是看文章的人。这就是它有一种一种长途跋涉之后不可取消，不可概括的自我证明的正当性，或者它有一种分量， 一种irreducibility，就是不可简化，不可化约的东西。
当你跟着围棋，真心走过这二里地之后，这二里地就是不能压缩的，它就是不能打车的。就不行，你把它交给AI，让它吐给你一个summary，你看它，你就不会再满意了。因为它就是一个不能再少了，那个东西就是好的东西。这就是我的描述， 就相对应于你刚才那个层层深进，成为一更大的图景的那个描述。我觉得你的那个美学里面有一点，就是照我自己总结，就是听众或者观众或者读者接受完你这套信息之后， 他的第一反应是，卧槽，这事不好跟别人讲。对，我完全理解你想说什么，但我，我让我讲给别人很难。对，他会， 就跳进去，这不好说，就说不清楚。 好
This is an experimental rewrite
汉阳：大家好，我是汉阳，欢迎大家收听本期的山有虎。我的嘉宾是本节目另一位制作人重轻。重轻不久前翻译了一本书，书名叫《创意行为存在及答案》。我们想借此机会聊一聊关于创作这件事。
汉阳：我对创作本身有很多问题，也想请教重轻。正好借这个机会来讨论一下。咱们先界定一个问题：什么是创作？我觉得有必要问这个问题，因为很多时候我们提到“创作”时，脑海中浮现的图景往往不同。
汉阳：有人认为，每天写一篇稿子就叫创作；有人觉得周更短视频，或者一天发五条小红书，也算创作；还有人认为，花五年拍一组摄影作品才是创作。我觉得咱们得先明确一下，今天在这个语境中提到的创作究竟是指什么。
重轻：是的，这本书讨论的创作其实是一个比较广泛的概念，它更多的是指将个人意图显现出来的过程，并且这个过程需要是相当有机的。它显然不是强调形式，比如作为一个画家的体验，更不是关于成品，比如怎么制作报款或达到什么效果才能算创作。
重轻：它更像是一个临床心理咨询师所讨论的，关于人内在心理过程的东西。比如，如果把写作视为一种创作，它就是在“寻找词汇中挣扎”的过程。你在脑海中费劲寻找词，把它们排列在一起。这种意义上，创作更多地指向这个过程。
汉阳：那创作和艺术之间的关系呢？在这本书中，作者频繁提到“创作”和“艺术”这两个词。许多人可能认为，艺术仅限于绘画或雕塑，其实这并不关乎形式、社会声望或体制化的定义，例如写书就被视为正经的创作，而拍vlog却不被怎么重视。
汉阳：在书的语境内，我想表达的是一种内在机制，即个人的意图如何被外化。只要这个过程足够自然、密集，便可以视为创作。相反，若是你只是在优化你的SEO，虽然自有意图，却也很难称之为创作。优化标题以提高阅读量的这种行为，我觉得就显得稀薄，可能就不算。
重轻：我自己特别喜欢一本书，叫《没有人是艺术家，也没有人不是艺术家》。这个书名我很喜欢，因为它传达的一个感受就是在这个语境下，创作和艺术本质上都是一种行为，意味着我有东西想表达出来，而我得费劲把它弄出来的这个过程，正是我们今天想讨论的创作。
重轻：是的，我对这本书特别有感触。之前我帮朋友们开过一门写作课。不是我教他们写作，而是陪他们写出一篇文章。课上报名了30个人，结果只有一个人写出了东西。
重轻：这让我意识到，其实一个人想写作写不好，与他会不会写关系不大。问题在于他是否理解什么是创作。大多数人并不是不会写，而是没有体验过从零到一的过程，把脑中的想法表达出来。这个过程其实是很多人没有经历过的。
汉阳：这本书讨论了很多关于这种情况的内容。一个人想从零开始把东西弄出来时，会面临怎样的挑战和顾虑。他会遇到什么问题需要解决。当然，这本书的段位相对较高，作者探讨的是那些已经很优秀的艺术家或创作者面临的一线困境。并不是那些简单的写作困扰。
重轻：确实，书中有很多内容可以普遍适用到我们的生活中。作者是一位在音乐制作领域非常重要的人物，甚至在过去30年中，能与他媲美的人不多。所以他从音乐制作人的角度出发，将自己的经验扩展到所有创作过程中。
汉阳：你对这本书感兴趣的原因，也在于它触及到你自身的困难。书中探讨的内容具有明显的普遍性，任何长期从事创作工作的人，读完后都会感受到与自己相关的部分。
重轻：最开始我读这本书时，感到有些奇怪。书里充满了类似“让宇宙穿越你”这样玄妙的表达。但是读完后我意识到，这本书和这些词并没有直接关系。虽然这些观点看似深入，但实际上它们是解决创作者面临的问题必须经历的过程。
重轻：确实，这些文字的形式与它们实际想传达的内容之间存在一定差异。看不到这个差异的人，可能会误以为这本书只是一位大师想要传授什么启示。如果只翻两页，可能会错失深入理解的机会。
汉阳：当我读这本书并回想自己的创作过程时，感觉很明显：创作需要强烈的主体性，也就是说，你得有想表达的东西。没有想表达的东西，那你可以不表达。然而，当你真正想要表达的时候，你会意识到，这件事和你个人并没有太大关系。
汉阳：书中强调了自我和协调自我的重要性。听起来似乎矛盾，但实际上每位创作人都能理解其中的意义。你需要有一个自我去理解想做的事情，但在创作完成后，那件事与个人又无关。
重轻：书中对自我的定义非常明确，作者用很多比喻进行了阐述，例如“容器”和“滤器”的概念。容器是指你容纳了某种能量或文化，而滤器则是经由你这种“过滤”后产生的东西。这种明显不同的机制其实就是你自己。
汉阳：对，这跟我们之前聊过很多次的选题问题有关。为什么有时我们感到选择困难？并不是被外部强制，而是内部的紧张。在没有强烈的表达冲动时，创作仿佛只是为了锻炼，而非真正的目的。
重轻：我个人在这方面的洁癖很严重。比如，如果有人每周一说写五千字，我就觉得难以接受。并不是说这些有问题，只是我自己做不到。想说东西和想说，是完全不同的感觉。
汉阳：很多人问我，怎么选择题材。我自己选题的跨度很大，但不得不承认，我从来不是主动寻找的，而是无意中“撞上”的。我感到有东西在我面前，我无法不去写，或者无法不去做些内容。
重轻：的确，这本书让我体会到当你面对抉择时，想从无到有创造出一个东西，你会遇到的各种挑战和顾虑。书中涉及的许多内容可以扩展到我们生活中的很多方面。这也是我认为这本书重要的原因。 对，是。这不就是未遭罪而遭罪。这是什么呢？这个问题应该不用说太多。确实，痛苦作为一种刺激物，它确实可以刺激到你。
所以，如果痛苦能在正向上发挥作用，那它就是好的。但它真的能够在你的意识层面上起到作用吗？我对此有很大的怀疑。你说不可以，感觉得遭点罪，我对此也真的很怀疑。
不过如果对于你来说，这样的方式有效，那你就该这么做。但我真的很难想象这样的情况。你要说俄罗斯的经典文学，痛苦对他们而言就是源源不断的能量之源。但是你不能说，像托尔斯泰或陀思妥耶夫斯基，他们每天都在想这世界上还有什么罪可以遭受，这显然不是事情的本质。
我觉得这更像是一个事后的总结。事后的文学评论者会这样分析他们，但这主要是一种文化的展现，与这个国家的历史、民族和个人的特质都有关系。这样的分析可以在事后进行，但很难作为一种方法论，或者说是自我施加的一种痛苦。我觉得这样的想法非常糟糕。
你将自己视作一种特定的机器，得有特定的按钮来刺激自己。甚至我觉得，仅仅是为了刺激自己，也没什么意义。
但是把这看作是特别的，就像我挑选更好的镜头一样。今天我做了一遍我所有的“应得的”创作，心满意足。那么这肯定是没有用的，它不应该是一个过于意识的过程。
我觉得书里有一点挺有趣的，它列出了很多规则。我想对我和你来说，这些都挺实在的。然而，今天当一个人想要创作时，他会遇到一套奇怪的规则，然后这些规则就会进到我们的脑海中。例如，在摄影圈，如果参与到艺术讨论中，他们会问，你这个题材信不信，你的东西有没有人拍过。哎呀，这个在上世纪90年代比较流行，现在拍就有点老旧了。
书里完全没有提到这些问题，但当有人想创作时，他在网上搜索如何做某样东西时，通常学到的正是我刚才说的那些内容。
这点其实挺有意思的。哪些事情是创作中真正需要注意的，哪些是根本不需要挂念的。这两者在今天分得很模糊。对的，艺术与艺术评论的混淆使得这一点变得更加复杂。你到底是在做样子，还是在创作真正的东西？或者说，你创作东西的目的是什么？是为了让它看起来像个样子吗？因为如果你的使命只是做样子，那它可以也算是一种艺术，尽管不会太了不起，但确实也能算上。
然而，大多数人连做个样子都没真正做到。他们只是在一种无意识的状态下，盲目地遵循一些固定模式，根本不思考为什么要这样做。例如，拍那个所谓的梦盒子，他们只是觉得要用灰色、发白的色调，配合长焦镜头，结果一看就像八九十年代摄影样式的房顶，这样就好看了。
你可以说那个作品不高雅，但你不能否认这并不是创作，因为他们用各种方式去寻找，然后接近这个东西。他们心里有痒痒的感觉，努力去挠。虽然或许技术上不是很高明，但依然不能否认他们在创作。
大部分人就像之前我们讨论的，如何正确曝光一样。他们甚至没想明白这个基本的问题。当你在问怎么曝光最正确时，你找着那个老师，但他始终没告诉你怎么曝光才能最好。你怎么读那个数值，怎么设置，然后看曝光值显示成多少，怎么用斑马线才是最优。
这样的盲目状态，正是我认为拦住99%人的地方。因为他们甚至没有在追求一个真正的样子。刚才说的梦盒，其实他们是在接近一个样子，而那些人甚至没有这点追求。他们只是在学习如何做一个样子而已。
他们觉得那些摄影师都缺德，知道怎么曝光却不告诉我。但问题是，为什么你需要一个特定的曝光方式呢？曝光是你拍照片的一部分。你把室内拍得很黑，而窗外却刚好拍得白得发亮，再把室内的人拍得很清楚，这到底是在想拍什么？
其实你心里有没有明确的目标呢？如果有的话，这还算问题吗？这根本就不算问题。就是因为你对想要表达的东西一无所知。你只是有一种不假思索的想法，觉得拍照就得有一个正确的方法。
我就是没努力，没把这一块覆盖全。等我把所有方法学会了，我就像机器一样，每按下一张就能在网上赚点钱。
确实，拍照不只是问怎样拍，而是关于你究竟想拍怎样的东西。很多人都搞混了。他们回到了那种状态，显然说明他们没有确切想要拍的事物。如果你心里有一个具体的追求，那所有的问题都会随着那个目标的确立而消失。
当然，你该那么曝光，不是废话吗？如果不那样曝光，拍不到好作品。但是只有当你心里空荡荡的，目标只是拍一张普通的照片，或者说就是想拍一张在798能见到的照片，这样的创作目标显然是极其糟糕的。这样的目标根本不是目标。
很多人的心态就是这样。他们根本没有思考过。当你细问的时候，他们会很不好意思地承认，心里就只是想拍出好看的照片而已。想要艺术，其实是极其糟糕的目标。这就是我现在所做的目标。
我当年刚开始写东西的时候，只想写得像某本特刊一样，或者在做播客时，想要录一期像不在场那样的节目。但经过这些年，我发觉自己真正喜欢的东西，与这样的目标完全无关。听起来还真不怎么样！
你想做成一个类似于不在场那样的节目，究竟是什么样呢？不在场到底是什么样？你有没有试着去拆解过？因为它就只是一个人表达观点而已，没什么特别的。重要的是有没有具体的内容。
我可以告诉你，其实我干过这样的事情，首先听所有有关创作的访谈，不停地重听同一期节目，观察他们是如何切入一个话题的。把你当作一种语法在品味，觉得这语法我没学会，我学会了也能说出这些话来。
我承认我做过这事，但我最初的确困在这方面。有段时间我觉得到节目做得不满意，是因为自己的对话太肤浅了。
当然，语法并不是不能追求的东西，或者说话的口吻和韵律感不是不能追求的。然而，很多时候，你连这个特色也没有追求到。也许这不算懒惰，但也让人感到不知所措。
最主要的是要真正深入去解决你到底追求什么。这个问题始终在门外，你允许自己逃避。你说，哦，我就是想写得像“纽约客”那种文章。每当我想，嗯，我到底该如何写时，你又退缩了。
并不是不想细想，而是心里只是想，我就想写成“纽约客”那样的风格。你知道我曾经最傻的事是什么吗？你有你的创作问题，我想我也有我的陷阱。
我记得听过一个故事，让一个人成为《纽约时报》的特约写手，然后给他一年的时间去写。那个人花了两年去调研，结果我看到自己写得那么差，就没有花那样的时间。
一切都差不多。说白了，我当年拍照时特别喜欢某个摄影师的照片。我会问他，你用的是哪个相机，有什么镜头角度，调颜色时用的是什么软件。我问过他很多这样的问题，而至今仍然是个谜。
我为此问过无数次，直到现在我依然印象深刻。这就是我曾说过的，那是一个永恒的挣扎。当你没有找到重要的事务去吸引你时，就会把那些无关紧要的事情摆上重要的位置去抓狂。你还是因为内心空虚，陷入了那个问题之中。但你刚刚提到的，其实在书中也有提及。
我之所以认为这本书好用，就是它帮助我治了不少病。而且这个症状非常普遍，78个症状中总有一个能与您产生共鸣，且每个症状中都有很多内容，任何努力尝试创作的人都能在书中感受到一种同情。
对，它确实是在治疗大家。当你阅读的时候，会觉得这实在太真实了。即使对于那些从未经历创作的人，听到这样的斗争也会让他们感受到一种深切的情感。实际上，这本书可能并不适合大多数人，因为大多数人的问题与这书无关。
但如果你觉得这与你有关系，那么我觉得在阅读到这一点时，你肯定已经意识到自己与之相关，那就可以买来读一读。我觉得我今天读这本书还不错，但如果早些时候读，我会感受到更强烈的共鸣。它给我带来了一种安慰，告诉我哪些问题是重要的，哪些是不重要的。
你要知道我有这样的体验。我并不是因为没有一个稳定的输出基地，才不得不到处寻找投稿机会，想办法找到能发表的内容。在这过程中，我会遇到各种各样的挑战。有人会问你，你的增量信息是什么，你的视角是否独特，大家是否关心你的内容。
所以我把所有的精力都放在了这些事情上。例如，发微信时最多只能放几张图，字数也有上限。在某种程度上，这就像是苦海无边。然而，今天思考这些数以千计的心魔，成为一个更好的自己，或成为一个被社会认可的艺术家只是其中一种流行的心理，甚至不是唯一。
这绝对不是唯一。实际上，例如你刚刚提到的种种心理，就已经有五六种不同。在这个过程中，最流行的情节就是，我并不想创作什么，而是想成为一个艺术家。而想成为艺术家的原因很简单，就是我想获得那种当艺术家的感觉。
就像在小红书上，讨论怎么当艺术家可能没这么火，但说当艺术家是什么感觉时，一定能引发共鸣。那是自然的，因为生活就是关于每件事的感觉。
这是主流的叙事。刚刚你提到的另一种情况是，有些人想要模仿某些具体的文章风格，比如说想成为某本知名杂志或栏目中某种特定风格的创作者。这就是一种态度，再往下的界限就模糊不清了。
就比如说拍纪录片，看到NHK拍的材料就可能特意地去追求那种悲悯和包容的人文气息。哎呀，简直了，哦，我一定想要那个感觉。但这样的期待只是停留在一种边缘化的事情上，想要那种感觉是不够的。
因为当你意识到你想关怀的普通人，其实就是你自己时，你需要众多平凡人来见证这个过程，来佐证你确实很悲悯，能深刻体会到他人的苦情。
这也正是艺术创作中一种复杂的体验，甚至是第一个复杂、传奇的创作故事。在这方面，这其实是《不在场》该讨论的内容之一。关于自发性、无敌努力、对手艺的奉献以及最终获得卓越作品的这种迷恋，实际上与其说重要，不如说根本没有意义。
例如，谈论什么特稿最好时，一年放假、闲逛、采访无数身边人的故事并不出彩。它包含的是什么？它想写的东西是什么？根本没有雏形。
这仍然只是在迷恋传奇故事。好的创作需要痛苦、奋斗，写了一万字不费劲的笔记，创作150万字，才能换来的并不是事物的质量。正如说35毫米和1.4比1.2的区别，然而光质更好、紫边更少，这之间有什么本质的区别呢？其实是一磊。
这些叙事在绑架我，让我局限在其中。当我确实创作出一个作品时，我会混淆这个过程。比如，今天我写出了两万字，但当我想把这些东西发布时，却没有耐心去一一整理所需的目的。有人可能会跟你说，视频若超过二十分钟，弯播率便会下降，这样就必须缩短它。
这种限制稍显健康，但它又是另一回事。你与媒介之间的冲突，或者与发行管道之间的冲突便是此类问题，并不属于某种心魔，这是实际需要面对的事情。
有的人觉得，不行，我不能删减，或者就算能删，也是觉得太可惜。这样的情况也容易让人困扰。书中对此方面讲得特别多，因为很多艺术家都是在这个过程中寻找解脱的方法。
也如著名音乐制作人Rick Rubin为西耶合创作专辑时，我们了解到的情况就是，他并不是一个制片人，而是一个“编辑”。他的任务是将成千上万的创作材料裁剪减缩，最终保留最基本的框架，出色的作品就是这样完成的。
在这个过程中，他帮助创建出了一种不可思议的作品。很多时候，这种形式呈现出一个另一个过程，它其实和创作并没有直接关系。还有一章提到，作品的编辑本质上是一个不断变化的、精挑细选的过程。
他在这样一个话语中抹平了“天真创作”与“世俗大众”、以及“出版发行”的不同之处。他指出，这一切都是创作过程的一部分，很多时候也是你心中的问题。
我自己还算幸运，能与优秀的编辑合作，因此能很清晰意识到，他们所做的改动通常比我自己的版本要好。每次，我想着能不能不改再去看。
当我看到他改完的版本，发现他用另一种眼光帮我做了一次创作，使我的东西变得更加出色。但有时候我也在分辨，为什么有些编辑我会接受，而有些时候则不太乐意。我现在意识到这其中的差别在于，是想把文章提升到更好的水平，还是对它进行标准化的编辑。
然而，很多时候产生的最终风格反而是世界流行的结果。我的一张照片并不是为了变成《国家地理》那样的效果，想要那样照片的人就应该请《国家地理》摄影师来拍摄，而不是请我。
就是你必须演绎出自己的风格。生动的作品不应仅限于单一模式，这是重要的。如果这句话对大多数人来说并不成立，那我们就不获取这样的叙述方式。
在当今社会中，追求某种样式的存在会让许多写作者感到困扰。无论是作品成型，还是关于期待的表达，很多创作者被现实无情拉扯，往往难以自处。
总之，爱好创作的人会在作品中追溯到更深层次处理的过程，而难以到达的也并不一定完全是坏事。真正的创作学习是在不断探索与反思中形成的。 对，是这样的过程。这是我们之前讨论的一类情况。另一个类型则是他对错误的零容忍，尤其是关于完美主义的观念。他认为创作是一个绝对改进的过程，越少错误就越好，甚至他有一种想法，作品中错误少一个就更好。这样的观念很多时候带来的是一种纯粹的负担，让你陷入无止境的自我折磨。这样的思维只会让作品变得更差。
书中提到，一些短诗的写作方式给我留下了深刻的印象。其中有一首诗提到，当作品中有五个错误时，它或许只完成了一半，但当错误变成八个时，作品就达到了最佳状态。这实在是一个值得深思的观点。我们是否在不经意间又陷入了一个特别严格的叙事，时常会想，我总是没有调整到尽善尽美。这又是另一种困境。
有一天我和他聊到即时摄影，他表示自己不喜欢即时摄影。我们有一本分歧很大的书，杨颜康的《神品的人》，他花了十年拍摄一个天主教村子的生活。对于他而言，这并不算现在的艺术。这种创作的方式实际上是记录别人生活的事情，或许别人拍摄也能体现出某种美。这并不能真正展现作者自己的意图。
现代摄影和现代艺术应当明确地表达出自我。然而有些创作方式却认为艺术是一条进化的线，似乎总是要朝着更好的方向发展。我们总是期待新东西出现，旧东西被取代，这个想法其实特别糟糕。我们常常将创作视作一种进化论，认为某类创作一定优于其它创作。
例如，当使用AI拍摄时便比不用AI的方式更为优秀。如今的创作更多的是反映某种模式。我们对创作的标准变得狭隘，认为即时摄影和黑白摄影已经过时，显然是一种误解。这是艺术与艺术评论之间的混淆。你在创作时，是否在书写艺术史？在你按下快门的那一刻，你是否还算是人类的一部分？
反倒是有些艺术家不需要去宽容他人。好的艺术家不必成为开放思想的艺术评论家。如果某人批评你说太狭隘，实际上是他太狭隘了。艺术家在创作时是否狭隘，这并不成问题。关键是他是否受到干扰，如果没有，便不存在问题。
狭隘的艺术观不必过于强调，而创作者的表达是否被妨碍才是重要的。若被对方的观点所限制，那么才是需要讨论的问题。正因如此，我认为狭隘并不算问题。艺术创作不同于艺术欣赏，我们所追求的不应当是一种全方位的开放。
在创作过程中，很多人却被这种观念禁锢住。创作者应该在做减法时，排除掉那些不符合自己风格的做法。即时摄影并不是不“真”摄影，其他的摄影方式也不应被简单排除。关键在于这条教条是否限制了你，引导你停滞不前，而非是否狭隘。 当你认真跟随围棋，真正走过这二里地后，你就会意识到这二里地是无法压缩的，是不能打车的。这是必经之路。如果你把这件事情交给AI，让它给你一个总结，你看了之后就会发现自己不再满意。因为这个过程是不可简化的，那种体验就是好的东西。
这是我对你所描述的美学的理解，和你之前提到的层层深入，成为更大图景的描述相对应。我认为你的美学中有一点值得总结：听众、观众或读者在接受完你提供的信息后，他们的第一反应通常是，“卧槽，这事不好跟别人讲。”
我完全明白你想表达的意思，但一旦我试图将它讲给别人，就发现非常困难。人们会想跳进去，但又说不清楚。
2025-04-05 08:00:01
EMERGENCY POD: Liberation Day with Tanner Greer of Scholarstage
Today is April 4th. Two days ago, we had Liberation Day, a tariff salvo that doubled as a bid to completely reshape the global economic order. Simultaneously, we had Laura Loomer walking into the White House and firing completely competent NSC staff who were MAGA enough to serve in Trump 1.0, but apparently not MAGA enough for her and the president. What is going on in the Trump administration and what does it mean for America’s relationship to China and its future place in the world? Thankfully, we have the great Tanner Greer of the blog Scholar Stage, who has written a guide for the perplexed. His new report entitled “Obscurity by Design, Competing Priorities for America’s China Policy” is the product of dozens of interviews and hundreds of hours reading the thinking of key Trump policymakers that will define America’s geopolitical and economic posture for years to come. We’ll explore the different strains of thought informing this administration and the longer-term implications of Trump’s policy management style. Nicholas Wells, longtime China Talk editor, will be co-hosting.
Tanner, what a day. Great to have you.
Thanks for having me on. Yes, it’s – when we originally agreed to have this podcast about the report, I knew it was relevant, but I didn’t know it would be this relevant.
All right, Tanner, let’s start with tariffs. How did we get here and what does this tell us about the Trump administration? This is what I spend the first part of my report talking about: how do we kind of model Trump’s behavior-making, decision-making, and why is it sometimes so difficult to predict what he’s going to do? I say there’s two reasons for this. The first is that Trump wants to be unpredictable. I think by disposition or personality type, he enjoys being somewhat impulsive and difficult to deal with. But over the course of his life and especially his first presidency, he came to realize that the less people know what he’s going to do, the better off he seems to do. I mean, whether or not that’s true for the country as a whole, but at least for him personally, there are a lot of advantages that accrue to being this kind of unpredictable, crazy machine where inputs come in and we don’t know what’s going to come out the other side. He believes this gives him negotiating leverage. He believes that this makes his strategies surer and more likely to succeed. There’s something self-serving about this, but he has taken this maybe disposition and elevated it to an official philosophy.
Totally, Tanner. In an interview in the lead-up to the 2024 election with the Wall Street Journal editorial board, he told them that there’s no way that she was going to invade Taiwan because, quote, “she knows I’m fucking crazy.”
Yeah, that was his exact quote. So, when it comes to the Chinese in particular, but world leaders generally, he wants them to think he is crazy. He wants them to think he could do just about anything. You can understand a lot of his behavior as acting in such a way to make that belief credible. He’s not the only president who’s ever thought that. Nixon had the same idea. If I’m mad enough, I’ll be able to make things happen in the world. The enemy’s rivals will be more likely to do what I want. This is a key part, in my view, of why Trump does what he does. He actually believes that if he nails himself down, if he ties his hands by explaining, this is what I’m going to do, this is how I’m going to do it in any detail, that will work against him. People will commit him to promises he doesn’t necessarily want to keep in the future, and it will remove his negotiating leverage. He often views international relations as a set of iterated negotiating patterns, as opposed to like, you’re charting a big long-term strategy, trying to get from A to B, and here are all the little inputs we’re going to plug in to get there. Instead, he views things much more iteratively: I’m trying to improve my position, you know, tomorrow, the next day, the next week, and so forth.
We’ve got a lot of competing impulses, right? You have these visions that have populated the GOP, where big tariffs need to happen in order to raise revenue, do better negotiating, revitalize manufacturing, and decouple us from China. And then you have this iterative game that Trump enjoys playing. I think the kind of mess of, you know, a lot of these tariffs seem to kind of be, even though this is a thing that Trump has been focused on for a very long time, it seems pretty slipshod, the way this was rolled out, as opposed to something like the progressive squeezing of Ivy League universities and law firms, which does seem to be a lot more cogent with it.
It’s interesting to me that tariffs, which is clearly something he has been focused on for a very long time, the rollout has not been a cogent one, but has been a much more sort of flat, you know, ADD craziness. I think there are essentially two reasons for this: One is kind of what I said, that Trump believes that increasing the uncertainty ratio of what will happen next, what he will do, is to his strategic advantage. He clearly believes this on an international sphere.
But there’s also a management style problem involved here. Trump doesn’t view it as a problem; he views it as the way in which he should do things. His strong preference is to have a team with conflicting opinions. He wants personal loyalty. But as long as he has loyalty, he prefers to have strong personalities who have, in some ways, widely divergent opinions on what should happen and how it should happen. His management style is to pit these people against each other, pit these factions against each other, and then act as the kingmaker, who swoops down and chooses the winner of these various discussions. You can think of it as his way of solving the principal-agent problem.
How do you keep a bureaucracy in line when it has its own interests? Well, if you can pit other parts of it against each other, that’s how he likes to do it. This style, I mean, has some advantages. Multiple people who I interviewed as part of writing this report, I interviewed more than 30 former Trump officials, people involved in this process from the cabinet level down, point out that this is in some ways very different from how Condoleezza Rice ran the NSC when we were in the lead-up to Iraq, where there was this attempt to find a consensus position and present it to the president instead of allowing the arguments to go directly up to the top. If you are a Republican whose goal is to not repeat the George W. Bush experience, you can understand why that might be attractive, even to some people in the administration itself.
So, the weakness here is twofold. The first is that it’s difficult to do long-term planning. It’s difficult to have long-term coherence, you know, month to month, day to day, over time. The second is that you run the risk of having competing priorities, competing ideas of what you want to accomplish, get bound up into the same policy. Because the president is not starting out by saying, we want to go this way. Instead, he’s letting things kind of come up to him. He’s not always making decisive decisions between the various options given. So, you’ll end up with a situation where multiple people will be supporting the same policy for conflicting reasons. I suspect when we look at how the tariff situation has gone from the start of the administration to today, that’s part of the story here.
There are multiple different groups who have different rationales for what tariffs should be, what they hope to get out of economic policy, especially regarding China, but economic policy broadly. I don’t know if the administration has been successfully able to integrate all these different views into a coherent policy. It seems at least plausible that ChatGPT outputted their formula for determining reciprocal tariffs, like they’re people supporting the tariff idea, but for conflicting reasons. They settle on this formula, which is simple enough, but it’s not how one would normally calculate a tariff rate that another country imposes.
Is this policy how you get that kind of outcome, or is something else going on? There’s certainly been for a long time this idea, and many people have been saying this for months and months, years, that one, you need something like reciprocal tariffs and that lots of countries are doing things unequally. But also two, that you need to like have something else in addition to those reciprocal tariffs, to have it be truly measured because countries will have all kinds of restrictions and their own industrial policies and things like that, that don’t make the playing field equal.
And that’s plausible enough. The question is, if you are given a deadline to calculate the entire world’s kind of on a bespoke basis, all those different policies that might make trade advantages or might make trade relations less advantageous or less fair in some way. I think that’s quite hard to do, and I don’t have any special insight on how the process for creating these happened this week. All I can say is that I’m pretty sure it happened this week.
Yeah. Media reports from a week ago.
Which is just a wild thing. And it comes back, I think, like, you know, we have all these competing theories of what’s going on and how they’re going to revitalize manufacturing or whatever. But there’s this famous note that Trump wrote to a Sherpa at a G7 meeting where he goes, trade is bad. The concept of a trade deficit being America is losing and getting cheated is something that has been a longstanding theme of his intellectual arc.
Aside from the whole, did ChatGPT come up with the formula, like, basing it on trade deficits as the thing we are trying to correct for does actually kind of line up with Trump more than it lines up with any of these big 3D chess, we’re going to remake Bretton Woods type thought processes that we’ve seen come out of the GOP ferment over the past few years. I think that’s in some ways correct. I think, though, that if you look at these tariffs, they’re actually not that far from what Trump said on the campaign trail he wanted to do.
So Trump said two things on the campaign trail very persistently. First, he said, I want something like 10% tariffs across the board. Second, I want something like 60% tariffs on China. Those were two things he said on multiple occasions.
Yeah. If you look at the numbers that we have, so China effectively between what they did earlier and what they’re doing now has effectively a 60% rate that’s being added to it. Then if you look at everybody else, more or less what you have is you have 10% applied everywhere. You have, on top of that, this reciprocal tariff stuff that, in some cases, has really kind of large numbers, like Vietnam; some countries are much smaller.
My guess, and this is just an educated guess; I haven’t talked to anyone in the administration in the last week. They’re all too busy right now to have this conversation with me, and I’m not going to pester them. Steve Besson, you’re welcome to come on China Talk. I don’t think it will be fun. I would not recommend it, but, you know, the offer’s open.
Is, I suspect, if I had to guess, the logic, even Trump’s logic, is something like this 10% is going to stick no matter what. Then all those other numbers are maybe negotiating leverage. Now, negotiating for what? This is an interesting question, to which, again, I’ll come back to my earlier point. What are you actually trying to achieve? There are multiple different answers to that. But I suspect that what Trump wants to have happen is to have all these world leaders come and kowtow and say, okay, what do we need to do to get closer to that 10% and not be at whatever?
For some countries, like Cambodia, I don’t know what they could do, really, in a trade sense. They could maybe say something like, well, we’ll also kick the Chinese out of the Siem Reap naval base or whatever. If that’s the sort of thing that Trump cares about, if that’s the sort of thing that USPR is willing to muddy the waters with. But I think they’re probably just in trouble.
Whereas a country like Japan has a lot more room to do that sort of negotiating back and forth. Here’s the thing, though, Tanner. You do something like this, and you expect everyone to play the game that you want to play, right? Trump wants this to be an iterated game, and he wants to have a lot of fun calling folks and cutting deals left and right. But there is a point where you do this enough times and people get sick of you and don’t want to play anymore.
Is there a breaking point? At what point, if ever, do countries just say they’re no longer interested in being on America’s global ledger from a geopolitical and economic perspective if this is the way that they’re going to be treated? I mean, the question is, is that even possible?
If you’re Japan, to take an example here economically, is it possible not to be on the American ledger? Militarily, is it possible not to be on the ledger? I don’t know if it really is. If you’re a country like Vietnam, militarily, geopolitically, I think, yes, it is easy to balance that way. Economically, it’s much more difficult.
I just think a lot of these countries, if Trump’s calculation is that we, because we have the consuming power, because our economy is so essential to the world economy, that a lot of these countries will have no option but to face the music. I think in the short term, especially, that’s somewhat true. The question is more in the long term, does this basically create conditions where lots of countries over the 10-year term say, okay, maybe we should look towards something more like autarky for ourselves? Or maybe we should balance away towards some other version?
I can see that as maybe a realistic response from some of these countries. But in the short term, I don’t think there’s a, we’re not going to play with the United States anymore. I don’t think it’s possible. I think in a 15, 20-year scope, maybe. Because, you know, there were some expectations that you could make about America, you know, really post-1945, right? That this is a country that is excited to trade with you and help make you rich.
Insofar as Trump’s idea that trade is bad and other countries are ripping us off and we have no interest in, or that it is not at all a part of America’s strategic vision that the countries that we’re friends with should also succeed economically, that is a very fundamental shift. Yes, on a 10- to 15-year calculus, it may make you really question some assumptions.
I think I would actually rephrase what the problem is. I anchored this first part of the discussion talking about how Trump enjoys and finds strategic value in uncertainty. In my mind, the truth is, when you look at most foreign countries, what they want from the United States—those who are in our alliance system, as well as those who are major trading partners—the vast majority of what they want from the United States is just actually certainty. They want to know what will happen, at what speed, in what way, so they can kind of prepare.
It’s like, you know, there’s the phrase about how it’s no fun to be a mouse among fighting elephants. If you’re a mouse, you really want to know where the elephant is going to be stepping. Trump believes that he has a lot of advantages to not letting anyone know where he’s going to step. I think that if you’re an American ally, I actually believe that the ability of American allies to accept a lot worse conditions relative to what they’ve been given is actually pretty high.
What I think will be much harder for them to do is to not know what conditions they can accept at any point in the future, right? Which kind of goes back to your question, like when do people decide they want to leave the game? I think that if they’re not given some actual sort of enduring deal that they believe, this is what the new reality is, and they can assess those terms and say, okay, this is the new reality, we do this or we do, you know, the Chinese way or whatever, and they can make that decision.
If the way is, okay, the Chinese are very stable in what they want, but the Americans are all over the place. We don’t know what they’re going to say, not just administration to administration, but month to month, year to year, that’s going to be a bigger problem.
That will be even more than just treating the allies as if they’re not treasured allies that share values and all these other nice things. I think that this sort of capriciousness or arbitrariness, if they perceive it to be as such, if it doesn’t seem to come from any principles that they can discern, predict, and act upon, that will cause issues in the long term.
I guess the question is, Tanner, can you come back from something like this? We had big fights in Trump 1, and then we had USMCA, and that was his vision of the greatest deal since sliced bread. You talk a lot about how the sort of constellation is ever-changing, and there are winners and losers, and they don’t necessarily happen for policy reasons. They often happen for personal reasons. But I mean, if this is the most fun thing for him, is playing these games, having negotiations, and three months later reopening it and trying to get more—like, is this just the base case for the next four years?
I don’t necessarily think so. I mean, I don’t know. This is an interesting case because one of the—we have a lot of data on how Trump ran the last administration. That was a whole year, I mean, sorry, four whole years of how it worked. One of the interesting questions is, have the way things have worked for the last three months been more predictive than how things worked in the four years previous?
There are some discontinuities. A big one, like taking these tariffs. When Trump announced the China tariffs in the first administration, they were in installments. They started small. They were well in advance. It was known it was going to be at this level, this level, this level, this level, going up as we had kind of graduated pressure against the Chinese. It was not 25% tariffs on this country tomorrow.
One of the questions you kind of have to ask is, so, okay, what’s the difference between the two administrations? Why did the first one have this kind of more measured response whereas the second one does not? Even if you’re kind of playing the same game, I think the markets responded much better to the first one because they had time to plan and prepare.
You could say a few things. Bob Leitzinger isn’t here right now; he had an extremely respected individual. He had Trump’s ear. He was good at talking to Trump, getting Trump to say what he wanted to do. He was very much of the mind that if we’re going to do a tariff schedule, we’re going to do it above board. It’s going to be completely legally nailed down so that the courts don’t challenge us and bring it down. We’re going to go slow. We’re going to go measured. But we’re going to do it.
I’m not sure that the corresponding figure on trade policy in this administration seems to be Howard Lutnick, and he doesn’t seem to have that same sort of slow and steady wins the race sort of personality. This is just an outside observer. I don’t have special insight on how it is, but just based off of what I’ve seen him say, media reports say, it seems like he wants to push maybe faster than USTR did in the previous administration.
There’s like a version of this where, okay, well, if he leaves, then maybe things shift somewhere else. I could see that. The whole idea is like you just apply a new coefficient of shitty staff work to all the policymaking. You have, like, the Trump madmen, I want to be on the phone hustling people, and you have, like, poor execution. So whatever outcome you’re trying to get, you can just apply like a 20% discount because you’re going to do stuff like tariff penguins.
When you have an exemption for semiconductors, you’re going to put it on CPUs instead of GPUs. They clearly wanted to have some carve-out for AI, but they just did it wrong. The last-minute staff work, so that you’re blowing up Madagascar’s economy, and it’s not like America is going to grow vanilla beans in the U.S. anytime soon to compensate. If you are going to let Laura Loomer fire people, are you going to get better staff and better execution out of it? Probably not, right?
So that seems like a constant you can hold. If this is a question just about, like, is the Trump administration version 2 going to be competent, I mean, I think that’s a slightly different question from what it is they want to do, right? Or what is it that people inside it want to do? I’m sympathetic to the argument that what they want to do doesn’t matter if they can’t do it well. Especially if you’re doing something like trying to rejigger the global trading economy.
In preparation for this, I reread the council, the guy, Stephen Morin, who’s the Council of Economic Advisors Chair for the Trump administration. He wrote this big thing in December about how we should— it’s like a 40-page paper, all kinds of academic citations about how we should use trade as leverage to redo Bretton Woods, redo the existing financial trade order to be more in favor and more sustainable, more in favor to American manufacturers, more sustainable for American budgets, so on and so forth.
I think it’s a pretty smart and very interesting approach. But what struck me in reading it now is how he talks a lot about how when we do this, we need to be cautious and careful. When we do this, we need to be slow and graduated just like we were during Trump 1. He says it a lot. He says it just like we were during Trump 1. We need to do it that way, just more ambitious.
Obviously, not everybody has his same agenda. He’s not in charge of any; he’s not in the cabinet. He’s not on a decision-making authority. That’s a consultative body that he runs. But it does kind of point to the weaknesses of some of these approaches. If you were doing this in a measured way, I wonder if the responses would be the same throughout the commentariat or if they would be different. I suspect for many people it’d be the same. They’d be just as upset no matter what.
But I think the markets would probably respond differently. I don’t think you’d have the big run on the markets today that you’ve had if there was a slower, measured, more carefully articulated version of what’s happening. This idea of temporal claustrophobia, something we’ve covered in China Talk in the context of, like, Japan bombing Pearl Harbor, Hitler invading the Soviet Union, is this idea that sort of the walls are closing in on your strategic ability to realize your strategic vision. Every day you wait lowers your percentage chance of making the world you want to live in.
Then you end up blundering into some of the worst strategic errors in world history. I do find it interesting how Trump and JD and a lot of other folks talk about what they’re doing on the economic and geopolitical side as the clock really being almost about to run out. We need to act big. We need to act fast now. Otherwise, America’s fiscal health is shot. America’s ability to sort of throw its weight around the world is a wasting asset, and we need to use it now while we still have the chance.
Thoughts on that, Tanner?
Yeah, I think you can look at this at different levels of analysis. At the level of individuals who have programs, one of the things my report emphasizes is that historically, Trump administration has been very much; Trump goes through people quickly. What is policy today will not necessarily be policy tomorrow as these people change. This does incentivize individuals with a program to, one, implement that program as soon as possible and, number two, to implement it in ways in which it’s hard to reverse.
This is one reason why you might have a penchant for drastic actions. Say something like, let’s just get rid of USAID on questionable legal grounds. But let’s just get rid of it so that when people with other opinions come in later or when Congress gets their act together, there’s not going to be a chance to really reverse the situation. In some ways, these tariffs look a bit like that. That’s one way to look at it.
Another way is to look at Trump personally. This is his last administration. This is his last chance if he’s going to make—I mean, people keep talking about, oh, he’ll try to do a third or whatever. But I don’t think—like, the fact that he’s moving so fast in my mind is actually evidence that he doesn’t see it that way. He wants to make the changes he wants to make, and he believes it will have impacts. Impacts need to be seen soon. That’s true too.
Then there’s this kind of broader question that if you are really interested in upturning the global trade order, the global political order, the national federal government’s way of working, I think a lot of people fear that if they do things slowly, all they’re doing is giving ammunition to enemies and giving veto points, like, people who might have veto points in the process, giving them the chance to activate it. You need to move before that world of resistance is summoned.
I think that’s probably a direct lesson learned from the first administration, where all the time, the first things the first administration wanted to do were frustrated, sometimes for good reasons, often for completely crazy reasons, right? You would have things all over the place, kind of very slow and frustrated. I think that leads Trump and a lot of people around him to be like, okay, we can’t be like the first one. We have to just go in and do things. Screw staff work, right? This needs to be done.
It’s more important to do the big thing and get directionally in the right direction than it is to look like we’re doing the right thing, but never actually have it happen. Because there’s enough chance if we go slowly for veto points in the bureaucracy, Congress, and so on to react. That probably goes back to Trump’s whole philosophy too: I’m much better off if people don’t know what I’m going to do. If I just act and everyone has to react, that’s how he prefers things.
Doing things slowly, doing things in a measured way, forces you to react to others just as much as they react to you. There was this one, you know, the Trump put, right? Of, like, okay, the market goes down enough, Trump will stop being crazy. That was like a breaker that you saw for the first administration, which is basically gone now. Or we’ll see. This is our first real test, I guess.
The sort of psychological shift manifested in that in particular, I think is a really interesting one. I think it’s mostly gone, although it will depend a lot on how the markets look over the next six months or so. I think Trump earnestly believes that there’s going to be a period of problems that will be, but not forever.
If we have a recession that lasts two years, I think there’ll be a lot of rethinking on his part. Assuming there will be rethinking is one of the assumptions of that. Well, no, I don’t predict which way that rethinking will go. I think there will be rethinking on his part because that’s constantly been one of the truths of Trump in power. He’s always rethinking. He’s very, very—he doesn’t stick to ideas or people.
He sticks to some very big level ideas: immigration, trade, not so great. But he has a lot of flexibility in his persona, and his base lets him get away with a lot of flexibility in what he wants to do. I think that the expectation that what he does now is exactly what he’s going to do in two years is almost certainly false. The uncertainty for everybody else, though, is like, in which way will it diverge?
And there are many paths.
Yeah. I mean, okay, so I had a very off-color comparison. I was rereading Ian Kershaw’s Hitler biography last night, particularly the last 200 chapters. It’s April 1945. The walls are closing in. This is Ian Kershaw writing about Speer’s memoir, saying, Hitler at the time gave an intimation that the German people might not deserve him, might have proved weak, have failed its test before history, and thus be condemned to destruction. It was one of his hints, whether in public or private, I mean, the continued outpourings of optimism about the outcome of the war that Hitler indeed contemplated, at least momentarily, the possibility of total defeat.
So, I don’t—like, okay, you’ve talked me out of this, Tanner, but I do see a world in which there is a King Lear-Trump polling at 10% in 2027, just like, “Fuck it, I want to burn the whole world down” timeline, which we shouldn’t entirely discount.
I think that’s not the thing I would be worried about. The trouble of Trump running at 10% in 2027 is not that the world needs to burn down. It’s that he’s going to worry that if he loses the next—if a Republican doesn’t win the election afterward, he’s going to jail. That’s going to be his worry.
I think the difference between someone like Hitler and Trump is that Hitler was extremely ideological, right? Trump—like, Hitler has this whole thing of, like, “Oh, the people don’t deserve me and my brilliance.” Trump—I don’t think Trump thinks like that about himself. He’s much more of a—I mean, it’s kind of like, you know, saying P.T. Barnum doesn’t—if people aren’t going to his show, being like, “Oh, it’s obviously because they didn’t deserve the goodness I was about to show them.” No, he’s more like, “Oh, I played my hand wrong. They didn’t believe my game.”
I think Trump is much more…
And he’ll just find a new act.
Yeah. Well, I mean, he might not be able to. He’ll also be older and all this other kind of stuff. But I think that this sort of messianic thinking is certainly there on the right. There are certainly people on the right who think that way.
Yeah.
I don’t think Trump is that guy himself. It just doesn’t mesh with anything I know about his character, where he thinks—I mean, he thinks, yeah, he probably does think, “I’m somewhat God’s gift to humanity. God saved me in some sense.” But I don’t think he thinks that, “Oh, I am the embodiment of an abstract ideology that the people just weren’t ready to receive yet and weren’t ready to purify themselves for in the grand struggle.”
You know, that’s—I don’t think that’s how Trump thinks of the world at all.
All right. Fair enough. You’ve convinced me, Tanner. For the record, I did ask ChatGPT for better analogies, and it didn’t have one that I liked. Let’s talk about actual things that people in Trump world believe. I mean, I guess maybe Laban’s wrong. But anyways, let’s focus on the economic dimension. We have two axes that you chart Trump world economic thinking on. One focused on industrial renaissance versus emerging technology advancement. And another axis that is focused on—that believes that the administrative state can do things. And another that thinks the administrative state is, by definition, weak and ineffective and not a useful tool for changing the world. Tanner, talk us through this quadrant of thinking. Maybe assign some human beings to better populate our four corners for us.
Yeah, sure. I think the first thing to emphasize here is that there are certain things that every MAGA person kind of has some consensus on when it comes to the economic vision they want for the future. And as I frame it in the report, especially the economic vision with China, because the way people frame it when they’re talking about China is that we need to win the economic competition with China. And what does that winning mean? It usually means some sense—a word that comes up a lot is some sense of independence. There’s this sense that the United States is too dependent on the world abroad, and it’s not free. Or, to use Trump’s word from earlier this week, it’s not liberated. He said this himself, that this is going to be a new Independence Day declaration.
And the idea there is that all these ties with foreign countries mostly just tie down our hands, mostly just make it so that we are dependent for basic goods, for basic things that are secure to our national security, that are core to the future of our economy, that we can only have these if we treat other countries properly. And that we want a world in which American strength and American wealth is not dependent on the goodwill or the good graces of foreign countries, but rather the reverse, where foreign countries’ prosperity and security are dependent on our goodwill. That’s more or less the unifying vision. So the question then is, like, well, how do you get there? And what does that mean specifically?
So staying on the unifying vision for a second, it’s this vision of, you know, that’s an autarchic dream, right? No, I don’t think it’s necessarily autarchic. It can be autarchic, but the point there is, like, who is depending on the good graces of who is more or less the argument there, right? Like, is everybody—so, I mean, as an abstract idea, America in 1946 is the entire world’s economy, has a lot of trade relations with the whole world because basically the whole world is buying our stuff. I don’t think any Trump person looks at America 1946 to 1955 on the economic side and says, oh, yeah, like, that was terrible. They, in some ways, look at that and say, no, that’s great. If we could have that world where America is the world’s factory and everyone’s buying from us and they can cut off us, but we can’t cut off them, then they’d all be fine with that.
And I think the question there then becomes what level of autarky is needed in a world in which other countries are richer? What stuff do we need to replicate at home? What stuff can we maybe get abroad? But generally speaking, all quarters basically agree that globalization reduces America’s field of action. And in particular, this is dangerous because globalization is mostly redound to the benefit of China. And not only have we—it would be one thing if we were giving up all this capability to Japan and the EU, which a lot of them view as a big problem. Even worse, if it’s going to China, which is mostly what has—the lion’s share of the story of what has happened is that China is the one that has gained a lot of these capabilities that America is now dependent on.
And so if you have a geopolitical rival upon which you are economically dependent, that is a problem in their mind. Are there some 1930s vibes of that? Yeah, a little bit. But I don’t think the vision is complete autarky. In each of the four quadrants that you have for economic policymakers in Trump’s orbit, what is the limiting principle in each of those? Like, I think maybe dynamists, people in the lower right quadrant, people who are skeptical of the administrative state’s capacity and want tech revolution, they might say, you know, we need to reshore manufacturing of critical national security things like chips.
And then people on the other side of the spectrum might say, like, I don’t know, we need to reshore tons of manufacturing capabilities. Like, is the limiting principle, do we need to reshore t-shirts for American national security as well? Like, where does each camp draw the line as to what we need to bring back to America? I think that’s a really good question for kind of getting at the divides between the camp, right? Especially this kind of question I have. So, like, my first axis that kind of divides the group is the people who view winning the future, securing American national security, taking control of the currently heights of the 21st-century economy in terms of developing new technologies or being at the leading edge of new technologies versus those who, like, want a much wider spread industrial renaissance.
And you kind of think of it as a spectrum with extremes on each end. So, at the most extreme version is going to be the guy who basically thinks the only thing that matters is AI. Like, nothing else really matters. But if America wins AI, then everything else just kind of falls into place. And there’s nothing that really needs to happen for AI except for us to make sure, you know, OpenAI has the money and the energy that they need. Or Anthropic or whatever. And then on the complete opposite side, you’re going to have someone who’s going to be like, we need everything, including t-shirts.
In reality, most people are not on either of those extremes. But that’s kind of the spectrum, right? And so, I tend to find that people on the right half, people in my right two quadrants, or the leading tech side, they tend to, I would say, either come from national security backgrounds or come from tech investment backgrounds. Sometimes finance as well. And their model of the world, the way they think about this problem—we’ll start at the bottom right corner, the dynamists in particular, who, like, you know, this is somebody, like, to choose a very prominent example, someone who’s outside of the administration, who’s written very publicly. Someone like Marc Andreessen, I would definitely put there.
And there’s lots of people from his circle who’ve kind of gone to the administration, and you can kind of see where they’ve done. But they look at American success over the last 30 years, 40 years, and they say that the successes of that time need to be replicated. There’s this chart that went viral on tech Twitter a few months ago that showed the productivity gap between the United States and Europe between 1990 and now. And it basically shows that the Europeans have not had very much gain in labor productivity, whereas the Americans have almost tripled it. And this is one explanation for what has happened to the American economy, why it’s been much better than the Europeans. In the year 2000, people thought the EU might be up at the American level, and it just hasn’t been.
And the answer these people will give is, well, it’s because America had the most important firms and the most important technological advancements of that time. We had Apple, we had Meta, we had Amazon, and we have Silicon Valley. And Silicon Valley, as focused on specific firms, they often think about this in terms of firms. They want to make sure we have the winning firms for the future. They’re going to implement the next round of a technological revolution. And if those firms are Chinese, that productivity gap that you see between the EU and the United States, that’s going to be China and the United States. And so we need to make sure our firms are it.
And the rest of the story doesn’t matter. It doesn’t matter if we have, you know, t-shirts. It probably doesn’t even matter if we have steel. We can import that from somebody else. What matters is that the firms that will capture the largest percentage of global growth in the future—and those are going to be the ones at the forefront of new technologies—those ones need to be us. And so when they look at what these suite of technologies will be, obviously AI. There’s concern about things like semiconductors and stuff like that, especially if in the future the Chinese might cut us off or something along those lines. There’s things like quantum computing, things like automated systems, things like robotics, UAVs, new energy, aeronautics, and space. That’s the realm in which these guys kind of think.
And the guys on my top right, I call it the techno-nationalists, they have a very similar perspective, but they’re even more national security oriented. Like, their argument is less about we need to have the firms that have the most economic growth and more about we need to have the technologies that will have military applications in the future. And we need to make sure we’re pouring national resources into that. And then on the two left quadrants, the people who are in favor of an industrial renaissance, you write in your report that people in those camps see Silicon Valley not as this model of productivity success, but as a cautionary tale.
So, there’s a national security oriented way in which they see them, and there’s a more economic and political way. Let’s kind of start with that. The economic and political thing is Trumpism in some way starts out because certain parts of the country feel like they’ve been left behind by this fantastic economic growth that we just described that happened between 1990 and then 2015. If you are a person in West Virginia, Buffalo, New York, Winesburg, Ohio, places like that, the vast increases in American productivity and the lowering of consumer prices haven’t been sufficient to offset what you’ve lost in the deindustrializing of your areas.
And so, as a practical political matter, these guys point out that if we don’t have an economy that meets the needs of these people who live in these swing states and are at the core of our movement from its very beginning, then we’re doing something bad. There’s the economic, and then it will just create even worse populist eruptions in the future if we can’t meet this need. There’s the economic argument on top of this, which is that a lot of these guys look at China and say, if you look at China’s economic advances, there was this idea that you could, in the past, have American companies doing design.
And then you could export the middle tier, the hard-tier construction, off to countries like China. And then you would still be able to be a leader in design and in software, you know, the user end experience and the design experience; all the middle stuff, we can take to a country like China or other countries that are in the middle of industrializing. And that will make things cheaper, and we don’t really have to worry as long as we’re at the leading edge. A lot of people who are studying the Chinese economy now, and if you read a magazine like American Affairs or you look at American Compass, you’ll see a lot of this stuff kind of percolate up.
They make the argument that this is really not true, that actually, if you are doing really well at the production, you will soon have the skills needed to not only produce but also to design and engineer and develop the backends. And that’s what’s happening in China right now. That’s more or less their argument. And so if we follow this Silicon Valley way, where we just focus on, like, Apple focuses on the user end experience and the design, but then exports the hardware part to foreign countries because it’s cheaper, we’re going to end up where you have, you know, Huawei and Xiaomi and all these little companies that are leapfrogging America and developing new technologies because they have both design and engineering experience. And that’s the story of EVs or whatever. That’s kind of the logic there.
So that’s how we go from Vivek saying, you know, we should tariff the hell out of China, but it’s okay to do friend shoring and we also need 50% tariffs on Vietnam because those factories should be in America and not in these countries that don’t even like us anyways. Well, that’s right. And I think it depends on the particular thing. I mean, some countries like Cambodia or Vietnam, I mean, that’s producing t-shirts half the time. Right. Especially a country like Cambodia or Laos or whatever. And that’s probably the hardest argument any of these people have to make.
But if you’re talking about major industrial electronics or things like steel, these guys will say you need to have this. They like to use this phrase, like, a functioning industrial ecosystem, right? Where you have overlapping companies, overlapping supply lines, engineers inside your country that will develop these new technologies. And then they’ll add a national security point on top of this, which is that if there is a war, especially with China, it’s really dangerous not to have, like, even basic things like steel and aluminum not being produced inside our own country. If it’s being produced far away, that can be targeted in a war.
If it’s being produced in China itself, then we won’t have access to it. We need to have this capacity at home. And so that’s kind of the people on the left-hand side of the argument is that we need to have much more of this stuff here. Otherwise, we’ll have bad effects in developing new technology and meeting the political and economic needs of average people across the country and in secure national security.
Nick, let’s say you’re an old-school Republican and you still really like the ideas of free markets and tax cuts and deregulation. If you want to get in, if you want to be influential to Trump, which camp are you going to affiliate yourself most with to try to be in his orbit? If you’re an old school free marketer, probably the group that you have the most easiest affiliation with is probably the bottom right corner, which I call the decentralists. So as a reminder, like, the top and bottom of the graphs, this is basically people who believe that government should be used for conservative ends versus people who believe that they’re really skeptical that government can be used that way.
And people on the bottom right, like, they believe in this new technology paradigm and they also believe that the government mostly just needs to get out of the way. And so their program is, like, we can have a lot of economic growth and develop these new technologies if we just get rid of regulations. If we do all this, like, abundance NEPA reform stuff, it’s kind of in line with what a lot of these people say. Certainly, it’s in line with, like, a lot of what the DOGE people think and have been doing. That really the big problems that we have are inefficient bureaucrats, and we have all these DEI things holding down the economy, holding on foreign companies, that we have, you know, kind of crazy 1970s environmental regulation rules.
We’d obviously have nuclear power plants everywhere already if we didn’t have that stuff. And so that’s kind of their tech. And a free marketer can find some work, can work with them pretty easily. Although I think in terms of, like, personalities, they would often clash because a lot of these guys kind of come from tech perspectives where they’re very not old school in their way of thinking and talking. But in terms of, like, actual policies envisioned, I mean, that’s where someone like Vivek is at, right? And he says, yes, we should have tariffs, but only for the perspective of reshoring key national security stuff. Otherwise, our goal is just to take the government out of the economy.
Are we looking at a post-free markets America for the foreseeable future? Or is there going to be a swing back? I mean, there’ll probably be a swing back in time, especially if, like, I don’t know, we have a Great Depression-style thing. I mean, a lot of time, free market swings are the result of, like, swings in the reverse, right? They’re reactions. And if liberalism—and I mean, like, economic liberalism—seems very outdated to people, it usually doesn’t once it’s not being tried. So I think that that’s a certain possibility.
I think in Trump world, there’s not a coherent answer to this, partially because you have all these different positions, right? So on the top half are the people who believe that actually Republicans have been wrong since Reagan times and that we need to have some sort of much more serious intervention in the economy in support of building the new economy in the future. And there’s generally two precedents that are cited for this. People in the top right corner, those kind of national security hawkish folks, those techno-nationalists, they’re very apt to look back at, like Cold War-style interventions in the economy for the sake of creating new technologies.
And they know there’s a lot of those. A lot of technologies are basically the marriage of government and the private sector for national security needs. They say, well, if it worked for us in 1950, why wouldn’t it work now? People in the top left corner also believe that. I call them industrialists or industrial policymakers because they are strong believers in a big industrial policy across the board. This is kind of like Oren Cass is maybe a leading figure of this. I think J.D. Vance is certainly in that camp. Marco Rubio is in that camp.
And they would like to see the full suite of policy tools used to not just advance American technology but also to try and build American factories and make a much more equitable—although they won’t use that word—distribution of economic goods across the United States. And these guys, this top left industrial policy camp, my personal read at this point in the administration is they are not having too many big wins. They have a lot of intellectual wins. And they support tariffs. So if you consider tariffs a win for them, then they’ve got something. And there’s not a lot of appetite in the administration thus far for the sort of spending that these guys imagine.
And that’s maybe the weakness of their approach is that if you’re going to do hard-level industrial policy, it’s hard to do without also raising taxes or raising the deficit. And Republicans are very allergic to both of those. And so that gives you three of the four camps. I’ll just say one more thing just about the bottom left camp, which is the trade warriors, because there’s something about this that’s not obvious to people.
A lot of people who are attracted to tariffs are often critics of industrial policy, rich, large. They, like, say, didn’t like the Chips Act. And they actually are in their own way, like, they’re critics of the administrative state. They don’t believe that government can do this well. But they nevertheless believe that tariffs might be able to. And this kind of seems like a contradiction in terms, but it isn’t necessarily. Because if you think about it, tariffs are extremely, although high impact, they’re low bureaucracy. USTR has, like, 200 people. Maybe they need to sign, like, 100 people more on to have negotiations with every country in the world right now.
But if you want to basically have the sort of industrial policy that you could run on, like, a night watchman state, the sort of state that America had in the 19th century, well, that’s how America did it in the 19th century. It used tariffs as your lever. And so if you want to kind of do the DOGE thing where you’re tearing down the federal government without reducing the ability to bring back this industrial renaissance, you’re often very attracted to the tariff position.
Okay, Tanner, a challenge for you. Let’s use Laura Loomer as our hinge to explaining Trump world’s views of geopolitics. Well, maybe, I mean, do we need to give background for people who don’t know what happened with Laura Loomer? Maybe you should tee it up first. I think not everyone will have seen that piece of news in the middle of all the tariff stuff.
I don’t know if I can, like, tee up Laura Loomer, but all right, I’ll try. Okay, so. Yeah, I mean, why don’t you, can you explain her in four sentences? I feel like you have a better sense of her mind space. I’m sure. Laura Loomer is a media personality slash activist on, I would say, the MAGA right. And she was influential in the election, and she was a constant ally of Trump during the election.
She stayed with him during the primaries. And one of the things she’s been big about is policing Trump world to make sure that rhinos, Republicans in name only, do not get important administrative posts in this new administration because the theory is that a lot of the stuff that Trump wanted to do last time around didn’t happen because he had people who were working against what Trump wanted. I dispute this theory, but we can talk about that in a minute.
The key thing here is that the other day she came to the White House with a dossier of people. That she had evidence of some sort that they were never Trumpers at one point in their life and were disloyal to Trump’s current program. And they need to be let go. And it looks like Trump let go several of them. Importantly, the people that have been let go by my count, there were three people in the National Security Council. This morning, it was released that the head of the National Security Administration or agency, sorry, the NSA and the number two of the NSA were also both let go, presumably because of whatever Laura Loomer was presenting in this dossier.
But on the flip side, not everybody that she’s targeted has been let go. There are people like Alex Wong or Ivan Cappanathy on the NSC who she’s targeted many times in public who still have their jobs. So that’s basically what happened. She came in, gave a briefing, people got fired. Try as she might, though, Laura Loomer will have a very difficult time firing once-never-Trumper Vice President J.D. Vance. So good luck to her.
She’s also a member of the tribe and has a huge bone to pick with Elon, particularly for the China connections that he has. Which is interesting because this is not really something that’s come up all that much. And she’s not the only one from that faction, right? Like Stephen Bannon also has gone on the warpath against Elon for months for very similar reasons. I mean, so there’s two ways you can look at what happened. You can look at it kind of as the way you were suggesting in the very beginning of this podcast, which is, oh, isn’t it crazy that we have far-right Internet personalities firing people in the National Security Administration? I mean, in the National Security Council.
Obviously, we’re not going to look at this as a fight within the Trump administration itself between these different factions that I’ve kind of identified. And I suspect the second is the right way to look at it. I suspect that Loomer is just as much an agent of these forces or of these people inside the administration that she wants to help as she is a completely outside force. And this is a way for people who dislike some of the policies these people might be pushing for to bring it to Trump’s attention that doesn’t seem self-serving in their own way.
I don’t know if that’s true, but if true, it’s very clever. But it wouldn’t surprise me at all to find out that, like, there are people in the administration who helped tee this whole thing up. Because where does Loomer learn all this stuff in the first place? How does she know, you know, the long details of what David Fyfe thinks or not? She doesn’t, she’s not part of that sphere. But there are people who are part of that sphere who know Laura Loomer. And so that’s my guess at how this actually happened. It’s a hit job.
We’ve got people paying libs of TikTok and other influencers to dig up dirt. It’s like internal cancel culture. It’s beautiful. Well, you know, it’s not that new or unusual, frankly, in like the long history of American politics for people to do this sort of thing to take—I mean, it’s just not that different, actually, from leaking something to The New York Times to discredit somebody internally. It’s just the difference is Trump doesn’t care about what The New York Times thinks, whereas he will give an audience to Laura Loomer.
And the dynamics are not that different. And make J.D. Vance and other cabinet officials sit there while she berates them, which is kind of wild, which is another level of wildness, I think. But did J.D. Vance mind? I mean, for all I know, he was one of the people who was he’s on the opposite side of these debates from the people that released. So in some ways, like, this is to J.D. Vance’s benefit, assuming he’s actually, you know, he cares about the political program that he wants to do, which I think is true. I think unlike Trump, he’s more of a true ideological believer.
Maybe Vance could say that thing that you had Hitler saying in the bunker one day in the future. But I like he’s enough of an ideologue to actually believe in ideas as such. For sure. There was this quote from Time magazine. Vance had an interview there back in 2021. Vance told Time, Trump is the leader of this movement. And if I actually care about these people, like his constituents and the things I say I care about, I just need to suck it up and support him. So that’s 2021 Vance, I think. Yeah, I’d agree. I think it’s very consistent with how he is now.
He does have a definite ideological vision. I mean, I think the two consistent things with Vance in this time are, number one, he views his job as vice president to be the prime public defender of Trump, period. Kind of like how Nixon was to Eisenhower. The idea being that Eisenhower could say about the fray and Nixon would do all the hard stuff. Trump doesn’t necessarily want to stay above the fray all the time. But I think it’s a very similar sort of dynamic.
And the second thing is, like, when it comes to personnel or people and influencing policy that is one level below where Trump is at, you see his concerns manifest. He definitely has a project. I will say, I know you dismissed earlier the sort of quality of staff work, but there is not a deep bench for this stuff. And, you know, if you want to press a button as a president and have it create a certain outcome, like, you are going to need people to cross I’s and dot T’s. And the more this happens, the more you have to resort to more and more junior people who are just not going to know how to fly the plane.
So, I mean, if I was dismissive of that, I didn’t mean to be. I think it’s a real concern. And it’s a real problem. Look, the Trump administration has what might be called a red and experts problem. If you know, you’re going to go to 1930 Stalin as well. So I’m glad we’re on the same level, Tanner. I’m going to give you I have an essay that I’m writing about this, then you’ll eventually see it. And it’s going to be called Red and Experts. And I’ll give you a non-totalitarian example of this, though, once I introduce the idea of people who don’t know.
Red and Experts is the phrase that basically has been used in communist systems, especially in China. To describe the problem, like the kind of people that Mao wanted to have running his systems. You know, communists come into power. They have a problem. The median person in the bureaucracy belongs to the bourgeois class that they’re trying to overthrow. They believe rather strongly that the preferences of people from this class are orthogonal to the program they want to implement. So what do you do? Do you have these, you know, bad class element experts in charge? Or do you have reds who don’t know what they’re doing but are believers in the program?
Ideally, you can get people who are both red and expert. But otherwise, you have to find a way to triangulate this program. And this has been a consistent problem throughout much of human history. Whenever you have one group take over. The example that comes to my mind as a very compelling example of this is when Republicans first came to power ever. When Republicans took control of Congress and the executive branch in 1860. It was after a period in which Democrats controlled it for a very long time. Most of the military was Democrats.
Half of the military seceded to the South. Most of the rest of the military voted Democrat. And you had Republican after Republican in Congress drag these generals through hearings and protests to Lincoln all the time. Saying the reason that we’re not winning this war, the Civil War, is because we are not putting in power people who believe in the program. We keep putting in power these people who are sympathetic to the South. Who like the old Southern buddies. They are fighting in the war. Who don’t want to get rid of slavery. If we put those guys in power, they would fight the war much harder. They would do the tough stuff that needs to be done.
And Lincoln tried it in a few cases. And a lot of those generals who he did try to put, like John C. Fremont, didn’t do very well. It turns out that the generals who ended up doing the best for the Union were often people like Sherman. Who, before the war, did not want it to happen. Who was teaching in the South at the time. Who, on his first meeting with Lincoln, came away thinking, oh, this guy’s an idiot. He says it in his memoir. I hated him when I first met him. I thought he was an idiot. It was his fault this war was happening. But Sherman liked to fight. Grant liked to fight. They were very good at fighting.
And if you could get out of their way and let them do their job, which was killing people and burning things and stuff like that, you can have a lot of success. And so if you look at the class of generals and admirals who kind of won the war, Farragut and Meade and I guess Sheridan was very Republican. But if you look at this group of people as a whole, it’s not an extremely ideological group. What matters is their competence. And there was a problem in the U.S. Army in 1816 is there was a lot of incompetent generals who hadn’t been fighting in a war.
And so if the Republicans were looking at the current government now and saying the problem is that we have too many incompetent people who have not had the pressure, like, you know, wartime pressure sort of things to raise to the top. And what we need to do is just find the people who will be very good and will do what we tell them to do. And they will do it well because they’re American. If they cared more about that instead of these loyalty tests, I suspect they would do better. I think the general history of this Red and Experts problem is that you only really do well when you learn to have Reds who can be in charge of experts.
We do have a two-hour Civil War history show which will be coming out soon, I promise, guys. But the dynamic that Tanner is talking about is written up absolutely beautifully by Bruce Catton in his book Glory Road. It’s part two of a three-part trilogy about the Army of the Potomac. And yeah, I mean, like, when I was reading that, it felt very much like Stalin 1930s of there are all these snakes in the grass who are not, you know, down with the program. And I mean, we’ll do Hitler again. Like, my generals are betraying me. Like, they did Operation Valkyrie. So, of course, they were always against me from the beginning.
Like, this whole, like, I am losing because I don’t have loyalty in my people who I want to do the things is a great sort of psychological excuse for losing on other dimensions. But in fact, like, there are lots of human endeavors where you actually need the person with the PhD or the person with the 15 years of experience as opposed to the person with the right ideological orientation. I don’t want to diminish this. People can make a good argument that say, like, George McClellan in 1860 prosecuted the war with less ferocity than he should have because he was sympathetic to the South. People have made that argument. It might be true.
I think over human history as a whole, there are lots of examples of people in the bureaucracy, not loyal, subverting a program. But in my mind, this is less an argument for, okay, you need to just, like, replace everyone in the bureaucracy with loyalists and more an argument for, you just need to get people who are good at getting bureaucracies to do what you want. Right. I mean, like, Don Rumsfeld had a Pentagon full of people that he did not—had a Pentagon full of people who didn’t agree with his program. And he was very good at getting them to do what he wanted anyways.
And that’s the sort of thing that you should be selecting for. And I do think they probably overestimate or underestimate the number of people who are not necessarily Trump voters, but who will just do what they need to do because they believe in doing what the government says. There’s actually a lot of people like that. But here’s the thing.
And this comes back to the beginning of our conversation, Tanner, where if we don’t actually have a vision, if the vision is, I am Trump, I like negotiating, I like keeping my cards close to my chest. The people who are not down with that, oh, let’s just defer to this guy. It’s hard to point a Donald Rumsfeld in a particular direction because that guy’s going to get pointed in a different direction two months from now. I mean, that’s fair. Although, the problem with Donald Rumsfeld, too, is that in some ways he had his own vision that wasn’t matching up with Bush’s.
So, especially at that top level, I’m a bit more sympathetic. Trump feels like, especially at the level of his principles, that various principles of the National Security Council are, and this is the level of Rubio or Waltz or Hezgeth. These people are not pursuing something in contradiction of what I want to do, which is what he thinks about Mattis and stuff at that time. I have a lot of sympathy for him saying, okay, I need people in my decision-making council to at least not be pursuing their own programs, but bringing the decision up to me. I have sympathy for that.
I’m not sure that’s what happened here with this Laura Loomer thing. I’ve seen no evidence that David Fythe was off doing his own thing. I think it was more just a pure loyalty test sort of thing, where at some point between 2020 and now, he must have said something that was never Trumpy sounding. And you’re going to kind of cut him off. He doesn’t get the J.D. grace.
Yeah. That’s right. Well, should we talk about quadrants? Do we have time? Let’s do quadrants. Do you guys have time? 30 minutes on Laura Loomer. I love it. Dude, I’m so happy we got to the Civil War.
All right. Let’s get back to our geopolitical quadrants. We have one axis that is optimism for American power and capability, as opposed to pessimism. And one is power-based viewpoints versus value-based viewpoints of how America should think about and interact with the world. The first and most important of these axes is just how do you feel about American power? Do you feel confident that America is strong, that its potential is underutilized or unrealized at the moment? Or do you think that America is basically in trouble culturally, fiscally, politically, militarily, unable to do what it once was able to do?
This matters, especially when it comes to China, because it impacts how you think about what kind of posture America can afford to take towards China. If you think America is fundamentally weak, you think that the range of possible outcomes that we can have with China is much constrained. On the flip side, if you believe that America is fundamentally strong, you are often much more willing to seek kind of maximalist solutions with China. You are often more likely to think that there are not trade-offs between China as a problem and other problem spots across the world.
We don’t necessarily have to go all the way out of Ukraine in order to focus on Taiwan because we have the potential resources to do both. We just aren’t using them smartly enough or we’re not having enough defense budgets. But that defense budget is not a law of the universe. It could be changed. What Trump wants to have happen will happen. So let’s get him to make our defense budgets bigger. That’s one axis.
The second axis is whether or not you analyze American foreign policy primarily through the lens of some sort of realpolitik hard power. Or if you think there are other values-based things that matter. The top half of my diagram is these realpolitik people. They’re very easy to understand. I think they’re a pretty easy model. You think that force is the most important thing in the world. And what matters is whether or not America has it and whether or not alliances contribute or take from it, that sort of stuff.
The people on the bottom quarter, they’re a little bit different. The two different camps of values-based perspectives actually differ in the kinds of values they’re assessing. Can you describe how those two camps differ?
Yeah, that’s right. They’re a little bit less parallel than the top ones. The top ones, more or less, if your calculation of American power changed, you would go from one camp to the other. Right. But for the bottom people, they have a similar way of thinking about the world, which is that the international order and the international environment has a sort of feedback relationship with the domestic order, domestic values that we have.
And just like domestically, statecraft should be informed by a positive vision of what we want to do in the world. It should be informed by a positive vision of the good. Likewise, there’s no reason that’s not true in the international sphere. People at the top say it’s not true. All that matters is really kind of power stuff. And that other stuff is a distraction or it takes away from the different logic that operates in the international sphere.
The people in the bottom two quadrants, they disagree with that. So what do they say? I mean, I think the people in the bottom left corner, who I label crusaders, think that you need to forcefully defend things like democracy and liberty as abstract ideals on the international stage. That by defending them on the international stage, you’re enshrining them at home. And that things like China pose a threat to them at home. They often look at things like influence operations and stuff like that.
They often view things like influence interference operations as the big problem that China poses for the United States. And then the guys will kind of have the reverse version. A paramount guy here would be someone like Miles Yu, who believes strongly that things like democracy and human rights, these aren’t just good ideas, but they are potential weapons that should be used against the Communist Party of China. Where the communists say all these things are meant to undermine us. All these ideas, these things in the international order are eroding our internal regime. Someone like Miles Yu would say, that’s great. Let’s do it consciously instead of unconsciously.
All right. And then, I don’t know, do you have a question for the other side? Or, you know, let’s see. You say that’s not what J.D. Vance thinks. I mean.
Yeah. And then, okay, let’s go to the other side of that. The people who are values-based but pessimistic in American power. I noted, in sort of a preview of this report back in October, you called this quadrant restrainers. Now you call them culture warriors. Can you describe what the restrainer slash culture warrior people think?
Yeah. I don’t call them restrainers anymore because I’ve realized that this word was getting confused a little bit too much. Restrainers is like a long academic tradition of people being like, the United States needs to come home for all these strategic reasons. We need to be more isolationist. It would be better for the whole world if we were to be that way. And a lot of people in the culture warrior camp think that, but they get there through slightly different logic. Their main concern is winning the culture war. Their main concern is recreating the American economy, the American politics, American culture, along grounds they find less hostile to their worldview.
They have like a thousand little divisions. We don’t have to get into it now, like what those divisions are. The main point is that they don’t like the kind of standard progressive liberal orthodoxy, which inhabits most of the American bureaucracy, American academia, NGOs, and much of the corporate world. These guys tend to view the liberal international order as an extension of the same progressive order at home that they’re fighting against. They view it that way in two ways. One is that the national security bureaucracies that maintain this order, they view as strongholds of the worldview that they oppose, right? CIA, FBI, even the military officer corps full of progressives.
And the second sense is, again, they view there’s a sort of feedback relationship where the complex of maintaining the international order encases a set of values that is weaponized against them at home. And so there’s no real reason to try and sacrifice American dollars, American blood, for upholding a system that they believe is trying to squash them out of existence, squash their cultural commitments out of existence. So that’s their basic belief.
I mean, you could add to this maybe like a third subset, which is that traditionally Republicans are elected, they believe, to kind of roll back certain social, cultural conditions. This never happens because Republicans always get so concentrated on foreign affairs. And so we need to turn down the dial on foreign affairs so that those things can take precedence. That’s kind of the logic of these folks.
Tanner, why don’t you connect this quadrant to caring about defending Taiwan? Hmm.
So what does Taiwan mean to these four quadrants? The Taiwanese, for the last few years, have been very big into describing how, oh, we are like a democracy. We’re a liberal democracy in Asia. They’ve often said things like, we know we’re the only liberal democracy in Asia that has gay marriage allowed under normal ways.
Things like that. This is not only not convincing to these people, it’s an anti-signal. It’s an argument against their defense. And so a lot of these guys, for sure, would not be willing to go to Taiwan’s defense for the sake of values, which they don’t necessarily hold. They’re often going to look at something like competition with China and look for off-ramps for it. They’ll view competition with China as a problem only in as much as China has a hostile relation with the United States because we need to do the necessary stuff on the trade side.
And so they want to have a military guarantee so they can do kind of crazy stuff in the trade world and not have the Chinese escalate to military options. This is kind of how they’ll say it sometimes. Right. Otherwise, they don’t feel like the United States has a huge vested interest in fighting a war with China. They would rather cut and run in that sort of situation. They don’t, especially if we’re over Taiwan. Some of them might say there’s a bigger question about how much power China has over us, if they’re going to do things that will give us independence. They have an interest in that.
But they don’t necessarily see the defense of all these allies all over the place as being the fast route to protecting American independence and often use it as an excuse that the national security state uses to increase its own power relative in the coalition and increase its position in American society. Can we map Taiwan policy onto the remaining three quadrants? We got prioritizers, those skeptical of American power, power-based viewpoints.
And then I think in particular, can you help spot the difference between a primacist, someone who’s power-based, optimistic, versus a crusader, someone who is optimistic, value-based? Because I think, yeah, primacists, they’re not going to say, I don’t care about democracy at all. Like, they’re in America, they’re probably going to say things that sometimes sound like crusaders, even if it’s not as explicit as someone like Miles Yu.
I think the restrainer types, they do care about democracy as majoritarianism for America. They care about freedom of speech in America. And so they often view these national security stuff as being something that harms those things they care about in America. And so someone like Mike Lee, to choose a very prominent senator in this world, constantly talks about how those two things, democracy and liberties, are being sacrificed at a foreign policy that doesn’t need it.
Or someone like Ross Vogt, who’s now head of OMB, who more or less makes the same argument that this focus on foreign policy has come directly at the expense of these things we care about doing at home. So, they’re also skeptical of American capability and power. They also are somewhat doomer on where America might be. And this affects what they think about Taiwan and China. Most of them, their standard position, the position that seems to be encoded in their stronghold at the moment is the office of the Secretary of Defense at the Pentagon. A lot of these guys have been stalked there.
And I don’t know if you guys saw the Washington Post report from a few days ago that went through the memo that was released. They basically, there’s a new memo that says that Taiwan must be the organizing contingency for the entire United States military. That’s not a very restrainer way of looking. That’s not a very culture warrior way of looking at the problem. The prioritizers, their view is basically those things that they care about what the stuff the culture warriors care about. But they think that those guys are a little bit too blasé about what happens if the Chinese become the global superpower.
The only place they can stop being stopped from becoming a global superpower is Taiwan. And thus, everything else needs to be stopped in order to focus on this. We got to get out of Ukraine. We got to get out of the Middle East. We got to focus everything we got on this Taiwan thing because America is in such a weak position that unless we do that, there’s no chance of victory. Or deterrence.
You make this point at the end of your paper where you tie domestic success on culture war-type DEI fighting stuff to America’s ability to think it can do things on the world stage and project power and win wars and whatnot. Expand on that for us, please.
Okay, well, just think of it this way. If the number one difference between the different camps and the policy approaches you’re taking is, how strong do you think America is? What are the inputs that are going into this function where outside is like, you know, people’s internal, okay, America’s strong or not? And this will change from person to person. Some of these things are very obvious things, like the number of ships we have, the number of ships they have. Some of the things are more intangible. A lot of the rumorism on the Trump side is that they honestly need to make America great again because America is not. Because America is culturally falling apart; it’s becoming progressive.
It’s becoming a sort of culture that is disintegrating where people don’t feel loyalty. They will not be willing to sign up to fight for the military. There’s no longer this martial spirit inside the military. There’s no longer this cultural coherence outside of it. We can’t get the bureaucracy to do what we need. And so if there is a contest, we’re going to be fighting against ourselves the whole time. Things like that will go into calculations that people have over whether or not they believe that America is sufficiently strong enough to deter a war with China or to do anything else. This is important and very underemphasized.
I recommend people look up an essay written by Michael Anton in 2021 for The American Mind called, or maybe it’s for The Federalist, called Why We Should Not Defend Taiwan or something of that. Regardless, remember, Michael Anton is now in the State Department as at the head of policy there. And he lists all these reasons why we shouldn’t defend Taiwan, and most of them are about lack of American strength to do it. But if you see why he says we’re not strong, he points out the normal things about shipbuilding numbers and stuff. Then he says things like, and we keep crashing our ships. And we have pride flags everywhere.
And we have a military where Mark Milley cares more about transgender troops than about readiness. He goes through a whole list of things, which I think most analysts of American foreign policy, be they abroad or at home, do not really view as something separate from foreign policy. And for a lot of Trump world, that’s simply not true.
Well, here’s where I kind of see the Nero decree energy coming back into it, right? If the woke people seem like they’re winning in 2027, then I think that the sort of the Taiwan commitment, I imagine, would be a little less clear. You know, yes, I think that there’ll be more forces for restraint in a world in which the Trump people feel like all the things they’ve wanted to do, Doge tariffs, remaking the global order. If all this stuff turns to crap, you know, maybe there’s the argument like, oh, they’ll look for a way to have a war because I’ll make him look strong and people will trust them more than Democrats or whatever.
Like, I suppose it’s a possibility. But my intuition is that if we look like Trump has been in power for two or three years and America is in a worse position culturally, economically, politically than it was when he came into power. Then this will translate into a larger percentage of Trump people around Trump, feeling like America is not capable of standing up to China. Or that it needs to compromise and regain strength before it can do so. Hide and bind, per se.
You note in this paper, Tanner, that they’re playing half court tennis, thinking about China’s aims and ambitions and second-order consequences of what Beijing would do if they went this way or that way. It was never the sort of first layer of thinking that got all these people to all their different quadrants, both from the geopolitical and economic side.
I asked all these people about what’s your ideal China policy. What do you want to have happen? You know, what does the world look like after you’ve done this? What are the tools you want to use? And everybody had lots of answers on these questions and expanded freely on them. But when I asked the question, OK, so when you do X, what do you think the Chinese will do Y? First of all, no one ever brought that up on their own. Some of them were able to get pretty good answers when prompted, but they all had to be prompted to kind of view this as a we do this, they do this, we do this, they do this sort of thing.
And in some ways, this is almost kind of the opposite of Trump himself. Right. Trump is a super tactical guy who just wants to go from one thing to the next, whereas everybody else has these very big plans. They have these, you know, here’s the end state we want with China. Here’s this program of things we want to do to help us without thinking, OK, well, what’s their response going to be? Whereas Trump almost is the opposite, where he’s thinking, OK, what’s their response going to be? Let’s do the thing where they’re going to respond to us.
And I do think that’s probably a problem. It’s a problem both in that it probably means that branching from or bridging Trump’s way of doing policy to what these guys want to do will be difficult. Even if they weren’t all trying to do different things, it would be difficult. And then the Chinese will do stuff. And so I would feel better if more people had thought through what they think the Chinese response would be, especially on the economic side.
On the economic side, I think a lot of the people, whether or not China existed, they would have many of their similar economic ideals. For some of them, China is a convenient boogeyman. For some of them, China is a good piece of evidence to use as to how American economic policy could be better. But very rarely is China the reason they got to where they were so much as just a case study in what they want to do. And I suspect that there might be a few surprises when we act and the Chinese act in return.
Well, let’s close on that then.
Okay. Tanner, Tanner Greer, Mandarin speaker, guy on the scene for the Council of Foreign Relations doing the open translation project. Translation, one more time. How the hell are they going to make any sense of this?
Well, so the first question, and I don’t know the answer to this, is how much discernment do they have of the intricacies that I’ve kind of described? If they were very smart, they would try to look at these different factions and play in such a way that they would strengthen some over another. I doubt they will be able to do that. I don’t think the Chinese have a very good track record of understanding kind of American politics and manipulating it in a way that works in the era of Xi Jinping.
I don’t think Tanner is going to be consulting anytime soon. This podcast is all you guys are going to get. I know. I guess they could read my report. It’s out in the public. I mean, they will be filled, like, whatever little intern at the embassy reads my report, it getting filtered up to Xi Jinping, I think those steps probably matter.
Because I’ve met or seen some of those people at the embassy before, and a lot of them are actually pretty savvy on American politics. So it makes me think that there’s something between the step of the guy on the ground in D.C. and see where things get messed up. Or there are other pressures in their own domestic system where people are doing things for reasons that aren’t just what the Americans will do back, right? There’s a mere problem here. What will they do?
My guess in the short term is they’re going to just position themselves as being the nice guys on the international scene in contrast to the chaos of the Trump people. If they’re smart, they’ll emphasize this whole predictability, unpredictability element I’ve mentioned already. You can see they’ve already started leasing propaganda, talking about how they’re all for trade and all for helping people out and stuff. They’ll continue doing that.
The more interesting question is, will they take a harder or maybe more accelerated view of the Taiwan question? And I suspect the answer, unless the Taiwanese do something different, is no. I think they’re going to look at the chaos going on in the United States and interpret this as reason for them to have some sanguinity about their previous proclamations that the East is rising and the West is falling.
The time is on their side. I think that’s probably my guess. They’re going to look at their, they’re going to say time is on our side. We just need to be the calm guys while the Americans are the crazy ones. And everyone will see who’s the better bet. Taiwanese will see we’re only getting stronger in military power. We don’t have to pull the trigger right away. But that’s just my guess. There’s lots of things that could get in the way of that.
What one last one for you, Tanner. So, you know, history is contingent. Trump is a unique figure. DeSantis could have won the primary. He could have gotten impeached in January. He could have gotten shot twice over the course of the election. What was and wasn’t baked in given a Republican president winning in 2024?
What has happened with Ukraine was not baked in. What has happened in Europe was not baked in. That’s the first thing. I think that the general trajectory on the geopolitical side of the department orienting strongly towards Taiwan was to an extent baked in. Now, the really interesting question is, what about tariffs and trade?
And I think that a larger reckoning with the current international trading order, I believe personally that this order is not sustainable as it has been. The costs, you know, I hear a lot of neoliberal people kind of talk about how we just need to kind of go back to Obama-era stuff. It’s all good. Like, you know, it’s fine. I think this is utterly insufficient, especially if you’re a liberal who doesn’t oppose, who doesn’t like Trump. You have to see that this order in some ways produced him.
And so I think that whether or not Trump died last summer, we would be having some sort of reckoning with what the order would look like. Would it happen this way? Probably not. But I think there would be, like, I think that this the era of the post Bretton Woods era, you know, the globalized era from 1970s to now, whatever we want to call this world, is coming to its concluding point, naturally, at this point.
And the question is just how much agency are we going to try and have over this process? And how much agency can anyone have over this process? I don’t have simple answers to that. I think that the Trump team’s divisions and Trump’s own kind of erratic personality, of course, make that a little bit more difficult to exercise some sort of control against the friction of the universe. But I don’t think there was a way where we go back to 2016 at this point.
All right. I think we’ll call it there. Tanner Greer, thank you so much for being a part of the show. Thank you very much.
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This is an experimental rewrite
Tanner: Today is April 4th. Two days ago, we had Liberation Day, a tariff salvo that aimed to reshape the global economic order. Meanwhile, Laura Loomer walked into the White House and dismissed several competent NSC staff members who were sufficiently MAGA for Trump 1.0 but apparently not for her or the president now. What’s going on in the Trump administration, and what implications does this hold for America’s relationship with China and its future place in the world?
Host: Thankfully, we have Tanner Greer from the blog Scholar Stage, who has written a guide to help us understand all of this. His new report, titled “Obscurity by Design, Competing Priorities for America’s China Policy,” stems from dozens of interviews and hundreds of hours of studying key Trump policymakers. This research will define America’s geopolitical and economic posture for years to come. We’ll delve into the various viewpoints shaping this administration as well as the long-term implications of Trump’s management style. Nicholas Wells, the longtime editor of China Talk, will be co-hosting today.
Tanner: What a day! It’s great to be here.
Host: Thanks for joining us!
Tanner: Yes, when we originally agreed to have this podcast about the report, I knew it would be relevant, but I had no idea it would be this relevant.
Host: All right, Tanner, let’s start with tariffs. How did we get to this point, and what does it reveal about the Trump administration?
Tanner: I spend the first part of my report discussing how to model Trump’s decision-making behavior and why it’s often difficult to predict his actions. I identify two main reasons for this unpredictability. First, Trump inherently wants to be seen as unpredictable. It seems to be part of his personality; he enjoys being impulsive and challenging to work with. Over time, especially during his first presidency, he realized that when people don’t know what to expect from him, he tends to do better, at least personally. Since he believes this gives him negotiation leverage, he views unpredictability as beneficial for his strategies. There’s something self-serving about this, but he’s escalated this idea to an official philosophy.
Co-host: Absolutely, Tanner. In an interview before the 2024 election with the Wall Street Journal editorial board, he even claimed that Xi wouldn’t invade Taiwan because, quote, “she knows I’m fucking crazy.”
Tanner: Yep, that was his exact quote. When it comes to China in particular, as well as world leaders in general, he wants them to see him as unpredictable and potentially capable of anything. A lot of his behavior reflects this desire to make that belief credible. He’s not the first president to think this way; Nixon adopted a similar strategy. The idea is that if you present yourself as unpredictable, rivals may be more inclined to do what you want. This aspect, in my view, is key to understanding why Trump behaves the way he does. He believes that clarifying his intentions would limit him, locking him into promises he may not want to keep and reducing his negotiating power.
Tanner (cont’d): Instead of pursuing a long-term strategy to reach specific goals, he approaches international relations as a series of ongoing negotiations aimed at improving his position in the immediate future.
Host: There are clearly competing impulses at play, right? On one hand, you have these views within the GOP that advocate for high tariffs to raise revenue, negotiate better, revitalize manufacturing, and decouple from China. Then there’s the iterative game that Trump enjoys playing. The rollout of these tariffs seems somewhat chaotic, even though it’s an initiative he’s been focused on for a long time. It feels less orderly compared to more systematic actions like the progressive pressure on Ivy League universities and law firms.
Tanner: It is interesting, isn’t it? Tariffs are something Trump has long cared about, but their rollout has been erratic, almost frenetic. I think there are fundamentally two reasons for this. First, as I mentioned, Trump believes that increasing the uncertainty surrounding his actions works to his advantage, and he clearly subscribes to this idea on an international scale.
Tanner (cont’d): But there’s also an issue with his management style. He doesn’t see it as a problem—he views it as how things should operate. He strongly prefers having a team with conflicting opinions and values personal loyalty. As long as he has that loyalty, he seems to enjoy pitting strong personalities against each other and acting as the kingmaker who decides the winner of these internal debates. Think of it as a way for him to navigate the principal-agent problem within a bureaucracy.
Host: How do you ensure a bureaucracy stays aligned with your goals when it has its own interests? Well, if you can pit different parts against each other, that’s how he prefers to operate. This style has some advantages, as multiple former Trump officials I interviewed pointed out. This contrasts markedly with how Condoleezza Rice managed the NSC during the lead-up to Iraq by seeking consensus before presenting it to the president. If you’re a Republican looking to avoid repeating the George W. Bush experience, it makes sense that this approach would be appealing, even to some inside the administration.
Tanner: The drawbacks here are twofold. First, it makes long-term planning and coherence difficult, complicating continuity from month to month. Second, it risks creating competing priorities and ideas for what the administration hopes to achieve, leading to tangled policies. Since Trump isn’t upfront about his preferred direction, he allows various ideas to surface, often without making decisive decisions among them.
Tanner (cont’d): Consequently, multiple people might end up advocating for the same policy for entirely different reasons. When examining how the tariff situation evolved from the beginning of the administration to now, I think this dynamic contributes significantly to the story.
Tanner (cont’d): Many groups hold different rationales concerning tariffs and their desired outcomes for economic policy, particularly regarding China but also more broadly. I’m not sure if the administration has successfully integrated these divergent views into a coherent policy. It seems at least plausible that their formula for determining reciprocal tariffs emerged from conflicting motivations among those supporting the tariff idea.
Host: So, is the policy responsible for such an outcome, or is there something else at play? A long-standing narrative suggests that reciprocal tariffs are necessary since many countries operate with unequal advantages. However, it’s also clear that something additional is necessary alongside those reciprocal tariffs to ensure fairness, given various restrictions and industrial policies that can skew the playing field.
Tanner: That seems reasonable. However, calculating the effects of all these different policies worldwide to level the trade field is a daunting task. I don’t have special insights into how the recent decisions were made or the processes involved this week, but I believe this occurred quite recently.
Host: Yes, media reports indicate these developments happened just last week.
Tanner: It’s wild! And it ties back to the numerous competing theories surrounding how to revitalize manufacturing. There’s this famous note Trump wrote to a Sherpa at a G7 meeting where he stated that trade is bad. His perception that trade deficits signify America is losing and being cheated has been a recurring theme for him.
Tanner (cont’d): Setting aside the question of whether ChatGPT created the formula, basing it on correcting trade deficits aligns more with Trump’s views than any complex strategies we’ve seen from the GOP recently—like reconfiguring Bretton Woods or other grand designs.
Host: That’s a fair point. However, looking at the tariffs, they aren’t too far removed from what Trump said he wanted to do during his campaign.
Tanner: Exactly! Throughout the campaign, Trump consistently emphasized two points: he wanted around 10% tariffs across the board and about 60% tariffs specifically on China. Those statements were made on multiple occasions.
Host: Right. If we analyze current numbers, it appears that China now effectively has a 60% rate added, while a general 10% applies elsewhere. There are also reciprocal tariff adjustments, with some larger figures for countries like Vietnam and smaller ones for others.
Tanner: My guess—though it’s merely an educated guess, as the administration is swamped right now and I won’t pester them—is that the logic even in Trump’s thinking is that the 10% tariff will stick no matter what. The other tariffs could serve as a sort of negotiating leverage. The real question is, negotiating for what exactly? There are many potential answers, but I suspect Trump wants world leaders to come to him, seeking clarity on how to get closer to that 10% rate rather than facing higher tariffs.
Host: If you take a country like Cambodia, I’m unsure what they could do in trade terms to affect this. Perhaps they could offer to remove Chinese military presence at some point, but is that something Trump cares about?
Tanner: Countries like Japan have much more room to negotiate back and forth. But here’s the thing, Tanner: Trump wants this to be an ongoing game where everyone plays along, making deals. But there’s a limit to how many times you can play before countries lose interest and stop wanting to engage.
Host: Is there a breaking point? At what moment, if ever, would countries choose to disengage from America’s geopolitical and economic ledger if this is how they’re treated? Is that even possible?
Tanner: For Japan, can it truly opt out of the American ledger? Militarily and economically, is that a feasible option? On the other hand, for countries like Vietnam, militarily and geopolitically, it seems easier to balance, but economically, it’s much harder.
Tanner (cont’d): I think many of these countries might adhere to Trump’s belief that due to the United States’ consuming power and pivotal role in the global economy, they will feel compelled to capitulate in the short term. That seems somewhat true now, but the bigger question is whether this creates a long-term environment where countries over the next decade consider a more self-reliant, autarkic approach or tilt toward another balance.
Host: I see that as a genuine possibility for some countries. However, in the immediate term, I don’t believe we’ll see a mass withdrawal from interaction with the United States—the stakes are too high. In a matter of 15 or 20 years, there might be a turning point. Post-1945, expectations were that the U.S. would engage in trade and support economic growth.
Tanner: Exactly. If Trump’s idea remains that trade is detrimental and countries are taking advantage of us, or that successful allies are no longer part of an overarching American strategy, that represents a fundamental shift. It could indeed challenge many assumptions over a 10- to 15-year timeframe.
Tanner (cont’d): I’d reframe the issue. Historically, my report indicates that Trump benefits strategically from uncertainty. In contrast, many foreign countries, especially those within our alliance and major trading partners, crave certainty from the United States. They want to understand what will happen and when, so they can prepare accordingly.
Tanner (cont’d): It’s akin to the saying, it’s no fun being a mouse among fighting elephants. A mouse really wants to know where the elephants might step next. Trump believes he gains advantages from keeping others guessing about his next move. However, I believe American allies would tolerate much worse conditions than in the past, but they struggle to accept uncertainty about future terms.
Host: Exactly. When do allies decide they want to bow out of the game? If they’re not presented with a solid deal they view as the new status quo, they may find it challenging to make informed decisions about the future.
Tanner: Right. If the scenario is that the Chinese government offers stability while the Americans are unpredictable, where circumstances change frequently under various administrations, that creates a more significant problem in the long run. Treating allies as though they are not valued will damage relationships.
Host: That’s an important point, Tanner. Can you recover from these circumstances? There were major disputes during Trump 1, but then there was USMCA, which he touted as a historic achievement.
Tanner: I’m not sure. This is an intriguing case because we have extensive data on how Trump operated during his last administration—four years’ worth of information. A key question now is whether the dynamics we’ve seen in the past three months are more indicative than what occurred in the previous four years.
Tanner (cont’d): There have been some changes. For instance, when Trump initially announced the China tariffs during his first term, they rolled out progressively with advance notice. It wasn’t a matter of implementing 25% tariffs overnight.
Host: We need to consider what’s changed between the two administrations. Why was the first more measured in its approach compared to this second one, especially while ostensibly playing the same game? I believe the markets responded far better during the first administration because stakeholders had time to prepare.
Tanner: There’s been a significant difference, for sure. In the first term, Bob Lighthizer had Trump’s ear and understood the importance of executing the tariff schedule transparently and legally, so the courts wouldn’t challenge it. His approach was deliberate and methodical.
Tanner (cont’d): It seems that the current trade policy figure, Howard Lutnick, may have a different approach—one that leans toward a faster pace than what Lighthizer advocated. This observation comes primarily from what I have seen him say, along with various media reports.
Host: I understand that if Lutnick leaves, things could shift again. There’s a narrative that the Trump administration’s second term can be evaluated based on the quality of staff work involved. We’re seeing a blend of Trump’s unpredictable style and a level of poor execution.
Tanner: Exactly. Whatever outcome you’re aiming for can inevitably be compromised if the staff work doesn’t meet certain standards. If Laura Loomer is in charge of firing people, it’s unlikely that you’ll see improved staff quality or execution as a result.
Tanner (cont’d): If the question is whether the second Trump administration will be competent, that contrasts with an entirely different inquiry about what its goals entail. I sympathize with the argument that objectives might become irrelevant if implementation is poor—especially when attempting to reorganize the global trading system.
Tanner (cont’d): As preparation for this discussion, I revisited a lengthy report from Stephen Moore, who was Trump’s Council of Economic Advisors Chair. In December, he authored a detailed paper advocating for using trade leverage to reform the Bretton Woods system and adjust the existing financial trade order toward more favorable outcomes for American manufacturers.
Tanner (cont’d): What struck me in reading it now is his insistence on approaching these changes cautiously and gradually, akin to the methods employed during Trump 1. He advocated for caution but with ambitious goals.
Host: Not everyone shares this agenda, of course. Moore isn’t part of the cabinet and holds consultative authority. Yet, it highlights the weaknesses of such approaches. If a more measured method had been deployed, I wonder if the marketplace response would have been different or led to similar complaints.
Tanner: I believe the market reaction would have varied. The current tumult we’ve seen might not have occurred under a slower and more gradual approach. When we discussed temporal claustrophobia, akin to how pivotal moments drastically altered timelines—like Japan’s attack on Pearl Harbor or Hitler’s invasion of the Soviet Union—it emphasizes the urgency of the moment in determining strategic vision.
Tanner (cont’d): Many commentators believe that the clocks are ticking fast for America, and there’s pressure to act quickly. They feel that if we don’t act decisively now, America could significantly lose influence, as its fiscal health deteriorates.
Host: What are your thoughts on that, Tanner?
Tanner: The analysis can be broken down through various lenses. At the individual level, one key point in my report is that historically, the Trump administration has rapidly cycled through personnel. Policies can change drastically when leadership shifts, and this encourages those with initiatives to act swiftly and in ways that are difficult to reverse.
Tanner (cont’d): For instance, consider a drastic action like eliminating USAID on tenuous legal grounds—it’s an approach designed to formulate irreversible changes. The way tariffs are being constructed seems to lean in that direction.
Tanner (cont’d): Looking at Trump himself, this is his final opportunity: his last chance to make a mark. Amid discussions about his potential future bids, I believe his haste indicates he wants to enact changes and see tangible impacts soon.
Host: Then there’s a broader perspective. If someone aims to transform the entire global trade or political order, they might fear that acting slowly grants opponents time to regroup or gain the upper hand.
Tanner: Yes, this urgency stems from lessons learned during the first administration—there were so many initiatives hampered by unforeseen delays or resistance. I believe this mindset prompts Trump and those around him to think, “We can’t afford to repeat that—we need to take action!”
Tanner (cont’d): Their focus leans toward executing major reforms rather than looking as if they’re merely pretending to improve, which may allow bureaucratic obstacles to flourish. Thus, from Trump’s perspective, maintaining unpredictability clearly fits with his preference to act rather than wait for responses.
Tanner (cont’d): The previous expectation was that market fluctuations would result in checks on Trump’s behavior; that rationale seems to have faded. Nonetheless, based on observed behavior, I conclude that this upcoming period will be our first real test of these dynamics.
Tanner (cont’d): The psychological aspects of this situation are intriguing, but given past behavior, I think it’s likely that some challenges will occur, though the outcome will depend on market conditions over the next six months.
Host: If we consider a possible future, I was recently reminded of Ian Kershaw’s biography of Hitler, particularly his portrayal in April 1945—the walls closing in. Kershaw noted how Hitler intimated that the German people might not deserve him and contemplated total defeat.
Tanner: I get where you’re coming from, and while it’s essential to keep that kind of scenario in mind, the focus should remain on not entirely discounting the unpredictable. Tanner: I think that’s not the thing I would be worried about. The concern with Trump running at 10% in 2027 isn’t that the world needs to burn down. It’s that he’s going to worry that if he loses the next election, and if a Republican doesn’t win after him, he might end up in jail. That’s going to be his primary worry.
Co-host: Interesting perspective.
Tanner: The difference between someone like Hitler and Trump is significant. Hitler was extremely ideological. In comparison, Trump’s mindset is different. While Hitler might think, “Oh, the people don’t deserve my brilliance,” Trump doesn’t see himself that way. He’s more like P.T. Barnum—not thinking that if people aren’t attending his show, it’s because they didn’t deserve it. Rather, he thinks, “Oh, I played my hand wrong. They didn’t believe my game.”
Co-host: So, in that sense, Trump would just find a new act?
Tanner: Yeah, he might do that. But he will also be older and face other challenges. Still, I think this kind of messianic thinking exists on the right. There are definitely people who view things that way.
Co-host: Right.
Tanner: But I don’t think Trump sees himself as one of those people. It just doesn’t mesh with anything I know about his character. He might think, “I’m somewhat God’s gift to humanity,” but he doesn’t see himself as the embodiment of an ideology that the public isn’t ready to accept yet.
Co-host: Fair enough. You’ve convinced me, Tanner. For the record, I did ask ChatGPT for better analogies, but it didn’t provide one that I liked. Let’s pivot to discussing actual beliefs in Trump world.
Co-host: We have two axes that you chart Trump world’s economic thinking on. One focuses on industrial renaissance versus emerging technology advancement, and another differentiates between those who believe the administrative state can effect change, and those who view it as weak and ineffective. Tanner, talk us through this quadrant of thinking. Maybe assign some human beings to better populate our four corners.
Tanner: Sure! First, it’s important to note that there’s a consensus among MAGA supporters about their economic vision for the future. In my report, I frame it especially around the economic vision concerning China. Many frame it as a need to win the economic competition with China. But what does “winning” mean? It often relates to a sense of independence. There’s this belief that the U.S. is too reliant on foreign nations, which limits our freedom. To use Trump’s words from earlier this week, we’re not “liberated.” He even mentioned it would be a new kind of Independence Day declaration.
Co-host: Interesting take!
Tanner: The idea is that our connections to foreign countries tie us down and create dependency on basic goods essential to our national security and economic future. The goal is to ensure that American strength and wealth don’t depend on the goodwill of other countries; rather, it should be the opposite, where foreign countries’ prosperity relies on our goodwill. That’s essentially the unifying vision. The question is, how do we achieve it?
Co-host: So, staying on this unifying vision for a moment, is it an autarkic dream?
Tanner: Not necessarily. It can be autarkic, but it’s important to consider who depends on whom. For instance, in 1946, the U.S. was the world’s economic powerhouse, engaging in extensive trade because other countries were buying our products. I doubt many MAGA supporters view that era negatively. They might even see a world where America is the factory for the globe, as a positive one.
Co-host: Makes sense.
Tanner: The question becomes how much self-sufficiency is required as other countries grow wealthier. What should we produce at home versus what can we source abroad? Overall, there’s agreement that globalization limits America’s influence, which is viewed as a danger, especially since it benefits China most.
Co-host: There’s definitely a historical echo in that.
Tanner: Absolutely. Having a geopolitical rival that we are economically reliant on is a significant concern for this faction. Do they see parallels to the 1930s? Sure, to some extent. But I don’t think the vision reaches complete self-sufficiency.
Co-host: What are the limiting principles across the four quadrants you identified for economic policymakers in Trump’s orbit?
Tanner: Great question. The divide I observe involves those who focus on securing American national security and leading in technological advancement versus those who advocate for a broader industrial renaissance. At one extreme, you might find someone who thinks only AI matters. On the opposite end, you have someone insisting that everything, including t-shirts, is essential. In reality, most people fall somewhere in between those extremes.
Co-host: Understandable.
Tanner: Individuals in the two right quadrants generally come from national security or tech investment backgrounds. They view American success in the past decades as something to replicate. A viral chart recently illustrated productivity comparisons between the U.S. and Europe, showing the vast productivity gains the U.S. has achieved, largely due to major firms like Apple and Amazon.
Co-host: Got it.
Tanner: In the lower right corner, I’ll use Marc Andreessen as an example of a “dynamist,” someone advocating for a tech revolution focused on critical sectors. They believe if the U.S. wins in AI, everything else will follow. In their view, if these firms are Chinese, that productivity gap between the U.S. and China will widen.
Co-host: It seems they prioritize firms that will foster significant technological breakthroughs.
Tanner: Exactly. Rather than concerns over manufacturing shirts or steel, the emphasis is on leading firms driving future economic growth. So when they consider tech revolutions, they focus on sectors like semiconductors, robotics, UAVs, and new energy technologies.
Co-host: What about the techno-nationalists?
Tanner: Yes, they share a similar outlook but emphasize national security more. Their argument shifts from gaining economic advantage to ensuring we maintain technologies with military applications. They advocate for national resources to be directed towards those areas.
Co-host: Quite logical.
Tanner: In contrast, the left quadrants advocate for an industrial renaissance. They see Silicon Valley not as a success story, but as a cautionary tale. There’s a geopolitical and economic lens here, stemming from a belief that certain areas of the country have been left behind amid the incredible economic growth over the last few decades.
Co-host: These perspectives frequently arise in discussions about tariffs and manufacturing.
Tanner: Exactly. People in areas like West Virginia or Buffalo haven’t benefitted from the overall productivity increase, suffering instead from deindustrialization. They advocate for an economy that meets the needs of these areas. Economically, they argue that not addressing these issues could lead to populist backlashes in the future.
Co-host: Very true.
Tanner: On top of that, they’re critical of the belief that we can maintain leadership in design while offshoring manufacturing. They argue that if China excels in production, they’ll inevitably gain the skills for design and engineering, further increasing competition.
Co-host: It’s a valid concern.
Tanner: That’s how we end up with situations like Vivek’s comments—pushing for tariffs while advocating for friend-shoring. Some argue that we need to reshore essential manufacturing to protect against dependence on countries that don’t align with U.S. interests.
Co-host: Right, and in terms of lesser-value products like t-shirts, it becomes trickier.
Tanner: Exactly. It’s easier to advocate for resourcing vital industrial sectors. The argument for a functioning industrial ecosystem arises here, emphasizing overlapping companies and supply lines within the country—especially when considering national security risks associated with wartime situations.
Co-host: Great points!
Tanner: There’s significant concern that if essential goods are sourced too far away, we may find ourselves unable to access them during a conflict, which is why those advocating for a more significant domestic production emphasize the need for basic industries like steel and aluminum to remain within U.S. borders.
Co-host: Thanks for clarifying that, Tanner.
Co-host: If you consider yourself an old-school Republican who values free markets and tax cuts, which camp would you be most inclined to affiliate with in Trump’s world?
Tanner: If you’re an old-school free market supporter, you’d likely feel most comfortable in the lower right quadrant, which I refer to as the “decentralists.” This group believes in the new technology paradigm while generally viewing government interference with skepticism.
Co-host: Would these decentralized arguments resonate with how some see the economy?
Tanner: Certainly! They argue that economic growth and technological advances could thrive if the government stepped back. This group seeks to reduce bureaucratic hurdles, with personalities like Vivek advocating for tariffs mainly to ensure key national security elements are reshored.
Co-host: Is a post-free market America on the horizon, or will there be a rebound?
Tanner: I think there will eventually be a reaction swing back, especially if we face economic downturns reminiscent of the Great Depression. Historically, shifts in free market philosophies often stem from reactions against earlier failures.
Co-host: That’s an insightful perspective.
Tanner: Considering Trump world, there’s no coherent answer due to its various positions. In the top half, you’ll find people arguing that Republicans have been mistaken since Reagan and advocating for a far more serious interventionist approach.
Co-host: So, the national security-oriented techno-nationalists…
Tanner: Yes, they’re apt to reference Cold War-style interventions as precedents for creating new technologies, suggesting if it worked in the past, why not now? Meanwhile, the top left consists of industrialists like Oren Cass, J.D. Vance, or Marco Rubio, who support comprehensive industrial policies—seeking more equitable distribution of goods without explicitly using that term.
Co-host: But have they had many significant wins?
Tanner: Not many at the moment. They support tariffs, seeing that as a victory, but their vision for larger spending initiatives has not gained significant traction within the administration. This may limit their potential for success.
Co-host: Interesting dynamics at play.
Tanner: As for the bottom left camp, the trade warriors, they often criticize industrial policy and may be generally skeptical of the administrative state. However, they see value in tariffs as impactful but lower in bureaucratic requirements.
Co-host: It’s an ironic position.
Tanner: Yes, it does seem contradictory. However, they argue that a simple tariff strategy provides a historical precedent for achieving goals without extensive bureaucratic intervention, similar to 19th-century practices.
Co-host: Moving forward, how would you tie this back to Laura Loomer’s role in Trump world’s geopolitical views?
Tanner: Right, we should begin by providing some context for those unfamiliar with Laura Loomer’s role. She’s a media personality and activist aligned with the MAGA right, who has consistently supported Trump and aimed to influence his administration by ensuring that disloyal Republicans don’t occupy key positions.
Co-host: Got it.
Tanner: Recently, she brought a dossier to the White House, claiming to have evidence against certain individuals, labeling them as “never Trumpers.” Subsequently, several people, including members of the National Security Council, were let go.
Co-host: And this was rolled out quickly?
Tanner: Yes, notable figures were dismissed, though not everyone targeted was removed. Some still retained their positions, like Alex Wong and Ivan Cappanathy.
Co-host: Context matters here.
Tanner: It certainly does. Loomer may be seen as an outsider waging internal battles within the administration, but she could also be working alongside some insiders serving as informants. I think that makes her role even more complex.
Co-host: Clever, somewhat of an internal push!
Tanner: It’s reminiscent of political tactics where internal factions seek to discredit opponents, similar to how leaks would discredit people in traditional politics. It’s just that Trump gives an audience to voices outside the political mainstream, like Loomer.
Co-host: Right, and she effectively critiques individuals while possibly being supported by allies within the administration.
Tanner: Precisely. There’s a strategic play at work—the potential for internal tensions to surface through her actions while still aligning with allies who share similar views.
Co-host: And that internal dynamic is quite fascinating to watch.
Tanner: It really is! It illustrates the complex web of influence and loyalty in Trump’s orbit, as well as the high stakes involved in navigating those relationships. Tanner: That’s exactly the kind of mindset you should be looking for. However, I think there’s a tendency to either overestimate or underestimate the number of individuals who aren’t necessarily Trump voters but simply follow government directives because they believe in doing so. There are actually quite a few people like that.
But here’s the crux of the matter. This ties back to the beginning of our conversation, Tanner. If we don’t have a clear vision—if the vision is simply, “I am Trump, I enjoy negotiating, and I like keeping my cards close to my chest”—then it becomes challenging for people to align with that. It’s tough to point a Donald Rumsfeld in a specific direction because he might quickly change course. That’s a valid point. Although, the issue with Rumsfeld is that, in some respects, he had his own vision that didn’t quite align with Bush’s.
Co-host: Right.
Tanner: I’m somewhat sympathetic, especially at that senior level. Trump seems to feel, particularly with respect to the principles of the National Security Council, that individuals like Rubio, Waltz, or Hezgeth are not pursuing agendas counter to what he wants. This is in stark contrast to his feelings toward Mattis and others at that time. I understand his desire for individuals on his decision-making council to at least refrain from advocating their own programs, choosing instead to elevate decisions to him. I can sympathize with that.
Co-host: So, do you think that’s what happened in this situation involving Laura Loomer?
Tanner: I’m not so sure. I’ve seen no evidence that David Fythe was operating independently. It felt more like a loyalty test, where he must have made some comments that sounded “never Trump” at some point between 2020 and now. So, he ends up being sidelined, unlike J.D. who seems to have been treated differently.
Co-host: Yeah, that’s an interesting observation. Should we discuss the quadrants? We have time, right?
Tanner: Let’s dive into the quadrants. It’s a fun topic!
Co-host: Fantastic!
Tanner: We have one axis that measures optimism regarding American power and capability against pessimism. The other axis differentiates between power-based viewpoints and value-based perspectives surrounding how America should engage with the world. The first axis is crucial: how confident do you feel about American power? Do you believe America is fundamentally strong, with unrealized potential? Or do you think it’s struggling culturally, fiscally, politically, and militarily?
This distinction matters significantly, especially in relation to China. If you perceive America as fundamentally weak, you’ll likely believe that the potential outcomes with China are severely limited. Conversely, if you view America as strong, you’re often more inclined to pursue aggressive solutions regarding China, feeling that managing multiple adversities is within our capabilities.
Co-host: Interesting.
Tanner: For instance, proponents of a strong America might argue we don’t need to abandon Ukraine in order to focus on Taiwan because we believe we have the resources for both endeavors. After all, it’s not like our defense budget is set in stone. If Trump gets his way, our defense budgets could increase. That’s the first axis.
The second axis distinguishes whether you analyze American foreign policy primarily through a lens of realpolitik or through a values-based framework. The upper half of my diagram consists of those who adopt a more power-centric view. It’s straightforward: they view military force as vital for assessing American influence and the role of alliances.
Co-host: I see.
Tanner: The lower quadrants have different kinds of value-based perspectives, and they actually differ fundamentally in the values they emphasize. Can you explain how those two camps diverge?
Co-host: Sure!
Tanner: They are somewhat less parallel than the top quadrants. If your perception of American power changes for the top quadrants, you may easily switch between camps. However, those in the bottom quadrants hold a similar worldview that sees a connection between international order and domestic values. They argue that, like domestic policy, the state’s actions abroad should be anchored in a positive vision of what we want to achieve globally. Those in the top quadrants deny that connection, believing that such perspectives distract from the core power dynamics at play internationally.
Co-host: Got it.
Tanner: People in the bottom left corner, who I call “crusaders,” advocate for aggressively defending ideals like democracy and liberty on the global stage. They believe that safeguarding these values abroad bolsters their strength at home, viewing threats from entities like China as discouraging influences.
Co-host: That’s an intriguing perspective!
Tanner: On the flip side, you have perspectives like Miles Yu, who argue that democracy and human rights aren’t just noble ideas but powerful tools against entities like the Communist Party of China. While communists perceive these ideas as threats, Yu suggests the U.S. should consciously wield these ideals.
Co-host: Interesting dichotomy there.
Tanner: Exactly. Now, let’s explore the other side of this value-based but pessimistic American power quadrant. In your previous report, you referred to them as “restrainers.” Has that classification changed?
Co-host: Yes, it has! I now refer to them as “culture warriors” because the term “restrainers” was causing confusion. Historically, the restrainer camp argued for a strategic pullback, advocating that the U.S. should become more isolationist for broader benefits. Culture warriors operate on a similar foundation but reach that conclusion through different logic. Their primary focus is on winning the culture war and reshaping American society—politically, economically, and culturally—along lines that align with their views.
Tanner: Makes sense!
Co-host: They appear quite divided across many subgroups.
Tanner: They certainly are! We don’t need to dive into every division now, but it’s essential to highlight that they oppose the dominant progressive liberal order in American bureaucracy, academia, NGOs, and corporate structures. They see the liberal international order as an extension of the domestic progressive order they resist.
Co-host: That feedback loop you mentioned is intriguing.
Tanner: Yes! They view efforts to uphold this international order as aligned with values that harm their idea of cultural conservatism domestically. Consequently, they argue against sacrificing American lives and resources for an international system they feel is designed to undermine them.
Co-host: That definitely underscores their motivations!
Tanner: Additionally, they believe that Republicans are primarily elected to push back against certain social and cultural conditions, yet they often focus heavily on foreign affairs, preventing progress on domestic issues.
Co-host: That’s a compelling point, Tanner.
Tanner: Now, regarding Taiwan and its relevance to these quadrants: the Taiwanese have emphasized in recent years how they represent a liberal democracy in Asia and have highlighted milestones such as legalizing same-sex marriage.
Co-host: How does that resonate with the camps?
Tanner: Unfortunately, these arguments are often more of an anti-signal for those in the bottom quadrants. Many would be hesitant to defend Taiwan’s liberal democratic values because they don’t share them. For some in these quadrants, the geopolitical relationship with China is primarily about trade dynamics and competition, rather than values-driven defense.
Co-host: So, they would prefer practical solutions rather than ideological commitments?
Tanner: That’s right. They might see military guarantees as a means to navigate trade tensions without directly escalating military conflict with China. The notion is that they’d rather avoid military engagement unless it’s absolutely necessary.
Co-host: Quite pragmatic!
Tanner: Absolutely. In this context, the prioritizers may share concerns about the culture warriors’ knee-jerk responses to global conflicts like Taiwan. They believe that without a united effort toward this one critical point, the chances of American success diminish significantly.
Co-host: It’s all connected!
Tanner: Precisely! In my paper, I also explored the relationship between domestic success in certain cultural issues and American power projections abroad.
Co-host: I’d like to hear more about that connection.
Tanner: Let’s break it down. If the primary differentiation among policy camps is how strong they believe America to be, this perception can be influenced by both tangible metrics, like military assets, and more intangible qualities. Many in Trump’s camp express a belief that “making America great again” is necessary because they see America as culturally disintegrating.
Co-host: And that perception affects military commitment?
Tanner: Exactly! Many believe that cultural discord weakens the country’s resolve to fight. If there’s a perception of internal disunity, people may not feel inclined to join the military, which further undermines readiness. This becomes a significant factor in determining whether they view America as capable of effectively deterring conflict with nations like China.
Co-host: Makes a lot of sense.
Tanner: You could refer to an essay by Michael Anton from 2021 where he discusses why the U.S. shouldn’t defend Taiwan. Most of his reasons revolve around an assessment of American strength, highlighting various weaknesses that most traditional analysts might not attribute to foreign policy.
Co-host: That’s a thought-provoking connection!
Tanner: Yes, many in the Trump world diverge from the consensus among foreign policy analysts, arguing that socio-cultural deteriorations impact policies.
Co-host: A fascinating angle.
Tanner: Now, here’s where I sense a resurgence of the “Nero decree” mentality: if the progressive agenda appears to be winning by 2027, it’ll impact views on commitments like Taiwan. There might be a shift toward seeking off-ramps, a strategy to present strength amidst chaos and rally support for nationalist agendas.
Co-host: It’s a cycle, isn’t it?
Tanner: Absolutely! If by the end of Trump’s term America is perceived to be in a worse state culturally, economically, or politically, many supporters may rethink the country’s capacity to face challenges like China.
Co-host: That brings a lot of clarity to understanding the future landscape.
Tanner: It’s critical to recognize how internal conditions will likely shape responses to external threats, suggesting a potential retreat rather than a proactive approach.
Co-host: An interesting prediction! You mentioned that players involved aren’t considering the intricacies of this dynamic—how the Chinese might respond based on American actions.
Tanner: That’s right. There seems to be a gap in strategic thinking. While various factions articulate their desired China policies confidently, they often neglect to consider Chinese counteractions to their proposals unless prompted.
Co-host: So, there’s a disconnect on anticipating responses.
Tanner: Precisely! It’s almost the opposite of Trump’s highly tactical approach. While he might consider the immediate reaction from China, his opponents are focused on their long-term goals without fully absorbing the cause-and-effect of their actions.
Co-host: That’s a significant insight into their planning.
Tanner: Yes. The economic side, in particular, is where motivations may not even hinge on China directly. For some, China’s presence serves as useful evidence for their broader economic ideals rather than a definitive focus for their policy outlook.
Co-host: It’ll be intriguing to see how these dynamics unfold!
Tanner: Certainly! The interplay of domestic perceptions and international realities will remain vital in shaping response strategies.
Co-host: Well, let’s wrap this up!
Tanner: Thank you for the engaging conversation!
Co-host: Appreciate your insights today, Tanner!
Tanner: My pleasure!
2025-04-04 08:00:01
A riddle for you before we move on. Yes. We’ll do Salesforce, Netflix, Square, Amazon, Palo Alto Networks, Facebook, Snap, Proofpoint, NetSuite, and CoreWeave have in common. No idea. They all broke issue. Oh, wow. And we’re back. Bill, great to see you. Good to be seen. I mean, you have to be pretty stoked coming off those wins last weekend in San Francisco. Yeah, I’m repping the Gator hat still. I’d say we kind of eked by for those that did watch. That was an incredible final few minutes. Explain it. Take us through the final few minutes.
Well, I mean, to be honest, I was afraid. I didn’t think there was a chance at this point because they were behind by so much as you were heading into the end of the game. And they basically scored four three-pointers against zero from the other side. They intentionally fouled and had missed free throws. So, I think someone said the Yahoo game predictor had it at about 98% Texas Tech with very little time left on the clock. And they somehow eked it out.
Now, the bullish people will say, oh, you lived through something like that. Now, you have confidence to deal with anything. But, you know, the odds makers have put Duke in front of Florida at this point. And that started in the opposite place. So, anyway, one game at a time. Super exciting. I was out in San Francisco at the Chase Center. Got to hang out with the coach a bit and some of the players’ parents. And it was a good trip. And now, since I’m in Austin, it’s right down the street here to San Antonio. So, a lot of friends coming in. And we’ll see what happens. It’s going to be a heck of a weekend.
And maybe if a certain couple of teams end up in the championship game, I’ll sneak in there on Monday night. I guess as long as we’re repping and rolling. I have two degrees, one from Florida and one from Texas. The Texas ladies are playing in the Final Four in Tampa. So, a lot of good stuff happening.
Speaking of a lot of good stuff happening, I think there are some people in the world that think a lot of not-so-great stuff is happening based on market reaction to the president’s announcements of these tariffs. So, why don’t we kick it off talking about Liberation Day? Yeah. So, we’re recording this right after Trump’s presentation. I think you and I both watched it and then we jumped on the pod. You’ve been talking about this. It’s obviously been choreographed that this was coming.
And, you know, you’ve been saying for a very long time that Trump and his team are very serious about this rather than the argument that it’s just a means to an end and a way to get a negotiation started. You have made the point that you believe that they believe this is actually where we need to take the economy. And as such, you’ve been conservative and worried about where this would go. You gave a presentation last week on how to think about this. What did you talk about there?
I think it was at the JPMorgan Tech Conference. It was pretty clear to me and you, and we were talking about it in early Feb, that this was doctrinal. There was a philosophical belief around trade that they wanted to create a more fair and level playing field. And the real debate has been going on is like how big? And there are a couple of different camps.
And so JPMorgan had this great event in Montana last week with 100 tech CEOs. They had Howard Lutnick, Elon Sachs, Doug Bergrum, all talking about various aspects of this. They asked me to do a little bit of a presentation on decoding the Trump economic agenda. And really, it boils down to this, Bill. At the top, I think that all the CEOs in the room are pretty excited about the golden age that people have been talking about.
You know, a pro-growth administration, pro-business, pro-investment, lower taxes, less regulation, pro-M&A. We’ve seen this M&A flywheel starting to kick up, this AI super cycle. But everybody’s been pretty terrified about these tariffs. And the real question going into today, Liberation Day, was were tariffs going to land closer to the $600 billion trillion tariff level that Peter Navarro had been talking about and Howard Lutnick had been talking about? Or maybe at a little bit lower end of the spectrum, which we heard a little bit more from Scott Bessent and Kevin Hassett. And I think everybody was holding their breath.
Well, we got the answer. We got the answer today. And so I was framing that at the J.P. Morgan conference. I showed this slide you’ll get a kick out of. I ended the presentation with two planes coming in for landing. They both actually land, believe it or not, but it’s like the glide path that we land here with tariffs and budget cuts matter a lot. And so, you know, we got the news tonight. You’re right. We just listened to the president talk, and he came in on the larger end of this.
I mean, there’s no other way to slice it. There was a headline that hit right after the market closed from the Wall Street Journal, I believe, that it was 10 percent across the board. And the markets jumped up like two and a half percent. Right. And then as the presentation started unfolding, they started coming in. People saw this chart that they presented on reciprocal tariffs. Right. Where they say tariffs on China going to 54 percent. Can you believe that?
Thirty-four percent on top of the 20 percent that already exists. And he starts going through this list and the futures, the S&P futures, the Q, the Nasdaq futures start sinking. They had a 600 basis point fall between where they initially jumped and where they ended up. So the market is not liking this at all. And depending on what index you were looking at, we were already down eight to 15 percent on the year. Now, whatever comes in tomorrow will be on top of that.
So that’s the chart. That’s kind of the initial reaction out of the market. We can break them down a little bit if you want. But that was the initial reaction. Right. And one of the pieces that I witnessed, and I think everybody else did as well, maybe you have more data on it, is when they said reciprocal tariff, they kind of redefined what a tariff is for the reciprocal calculation. And they are including others, whatever.
I don’t know what else goes in there. Maybe you know exactly what fills this in. But basically, the Trump administration is calculating an effective tariff, if you will, for each and every other country, which may not be the explicit tariff. Correct. I mean, they call them non-tariff trade barriers. This is everything from what they call currency manipulation to things like judicial actions that restrict free and fair trade of our products into their countries.
And by the way, we know there are non-tariff trade barriers. So, like, it’s not totally surprising. But if you and I were to do the math on these, you can kind of make those numbers whatever you want to make them. And so, you know, if I had to go through this, the tariffs largely break down into let’s call it three or four big buckets. Right.
There’s the auto tariffs. That’s largely on Mexico, Canada, and Germany. And the auto tariffs were imposed at 25 percent. In fact, he had 20 members of the UAW union in the front row at the event. He invited, I think, the president of the UAW up on stage to make some comments. And he said these people used to be Democrats. The Republicans now were the only ones who fight for them. And by the way, this is really, you know, he said we won the state of Michigan because of this.
This is what I campaigned on. You know, these are the promises we made and we’re delivering on the promises. It is striking to me that just politically, this is what Democrats were running on 20 years ago. And it just shows how much the political parties have changed. So this is a big tariff differential as it relates to autos. Then he had the reciprocal tariffs, which are the ones that I outlined here, Bill.
Remember, Trump is the negotiator in chief. This is the starting point. All these tariffs go into place, and we’ll put these charts up on April 9th, country by country. And so we’re going to hear all these ad hoc negotiations going on, some of which I’m sure like Vietnam, he’ll declare victory on even before we get to April 9th, because they’ve already capitulated on a bunch of tariffs. He’s also declared that there are $6 trillion of new investments that people have committed to in the United States.
He mentioned NVIDIA, Apple, TSMC, SoftBank, OpenAI in his remarks. In fact, I particularly noted when he talked about SoftBank and OpenAI, he said great companies. So, you know, for the people who are watching the battle between OpenAI and NX, that was notable. And then he said, we’re going to have a minimum tariff of 10 percent on all countries. So even if you’re not on this list, we’re going to have a minimum tariff of 10 percent on all countries. And then, of course, China is kind of in this bucket on its own, right?
That’s going to be a huge negotiation on its own. There are a lot of things that go into that negotiation, everything from the Panama Canal to the TikTok stuff. So set that aside, if you will, for a second. There’s no way that lands, I think, at 54 percent. That’s the headline tariffs, okay? When we do the math and we add all of these up and say, what does this come up with?
The headline is we were at $77 billion last year, and we end up at about $750 billion. Remember, Peter Navarro, the hawk, the person who had been saying we’re going to land big tariffs, he was estimating $600 billion. So this definitely landed on the larger side. But then they came out right after that, and there was a footnote about things that were exempted from the tariffs. Exemptions, which included pharmaceuticals and, notably for you and I, semiconductors. Wow. Right?
So Taiwan’s got a 32 percent tariff, but semiconductors are exempted. And so we’re going through the value of those exemptions right now. My hunch is that this is going to land right around $600 billion. But, Bill, I have to ask you, you know, here we are. You and I, we’re trying to make our way through this, make sense out of it.
I don’t think there are many CEOs we know who support this or like this. In fact, I think a lot of congressional Republicans don’t like this. You’ve made an eloquent defense of the benefits of free and largely fair trade. When you start hearing things like this, like, okay, this category got exemption, or this category got exempted, just give me your reaction, right? As somebody who I think totally understands the benefits of free trade, when you see the Republican Party doing this, how does it make you feel?
Well, yeah, I mean, at a high level, I’m a believer in open markets, free trade, and comparative advantage. And that’s been studied for a very long time. There are very solid mathematical arguments why if you pull up the trade walls between multiple countries, you’re going to hurt the efficiency of both of them in the long run. And I, at least from a theoretical perspective, I’m a believer in that.
I think there’s another issue for the markets and for the CEOs, which has to do with both the slow pace at which they could realistically respond to this, and then the amount of ambiguity that’s been out there. And so, let’s say what the administration wants to encourage is for you to relocate a factory that you have or let’s call it production that you have in Thailand and put it on American shores. That’s not a quick process.
And if you start that process today, it might take three years before you’d have the type of volume to be capable of bringing that back. And probably at a higher cost. I mean, one of the things that I’ve said over and over again, I don’t think our labor is globally competitive, nor do I think it wants to be. And so, even bringing it back, you’re going to have a higher cost of production because we’re going to have a higher cost of labor.
That being said, this ambiguity, you know, there’s a lot of people even going right into this announcement that didn’t know what percentage of it was real versus bluster. And there’s, I think, at this point, just reading the papers, not making my own assessment, the administration has a reputation of maybe some of this is for negotiation, maybe some of it’s not real.
And so, you’re left not knowing what’s going to be the policy three months from now, six months from now, or 12 months from now, which makes it very hard to allocate CapEx in any meaningful way whatsoever. I think it’s so well stated. You and I have said, you know, markets and business abhors uncertainty. Yeah. Right?
It can deal with almost anything, but it has to have predictability so it can build a forecast, so it can look at an investment and say, is that NPV positive? I was literally texting with some big CEOs during the president’s announcement, and they were asking questions. Do you see us exempted? You know, are we in there? And this is just amazing to me, right, that you have this level of uncertainty.
I was with those hundred CEOs in Montana last week, and I would say almost to a one, they said things are slowing down in February and March because nobody knows what to do. And remember, the Fed had just come out last week and had taken down their forecast for GDP growth, had taken up their forecast for inflation, had taken up their forecast for unemployment by the end of the year.
So, the economy, you know, most major economists are increasing their probability of recession and are slowing the rate of growth. And the question today was, was Liberation Day a clearing event? Does everybody have clarity now? And I think your point is a great one. Even though they may have gotten an exemption or they may not have an exemption, the question is, can I count on this?
And how long can I count on this? And can I really plan a year out based on this? Or is this going to change yet again over the course of the year? And so… And by the way, there’s cascading effects. So, if you’re unsure about things like this, you’re not going to hire a bunch of people, for example. You’re probably going to pause hiring because you don’t know how much earnings you’re going to have.
So, those kinds of things can proliferate downward and affect unemployment and eventually affect consumer spending just by freezing nearly everything in the economy. Let me tell you two other cascading effects. I heard from one of the CEOs last week that four contracts with him had actually been canceled, right? Because they had three European contracts canceled and one Asian contract canceled because the countries were so upset with America that they’re going to do deals instead with European country companies or whatever the case may be.
And then I saw a couple of tweets, Bill. One was that China and Korea and Japan were actually going to collaborate in response to the U.S. tariffs. Somebody said, you know, we haven’t seen the Koreans, the Japanese, and the Chinese combine forces on anything since the Mongols caused them to get together. And so, it is causing a lot of strange bedfellows.
I sent you the tweet where the Europeans, the president of Europe, President Macron, they’re all going to China and they want to talk about closer trade negotiations with China. And you warned us about this. I think you said the meeting was in Vietnam. And obviously, I got that from the president of the Economist who had predicted that that would happen. But yes, you know, there’s no reason that that wouldn’t happen.
And there’s a lot of ramifications of this. I don’t think that anything came out of it that’s going to be good for the markets and good for stocks. But that’s more your world than mine. I’ve always shunned macro analysis. Let me just maybe opine on that for a second. Like, where do I think we go from here? After hours with the NASDAQ down, peak to trough post-Trump at almost 18 percent. Right?
That’s down a lot. A lot of names in the NASDAQ are down 40 percent or 50 percent. So, we’re starting to get some of that fear into the market. Somebody asked, and I said, as very rude, I do believe the president wants to do deals. He believes in fairer trade. I think we’re going to land the plane closer to $300 billion or $400 billion in tariffs, not $600 or $700 billion, and certainly not a trillion in tariffs, even though it feels today like it was bigger than that.
And one of the things I think that’s going to kind of force the president’s hand, he talked at the press conference. He had a bunch of senators there and House members. The senators and House members are hearing from their constituent CEOs that they don’t like these tariffs. And remember, most important to the president, he wants to get this reconciliation package passed, which he calls a big, beautiful bill.
He wants to get this thing passed, which puts in place no taxes on tips and the permanency of the tax cuts which he passed in his first administration. He can’t afford to lose a single Republican vote. And so I think that that also is going to guide him a little bit more to the center. And that’s what, you know, we’ll see whether the market believes that. Certainly didn’t believe it after hours today.
We’re going to get a little bit more positive on the companies we like the best because we think some of this fear is now getting priced in. What will you be looking for in the next 30 to 60 days as this plays out? Yeah, I think we’re still in the fog of war, certainly. But I will be looking at, do these exemptions on things like semiconductors and pharmaceuticals hold?
Are we seeing the country-by-country renegotiation on some of these things? And probably most importantly, Bill, it’s really about China. China is the second largest economic power in the world. It scares me how big the tariffs are that we are suggesting are going to go in place on China. And I think, you know, it’s imminent that he and Xi are going to have to talk and get a big trade deal done.
And so those are the things I’m going to be watching for. I don’t think I see any clearing event here for at least another 30 to 60 days. But remember, the best opportunities to buy something are when people are a little fearful. So you may have to just take a bit of a leap of faith on this one if you want to purchase at the best prices. Makes sense.
You know, another thing that we heard a lot about this week, Bill, speaking of China, is some developments in Chinese open source and some developments on the U.S. open source front, particularly with respect to these frontier models. You have a lot of, I think, understanding about the history in China around open sources and about the history of the United States around open source.
So help us unpack, if you will, what do you think strategically is going on in China with respect to these open source models? I’ve seen some people tweet that maybe DeepSeq was forced into open source by Xi. Do you think that’s going on or is there something else going on here? And by the way, I mean, all this culminated in the past week with OpenAI moving, you know, are talking again about open weighted models, which is a really important data point.
So how did we get from where we were to where we are? So, yeah, I read that same tweet and I think it was remarkably misplaced. China has been supportive of open source for well over a decade now. If you look on most of the major open source products and look at the management page and who the sponsors are for these, like Linux, you’ll see many of the major Chinese companies have been there and been supporting it for a while.
Why? They’ve been accused of stealing tech IP for years. And so when something like open source comes along, this looks like the best thing possible, right? There’s no one that can accuse us of IP theft because there is no IP ownership in an open source world. And so having dealt with those accusations for probably 40 or 50 years, I think everyone in China, the government and the entrepreneurs writ large view open source as a very positive movement for their country relative to the West.
And so they’ve been in on it for a long time. They’re very adept at it. They’re very big believers in it. You know, when we talked about the interview with the DeepSeek founder, I would say he had as much kind of emotional mindset that his entire emotional mindset was tied to open source. He believes in it and wants to support it. So that’s an important backdrop.
So I don’t think China got there in some calculated way or do I think it was some recent move. I think they embraced open source over a decade ago because it made a ton of sense for them in a world that had pointed a finger at them from an IP standpoint. So, let me just double click on that. So basically what you’re saying is they may have been fearful that they would have been cut off from other types of software products in the United States.
Like, you know, there might’ve been export controls or other things put on them. So they said, I may as well support Linux because I may not be able to get Windows. Right. But I think that discussion happened a long time ago. Like it wasn’t recent. But I think it’s important because that lays the foundation, right? That if that’s a foundational belief among Chinese entrepreneurs and Chinese companies, then it’s understandable that this new generation of entrepreneurs might also see the advantages of open source.
Another thing that I think people have to remember is that within, also within the past decade and maybe 15 years, many U.S. companies have learned to use open source as a defensive tool rather than just an offensive tool. Say more. Yeah. So this is the biggest companies out there. If they get in a position where they feel like they’re behind the eight balls, so they’re not in a leadership position, they will embrace open source as an attempt to level the playing field.
And a great example is Kubernetes. So Amazon took this huge lead with AWS in the hosted server business. Everyone was afraid of that. Google had a piece of technology called Kubernetes that was orchestration that would allow you to move a workload if that became a standard from one large server vendor to another.
It basically created ease of distribution so you could run on multiple clouds. They went to the Linux Foundation. They recruited IBM, a whole bunch of other people, got everyone behind it. And it gained so much momentum that Amazon had to embrace Kubernetes. So it worked. And we don’t have a monopolist in that cloud business right now, perhaps because of that deft move made by Google.
But they did it with Android against Apple, you know, being notable. And Meta did it with Llama here, right? They came to the table. They weren’t first to the table in the AI space, but they were disruptive with open source. One other thing I would point out about that type of move and attention, in addition to saying it’s defensive, I think it’s great for consumers.
Like if you study economics in business school, you know, there’s this notion of pure competition. Where do you have the most fluid competition, which leads to the lowest prices for a consumer? And certainly open source does that versus proprietary code. Like it’s just hyper-competitive. And that’s why it’s disruptive. And that’s why people use it in this way.
So that’s a huge backdrop to where we are today. I believe DeepSeek has been remarkably successful in the enterprise. And that’s, you know, it’s hosted by AWS. It’s hosted by Google. It’s being used around the world. I’ve heard from Hugging Face that it’s been forked over 1,500 times on their site. And so it’s prolific. I’m beginning to hear concerns that D.C. may take action to limit the use of DeepSeek.
You’re saying Washington may intervene to take action because there are people perhaps lobbying against or other concerns that Washington may have about open source Chinese models being used by American enterprise. I think it’s safe to say both of those things are happening. There are people that are really concerned about, you know, Chinese technology getting underneath our products.
Whether or not this particular code could be bad or not, they just might have that default. And then I think there are people that are lobbying because it would benefit their company. Either way, if that gets traction, you have a window in the U.S. right now where someone might move to go left of DeepSeek in terms of openness on one of their models, either in an offensive or defensive move. And I think it’s a short window. And so it’ll be very interesting from my point of view to see whether Google does that or Meta. I think they have an announcement coming up in a few weeks of their next model. I don’t think Anthropica would do it. They’ve been so anti-open source, it would be very out of character for them. But it will be interesting to see what happens on this front. And that leads us up to OpenAI making an announcement, which I’ll let you describe.
Well, listen, we’ve talked here and Sam has been dropping the breadcrumbs on Twitter, right? That they wanted to launch an open-source model. They’ve been GPU constrained. They’ve been bandwidth constrained. But he got the announcement out this week, which I was thrilled with, where he said, we’re excited to release a powerful new open-weight language model with reasoning in the coming months. And we want to talk to devs. So he’s inviting all these developers to participate and give feedback. He said, we want to make it a very, very good model. We’re planning to release an open-weight language model, one of our first since GPT-2. And he says, we’ve been thinking about this for a long time.
And the interesting thing is somebody in the replies I saw said, are you going to make people buy licenses if they get a lot of users, like Meta is doing with Llama? And he kind of takes a jab at Meta and he says, no, we’re not going to. He says no. Yes, he said no. He says no, which indicates maybe we’re going to out-open Llama. So that’s on the one hand, kind of opening AI. And by the way, just hold your thoughts. In case people don’t know, openness is a continuum. It’s not black or white. And that’s true of all the open-source technologies. In the open-source model world, some of the players, most notably Meta with Llama, have a usage restriction against the free use of the model at $700 million. And that’s what you were referring to.
And so at least in a tweet, Sam suggested they won’t have that in theirs. So back to you. Yeah, no, which I think is notable. Because remember, at the end of this month, we have LlamaCon, which is the developer event for Llama. It’s a big deal for Meta. The launch of Llama 4 has been rumored for a long time. In fact, I think there are just a lot of people who are surprised they haven’t released it. But it seems to everybody they’re going to have to release it ahead of LlamaCon.
What I’m hearing, Bill, is that it’ll be a 400 billion parameter model. It’ll be a mixture of expert model using 50, 60, 70 experts. It’s going to have huge context window, like 10 million context window, and it’s going to launch this month. I think it’s terrific that OpenAI has now fully committed. Everyone on the team, Brockman, Kevin, et cetera, we’re all retweeting this. And maybe even suggesting that this is going to be a more capable model and even more open. I think it’s good that we have competition in the U.S. for an open-source model.
And when it comes to the administration and what Washington, D.C. is going to do, my best sense is they do not want to see a Huawei DeepSeq Belt and Road, either with chips or with open-source models. They do not want to see the world run on DeepSeq on Huawei 910 chips. And so, this gets back to the AI diffusion bill and how we’re going to restrict these things. I think they would love to see the world continue to run on U.S. compute, U.S. silicon. And they would love to see Llama and perhaps OpenAI’s open-source model around the world. They know it has a lot of distribution.
So I think this was a really positive step forward on that. And you bring up an important point, which is, you don’t have to be, let’s say something does happen to limit DeepSeq usage. Whoever’s going to jump in to try and lead the open-source movement in the West, if they want to be a global player, they don’t just have to get left of everyone else in the West. They still have to compete with DeepSeq on a global basis. And I don’t think it’ll be interesting to watch. I’m very, very curious how this plays out.
I’ve already asked Clement at Hugging Face if maybe he will create a continuum so we can rank all these different models from an openness perspective. Because there’s so many different facets by which you could be open or not. Oh, but actually, I had one more thing. Since you’re involved at OpenAI, you can correct me if I’m wrong. But you have been saying for a couple of quarters now that the real opportunity for OpenAI is on the product side versus the model side, which hints at being more of a consumer product than, say, the enterprise API business that they’ve also been in.
If they think that also, and I’m not involved, so this is a conjecture on my part, being more open with your model is a really deft move. Because it will put more pressure on other players to try and keep up. And it will allow your model to have more pervasive usage globally. And you talked about running out of compute. The minute you put that model out where other people can download it, they’re doing that on their servers, not on yours.
And so, I just think it’s a very clever move for the same reason Google would have supported Kubernetes. You’re kind of wiping out the business opportunity for other models to play on the API side if you make yours open, which helps protect the competitive flank. And, once again, great for consumers. Yeah, I think it makes sense on a bunch of fronts. On the first front, I think they want developers to develop on their platform and build applications for OpenAI. And so, this brings them into the ecosystem.
Number two, I think that they fundamentally have a view that they want to build products and applications that move humanity forward with AI. And this is another way to do it at scale. Sam has said publicly now, I’ve heard him several times say that he thinks that models are commoditizing. They’re anti into the game. They will continue to push the frontier. So, he thinks they’ll have the best models. But that general intelligence, that average level intelligence, as we already see, is going to be widely distributed.
And that the battleground, Bill, is really going to be fought around products and services. I wouldn’t say that they view themselves as exclusively a consumer company. But clearly, ChatGPT is a major thrust, a major focus of the business. It’s the market-leading consumer application, probably has 80% to 90% market share. I think network effects are kicking in and other things. But I also think their enterprise business is, if not the biggest, one of the biggest and also growing at the fastest rate.
Because remember, the consumerization of the enterprise. One of the fascinating things that’s happening in the enterprise is these are all users of ChatGPT. So, when the CEO shows up in the boardroom and somebody says, yeah, we’re looking at bringing AI into the company. And we’re looking at ChatGPT enterprise. It’s an easy yes. But it reminds me of when every CEO said, hey, get me an iPhone in the enterprise. And they were on BlackBerry and they all switched to iPhones because they loved using them at home.
I think the natural thing for them to do. So, it’s not to say enterprise is going to be a battle. It’s not going to be winner-take-all. But I think these guys do have their eyes squarely set on building a big enterprise business. And there’s probably two different types of enterprise. There’s probably a product that people buy user licenses for, for doing white-collar research-type work where they may, where I think what you said will be very relevant. Like having the UI they’re used to.
And then there’s the separate side, which is models that underlie the types of business processes that you’re building. Well, one of the things you pinged me on this week was the investment round around OpenAI. And I’m happy to share what I can share. But you had some—
Tell us what happened. What was announced? Yeah, well, I mean, they announced the long-rumored investment that was led by SoftBank, which many people described in the headline as a $40 billion investment round. I think if you read the breakdown of it, it comes in a couple of tranches. The first tranche being closer to $10 billion. The second tranche being closer to $30 billion.
And it’s an extraordinary amount of money. It’s bigger than any, bigger than I think the largest IPO sans maybe one or two that has ever been done. I’ve often described these as private IPOs. Altimeter participated along with several others who were reported. And the valuation was like $260 pre, which would make it, if all the money were to come in, a $300 post valuation. And so it certainly got a lot of attention this week.
And you asked me the question, I think, Bill, just around kind of valuation, right? How did we think about valuation? The first thing I would say is market leaders never look cheap. When I invested in Google in 2005, when I invested in Meta, when the IPO broke and we looked at those late-stage private rounds, I certainly remember the Microsoft round in Meta at $15 billion that was roundly criticized as being incredibly expensive. None of these things, you’re certainly not going to buy a market leader on the cheap.
But if you really look at this, I think that they’ve said publicly they expect their revenues this year to be around $13 billion, right? To do $13 billion in revenue probably means you have to exit the year closer to $15 to $18 billion in run rate revenue. So as I look at this on a forward run rate for this year, you’re paying something like 20 times revenue for the business.
Now, we also had a couple other announcements this week. There’s the Anthropic funding round, and there’s talk that they’re doing a billion to $2 billion in revenue, a $60 billion funding round. So that to me looks like something like 50 times revenue. So again, you got OpenAI at 20 times, Anthropic at 50 times. And then we had the merger of X and X.AI. Correct. Which are rumored to have around $3 billion in revenue. And the combined market cap there is like $125 billion.
So that looks like closer to 80 times revenue. So the market leader here, which usually trades at a premium, not at a discount, to me, again, we can argue about the sustainability and could somebody disrupt them? And is 20 times a good valuation in this environment? Yeah, but aren’t they spending a lot of money on compute? And is it really high-value revenue? But apples for apples relative to their peers, it certainly appears to me like 20 versus 50 versus 80. It’s hard to say that this would be more expensive on a multiple basis than Anthropic or X.AI.
Yeah. And I also read that there are still contingencies on whether the full conversion from a nonprofit to a for-profit happens. So I think there’s some stuff. If that’s true, there’s still some stuff to play out. But one thing I would say when I witness this from afar, and once again, you’re involved, I’m not. So correct me if I’m wrong, but having lived through the Uber Lyft situation, which oddly had Masa coming to the table also, our world has just evolved into one where so many people believe in power loss.
So many people believe in network effects and that these markets are winner-take-all, that you end up with these massive capital raising rounds. And, you know, it’s not lost on me that both with Stargate and with this one, the headline number is bigger than the piecemeal when you start to unpack it, which, for better or for worse, from my position, smells of being promotional. And so why would you do that? Why are you trying to have a bigger headline number? And that number does get repeated in the press.
So it does work in that way. And I come back to, you know, I suspect the company’s sending a message to Anthropic and anyone else in the game that we’re here for the long haul. And they probably didn’t anticipate all the moves that Elon’s made with X and Twitter. And obviously that is another deep-pocketed player. But, boy, if you’re on the Anthropic investment side, I’d be scared. I’ve lived through this before. It is a sport of Kings, if you will.
And then lastly, one thing that naturally falls out of that is unit economics get postponed. You have to believe in the power law and the network effect. They’ve, in addition to that headline number, I think they’ve said publicly they expect to lose five to seven billion this year. And with an employee count of, I think, six to seven thousand, that’s not going to run you more than a billion or two. So there’s some number between the revenue number you talked about and subtracting two billion for expenses.
And the rest is your operating cost of keeping this AI machine going. I ran some loose numbers and I come up around 15 or 20 bucks a year for a non-paid user, just to run the servers on their behalf. And, you know, eventually you might get to advertising. Eventually you may convert more of them to paid. These are things we’ve seen play out over time. They played out with ad models. But once again, if you’re going to try and lay chase to them, you got to be prepared to underwrite that cost yourself.
Listen, I think the analogy is a fair one, Bill. And obviously Masa was involved in the Uber Lift battle. So it’s an easy one, particularly with his involvement here to say you’ve referred to it before as weapons of economic destruction, all of this capital. But I would remind you, there was a moment in time in 2020 where the headlines were that Uber would never be profitable. It was a failed business model. It will never make money. And here’s a business that’s going to do six billion in free cash flow.
And so, and so the winner does take all and the winner does take most. I will tell you, as a shareholder, I speak with the leadership of the company all the time about unit economics. Obviously, if I’m investing in the business, I feel confident in their leadership around unit economics. One of the things that I think is really important here is just like what’s happening in the business. Sam tweeted this week, they added a million ChatGPT users in an hour.
In an hour, they crossed 20 million subscribers, paying subscribers for ChatGPT. They crossed 500 million weekly average users of ChatGPT. In fact, they’re going so gangbusters. They’re throttling all their demand. In fact, I don’t know if you saw David Sachs’ tweet where he said America’s leading AI companies are all reporting that demand is off the charts. So much so that they’re being forced to impose rate limits. And he said, fortunately, we have massive new infrastructure projects coming online, which gets me to the point of why are they raising so much money?
You and I are talking about taking the pod down to Abilene, Texas to see Stargate, to Denton, Texas, to see the CoreWeave facility that they’re standing up for OpenAI. And the fact of the matter is, I think that they need to bring on a massive amount of compute just to support the demand they currently have. I can tell you when you look at the product pipeline for OpenAI, whether it’s, you know, there are two or three models they already have completed on the shelf.
There’s a lot of agent stuff that they want to do that’s on the shelf. I think there’s a lot of stuff they want to do around pricing, but they can’t do these things today in open source with their current level of GPU demand. And Sam went online, Seth, anybody has a cluster of a hundred thousand GPUs, send me a DM. And you may say it’s promotional and hyperbole, but the round was already raised. It could be both. I actually think, in this case, it’s true. I know they were pulling a lot of things offline just to support the demand.
Now the irony is where, what was this demand coming from? Right. And the demand, because we didn’t mention Gemini 2.5 that happened to release this last week. And part of the reason we didn’t mention it is because literally on the day that they launch it, OpenAI launches this upgrade to Imogen where people are making all these anime photos of themselves that literally blew up. Demand for a billion anime photos a day from the United States all the way to India, and they can’t support it.
And some people may say, oh, well, this is an example of how dumb AI is. People are using it to make anime photos. But I would point them to Chris Dixon’s blog that he wrote some time ago where he says, listen, the next big thing will first appear as a toy. There are a lot of things that we do for entertainment. A lot of things that we know that OpenAI and ChatGPT are being used for a lot of deep research.
But the fact of the matter is, at least as to this one, and I’m not going to get into the other valuations for the other models, but I’d say at least as to this one, I was an early investor in Google. I was an early investor in Meta. I saw what those early consumer products look like, what those demand curves look like, what that cohort retention look like. And I would just say to you that what I see out of ChatGPT reminds me a lot of kind of those winner-take-all consumer applications. They’re not infallible.
It’s not that they can’t be assaulted. Grok has been a great model launched by Elon. But I think they really do have a big moat, and I think the network effects are kicking in. And I think that not only are they an order of magnitude bigger, but they’re also growing a lot faster. So, I think that the consumer business here will ultimately be valuable. But to your point, the unit economics can be crap along the way, and it’s up to the company to launch the things like advertising, paid, different pricing tiers, etc., that bring all those things.
Let me ask you a question, since you went down that avenue. I think they’ve announced, is it 20 million paid users and 500 million total users? So you have a 4% conversion rate. How do you think about paid versus advertising that conversion rate? How do you think about the business model with those facts on the table?
Yeah, I mean, honestly, I think that right now we probably, you know, we’re throttling ChatGPT. So when you bring on more compute, all those numbers would be higher, right, if you just have more compute. Secondly, I think most of ChatGPT users are using a model that’s like a year old, right? Because we haven’t been able to upgrade the, they haven’t been able to upgrade the models because I don’t think they can support the things they want to do in the upgraded models from a GPU perspective.
So my suspicion is when they’re able to do that, they’re going to have a lot more flexibility around things like pricing tiers. Sam has said he doesn’t particularly like advertising, but at some point, they will obviously have something that they think is beneficial to consumers that will be around that. If you look at Operator, if I say to Operator, book me the Four Seasons Hotel in New York next Tuesday and it does that for me, which I think they’re getting a lot closer to you and I have this back and forth on that.
But, let’s just stipulate that even you believe at some point they’re going to get there. And if we’re driving that kind of value for users, either the user will pay or the end merchant will pay. I think there are all sorts of business models that will evolve around that. So my hunch is you’re going to see a mixture of advertising. You’re going to see a lot of different pricing tiers. You’re going to see models.
I don’t think we’re going to have this long menu of model choices that forces the consumer to understand the difference between 04 Mini and 03 and 01 and all these different models. I think you’re just going to have a smart model, ChatGPT-5 or ChatGPT-6 that may be coming sooner rather than later. That’s just going to make those choices for you. And so I think there are a lot of ways.
But by the way, we’ve talked about this in the past, but I’ve often felt that one of the reasons that Google is so susceptible to disruption is how they’ve maximized the revenue per visitor. And I personally don’t think there’s any way when that world you’re talking about, that agent world evolves, that that partner in a hotel is going to pay a fee anywhere close to the fee that’s paid to Google by someone that’s marketing a service.
I always say using LTV math versus transactional math. I just don’t think there’s any way you can get there. And so that’s a huge disruptive advantage for OpenAI. Well, let’s click on that for a second. You know, we have our friend Glenn Fogel, who runs Booking.com and is an incredible CEO. They’ve built an incredible business. And they’re one of the largest advertisers. They’ve historically been one of the largest advertisers on Google.
I think it’s been reported that Google generates, it’s one of their largest advertising categories in the world, is travel, hotels specifically. Booking is one of their largest global advertisers. So you sell a $100 hotel room and you take $20, Booking.com, let’s call it, take 18 to 20 bucks. And then you pay a portion of that to Google, maybe half of it, maybe more than half of it.
Well, actually, to be fair, in many circumstances, they’ll be using what I call LTV math and they’ll pay more than 100%. Oh, they’ll pay $50 instead of $20 because, and then they’ll say, well, if the customer comes back twice in a year, we get to break even in the first year and we’re going to hold them forever. And this is why that won’t work in the agent world, which is no one’s going to think that way. Because if you’re a white-label service that’s underneath the agent model, the most you can share is 10 of the 20, right? Or whatever, you know, it’s a change.
So there’s no doubt there’s competition coming to that. You know, Google’s traded down from $200 to $150 and change. And, you know, they see that disruption coming their way. The irony here, right, is we still have this antitrust investigation with Google. I always get a laugh out of the fact that finally in 2025, the first time we’ve actually had competition for Google, it’s very clear that competition is coming to all those categories.
And now we get around to talking about breaking up their search monopoly. I mean, it’s ridiculous. I don’t think that’s, I think that’s the last of our problems. But I do think we’re going to see business model evolution around these different categories. So let’s just say, Bill, that it settles out at $10, right? That the hotels are clearly willing to give $20. Let’s say it settles out at $10. Well, hell, that’s all upside for OpenAI.
Right, but it’s replacing $50 for Google. That’s my only point. Yeah, no, very good point. By the way, another thing played out in this space, a little out of order of our competition, but there’s this acronym people are starting to use MCP that is a way for you to represent your service. Like if you were Booking.com to a model so that it’s not simply scraping your website, which is certainly not the ideal way to do this thing.
That standard, I believe, got started in Anthropic, but OpenAI has agreed to support it. And so another factor that plays out with whoever’s most aggressive with the open standards is they might be able to take a lead in defining these things, which could be advantageous to them. And I got to tell you, you know, I meant to say this earlier when we were talking about the open source stuff, but I got to believe the anxiety is high at Google.
I got to believe the anxiety is high at Meta. We’ve seen some high executive shifts and departures also at Apple in terms of who’s in charge of these things. And so I see those moves and I think that must represent anxiety. You know, there’s a lot at stake. And so it’ll be really interesting to see how open people are, how willing they are to be open with their models, how aggressive they are, because I think it’s a really critical window. I’m talking about like the next three to six months could dictate who’s standing on top of the hill five, ten years from now.
Well, the tectonic plates, as I’ve said, as we’ve said for now two years, this is the first time they’ve shifted in this magnitude in 20 years. This is a 20-year event for 20 years. The search paradigm ruled everything in consumer Internet. And Google stood at the top of that mountain and it took something, you know, it took an AI-level shift. It took ChatGPT moment and them getting to the scale of maybe a billion monthlies and 500 million weeklies to even lead to this conversation.
But things happen very slowly, Bill, as we know, and then they happen very fast. And I think that’s your point. Yeah. Before we run out of time, we had an IPO, which we haven’t had very many of. Let’s talk IPOs, both CoreWeave. But after that, let’s just talk about IPOs in general. That’s great. You know, like, as you know, we’re shareholders in CoreWeave. Since a couple rounds ago, we were one of the largest buyers in the IPO and we’re happy we did.
You know, I have to say on Friday, I was pretty damn nervous, Bill. It broke the offering price, went down to about $37 a share. I think today they had some announcements of this deal with Google where they’re going to. Provide NVIDIA, Grace Blackwells through CoreWeave.
So Google is going to be buying a bunch of NVIDIA chips through CoreWeave. And one of the big criticisms of this company was they were too dependent upon Microsoft. But now they’ve diversified. They have Microsoft, Meta, now Google, OpenAI, NVIDIA, Cohere, Mistral. And so I think they’ve really emerged as the leading AI kind of cloud.
And the stock in the last couple of days, despite the fact they took it public last Friday. I mean, talk about taking a company public into a Category 5 hurricane. I mean, we had Liberation Day staring us in the face and they had to fly into that. And, as you pointed out, it wasn’t the least controversial of IPOs. But I have to give credit to Mike Intrator and his team.
And listen, I’m here in Silicon Valley. They started this company five years ago. It’s worth over $30 billion today. It’s played a really important role in standing up OpenAI and a lot of the leading AI labs. And I just think that’s a good thing for all of us. But it’s also fair to ask the questions that you’ve asked around the durability, if you will, about CoreWeave in the revenue.
Yeah. And look, you’re absolutely right. It’s so funny. We sit around and complain about the IPO market not being open. And, for the entirety of 2024, the markets were up 30%. You know, the sunshine was out and no one was going. So here, someone finally gets the guts to go. And the markets, of course, have turned the other way.
And that’s why, man, anyone that tells you when you’re ready to go public that you have to wait on the markets to be in a particular place, I would tell them to shut the fuck up and take your company out. You can’t control that thing. That’s an external factor.
And I also believe a lot of people, because of my stance on direct listing, said was this a good IPO, a bad IPO? I mean, as you said, there’s stuff moving all over the place. You have a leader in their field, unquestionably, huge revenue growth. Their business model isn’t fully unpacked because the CapEx is invested so far ahead of the product.
So you can’t look at the income statement and say, oh, the right economics are perfect. And there’s questions about what is the appropriate depreciation schedule and all these things. But I am glad that they got out and I’m glad that it’s done fine. And they’ve had basically two customer announcements since they went out, which shows how fast this AI world moves.
And I think that’s why the stock went from 40 to 36 and way back up above that. Now let’s hope that it brings more IPOs to the table. This company needed capital because it’s a capital-intensive business. And those are the ones that tend to come to the markets eventually, no matter what.
We have this offsetting reality that the Stripes of the world and Databricks and others are choosing to delay being public and have massive access to private capital. And maybe that’s a discussion for another day, but there are a few others. Klarna’s in the pipeline. We’ve talked about Cerebrus being in the pipeline. And so I’m always hopeful.
I’m always just wanting for there to be more companies that are willing to move into the public markets, but the offset of what we talked about at the beginning is going to be there. And so we’ll see how those things interact with a choppy market. Well, yeah, and somebody else I should mention is Morgan Stanley.
I mean, they took a lot of heat last Friday on this deal. Why are you bringing it now? The stock went below 40. CNBC was roundly critical of the company and of Morgan Stanley and the stock’s at 60 bucks or whatever it ended today at. And so again, we feel good as shareholders. I feel good for the people involved in the company.
Obviously, there are still questions that remain about the business. You mentioned depreciation. The one thing I’d tell you about the depreciation argument, as it relates to this company, is a lot of people push on, there’s a statement by Jensen at GTC that hoppers may not have value because Grace Blackwell is so much better. He was a little more aggressive than that.
Okay. So say it. He called himself the chief revenue destroyer and basically made statements that I think, if you interpreted accurately, would imply that maybe you should have a two-year depreciation, not a six. Like he made it sound that way. It’s very abrupt. And he took a lot of heat and he probably wishes he hadn’t said it, but he said it.
My view on this is like, listen, because we’ve got to square this circle. We have the Saks tweet, we know the inference demand is off the charts. Everybody is demonstrating their need for more GPUs to run inference. Everything in the world is becoming inference. We’ve talked about that at length.
And so my view is this: when you talk about two years for GPUs, cutting-edge GPUs are going to be used for cutting-edge training for the frontier models in that first two-year period. But all these things are going to continue to get used for inference. So the right way to think about CoreWeave, and I think the consensus margins for this business are like 25% EBIT over the course of the next couple of years.
How do they get there? Think about their unit economics bill, their CapEx, their OPEX. So they’ve got to get a data center. They have to pay for all their operating expenses. Then they have to buy the servers. So the way this works, I think, is they sell a four-year deal to Microsoft or a four or five-year deal to OpenAI or a four-year deal to Google or whatever.
They expect to pay back all the CapEx, OPEX, and GPUs in three years. And so the fourth year, which is a four-year guaranteed contract, the fourth year is your profit margin, right? And then anything you earn past the four years, that’s all gravy on top. And the consensus earnings are not giving them any credit for anything after the end of those contract periods.
Now, what I’ll tell you is, and we’ve done a lot of research on this, there’s still a lot of A100s in use in the world today. In fact, Jensen has talked at length about that. That’s a 2020 product. So we’re in the fifth year and A100s are still out there being used by almost everyone that bought A100s. And then if you look at it, I think Jensen at GTC said last year that OpenAI had just retired the V100s.
That was a 2017 GPU. So that’s like a seven-year lifecycle that they were using those for. And so I think that we have a lot of comfort that at a minimum, people are going to be using these things for four years, a couple of years for training, a couple of years for inference. I’ve yet to hear of anybody throwing away any GPU because it doesn’t have value.
Remember the way CUDA works, the software that runs these GPUs, it constantly gets upgraded. It’s like my Tesla, right? I had an old Tesla Model S, like seven years old, but it felt like a new car because my software got updated all the time. And frankly, it still got me to the places I needed to get to. It wasn’t as good as the new model I bought in December with full FSD and everything else, but it didn’t feel like a really old car because the software was constantly updating.
I kind of think of that the same way for these GPUs. The GPUs are getting better every year, even though the hardware remains the same. So I’m not nearly as worried about that depreciation schedule. It seems to be a big hit on the company and lots of people are talking about it, but they’re out the door and kudos to them on this big new deal today.
But look, the pushback on that is obviously that it’s not a zero or one, like you make it sound like it’s binary. You either throw it away or it’s super valuable. And what inevitably happens is the earning power of that product drops over time. And so there is, I think, a reasonable question, should it be more of an accelerated depreciation schedule?
The idea with depreciation is to mirror the useful life. So you’d want it to mirror the earnability of the asset over time. And so six years straight probably isn’t the best fit for that, but we’ll see. We’ll see what happens over time. Their peers, the incumbents in this world were at four years ago and pushed it to six, which wasn’t. And the answer may lie somewhere in between.
And, like I said, I don’t think they need it to be more than four in order to achieve the margins that they have. But they’re also, to your point, it’s a highly levered business. They got to de-lever the business. So there’s a lot of things in play here with CoreWeave. That’s why, again, if you look at the multiples it’s trading at, well, I don’t know what they are today, but the multiples that came public at, we’re not overly taxing from our perspective, but there’s a lot of headwind for all these AI companies.
I mean, you have Nvidia trading at 19 times fully taxed earnings. And so there’s a lot of skepticism in the world, notwithstanding all the stuff we hear about demand, a lot of skepticism in the world about AI demand. A riddle for you before we move on. Yes.
What do Salesforce, Netflix, Square, Amazon, Palo Alto Networks, Facebook, Snap, Proofpoint, NetSuite, and CoreWeave have in common? No idea. They all broke issue.
Oh, wow. And so when the talking heads on CNBC and others are critical of a company because they trade below their IPO price, it’s just such a wrong way to look at things. I think one of the reasons those high-quality companies get priced to perfection is the founders are stronger-minded and have more leverage and negotiate more on this agreed-upon price, which would also go away with a direct listing.
But boy, what a silly way to think about quality, whether or not you give away more and pop. That’s what a lot of people think. You know, I don’t like that. You know, well, I kind of thought that maybe you thought this was a perfect IPO because it ended day one at precisely 40 bucks, which was the offering price.
Which was probably engineered. Let’s be realistic. You also, there’s some peculiar terms in this company that you may have played a part in. The series C has a put right at like 3875. No doubt in my mind, people wanted to make sure it priced above that, which may have played a factor here. Who knows?
But let’s move on. Let’s talk about one more thing before we go. So much news in one week. The tech talk thing, there’s new information as we speak to tell me. Well, I mean, listen, there’s a lot of rumors swirling, which not surprisingly, this deal is set to expire or need to be extended by April 5th under the terms of the first congressional extension that was made by Trump.
They’ve made very clear that there are a lot of buyers for the TikTok asset, and that the president wants to put together a deal. And, of course, we have all these tariffs going on, on China. So I’m sure this will end up as part of a big trade negotiation as it pertains to China. But as you know, just for everybody, we’re shareholders.
I’ve been a shareholder in this company since 2015, one of the earliest venture capital rounds in ByteDance, the parent company, which owns TikTok. For the last two years, I’ve agreed largely with Elon and sacks and others that we should engage with China. We shouldn’t just shut down TikTok. We should make TikTok abide by the rules and regulations that we have in this country.
And that’s what this whole legislative unwind was about, the for sale of spin-out TikTok US. So here’s what I’m hearing. I’m hearing that there will be a new company stood up, and I’m not privy to any information. I’m not party to these negotiations, but I’m hearing, let’s call it TikTok US, and that TikTok US will be partly owned by ByteDance.
But I think they have to keep that ownership threshold under 20%. So let’s call it 19.5% owned by ByteDance that it will be owned partially by just the existing shareholders. Remember the shareholders in ByteDance, 60% of those are U.S. investors like Altimeter. So that we’ll get our shares in ByteDance or in TikTok US.
And then the 50% of it or thereabouts will be new investors. So think folks like some of the rumors I’ve seen, Amazon and Dreesen, Oracle, et cetera. And these are investors who are not currently in the cap table of ByteDance. So Altimeter or Co2 were currently in the cap table ByteDance, so we’re not going to be part of the new investor syndicate, or at least that’s my understanding.
So imagine they stand that up. And then the question, where would that money go, Brad? So the money would go into this new company, right? So the new company would be capitalized with this new money. It would have a new board. So it would be new, fresh capital for the new company. It wouldn’t go to ByteDance.
No, that’s my understanding that it would go into new company, that new company would get a license to the algorithm, and it would be up to new company to audit that, to audit the data. Because remember, that’s the whole point here, Bill. Like we want to have some control over the algorithm and the data.
So it makes sense that Oracle would be involved in that. Because remember, TikTok runs on the Oracle cloud down in Texas. I think a logical question is, okay, what’s the big, what’s the so what here? And I’m hearing that the valuation for TikTok US could be pretty low, which I would expect.
Right. Because remember, Trump has said maybe we’ll put this in the US sovereign wealth fund. So he’s negotiating the deal. I expect that he wants to get a pretty good deal. You didn’t mention what percentage was for that. But is that part of the cap table too? No, no, no idea.
Yeah. One particular question. If you go back six months, maybe three or six months, there was a lot of discussion that I would suggest that the parent company, ByteDance, had no interest in this deal. They’d rather shut it down than do this. Have they changed their mind for some reason? Is there a new perspective from their side?
Well, I think, remember, if we go back six months, there was a camp that said shut it down. And there’s a camp saying, or we’ll just take it. Right. And like, I think that the company’s perspective, Yaming, the founder of the company, he basically said there’s no way to separate the algorithm between TikTok US and TikTok rest of world because creators in the US create content that go to the rest of the world and vice versa.
And so, if you took away all the US, it does so much damage. You would be better to shut down TikTok US and just invite the US creators onto the French platform or the United Kingdom version of this or the Australian version of this via a VPN or something. So I think the big change here, Bill, is this idea that US TikTok and global TikTok will continue to use the same algorithm.
And it’s just a license to the US TikTok would be my guess was part of that bridge or breakthrough. I think a key thing here is like, how do Altimeter or Sequoia or other US investors remember, 60% of the investors in ByteDance are US investors. And the investors in places like Altimeter, they’re pension funds, they’re teachers, they’re firefighters.
And if you think about the fair value for ByteDance, I think most people, although it only trades at, let’s call it 300 billion, most people think the fair value of this is closer to a trillion dollars or certainly to 800 billion. So if you take 60% of a trillion dollars, that’s 600 billion in locked up venture capital value for all of the endowments and pension funds, etc. For US investors, that’s more than almost every other unrealized venture gain put together, Bill, right?
And so if you’re able to take this company public, that turns into DPI, like hundreds of billions of dollars of DPI that goes out to the investors in these venture funds. This company being TikTok or ByteDance? This company being ByteDance. But we had to get the TikTok deal done as a condition required to get ByteDance public or ByteDance out the door.
And so remember, ByteDance, about 90% of ByteDance’s business is not TikTok US. 90% of the value of the company is things like Douyin, which is the Chinese version of TikTok, and Daobao, which is the Chinese version of ChatGPT, and TikTok around the world. And so there’s a huge and profitable business inside of China and the rest of the world.
And we’re just debating this piece in the United States. And so as a shareholder, I will tell you that whatever the dilution is caused by this, it’s nominal relative to the value of the total. And what I really want to see get done is just certainty, right? Certainty for the company. I think it’s good for the US that TikTok will remain.
My kids love it, and I’m glad we’re going to make them abide by the rules of regulation. I think it’s a win for Team Trump. I think it’s a win for ByteDance. But remember now, we just hit them today with 54% tariffs. So there may be a conversation that has to occur before Xi and Trump. I thought this deal would get approved by China. Now I’m not so sure.
And the Chinese government could probably block the deal. Exactly. So just because we announce a deal, if we do hear a deal announced over the course of the next few days or over the next week, doesn’t mean that it’s a done deal. But I’ll leave on an optimistic note. Okay, let’s do that.
I think that the president wants to do a deal with Xi. I don’t believe we’re going to have 54% tariffs against China. It’s too important to the rest of the world that we can cooperate with China on things like ending the war in Ukraine, things in the Middle East. Yes, there is a great competitive struggle between the two countries.
But I think that ultimately, the president will cut a deal. He said that he likes Xi, invited him to the inauguration. And as we know, he’s a dealmaker. And now we’ve got everything from the Panama Canal to negotiate over to TikTok and all the other trade deals between the two countries.
So I suspect that when we get back to what really came out of Liberation Day and what really matters, I think the most important thing that matters is U.S.-China bilateral trade relations. And I think that’s going to really dictate the direction of global growth and the direction of U.S. and China economic growth over the course of the next few years.
Important to watch. All right, man. Take care. Great seeing you. Have fun at the games. Take care. As a reminder to everybody, just our opinions, not investment advice.
This is an experimental rewrite
Speaker 1: A riddle for you before we move on. Yes. Can you guess what Salesforce, Netflix, Square, Amazon, Palo Alto Networks, Facebook, Snap, Proofpoint, NetSuite, and CoreWeave all have in common?
Speaker 2: No idea.
Speaker 1: They all broke issue. Oh, wow.
Speaker 2: And we’re back. Bill, great to see you.
Speaker 1: Good to be seen. I mean, you must be pretty stoked coming off those wins last weekend in San Francisco.
Speaker 2: Yeah, I’m repping the Gator hat still. I’d say we kind of eked by–for those who watched, it was an incredible final few minutes. Can you explain it? Take us through those last moments.
Speaker 1: Well, to be honest, I was afraid. I didn’t think there was any chance because they were behind by so much heading into the end of the game. They scored four three-pointers while the other side scored none. They even intentionally fouled and missed free throws. I think someone mentioned that the Yahoo game predictor had Texas Tech at about a 98% win probability with just a little time left. But somehow they pulled it off.
Speaker 2: Now, the optimistic folks might argue that experiencing something like that builds confidence for dealing with anything. However, the odds makers now have Duke favored over Florida, even though it started on different footing. So, one game at a time.
Speaker 1: It was super exciting. I was out in San Francisco at the Chase Center, got to hang out with the coach and some of the players’ parents. It was a good trip. And now that I’m in Austin, it’s just a short drive to San Antonio. Lots of friends are coming in, and we’ll see what happens. It’s going to be a heck of a weekend.
Speaker 2: Maybe if a certain couple of teams end up in the championship game, I might sneak in there on Monday night. I suppose if we’re repping and rolling, it makes sense. I have two degrees—one from Florida and one from Texas. The Texas ladies are playing in the Final Four in Tampa, so it’s a lot of good stuff happening.
Speaker 1: Speaking of a lot of good stuff, there are some people who think a lot of not-so-great things are happening, judging by the market reaction to the president’s announcements about these tariffs. So, why don’t we kick it off by talking about Liberation Day?
Speaker 2: Yeah. We’re recording this right after Trump’s presentation. I think you and I both watched it and then jumped on the pod. You’ve been discussing this; it’s clearly been choreographed.
Speaker 1: Exactly. You’ve been saying for a long time that Trump and his team aren’t just using this as a negotiation tactic—this isn’t just a means to an end. You’ve pointed out that they really believe this is a necessary direction for the economy. Because of that, you’ve been cautious and concerned about where this leads us. You gave a presentation last week on how to think about this. What did you cover there?
Speaker 2: I think it was at the JPMorgan Tech Conference. It became clear to us in early February that this was a doctrinal shift—there was a philosophical belief around trade that they wanted to create a fair and level playing field. The real debate has been about the magnitude.
Speaker 1: JPMorgan hosted a great event in Montana last week with 100 tech CEOs. They had key figures like Howard Lutnick, Elon Sachs, and Doug Bergrum discuss various aspects of this. They asked me to present on decoding the Trump economic agenda. It really breaks down to this, Bill: The CEOs in the room are excited about this golden age everyone’s talking about.
Speaker 2: Right—pro-growth administration, pro-business, pro-investment, lower taxes, less regulation, and a boost in M&A activity. We’ve seen the M&A flywheel pick up, alongside this AI super cycle. But there’s a lot of fear around these tariffs.
Speaker 1: The big question leading into Liberation Day was whether the tariffs would be closer to the $600 billion trend that Peter Navarro had discussed or at the lower end that Scott Bessent and Kevin Hassett mentioned. I think everyone was holding their breath.
Speaker 2: Well, we got the answer. I framed that at the JPMorgan conference and ended the presentation with two planes coming in for landing. They both land, believe it or not! But it illustrated that how we navigate the glide path of tariffs and budget cuts is really important.
Speaker 1: We just listened to the president, and he definitely came in on the larger end of what was discussed. There was a headline that hit right after the market closed—something from the Wall Street Journal about a 10 percent tariff across the board, and the markets shot up by about two and a half percent.
Speaker 2: But as the presentation unfolded, the mood began to shift. People saw a chart that showed reciprocal tariffs—where tariffs on China could hit 54 percent. Can you believe that? That’s a 34 percent increase on top of the existing 20 percent tariffs.
Speaker 1: As he went through that list, the futures—like the S&P and Nasdaq—started to drop. There was a 600 basis point fall from where they initially jumped to where they ended up. The market is definitely not responding positively to this news. Depending on the index, we were already down 8 to 15 percent for the year. Whatever comes tomorrow will be added to that.
![Placeholder for a chart showing market reactions]
Caption: Chart illustrating market reactions to the tariff announcements.
Speaker 2: That captures the initial market reaction. One thing I noticed, and I think others did as well, was their redefinition of what counts as a tariff, particularly for reciprocal calculations. They included other elements, but I’m not exactly sure what they all are. Do you have insight on what else is included?
Speaker 1: Right. They classify them as “non-tariff trade barriers,” which encompass everything from currency manipulation to judicial actions that inhibit free trade of our products into their markets. And yes, we know these barriers exist, so it’s not surprising. But if you or I were to calculate these, the numbers could be manipulated.
Speaker 2: When breaking down the tariffs, they can largely be categorized into three or four big buckets.
Speaker 1: Right. The auto tariffs are primarily aimed at Mexico, Canada, and Germany and were set at 25 percent. At the event, Trump had 20 UAW members in the front row and invited the UAW president on stage. He commented that these individuals used to be Democrats and now only the Republicans fight for them. He added that they won Michigan because of these policies.
Speaker 2: It’s striking that this approach mirrors what Democrats campaigned on 20 years ago. It highlights how much the political landscape has shifted.
Speaker 1: The reciprocal tariffs that I outlined earlier are just a starting point. These tariffs are now set to go into effect, and we’ll get charts on April 9th by country. I’m sure we’ll hear about ad hoc negotiations, and some countries like Vietnam may simply capitulate before we get to April 9th, allowing Trump to declare victory.
Speaker 2: He also mentioned $6 trillion in new investments committed to the U.S., citing companies like NVIDIA, Apple, TSMC, and OpenAI.
Speaker 1: Right, and it was interesting how he specifically pointed out SoftBank and OpenAI as “great companies.”
Speaker 2: That’s notable, especially with the ongoing competition between OpenAI and others. Then he stated there would be a minimum tariff of 10 percent across the board. So, even if countries didn’t make this list, they’d hit that baseline.
Speaker 1: And of course, China falls into its own unique category, presenting a huge negotiation challenge. Many factors are at play there, from the Panama Canal to the TikTok issue. But I’m skeptical we’ll reach 54 percent as the final tariff level.
Speaker 2: Right. The headline figure of $77 billion in tariffs last year ballooning to a potential $750 billion this year is alarming. Remember, Peter Navarro had predicted $600 billion, so this landed on the higher side.
Speaker 1: But immediately after that, they slipped in a footnote about exemptions—like for pharmaceuticals and, importantly for us, semiconductors.
Speaker 2: Wow. Taiwan has a 32 percent tariff, but semiconductors are exempt.
Speaker 1: Exactly. We’re currently assessing the value of these exemptions. I suspect the total could trace back closer to $600 billion. But Bill, where does that leave us? It seems many CEOs we know aren’t supportive—many congressional Republicans likely dislike it too.
Speaker 2: You’ve eloquently defended the benefits of free and fair trade. So tell me your reaction when you hear about exemptions and the Republican Party’s approach.
Speaker 1: At a fundamental level, I believe in open markets, free trade, and comparative advantage. There are solid mathematical arguments for why removing trade barriers benefits efficiency for multiple countries long-term.
Speaker 2: What’s the other issue impacting markets and CEOs?
Speaker 1: The slow pace at which they can realistically respond and the underlying ambiguity. If the administration wants companies to relocate factories from Thailand back to the U.S., that’s not a quick process.
Speaker 2: True. Starting that move today might take three years before there’s enough volume for it to be viable again.
Speaker 1: Exactly, and with higher production costs since U.S. labor is likely not globally competitive.
Speaker 2: Right—there’s a lot of uncertainty around what percentage of these tariffs will be realized versus what’s just posturing.
Speaker 1: The administration has a reputation for some of its actions being for negotiation, which leaves everyone wondering what will be the policy in the coming months. That uncertainty complicates any meaningful capital expenditure planning.
Speaker 2: As we’ve both noted, markets and businesses can’t stand uncertainty.
Speaker 1: Exactly. They can handle almost anything, but they need predictability to build forecasts and determine if investments are worth pursuing. I was texting some major CEOs during the announcement, and they were asking whether they’d be exempt.
Speaker 2: That’s astonishing given the level of uncertainty!
Speaker 1: Right—in Montana last week, nearly every CEO I spoke to mentioned that things have slowed down in February and March due to uncertainty.
Speaker 2: And the Fed recently downgraded their GDP growth forecast while raising expectations for inflation and unemployment.
Speaker 1: The economy is generally perceived to be slowing, with major economists increasing the probability of a recession. So, was Liberation Day a moment of clarity?
Speaker 2: That’s a great point. Even if someone secures an exemption, the main question is whether they can depend on that exemption to plan effectively for the future.
Speaker 1: And the cascading effects—if companies are uncertain, they’ll hold off on hiring, which impacts consumer spending and economic activity broadly.
Speaker 2: To your point, a CEO shared last week that four of their contracts had been canceled. They lost three contracts from European companies and one from Asia—all because those countries are seeking better deals elsewhere due to discontent with U.S. policies.
Speaker 1: I even spotted tweets suggesting that China, Korea, and Japan might collaborate in response to U.S. tariffs. It’s unprecedented, and as someone mentioned, we haven’t seen those nations come together since the Mongol invasions.
Speaker 2: It’s creating some unexpected alliances. I sent you a tweet about European leaders like Macron heading to China to discuss closer trade negotiations.
Speaker 1: Yes, you warned us about this. You said this would happen during a meeting in Vietnam—drawing from insights from the president of The Economist who predicted such moves. There are significant implications here.
Speaker 2: I doubt anything that emerges will have positive market effects. But that’s more your territory than mine.
Speaker 1: Let me share my thoughts. The NASDAQ is down almost 18 percent peak to trough since Trump’s announcement—some stocks have plummeted by 40 to 50 percent. Fear is definitely seeping into the market.
Speaker 2: And how do you anticipate this will unfold?
Speaker 1: I genuinely believe the president wants to engage in trade deals. I suspect we’ll end up closer to $300 billion or $400 billion in tariffs rather than $600 billion or $700 billion, even if it feels larger today.
Speaker 2: What might influence that shift?
Speaker 1: He mentioned Senate and House members during his press conference. Those members are hearing from their constituents that they dislike these tariffs. The president needs to pass his reconciliation package—the “big, beautiful bill”—which includes maintaining no taxes on tips and ensuring the permanency of the tax cuts from his first term.
Speaker 2: He can’t afford to lose a single Republican vote because that could steer him towards a more centrist approach.
Speaker 1: Exactly, and we’ll see if the market believes that. It clearly didn’t after hours today.
Speaker 2: Looking ahead, what will be critical in the next 30 to 60 days as this unfolds?
Speaker 1: We’re still in a fog of war, no doubt. I’ll be monitoring whether exemptions for semiconductors and pharmaceuticals remain intact, alongside observing country-specific negotiations.
Speaker 2: And most crucially, how things play out with China.
Speaker 1: Definitely. China is the second-largest economy globally, and I’m concerned about the sheer size of the proposed tariffs. I think discussions between Trump and Xi are imminent for a significant trade deal.
Speaker 2: Those are good points, and I don’t see any clearing event in sight for at least 30 to 60 days. But keep in mind, buying opportunities often arise when fear is prevalent. You might need to take a leap of faith for the best prices.
Speaker 1: That’s an insightful approach. Speaking of fear, we also heard significant news this week regarding developments in Chinese open source and progress on the U.S. open source front concerning frontier models. You have a deep understanding of the histories in both countries around open source.
Speaker 2: So help us unpack what’s strategically occurring in China concerning these open-source models.
Speaker 1: I saw some tweets suggesting that DeepSeq may have been forced into open source by Xi. Do you think that’s the case, or is something else at play?
Speaker 2: That speculation seems completely misplaced. China has supported open source for over a decade. Just look at most of the major open-source products—like Linux. Major Chinese companies have been backing these projects for quite some time.
Speaker 1: Why do you think that is?
Speaker 2: They have faced accusations of tech IP theft for years. When open source emerged, it provided an excellent opportunity for them to avoid those accusations, as there’s no IP ownership in the open-source realm. So, the Chinese government and entrepreneurs see it as a positively strategic movement relative to the West.
Speaker 1: I see your point. You’re saying they recognized the advantages of open source a long time ago due to the pressure they faced?
Speaker 2: Exactly. So, they fully embraced it long ago.
Speaker 1: And let’s not forget that in the last decade, U.S. companies have also started leveraging open source defensively—not just offensively.
Speaker 2: Right. Major companies use open source to level the playing field when they’re struggling to keep up.
Speaker 1: Can you provide an example?
Speaker 2: Sure! Take Kubernetes, for instance. Amazon dominated AWS in the hosted server realm. This worried competitors like Google, which had Kubernetes technology for workload orchestration.
Speaker 1: So, what did they do?
Speaker 2: Google rallied support for Kubernetes within the Linux Foundation, bringing in major players like IBM, which built momentum around it. This collaboration forced Amazon to adopt Kubernetes, preventing them from monopolizing the cloud sector.
Speaker 1: That’s fascinating!
Speaker 2: It’s similar to Meta’s approach with Llama—they weren’t the first in AI, but they made a disruptive entry with open source.
Speaker 1: There’s also an economic aspect to all of this, right?
Speaker 2: Absolutely! Economically, open source promotes heightened competition, which ultimately leads to lower consumer prices—making it a powerful disrupter in the market. Speaker 1: So, that’s a huge backdrop to where we are today. I believe DeepSeek has been remarkably successful in the enterprise. It’s hosted by both AWS and Google, and it’s being used globally. I’ve heard from Hugging Face that it’s been forked over 1,500 times on their site. So, it’s quite prolific. However, I’m starting to hear concerns that Washington, D.C. may intervene to limit the use of DeepSeek.
Speaker 2: You’re saying Washington may take action because there are people lobbying against or expressing concerns about the use of Chinese open-source models by American enterprises?
Speaker 1: I think it’s safe to say both of those things are happening. There are people genuinely worried about Chinese technology being integrated into our products, whether or not this particular code may be harmful. They might default to that assumption. Additionally, there are those lobbying for action because it would benefit their companies. If any of this gains traction, the current climate allows for someone in the U.S. to potentially move left of DeepSeek in terms of openness on one of their models, either as an offensive or defensive maneuver. And I think that window is quite short.
Speaker 2: It will be interesting to see whether Google or Meta makes any moves. I know Meta has an announcement coming up in a few weeks regarding their next model, which could be significant. I don’t think Anthropic would engage, though, as they’ve been very anti-open source, which would be out of character for them.
Speaker 1: Speaking of announcements, OpenAI has also made waves this week. Sam has been dropping hints on Twitter about launching an open-source model. He highlighted the challenges they’ve faced, like being GPU and bandwidth constrained. This week, he finally announced that they plan to release a powerful new open-weight language model with reasoning capabilities soon and wants to engage developers for feedback.
Speaker 2: That’s exciting! There was even a reply where someone asked if developers would need to buy licenses if they gained a lot of users, similar to what Meta is doing with Llama. Sam jokingly shot that down, suggesting they won’t restrict access in that way, possibly indicating they might outdo Meta in terms of openness.
![Placeholder for a model comparison chart]
Caption: Chart comparing the openness and usage restrictions of various AI models.
Speaker 1: Indeed, there’s a spectrum to openness in the open-source model world, and Llama has restrictions against free usage at a $700 million rate. Sam’s tweet implies they won’t adopt such measures.
Speaker 2: That’s notable, especially with LlamaCon coming up, where the launch of Llama 4 has been highly anticipated. Many expect Meta to release it before the event.
Speaker 1: I’ve heard it will be a 400 billion parameter model, utilizing a mixture of experts—around 50 to 70—and it’s expected to have a context window of 10 million. OpenAI’s commitment to being more open is encouraging; competition in the open-source domain in the U.S. is crucial.
Speaker 2: Exactly, and the administration likely wants to avoid a situation where DeepSeek, possibly linked with Huawei’s infrastructure, dominates. They also want the world to rely on U.S.-based computing and silicon, preferring models like Llama and OpenAI’s to hold their ground in the global marketplace.
Speaker 1: Correct. They wouldn’t want the global market running on DeepSeek’s models. However, there’s a unique challenge. Whoever aims to lead the open-source movement here not only needs to excel in the West but must also compete with DeepSeek internationally.
Speaker 2: True! I’m curious to see how this unfolds. I’ve asked Clement at Hugging Face to potentially create a continuum for ranking all these different models based on openness, given the myriad factors involved.
Speaker 1: And speaking of OpenAI, you’ve suggested their greatest opportunity lies in product development instead of just model advancements, hinting at a shift toward consumer products.
Speaker 2: If that’s the case, being more open is a strategic move that could pressure competitors to keep up, allowing OpenAI’s model to gain global traction. Plus, releasing a model for widespread use moves the compute demands to others’ infrastructure rather than just theirs.
Speaker 1: That’s a clever tactic. Similar to Google’s support for Kubernetes, this could eliminate other models’ profitability in the API space by opening the door for broader usage of their model, benefiting consumers in the end.
Speaker 2: Indeed. They likely want developers to build applications on their platform, expanding their ecosystem. Plus, their goal of moving humanity forward with AI is being magnified through scalable product and application development.
Speaker 1: Exactly. Sam has consistently mentioned that AI models are becoming commoditized, with the battleground shifting towards products and services rather than exclusively model capabilities.
Speaker 2: And while ChatGPT is their leading consumer application—capturing about 80 to 90% market share—they’re also focusing on their enterprise business, which is seeing rapid growth. This alignment mirrors the phenomenon of consumer preference spilling over into the enterprise market.
Speaker 1: Yes, it’s not about a winner-takes-all scenario. Their appetite for developing an enterprise business is clear and quite strategic.
Speaker 2: We should also touch on the recent funding round around OpenAI. It was a long-rumored investment, led by SoftBank, estimated at around $40 billion.
Speaker 1: Correct. The breakdown seems to indicate a first tranche around $10 billion and a second around $30 billion, which totals an extraordinary amount—likely larger than any IPO aside from one or two in history.
Speaker 2: That’s a significant milestone. They’ve shared expectations of around $13 billion in revenue this year, which likely means a run rate of $15 to $18 billion. In terms of valuation, that places them at about 20 times revenue.
Speaker 1: In comparison, Anthropic is rumored to be raising at a 50 times revenue multiple, while the merger of X and X.AI is around 80 times revenue. So, at 20 times, OpenAI seems relatively reasonable against those metrics.
Speaker 2: While still needing to clarify some aspects relating to the potential for their conversion from nonprofit to for-profit, there’s considerable optimism around these developments.
Speaker 1: It’s essential to remember, especially with our experience from the Uber and Lyft battles, that there’s an existing belief in power law dynamics and the notion of winner-takes-all.
Speaker 2: Right, and that can create massive capital raising rounds, but there’s no doubt that headlines can seem promotional. This leads to questioning the motivations behind these announcements.
Speaker 1: Yes, they could be trying to send a message to competitors like Anthropic that they’re in this for the long haul, especially in light of recent moves from other heavyweight players.
Speaker 2: Plus, it postpones the conversation about unit economics. If they are predicting losses of $5 to $7 billion this year, the path to profitability is foggy, especially with the operational costs associated with maintaining their AI infrastructure.
Speaker 1: Exactly. We’re potentially looking at significant operational costs just to sustain the AI model, factoring in server costs and the transition to future revenue models such as advertising or paid tiers.
Speaker 2: Indeed, the focus will be on innovating revenue streams and maximizing customer lifetime value without losing sight of economic dynamics that affect sales and market behavior.
Speaker 1: And let’s not overlook the competitive landscape. Companies like Booking.com, large advertisers on Google, reflect how revenue models could evolve.
Speaker 2: That’s an important point. How travel and hospitality firms integrate with evolving AI models reflects the broader implications for commercial strategies moving forward.
Speaker 1: Right. Google’s antitrust investigations make it clear there’s fierce competition ahead. The evolution away from their monopoly might open avenues for new players.
Speaker 2: And as these dynamics play out, the next few months could be crucial for determining the landscape of open-source models and who might dominate long-term.
Speaker 1: Absolutely. The large shifts we’re witnessing are unprecedented, especially as the consumer Internet has revolved around search paradigms for years. The introduction of AI technologies marks a significant pivot.
Speaker 2: Before time runs out, let’s discuss IPOs. We haven’t had many lately, but CoreWeave is definitely one we should highlight along with other emerging opportunities. Speaker 1: You know, I have to say, on Friday, I was pretty damn nervous, Bill. The stock broke the offering price and went down to about $37 a share. Today, they had some announcements about a deal with Google where they’re going to provide NVIDIA chips through CoreWeave.
Speaker 2: So Google is buying a bunch of NVIDIA chips through CoreWeave?
Speaker 1: That’s right. One of the big criticisms of this company was its heavy dependency on Microsoft. But now they’ve diversified their partnerships—with Microsoft, Meta, Google, OpenAI, NVIDIA, Cohere, and Mistral. I think they’ve really emerged as a leading player in the AI cloud space.
Speaker 2: Interesting! The stock has been volatile, especially considering they just went public last Friday, which seems like a risky move.
Speaker 1: Absolutely! It’s like taking a company public into a Category 5 hurricane. We had Liberation Day staring us in the face, and they had to navigate that unpredictability. As you pointed out, it certainly wasn’t the least controversial of IPOs. But I have to give credit to Mike Intrator and his team.
Speaker 2: Definitely. And it’s impressive that they started this company just five years ago. It’s worth over $30 billion today and has played a crucial role in supporting OpenAI and many leading AI labs. That’s a positive development for all of us.
Speaker 1: While that’s true, it’s also fair to ask the tough questions you’ve raised about the long-term durability of CoreWeave and its revenue streams.
Speaker 2: Right, and it’s funny. We often sit around complaining about the IPO market being closed. Yet in 2024, markets were up 30%, and still, no one was ready to go public.
Speaker 1: Exactly! When someone finally has the guts to go for it, of course, the markets shift downward. Anyone who tells you to wait for the market to be in a specific state is mistaken—it’s an external factor beyond your control.
Speaker 2: I agree. And there’s debate about whether this was a good or bad IPO. Like you said, things are moving all the time—they have a leader in their field and huge revenue growth.
Speaker 1: Right, the business model isn’t completely unpacked yet because they’ve invested so far ahead of the product lifecycle. You can’t just look at the income statement and determine if the economics are flawless.
Speaker 2: There are certainly questions about the appropriate depreciation schedule and such. But I’m glad they got out there, and it seems to be doing well, especially with two customer announcements post-IPO—indicative of how fast this AI world is evolving.
Speaker 1: Yes! That’s likely why the stock fluctuated from 40 to 36 and then back up. Let’s hope this leads to more IPOs. This company needed capital because it operates in a capital-intensive sector.
Speaker 2: It’s a shame that companies like Stripe and Databricks are choosing to delay going public because they have access to private capital. That’s a conversation for another day, though.
Speaker 1: True. There’s also Klarna and Cerebrus in the pipeline, right?
Speaker 2: Yes! I’m always hopeful for more companies willing to move into public markets, but the choppy market dynamics we discussed earlier will be present too.
Speaker 1: On another note, we should talk about the recent situation with Morgan Stanley. They received a lot of scrutiny last Friday regarding this deal.
Speaker 2: True. They faced backlash when the stock dipped below 40, and CNBC was quite critical of both the company and Morgan Stanley. But look where the stock ended up today.
Speaker 1: Right! As shareholders, we’re feeling good. I’m happy for everyone involved in the company. Of course, questions remain about the business—it’s good to revisit the depreciation topic.
Speaker 2: Indeed. And about that depreciation argument, many people reacted to what Jensen said at GTC about how Hoppers might not be as valuable due to Grace Blackwell’s capabilities.
Speaker 1: Yes, he called himself the chief revenue destroyer and made statements that could suggest a two-year depreciation rather than a six-year one. That was a bold move from him.
Speaker 2: Definitely! The inference demand is off the charts, and everyone is clearly in need of more GPUs for that purpose.
Speaker 1: Agreed. With cutting-edge GPUs, they’ll still be in demand for both training and inference over time. There’s a consensus that CoreWeave’s margins are likely to land around 25% EBIT in the coming years.
Speaker 2: To understand how they get there, you have to consider their unit economics, CapEx, and OPEX. They need to establish data centers, cover operational expenses, and purchase servers.
Speaker 1: Yes, effectively, they sell contracts to major players like Microsoft, OpenAI, and Google. They expect to recoup all those costs within three years.
Speaker 2: Exactly! So, in year four of these four-year contracts, their profit margins come into play. Anything beyond that is just additional profit.
Speaker 1: That’s right. However, the consensus earnings seem to ignore any returns post-contract period, which seems problematic.
Speaker 2: And there’s still a lot of A100s out there, as Jensen mentioned during GTC. They’re still being utilized, even five years after their release.
Speaker 1: Exactly. Even the V100s from 2017 have a long lifecycle—they were used for around seven years. We can feel secure that these GPUs will be valuable for at least four years.
Speaker 2: The software updates on these GPUs keep them current, much like how my old Tesla felt new because of constant software upgrades.
Speaker 1: So, I’m not overly concerned about the depreciation debate. It seems like a significant issue for the company, but they’ll be fine given their recent deal.
Speaker 2: Of course, the pushback suggests that it’s not simply black-and-white. There’s a question of whether the depreciation schedule should mirror actual asset earnability over time.
Speaker 1: That’s a fair point. The idea of depreciation is to reflect useful life, and a straight six-year schedule may not perfectly fit the actual lifecycle.
Speaker 2: Yes. As we discussed earlier, their peers initially operated under a four-year schedule before extending it. The ideal answer might lie somewhere in between.
Speaker 1: I don’t believe they need more than four years to achieve their projected margins, but they still have to de-leverage the business, which adds another layer of complexity.
Speaker 2: Definitely. These multiples they’re trading at aren’t overly taxing from our viewpoint, but many AI companies are facing considerable headwinds right now.
Speaker 1: Agreed. Nvidia, for example, is trading at 19 times fully taxed earnings, which only amplifies skepticism about AI demand, despite the narrative around it.
Speaker 2: Before we move on, I have a riddle for you. What do Salesforce, Netflix, Square, Amazon, Palo Alto Networks, Facebook, Snap, Proofpoint, NetSuite, and CoreWeave have in common?
Speaker 1: No idea. What’s the answer?
Speaker 2: They all broke issue!
Speaker 1: Oh, wow! It becomes a flawed perspective when the media criticizes a company for trading below its IPO price.
Speaker 2: Right! Those high-quality companies often get priced to perfection, largely because stronger founders negotiate better terms.
Speaker 1: I suspect that some peculiar terms in this company played a role in the stock performance.
Speaker 2: No doubt! Let’s shift the focus. There’s a lot of new information coming in about the TikTok situation.
Speaker 1: Yes, there are numerous rumors swirling. This deal is set to either expire or be extended by April 5th due to terms that Trump established in the first congressional extension.
Speaker 2: It seems evident that there are many buyers interested in the TikTok asset, and the president is looking to put together a viable deal.
Speaker 1: That’s right! However, with the tariffs on China, this is likely to factor into a larger trade negotiation.
Speaker 2: Remember, we’ve been shareholders since 2015, one of the early venture rounds in ByteDance. I believe we should engage with China to make TikTok comply with our regulations instead of shutting it down.
Speaker 1: I agree! This whole legislative effort is about ensuring TikTok operates within U.S. guidelines.
Speaker 2: Here’s what I’m hearing about the potential deal: a new company, let’s call it TikTok US, will be formed. ByteDance will retain ownership under 20%.
Speaker 1: So ByteDance would own about 19.5% of TikTok US, while existing shareholders will share in the benefits too?
Speaker 2: Exactly! The new company would be capitalized with fresh money from new investors like Amazon, Dreesen, and Oracle.
Speaker 1: I assume this capital won’t go to ByteDance directly but toward the newly formed company?
Speaker 2: Yes, that’s right. The new entity would be licensed for the algorithm, which can then be audited to ensure compliance.
Speaker 1: What’s the expected valuation for TikTok US?
Speaker 2: It might be relatively low, especially with Trump suggesting it could tie into the U.S. sovereign wealth fund.
Speaker 1: That raises an interesting point since ByteDance had previously seemed disinterested in a deal. What shifted their perspective?
Speaker 2: Yaming, the company founder, believed it was impossible to separate the algorithm for TikTok US from TikTok globally. Without U.S. involvement, the ramifications would be detrimental.
Speaker 1: So, the realization that TikTok US and the international version would share the same algorithm could be a pivotal factor.
Speaker 2: Right! It’s worth noting that 60% of ByteDance’s shareholders are U.S. investors. That makes the situation critical for them.
Speaker 1: Absolutely! If ByteDance goes public, that would unlock significant unrealized gains for those investors.
Speaker 2: Right. The majority of ByteDance’s revenue still comes from its international branches, like Douyin and Daobao, not just TikTok US.
Speaker 1: As shareholders, we’d ideally want certainty. Protecting TikTok in the U.S. is a win for both the users and ByteDance.
Speaker 2: Definitely! However, with the recent tariffs imposed, we must realistically consider the challenges this may introduce.
Speaker 1: It’s true that even if a deal is announced, it may not be a done deal. The Chinese government could still step in.
Speaker 2: Exactly! I remain cautiously optimistic. I believe the president is interested in making a deal with Xi-for the benefit of both countries.
Speaker 1: I think you’re right. There’s too much at stake economically for a deal not to happen. The ability to cooperate on issues like Ukraine and the Middle East is critical.
Speaker 2: True! While there’s an ongoing competitive struggle, I sense that the president, being a dealmaker, will likely look for common ground on matters like TikTok and other trade discussions.
Speaker 1: Ultimately, U.S.-China bilateral trade relations are crucial and will play a significant role in determining future economic growth for both nations. Speaker 1: Important to watch.
Speaker 2: All right, man. Take care! Great seeing you. Have fun at the games.
Speaker 1: Take care! And just as a reminder to everybody, these are just our opinions, not investment advice.
2025-04-04 08:00:01
Lambda Days 2015 - Torben Hoffmann - Thinking like an Erlanger
Thank you.
Thanks for coming so last year.
I think I spoke down here as well, and there I did one of my normal urban priesthood things of propaganda, how wonderful learning is and how it turns into money, and all those things that you need to convince people about doing business and all those things there. That’s also normally how I get contacted to talk at conferences because we want to spread the happy gospel of Erlang and get the church a Berlin goal.
I got invited last year to do a talk at NDC in London by a good guy called Brian Hunter. We agreed, yes, we’ll do this thing. Then he comes up, but I’d like to know how you think. I must think how I think, I don’t think. When I program, I feel. Like Garrett said, I feel, and I feel good because I do it in real life. So I feel really good there, but then, okay, fine.
I put together something on thinking like an Erlanger, and it has evolved over time. I’ll try to take you through the mind of at least my head as an Erlanger, but I think the things I’m talking about apply to how a lot of people think. I also have some ideas about how you can approach Erlang programming because one of the things Garrett also has done some story studies, and you saw the keynote that learning is hard to learn.
One of the reasons learning is hard to learn is because it’s different. It’s not like all the other languages out there; it’s different, it’s special. Yeah, so yeah, and you like to do it. But the thing is, if you want to see for yourself, go check. Take one of those maps that show the lineage of time over programming languages, and then there will be this. You’re going, but Erlang is not on the list. What are you doing?
You have C, you can see that C turns into C++, and then you get Java, and then you get other things that are even worse. So there you go, but Erlang’s not on the list. There are reasons for that, but there’s also one good thing from that. Erlang is not on the list; it’s your number one reason why you want to learn programming because it teaches you to think in a different way, and that is very important.
With this in mind, a little question for you: if you want to see the Erlang code, you can’t handle the Erlang code. That’s important here. If you behave nicely, I might show you a bit of code. But the thing here is, this is very, very important when we talk about these things. Syntax is utterly irrelevant.
When I was…this the first time I saw Erlang, I thought syntax was horrible—capital letters and extra hours and everything—horrible. That was when I was 28 or something like that, so I’ve aged a bit since then. I’ve actually come to realize that syntax doesn’t matter, except that if you’re doing Java, then it matters a great deal and then it’s a different thing.
But there we go, but what does matter? Thinking is everything. So if you leave here with anything today, it is forget about syntax. It doesn’t matter; thinking is everything. This is what you need to take into whatever language you’re forced to write in, because if you’re not forced, of course, do it in your life.
So there we go. Now we come down to thinking. We all know that Erlang came out of telecom. So when you look at these things, you can also abstract it away. But basically, a domain like telecom has a number of things you need to do in that domain in order to solve the problems. If you’re not solving the problems, you’re not being paid, and yeah, then it goes bad.
So you can take the standard approach, and you can pick something like C++, Java, or something equally monstrous. You can try to solve the problem, meaning that you need to fill out a huge gap. This is good if you’re a manager. Now why is it good? It’s good because that means you need to have an army of people reporting to you. Having an army of people reporting to you means you’re important.
So there you go. That’s a good reason for languages to exist in big organizations because you can’t solve small problems with this; you can only make big problems, and you can get degrees in project management. I get tired. Okay, but then comes along Erlang. Erlang is a domain-specific language. It was created to solve the needs of telecom, which means that it fills out a lot more of the space in telecom. This means there’s a lot less you need to write.
Okay, now you can already see that the managers are going, “Scary! Scary! That means that I have less people reporting to me,” or maybe there won’t be a need for a manager. Scary stuff, but that’s actually important because this is all where this smaller gap means there are benefits. One of the good benefits is that you can express your thoughts more directly, so the feedback loop will be shorter, and you know what to say.
The breakfast of champions is feedback. The more feedback you get, the faster you can learn. This is about, again, in terminality: fail fast, learn from mistakes, and improve faster. It’s just the way things work. Another way of saying this is those benefits equal into money, but we will not dwell on that. It’s more about the feedback loop and actually the fact that this small gap makes it fun to program.
I had a colleague at Motorola—well, more than one, because it was a big company that did things in C and C++. So you see where that’s going. We were two; we got onto this Erlang bandwagon, and then we convinced management that we should grow our team by 50. Yeah! We get one guy extra; that’s how percentages work. So we go around, and we should try shopping for a person willing to learn Erlang and work with us.
I don’t know which part of those that he was most afraid of, but anyway, he came to the conclusion. He said, “Guys, you’re interesting. I’d like to work with you, but I’m not sure about this Erlang thing.” Okay, six months later, we are asked to downsize our team by 33; he had to go. So basically, he’s just sitting there, and he went to his boss. “I know I can’t be on the team anymore, but can I sit next to these two guys so I can smell the Erlang?”
This is how much fun Erlang is—you don’t want to go. He was clinging onto his table. Yeah, so one of the things out of the telecom domain that’s very important for us for solving problems is protocols, and that is actually something that too few people are taught. There’s a feeling that protocols are about flipping small bits.
Telecom protocols are the sugar of the world. This is how you make things work. Then here are two books to recommend. Of course, you all have a copy of “Communicating Sequential Processes.” Please. Yes, that’s one there. Good. Then don’t be as stupid as me; don’t go on a summer school with Tony Hoare and not bring your copy to have it signed. Stupid, stupid. So I think I’ll fix this this year, but that’s another story.
Another thing here, and this is a Springer book, unfortunately, so it costs a whopping 120 dollars. There, I think I have hung onto that. Normally when you take that class at university, you sell on the book because it was just too expensive to have around. But this one was so good that I, with my limited funds, hung onto that one. This is very good; it talks about how you design protocols.
Funnily enough, it uses CSP to describe these protocols, and it’s brilliant. Yeah, it’s worth the money. The thing is, you use protocols for a lot of things, and you should be using them a lot more. So here we have one Paxos consensus protocol. Of course, they decide to write it up in ASCII so that it becomes really illegible and things like that.
But protocols are all around us, and that is what we should do and think about when we design programs. Even if you are so unfortunate that you have to write a Java program, you should be thinking about protocols. Anybody can be a single-page programmer and write a Java object. Anybody can do that; making Java objects work together is about protocols. That’s why you need to focus.
Then, of course, you learn to think about protocols using the one true language on earth—the golden trinity of Erlang. That’s, of course, all right. This is what makes Erlang special. The golden trinity is something I came up with while taking a walk. I took the walk because people will say you shouldn’t be using Erlang, so okay, I’ll take a walk and deal with this problem.
Then you have to think about these things. So I thought about if I had to strip something from Erlang, what would I strip and still code in it? Which things would I take out and make it go? Then I can code in something else. These are the things that are left in the language when you take that exercise—at least when I do it.
You have share nothing. You’re not sharing anything between the processes you have in your system. Sharing memory is bad, and when sharing nothing, they also have to send messages between these processes. So that’s one pillar. Another thing is you fail fast because trying to resolve everything by throwing exceptions around and catching them will lead you to one thing—one thing only: you will lose your hair. Period.
Find a Java programmer that has worked with this for 10 years; he has gone bald. It’s exceptions that do that. You can’t run around and have programs just fail all over all the time. Okay, time for little career advice: if you work in a company like Motorola that works on safety-critical systems that have to work all the time, don’t run around and say, “We just let it crash.” People think you are insane.
For me, of course, it’s for the wrong reasons, but never mind. Don’t say that, so in order to fix that, you add failure handling to the language, and then you can match these things out. So you have shared nothing, one pillar with message passing, that everything is nice. This is where the protocols live. You allow these protocols for processes to fail fast; then you supervise them and make you deal with the failures in a nice way.
That comes with the first one, the processes—they’re so dirt cheap; you just use lots of them. If you try to program in the Python style and keep everything in one process inside Erlang, you’re doing it wrong. It’ll feel like programming in Python, and that comes with varying degrees of fun—mostly pain. So don’t do that. You should use tons of them. You can spawn off on a Raspberry Pi, the old version; you can spawn off something like 10,000 threads.
In Java, you can save 135,000 Erlang processes; they are that lightweight. Don’t be afraid of using them. If you program Java, objects are cheap—yes, fine—and Erlang processes are cheap. Don’t be afraid—use them a lot. Then, coming back to protocols, focus on how they interact. Just having a lot of processes is nice, but you need to focus on how they interact because that’s how you solve problems in a learning setting.
So I’m going to do something, and I warn you now: don’t do this at home. It’s very important because some people take it very literally when I take the next example and show you how to use online thinking on this problem. Of course, you shouldn’t be using Erlang on this, or maybe you should, but we’ll come to that. Don’t take it too seriously; just take it as a good example to show.
Good Game of Life—Conway’s Game—how many are familiar with it? Exactly. Why do you think one chooses that example? Okay, yeah. Never mind; we’ll take the critics later. So, cellular automaton—very simple. It is about the evolution of cells in discrete time, and the way it works is you have one cell—you can see the one there in the middle—and the next evolution of that cell depends on the neighbors around it.
Those eight ones around it. If there are two or three neighbors around it, it stays alive and survives. If it’s empty and has exactly three neighbors, a new cell is born into that square. All others become empty. If you’ve seen these things run, then you can make wonderful patterns on your screen. Let’s just show it. So yeah, I’ve written up one here, and then a word of warning: the world wraps around, so the top and the bottom can see one another, and the left sides can also see each other.
That’s a common thing. Here’s at time one, and then it starts evolving. You can see…did I jump over time? Yeah, well trust me, it is this correct one, and you see how they evolve, and over time, they need to see what the neighbors are like and figure out what the next value should be. Then you just move on like this, and I will not bore you totally to death for this one.
This one is a configuration that I think lasts 18 or 19 generations, and then it dies. It looks so good here; it survives six time steps and it’s good, doing great, but eventually, it dies out. There are people actually spending an enormous amount of time figuring out which configurations survive and which don’t, but that’s a separate area of research.
So, the traditional approach to this…so now we’re thinking like a Java programmer. Trust me, after this talk, that kind of thing is out of your body, so don’t worry. Otherwise, if not, I will get you some Erlang patches you can put on, and it’ll make it go away. We’ll just for two seconds here; the normal approach to this—this is how you see any textbook program—is saying you take a 2D array, and then you take a new 2D array.
Then you compute from the one before the next one, and you do a for loop across the board and do all of that. Yes, that’s nice if you’re doing imperative programming. There are some issues with this, and this is why people say you can’t say this. But I can and I will. It does not scale well because if you do this unless you start doing nasty parallel programming things, you need to actually run this sequentially through all things there.
If you want to do it in Erlang, because why program anything better? These imperative data structures are really ugly. So now I want to solve this problem in Erlang. So, this comes down to the basic Erlang idea, and that is one process per cell. Some people find that utterly: whoa, can you do that? Yes, you can.
There, I’ve been running on my machine a grid of 300 by 300 processors, so that’s 90,000 processors having fun doing Game of Life. It works; it scales. You let the processes talk to the neighboring cells, because that’s what you do in Erlang. You have processes talking to one another by sending messages around.
Where does this leave us on the mark? That means we are down in the left-hand corner; we are down in the share-nothing message passing area of the golden trinity of Erlang. This is where we’re going to stay for a little while and deal with these things.
When I’m a cell and I need to progress to the next time step, I need to know the values of what my neighbors are at this time step. You do this with a little bit of protocol. You collect the cell content of all your neighbors to figure it out, and then you update your own content, and you say… now i’m at time t plus one so this is what you do. Normally in the 2D version there, having a quick look here, you just have a process. It goes out, talks to all its neighbors and says, “Okay, good, give me what your value is,” and then I’ll update myself when I’ve collected for all my neighbors. Very simple protocol there.
So then the question back is, is this Erlangy enough? What was one of the things I said about Burlingame processes? Use lots of them. Have I used lots of them here? Oh yeah, one per cell. Could I do a little more? I could try and do a little more, so we’ll see if it’s a good idea. Sometimes you have to experiment there.
So actually every time I need to collect for a new time step, I create a collect approach: a startup collective process that is responsible for contacting all the neighbors, collecting results, and once it’s done, it’ll report back to the mother cell. “You know what? This is what I figured out. Figure out what you want to do and how you want to take rest to the next time step.”
Again, notice this very nice. It’s a protocol thing nicely described with these MSCs that says messages going back and forth. And this, that’s a learning thing. This is how you design Erlang programs. You write up these things and start focusing on sending messages around. You do not spend your entire life writing objects inheriting trees.
This is not important; the static structure is not important. It is sending messages between entities that’s important. This is what we do here, and then the collector loop itself, you can see here the top one. As soon as it has nothing, it’s waiting on. It has a number of neighbors counted up, and then it sends a message back to the mother cell.
Next content should be this: go ahead and know the other times that it receives can receive like this is the receive statement here. If you receive from one of your neighboring cells, send a cell content message. You do some updating and everything, and then you continue until again you completed that entire list.
This is nice and easy and the naive way of doing this. So the question is, will this work? No, it will not work, so we’re going to see it. Actually, we can sort of put a little bit of a provision on that: it works if basically if—there’s another thing—if you ever start to become a project manager or in any way leading other people and your developers say it works if, then it means that I have bugs in the program.
So you need to—yeah—so this works if you only take one step at a time. So you only ask myself to do cells to do one time step in this simulation, and then we stop again and we see where we are. It’ll work as long as you do that, and if you let the cells run freely, that actually doesn’t work because there the cells get out of sync.
How can the cells get out of sync here? The thing is, you can request something from a neighbor where the neighbor has moved on to the next time step. So the neighbor is at time t2, and you’re asking, “What’s your value at t1?” and you just say, “Well, it’s in my past. I don’t know.”
So that won’t work, and you can also be ahead of the game. You could be asking for a cell value in the future for your neighboring cell. You’ve moved on to time two, and you ask your neighbor, “What’s your value at time two?” and he’s like, “No, I’m at time one. I don’t know what my value will be at time two.” We don’t have wormholes in our life, so we can’t transport things through time and space and things like that.
We have to respect these things. But this can be fixed, luckily. So if you have a request for an old time, something in your history, you just keep a history that’s straightforward. If you have a future time request, and somebody asks you for a value, you will compute in the future. You just queue the response.
This load sounds like, “Okay, this is an artificial way of solving the game of life.” What are you doing with these things? Well, the point here is in lots of places where you have what they say here, asynchronous protocols, you will run into these kinds of issues, which means you need to deal with these kinds of things.
These two tricks—keeping history and also queuing responses until you’re ready to reply—are typical patterns you come across a lot when you’re doing online programming. It all comes down to using the infamous message passing.
Good, now we move on to the next thing because, again, as I told you when walking, we came up with these things: failure handling. You also need to look at failure handling in this because the code will not be correct. All bad things will happen that you couldn’t foresee.
So how do you do failure handling? You start supervising the cells in your system there, and then if a cell dies, the Erlang supervisor just puts in a standard Erlang supervisor and then restarts the cell with the original arguments.
So if a cell started, we’re saying I’m cell number 7.5 in this grid, and I start with a value of 1, meaning I have something in me. Good, it has been started there. But the problem is if you progress and you’ve done a simulation down to time 100, all of the time you just computed went away when you died.
It’s just gone. The process and the memory of the whole process is gone, so you start from scratch. That means all state, whatever you have, is lost. This happens every time you have a process restarting in Erlang. The new one that will be restarted will look like the same one, but it has lost all state, all history, which is something to remember when you do things.
But again, there’s a fix for this. You just monitor all the cells so you can see if somebody’s dying. I can do a fix to this, and what you do is you monitor them, and when they die, you wait for the new cell to come alive. Then you say, “Please catch up with the rest of them.”
So if you have a simulation that has run on to time 100 and a cell dies when it comes back online, you just tell it, “You know what? Run to time 100. This is where the rest of us are. Please catch up.” That’s a way of fixing it there.
If you draw that up in a diagram to show what you have, you have your main top-level supervisor. Then over here, you have a supervisor to supervise all of the individual cells. You have the cells here, and then over here you have the cell manager that’s responsible for monitoring cells when they go down and when they’re restarted, telling the new cell—the one that is replacing the old one—this is what you need to do to catch up with the rest of the world.
This, of course, can be written as a protocol, and it looks like this. Yeah, that’s a bit in it. Here we’re seeing that when the cell goes down, it dies for whatever reason in Erlang. It means that it’s sending out a down message because the process is monitoring.
So the cell manager gets a down message for this particular process, and that means that it says, “Okay, the old cell here, I’m removing that from my registry.” The manager is keeping track of who’s around so we know which processes relate to a certain cell in our simulation. Then the supervising in Erlang, they’re that simple. They just restart stuff. Nothing else, just restart it.
It starts a new cell to represent the one that went away, and then if that cell registers itself with the cell manager, and then the cell might say, “Okay, now I know cell ij. I know the process identified you. I will monitor you now, and if you die, we’ll fix things.”
What it does is it takes track of a time module in this as well. It asks, “What is the maximum time that the rest of the cells have reached?” and then it turns it back to the cell. “Please run up until next time.”
Then the cell that died before has now been replaced by a new one, and the new one has taken the full role of the old one, and it has sped up to where the rest of them were. Again, this pattern also applies outside game of life. You have this situation where something goes down, and then you’re looking at it. “Okay, I restart the process. How do I get it into the way so it works with the rest of my system?”
Sometimes you do need to do things like this where you’re forwarding it forward by saying, “Do certain things.” In other cases, you can just restart it, but that’s for the simple stuff.
The code I’ll actually show some more code here. So here this is what it looks like. The blue stuff here is the down message coming into the cell manager, and you can forget about the rest here. But this is actually, I’m getting a down message; then I do some bookkeeping. Yes, you really need to keep track of the maximum time.
I mean, all the negative cells will be blocked by the new response, and they could trigger. We need the time module. Well, the thing is, the different modes of operandi. I didn’t go into the details of that, but I have different ways of running the simulation.
I can just say do a step, step to a step. I can also say run until you reach a certain generation number, or I can tell them to run freely. When I run into this situation, I can’t avoid having the time in there to make sure that we sync up on something because then you would have a new cell. You could just say, “Just run,” and it will catch up. But the problem is you wouldn’t know when that is when it’s done doing that, and if you’re running in a different mode of a modus operandi, you wouldn’t be able to say, “Now we go back to doing stepwise things.”
So that’s the reason why the time module is there. If you were just running them freely all the time, you wouldn’t need it. You just say, “Run and catch up with the rest of them.” Does that make good? Yeah, good question.
Good question, yeah, no. So yeah, you get the down message, you do a bit of bookkeeping, and then you continue. Then the next step from the protocol is you wait for the supervisor to restart it. There and then, that cell registers itself again with the cell manager, and then you go down here and then you monitor the cell.
So now you’ve got control again. The cell manager is at all times monitoring all the cells in the system so you can take appropriate actions when there. Then the thing that makes things come back to life is you have a kickoff cell here, and that is a function that tells it to do the right thing depending on what kind of simulation mode you’re in.
That looks like this: if it’s a step thing, you just get the end time from the time module there, and then run until that point in time. Then it switches over to doing the stepwise thing. If it’s a run until simulation, you just tell it directly to do that, and if it’s running, you just tell it to run.
I could simplify all of this by saying we don’t have anything but running cells and will just run forever. There, but then again you will have a very hot CPU after a while because one of the things, if you try this out—and there’s a link to the code later in the slides—if you try this out and you do things like I do, running ten thousands of processes together, they will be utilizing all your cores because that’s how it just works in Erlang by magic.
Now at this point, there’s actually a problem. That’s a deadlock, and this happens when you ask the neighbor for a value, and it’s a future value for him, and he queues the response. He says, “I can’t answer you right now,” so he queues the response for you and waits until he’s updated himself to go to the next step and then he’ll send a reply back.
Unfortunately, somebody comes along and kills him, or he dies for his own reasons, and then that queuing, yeah, is part of the state of the process. As we remember, processes that die—even if they’re restarted by a supervisor—all the state is lost. So he has to remember; he has forgotten about that. He has to queue back and he has a response due to us when he gets to a certain time.
If you try to do these things—and I’ve actually built this into the program so you can just take and kill a random process just to see what happens—there you will see this, and then everything will stop because it’s a deadlock, so we won’t get any further there.
So how do you fix this one? Because one thing is you can realize what this problem is. You could go fix the code. When you’re dealing with real-life stuff, the stuff that people pay you money to do—asynchronous protocols, they’re nasty. So don’t try and just fix it; you do something elaborate on the testing side, and that’s where you use QuickCheck.
How many of you are familiar with QuickCheck? Quite a few happy people. The rest of you, you can be even more happy by dwelling into QuickCheck, so word of advice: use QuickCheck and do it operation by operation.
That operation by operation then ties back to looking at the MSC you created for the protocol you’re looking at. That gives you all the clues you need on how to write the different steps of the QuickCheck test.
Some tricks are in order to do this. You can’t just snap your fingers and do this QuickCheck, or EqC, QuickCheck is built on the notion of doing synchronous function calls, meaning that it does something, it checks the return value, and then everything is good.
So if you want to do asynchronous stuff like protocols here, you need to do something, and that is called mocking in EqC. So it has that as one thing that solves that problem, then it has another problem: you cannot call your own module. So you saw before we have cells talking to other cells, so you’re calling out and calling another cell inside a little model.
You cannot do that, so you add a protocol module. I’ll show you how that looks, and then because QuickCheck is synchronous, sometimes you need to sync with your process that’s living its asynchronous life. So you need to add functions, helper functions, that allow you to sync at certain times in the life of the process.
This is what you need to do to test it. Trust me, the alternative to this is to run a randomly big grid of processes and then try to guess if they’ve been running for a while that they are in the correct state and that all sorts of interleaving of things are working. That is not doable.
That’s why some companies hire test departments with hundreds of engineers to do this. We do not want to do this. We buy one QuickCheck license. We have one good guy working on it—or girl—and then you fix the problem.
Yeah, of course, you don’t get 10 managers out of that; that’s a different thing. So the protocol module basically is if I query for the content, it’s in a separate module created for the content of a cell. You just call back to the cell.
You have this finger calling out and then calling back in because that allows you to do the mocking, and that is typical with these asynchronous things. So that’s a trick to keep in the book.
Then the syncing—as you saw earlier, I spawned off this collector process. So when I sync with that, it has a little receive clause. Part of the receive clause the collector loop has is that you can get a status out, and that is the typical way of doing these things.
Don’t just do a sync function; do a status function. You can use that for debugging. That is often a good thing to have anyway. So don’t just say, “Okay, I’ll just do this for testing,” but do something that also has meaningfulness in terms of debugging.
For Game of Life, that debugging is not so relevant, but for real-life problems, that kind of debugging aids is perfectly good to have. It saves your hair, among other things.
Now doing a step in this is—and this is the QuickCheck model for this—and this is where you have to do a little bit of trickery here. So here, you’re waiting until the collecting status changes from what there is no collecting status to something, and that is when you know that the Collective process has been kicked off, and this is the only ugly thing in the quick check model. Now they’re only really ugly things. There are a few other things that are not so nice, but this is the only place where you do. This is how you always have to do it with quick check if there’s Asian kind of stuff, and you need to wait for something to happen. Make sure that things have happened.
You need to do these things to ensure that you’re at the right place, and then you can evaluate things because otherwise quickjet does the function call and looks at the world and says, “Has everything happened?” In this case, we’re spawning a process and that takes a bit of time for that to actually materialize. So, that’s why you have to wait for that there.
When you’re doing this step here, one of the things you’re expecting - and this is you need to ask all your neighbors here - in quick terminology and mocking language, there are call outs. So, you’re expecting to see these processes in the process sending out messages to other processes. That’s called call outs in this case.
Here you can see that the callout expected to go to our protocol module, and that’s why these calls have to be mocked and put in a separate module to do it. Now, I don’t have a run of it, but that actually painfully highlights the deadlock thing. You can probably check it out on GitHub and check out at the right point. Okay, this shows the deadlock, and then you need to fix it.
Now, how you fix it is that you let the collector loop monitor the neighboring cells. It’s asked for a value, and it’s expecting a response back. It knows, given the design and the protocol, if that process that it’s waiting for goes down, it will not remember to send a response back. This is tough luck.
Here you have the fix for it: if your neighbor is going down because you’re monitoring them, then you go down here and you spawn a new little function to wait for the neighbor to come back. When it comes back, you get a new message. Then here, the neighbor is back; it’s a new one, and you can then monitor it again. You stay in control at all times.
Again, this pattern is not just for the game of life. Every time you have one of these situations, if somebody you’re depending on goes down, you take the down message, you wait for the replacement to come back online, and you monitor it again. This is how you build a robust system. I double dare you to try and do this in C++ growth hotel with frets; this is where you will lose your teeth as well.
The recap here is a process per cell. In this situation, short-lived processes for small tasks would also work in other situations. There, focus on the protocols between the processes. This is the thing that one should burn most computer science professors on the stakes for: that they’re not teaching enough of these protocols.
Go ask for it. If you’re still in university, go ask for classes on protocols. It’s the only thing that matters when you get out there. The thing is, if you do a little bit of protocol, this is a career-wise gain. If you know protocols and you know how to deal with this, you would be like among the blind; the one-eyed is king. You will look insanely intelligent by even being average just because you picked the right tool to solve a complex problem.
You’ve taken the hint here, and I will now take 50 of all your bonuses going forward. So good, I use the supervisors to restart things basic URL and stuff, and then you have this monitoring management process on the side to get things that are restarted up to speed.
Thinking in Erlang, trying to sum it up: focus on the protocols. The MSCs have said that a number of times. I’ll say it again: focus on the protocols; it’s the only thing that matters. If you have to do Java, focus on the protocols. If you have to do C++, yes, thank you.
This is where you can do this in Java as well. Ask what could go wrong here, and then within days you will be seeing a psychiatrist because in Java everything can go wrong. But this is what you have to do in your life: you just ask what could go wrong here because that’s a natural way of thinking about things.
We have this supervisor thing; we have the fail fast there, so please go ahead and do that. Use tools and do lots of processes. Spawn these small, short-lived processes for small things. Please, please do that. Use supervisors to keep things in order, link, and monitor where needed. That’s also important.
Then you have, as another trick, which didn’t work out for Eagle, but in many cases if you need to have processes and you need to have a name for them and look them up, there’s a library called g-proc that can be very, very good for some of these things. The problem is g-proc dies in a very hard way when you’re putting 90,000 game of life cells into it; it just dies under the pressure because it’s not supposed to handle that. So, I don’t use it incorrectly there.
Use some timeouts; they can also be useful. I haven’t shown anything, and then you can also have things like transaction logs, ledgers, but you can see some of my oil presentations to see more of the description of those. These are different techniques to solve this problem and do it well.
Remember, this asynchronous protocols are nasty; this is why people do not like to do them. But async protocols are what you need if you want to build a scalable system that’s robust. It’s like a chicken and egg problem: you need to accept it, but they are nasty.
I couldn’t generate blood running down from the nasty. I need to look at that, but they are really nasty. You embrace them because that’s where the money is. Use run a quick check for it. Probably focus on one process and mock the calls to the others.
If you want to see more of the code, you can go to this GitHub repository. I think most of the stuff is on the testable branch, but I’ll merge it into master soonish. You can see everything there. If you have questions on that code, don’t worry; write me a mail. Do a PI if you have a way of fixing my horrible code; that’s perfectly okay.
Then we in Krakow, so I have to say something about Elixir. Otherwise, we had a webinar recently, and people asked, “But why aren’t we just doing everything in Elixir?” Well, you could sort of bot lazily, but it’s built on top of the Erlang VM.
The Erlang VM is a wonderful piece of machinery if you’re into any sort of Erlang. Is Robert here? No, Robert Birding is around here; he’s one of the creators. You just go shake your hand and say thank you for the VM. You have to do that. It has more Ruby-like syntax for those that aren’t into it. Again, syntax is irrelevant, but for some people it’s a big thing.
You can also do some hygienic macros, so that means you can do domain-specific languages quite easily if you’re into that sort of thing. It has better support for data handling. I think that is probably one of the key selling points of using Elixir. Erlang is a ping pong language, created by Ericsson, and they play ping pong a lot in Sweden.
So, then you get a message; you send it back in Elixir. It’s like playing rugby or something because you get past something, and then you’re allowed to run with the ball, and then somebody could come and kill you, and then you pass the ball again. So, you can do a little bit more.
Elixir is like rugby compared to Erlang language’s ping pong, but the underlying thing is you can’t do good Elixir without understanding the Erlang programming model. You need to embrace the golden trinity of Erlang in order to work on either of these things on the other VM: share nothing, message passing, fail fast, link, monitor, and asynchronous testing build ecosystem.
Thank you, and one question: for every cell? Yep. Yeah, you can keep one; you can only keep the last one around, and then you can just progress. So, does this kind of—because it’s a big trade-off—in terms of memory, it doesn’t take.
In real-life applications, you wouldn’t probably keep the entire history around in the process. If you have a history you want to keep in a real-life program, you’ll start putting some of it away to disk. You restore it if you need it for later, but you will also go in and say, “Make a trade-off,” and say, “That’s part of my history; that’s important for snappy things,” and you keep that in the process.
There are things that I don’t really have a lot of chances where this will be necessary, so I’ll just put this away. That’s the normal thing you will do within a real system. You could do a snapshot thing for that.
So, that’s different. You wouldn’t normally—it’s a very good observation. You normally wouldn’t keep the entire history in a normal system around that. That’s also why I talk about ledgers because ledgers are like a way of saying, “Now we’ve agreed on a synchronous point; we agree on something, we put it down, and then when we start again, we’ll ask the ledger how far we are, and then we fast forward to that point in time.”
That’s again an example to show things. There are things where you need to go more serious.
More questions? How do you handle a supervisor going down? The supervisors are going down. That’s a very good question because that comes down to how do you deal with the fact that you can’t protect and protect and protect and protect and get things to work.
What you do is this is where a key thing a lot of people say, “I can’t use the Erlang supervisor; I’ll write my own.” Don’t do that. A supervisor is simple. It’s very simple, and it’s supposed to just restart.
The problem is a supervisor will die itself if it restarts its children too much. You need to have something above it to say, “What do I want to do here? Do I want to restart here or what?” The thing is then you have this like you saw here with the cell manager.
You might have another process on the side that is doing some business-specific logic to if the supervisor goes down because the supervisor above the supervisor will just restart it until it runs out of tries.
The key thing is that you only protect the cells with this extra process on the side because they are the important part of my system. If things are going so badly that you run out of restarts, you probably are better off that the whole program dies.
It is rare that it happens, but it can happen. So, you don’t stop all supervisors from dying; you need to take a trade-off here. The beauty in Erlang compared to Java is that you are not dead from one exception killing you. You can decide which things to fix and how the rules are for restarting, but you should not try and cover everything.
It’s not like that. But then the other thing is it’s separated from the rest of the code. All the supervisor stuff happens outside the wonderful joyous coding you’re doing that makes you feel good. It’s outside the golden path.
But it’s still a trade-off you need to make, even in Erlang. It’s not like the silver bullet solve all problems.
This is about guarantees of message delivery. In Erlang, there are no guarantees of message delivery, none whatsoever, because that’s the only sane decision to make operating in a system that has potentially distributed machines.
You cannot know, and the guarantees, if you wanted guarantees, the amount of stuff you have to build in is enormous, and it won’t work. So, there’s asynchronous message passing. What you will know is if you’ve sent a message to a process and it hits the mailbox of that process, it stays in that mailbox until the process takes it out or the process dies and takes the mailbox into the grave with it.
Therefore, it will be delivered only once there. But you cannot be guaranteed of it ever reaching if that process is dead. You have the process identifier and say send it a message, and that process is already dead; you’re not alarmed. You don’t get any message whatsoever somewhere on the network.
Yes, that’s why you use timeouts, and that’s why you use monitoring for things so you can get control over this. But the thing is most of the monitoring will happen outside the regular code, and it will be some extra error handling code you can think about at a later stage.
But you’re right, absolutely right. Sorry, somehow. Okay, so scaling timeout? Well, that is dependent on the system you’re doing because if you’re sending something you have distributed across a number of machines, you need to sit and look at what is the latency of my network.
What’s the reasonable timeout here? A little bit of calculation, but you can use rules of thumb for these things. You say, “Okay, if I’m not getting a response back in five seconds,”—five seconds is a long time—”then something is probably wrong, and I need to start doing something like kill myself.”
And say that there’s a problem. Higher, yeah? It’s what you do in Erlang, right? If things are not working, you kill yourself.
Actor-based programming in general: my take on that is it’s amazing because one of the things that’s good about actor-based programming is that it separates it, and acts up in most languages will have its own thread of execution, meaning that you’re hiding all the awkwardly fretting in programming languages, and you get a focus on sending a message to that actor.
If it’s not message passing as it is in Erlang, it’s something very similar to it conceptually. Actor-based is awesome, very good. Just don’t think you’ll be as happy in those languages as you will be in Erlang speaking.
Like you are treating yourself, you must do proper error handling. Now you can do it if you’re forced to; just take the ideas from Erlang and apply them to something else. But remember, it’s not the real McCoy. There are certain benefits, and the thing you will see is all this I’m talking about fail fast and supervision.
This is what you’re not getting as easily in other languages. There’s a reason why a company like WhatsApp has built the entire infrastructure on Erlang because these kind of problems will occur.
Errors will happen when you start doing a system with 500 machines. There’s a reason why a bad gambling company like Bet365, that are moving millions of pounds around every second, has their infrastructure on Erlang. This will happen, and you need to be able to deal with it when it does because the worst thing that can happen for a gambling company is that the flow of money through that system stops.
If you take out a little bit of that flow because one process is dying, everything is good; you’re still making a ton of money. If you have a Java exception taken down your entire website, the entire flow of money is just gone.
If you’re moving millions of pounds through the system every minute, you don’t want the system to go down. So, yes, okay, I think it’s done. Thank you.
This is an experimental rewrite
Speaker: Thank you.
Speaker: Thanks for coming back last year.
Speaker: I think I spoke down here as well, where I engaged in my usual role as an urban priest, spreading the propaganda about how wonderful learning is and how it translates into financial success. It’s all part of convincing people to engage in business. That’s typically how I get invited to speak at conferences — to share the joyful gospel of Erlang and help promote its mission.
Speaker: Last year, I was invited by a great guy named Brian Hunter to give a talk at NDC in London. We agreed to go ahead with it, but then he asked, “I’d like to know how you think.” I had to think about that. Honestly, I don’t think—it’s more about how I feel when I program. Like Garrett mentioned, I feel good about it because I apply it in real-life situations. So, that’s how I approached the talk.
Speaker: I ended up putting together a presentation on “Thinking Like an Erlanger,” which has evolved over time. I aim to walk you through the thought process of being an Erlanger. I believe the concepts I discuss can resonate broadly. I also want to share some approaches to Erlang programming. One issue Garrett highlighted in his keynote is that learning how to learn is hard.
Speaker: One reason for the difficulty in learning is that Erlang is different. It’s not like other programming languages; it has its own unique essence. If you’re curious, take a look at one of those lineage maps showcasing the evolution of programming languages; you’ll notice Erlang is often missing from the list. You’ll see C evolving into C++, and then Java, followed by various other languages. It’s noteworthy that Erlang is absent, but this absence points to a compelling reason to learn it: Erlang teaches you to think differently, and that’s crucial.
Speaker: With that in mind, here’s a little question: if you want to see Erlang code, it’s important to know that you can’t handle Erlang code just yet. If you behave nicely, I might share a snippet with you later. But really, what’s essential here is that syntax is utterly irrelevant.
Speaker: When I first encountered Erlang, I thought the syntax was terrible—capital letters everywhere, and more punctuation than I could handle. That was quite a few years ago. Since then, I’ve learned that syntax isn’t what matters — unless you’re programming in Java, then it does matter significantly.
Speaker: So, what truly matters? Thinking is everything. If you take away anything from this talk today, let it be this: forget about syntax. It simply doesn’t matter; thinking is what counts. Carry this philosophy into whatever language you must use, unless you’re fortunate enough to code in Erlang.
Speaker: Now, let’s delve into thinking. We all know Erlang originated in the telecommunications industry. This domain has specific requirements that you must address to solve problems. If those problems remain unsolved, you won’t get paid, and that’s not ideal.
Speaker: You can follow the conventional route and choose a hefty language like C++ or Java, which tend to create a massive gap in problem-solving. This is advantageous for managers because it necessitates having a large team under your oversight. More employees reporting to you suggests you hold an important position.
Speaker: That’s a compelling reason for some languages to thrive in large organizations. They can’t tackle small problems; they only complicate bigger ones, allowing for extensive degrees in project management. I often get tired just thinking about it.
Speaker: Then along comes Erlang. Erlang is a domain-specific language created to meet the needs of telecommunications, meaning there’s significantly less that you need to code.
Speaker: But you can already sense the trepidation among managers: “Scary! Less need for a team under my command, or worse, no need for a manager at all!” But this smaller gap offers benefits. One of the primary advantages is that you can express your thoughts more directly, resulting in a shorter feedback loop and clearer communication.
Speaker: The mantra for success is feedback. The more feedback you receive, the faster you can learn. This ties back to the concept of failing fast: learn from your mistakes and improve more swiftly. These benefits translate into financial success, although I won’t dwell on that. It’s more about the feedback loop and how this smaller gap makes programming enjoyable.
Speaker: I had colleagues at Motorola—well, more than one since it was quite a sizable company entrenched in C and C++. My colleague and I jumped onto the Erlang bandwagon and convinced management to expand our team by 50%. Yes! We managed to get one extra person—such fun with percentages! So, we began our search for someone willing to learn Erlang and join us.
Speaker: I’m not quite sure which part frightened him the most, but he eventually said, “Guys, you’re fascinating! I want to work with you, but this Erlang thing makes me uncertain.” Fast forward six months, and management decided to downsize our team by 33%. Sadly, he had to go. Yet, he still asked his boss, “I know I can’t remain on your team, but could I sit next to these two guys just to smell the Erlang?”
Speaker: That’s how enticing Erlang is—people want to stick around! He practically clung to his desk! One crucial aspect of the telecommunications domain worth highlighting is protocols, which is not taught enough. Many have the misconception that protocols are just about fiddling with bits.
Speaker: In truth, telecom protocols are the sugar that makes functioning systems possible. I’ve got two recommended reads:
First, “Communicating Sequential Processes.” Yes, you all have your copies, right? Good. Don’t be as foolish as I was—attending a summer school with Tony Hoare without bringing your copy for him to sign is just silly. I’m determined not to repeat that mistake!
The other recommendation is a Springer book, which unfortunately costs around $120. Typically, in university, you dispose of such expensive textbooks after your course, but this one was so valuable that I held on to it despite my limited finances. This book excellently discusses protocol design.
Speaker: Interestingly, it employs CSP to describe these protocols, and it is brilliant. You should use protocols frequently in your work; they have countless applications. For instance, here’s an ASCII representation of the Paxos consensus protocol. Sure, it’s not the clearest output, but it gets the idea across.
Speaker: Protocols are ubiquitous, and we need to consider them when designing programs. Even if, alas, you find yourself programming in Java, you should still focus on protocols. Anyone can write a single-page program and create a Java object; what’s truly challenging is making those objects interact effectively, and that’s where protocols come into play.
Speaker: Of course, you will learn to think about protocols using the only true language on earth: the golden trinity of Erlang. The golden trinity is a concept I conceived during a walk, prompted by the frequent dismissal of Erlang by some. As such, I took a moment to ponder what aspects I could strip away from Erlang while still being capable of coding in it.
Speaker: These elements remain after this exercise—at least from my perspective. The first is share nothing. You don’t share anything among the processes in your system; sharing memory is detrimental. Instead, processes must exchange messages to communicate. That’s one pillar.
Speaker: The second pillar is about failing fast. Depending on exceptions to manage failures will only lead one thing: hair loss. Seriously, find a Java programmer who’s been at it for ten years — they’ll likely be bald. Exceptions will wreak havoc on your mental well-being.
Speaker: Quick career tip: if you work for a company like Motorola that specializes in safety-critical systems that must operate continuously, don’t tell anyone, “We just let it crash.” Trust me, they’ll think you’re mad!
Speaker: Unfortunately, I speak from experience. But never mind that; to navigate around this, you need to integrate failure handling into the language, ensuring that your programs can manage exceptions gracefully.
Speaker: So, returning to our main pillars, we have shared nothing, which allows for message passing to keep everything amicable. This is where protocols reside. You allow these processes to fail fast, supervise them, and manage failures gracefully.
Speaker: Another interesting aspect is that processes in Erlang are incredibly cheap; you should utilize a multitude of them. If you try to program in the style of Python, keeping everything confined to a single process in Erlang, you’re doing it wrong. It’ll feel like a Python script, which lends itself to varying degrees of frustration—mostly pain. So avoid that path. Embrace Erlang’s potential by spawning numerous processes; a Raspberry Pi can easily handle around 10,000 threads.
Speaker: In Java, you could effectively save about 135,000 Erlang processes, as they’re lightweight in comparison. Don’t shy away from employing them abundantly. When we circle back to protocols, the focus should be on their interactions. Just having a plethora of processes is great, but it’s all about how they communicate. That’s the key to problem-solving in a learning environment.
Speaker: Now, let me preface the next part: don’t attempt this at home. It’s crucial, as some might take me literally. I’ll show you how to apply online thinking to a problem.
Speaker: We’re diving into Conway’s Game of Life—how many of you are familiar with it? Exactly. Now, why do you think I chose this example? Well, we can tackle that later.
Speaker: To summarize, cellular automaton is a concept involving cell evolution over discrete time. The way it operates is that each cell in the grid—like the one in the center—relies on the neighbors around it for its next state.
Speaker: If there are two or three neighbors, the cell remains alive. If it’s empty but has exactly three neighbors, a new cell is born in that spot. The others simply become empty. If you’ve seen this run, it can create beautiful patterns on-screen. Let’s demonstrate it!
Placeholder for possible screenshot of Conway’s Game of Life evolving over generations.
Speaker: So, I’ve developed one here. A quick note: the world wraps around, meaning the top and bottom edges can interact, as can the left and right sides.
Speaker: Here’s the state at time one—it starts evolving from here. You can observe…did I skip time? Trust me, it’s accurate, and you can see how they progress as they depend on their neighbors to determine their next value.
Speaker: I won’t bore you indefinitely. This particular configuration lasts about 18 or 19 generations before fading. It starts off strong, thriving for six time steps, but eventually, it vanishes. Many dedicate extensive time toward understanding which configurations endure, but that’s a separate field of study.
Speaker: Now, let’s consider the conventional approach to this problem, glancing through a Java programmer’s lens. Don’t worry, after this talk, that mindset won’t linger. If needed, I’ll supply you with some Erlang patches to dismiss those thoughts entirely.
Speaker: Typically, in a textbook program, you’d create a 2D array and a new 2D array, computing states based on prior ones while employing a for loop to navigate through the grid. That’s cozy if you’re engaging in imperative programming, but this approach has its drawbacks.
Speaker: It’s often stated that this method doesn’t scale effectively. Unless you delve into some complicated parallel programming techniques, you end up executing everything sequentially. So, let’s explore how to tackle this with Erlang.
Speaker: Here’s the crux: we’ll adhere to the foundational Erlang principle of one process per cell. Many find this shocking—can you genuinely execute that? Yes, you can!
Speaker: I’ve successfully run a 300 by 300 grid of processes, meaning 90,000 individual processes happily executing the Game of Life. It works and scales beautifully. Each process communicates with its neighboring cells, which is standard practice in Erlang: processes sending messages to one another.
Speaker: What brings us to this juncture? We’re positioned in the bottom left corner, firmly within the share-nothing message-passing realm of Erlang’s golden trinity. We’ll remain here for a while.
Speaker: When I’m a cell aiming to proceed to the next time step, I need knowledge about my neighbors’ states at that precise time. You achieve this through a specific protocol: collecting the content of your neighbors to discern the next state, updating your own content subsequently.
Speaker: When it’s time to advance to t plus one, here’s the logic. You have a process that reaches out to all its neighbors, requesting their values and then updating itself upon receiving their responses. It’s a straightforward protocol.
Speaker: The follow-up question is: does this comply with Erlang principles? Remember, I mentioned using plenty of processes. Have I reached that threshold here? Oh yes, one process per cell. Could I expand a bit more? Perhaps I can, so let’s explore if it’s a wise decision. Sometimes, experimentation yields fruitful insights.
Speaker: Each time I need to gather data for a new time step, I create a coordinating process responsible for reaching out to all neighbors, collecting their results, and reporting back to the originating cell: “Hey, I gathered this information for you. Decide how you want to proceed to the next time step.”
Speaker: Also, notice how elegantly this is structured with protocols, effectively illustrated in MSCs, mapping out the messages exchanged. This is essential: designing Erlang programs demands an emphasis on passing messages around.
Speaker: You shouldn’t spend your life crafting elaborate object inheritance trees—those are not crucial. The focal point should be communication between entities.
Speaker: In the collector loop, visually at the top, as soon as it gathers nothing else, it takes stock of how many neighbors have responded thus far, then sends a message back to the originating cell.
Speaker: The content should reflect the values received as statements. If you receive a message from a neighboring cell, relay a cell content message. You update your own state accordingly and persist until you’ve received replies from the entire list of neighbors.
Speaker: This approach is straightforward and naive. Now the question arises: will this work? The answer is no, it likely won’t, but we can anticipate some conditions that may apply: it works only if—another critical note—if you ever find yourself in a position of project management or leadership. If your developers say, “It works if,” it typically means there are bugs in the program. Speaker: So, you need to ensure that this works only if you take one step at a time. In this simulation, you should only ask the cells to complete one time step at a time, then pause and see where you are. It will function as long as you adhere to that. However, if you allow the cells to run freely, they can get out of sync.
Speaker: How can the cells fall out of sync in this scenario? The issue arises when you request information from a neighbor that has already advanced to the next time step. For example, if your neighbor is at time t2 and you ask, “What is your value at t1?” they might respond, “Well, that’s in my past; I don’t know.”
Speaker: You could also be ahead of the game. Suppose you inquire about a cell’s value in the future while your neighbor is still at time one. You’ve progressed to time two and ask, “What’s your value at time two?” and they respond, “No, I’m at time one. I cannot provide my value for time two.” Unfortunately, we don’t have wormholes in real life to transport things through time, so we have to respect these constraints.
Speaker: Fortunately, this problem can be resolved. If you’re requested an old time value, you can simply keep a straightforward history. For future time requests, when someone asks for a value, you can queue the response until you’re ready to reply.
Speaker: You might think, “Okay, this is an artificial way to solve the Game of Life.” But the point is that in many situations involving asynchronous protocols, you will encounter these kinds of issues, which means you need strategies to handle them.
Speaker: These two tricks—maintaining history and queuing responses until you’re ready to reply—are common patterns one comes across when doing online programming. It all revolves around using effective message passing.
Speaker: Now we move on to the next critical aspect: failure handling. You must also consider failure handling in this context, as the code will inevitably face unforeseen issues.
Speaker: To manage failure handling, you need to supervise the cells in your system. If a cell dies, the Erlang supervisor can simply employ a standard Erlang supervisor to restart the cell with its original parameters.
Speaker: For instance, if a cell is identified as number 7.5 in this grid, and it starts with a value of 1, that indicates that it possesses some information. Everything seems fine. However, the problem occurs if you’ve progressed to time 100 in the simulation; once that cell dies, all previously computed information is lost—it’s gone. When the process is revived, it starts from scratch. This means all state and history are eradicated, which is worth keeping in mind while developing.
Speaker: Thankfully, there’s a solution. You can monitor all the cells to detect if any of them die. When one does, you wait for the new cell to come online and issue a command, “Please catch up with the rest of us.”
Speaker: So, if you have a simulation that has advanced to time 100 and a cell dies, when this cell returns online, you instruct it: “You know what? You need to run until time 100; this is where the rest of us are. Please catch up.” That’s one way to rectify the situation.
Speaker: To visualize this in a diagram: you have your main top-level supervisor, and then you’ll have a supervisor responsible for monitoring individual cells. You can depict the cell manager, which oversees the cells as they go down and come back, explaining to the new cell—the one replacing the old one—what steps it needs to take to reintegrate with the rest of the world.
Speaker: This process can be written as a protocol, as shown here. It’s apparent that when a cell fails, it sends out a down message, which the process is monitoring.
Speaker: The cell manager receives that down message and acknowledges it by removing the old cell from its registry. The manager keeps track of currently active cells in the simulation. The Erlang supervisors are straightforward—they simply restart processes with no fuss.
Speaker: When the supervisor starts a new cell to substitute the one that went offline, if that cell registers itself with the cell manager, it may say, “Okay, I recognize the process that identifies you. I will now monitor you, and if you fail, we’ll resolve the issue.”
Speaker: What’s vital here is tracking a time module. The manager asks, “What is the maximum time achieved by the rest of the cells?” and relays this information back to the newly coming cell, prompting it to “run until the next time.”
Speaker: Now, the new cell that replaced the old one has resumed its role, and it has successfully accelerated to match the state of other cells. Again, this pattern can also be applied beyond the Game of Life context. You’ll often face scenarios where something goes down, and you’re left with the question: “How do I manage this to ensure everything continues to function seamlessly?”
Speaker: Sometimes, you may need to implement measures like this, where you specify particular actions. In simpler cases, a straightforward restart can suffice, but that’s generally for the less complicated scenarios.
Speaker: Now, let me show you some code. Here’s what it looks like. The blue elements represent the down message reaching the cell manager. You can disregard the other details for now. What’s critical is that I receive a down message, followed by some bookkeeping. Yes, you need to keep track of the maximum time.
Speaker: Essentially, cells that are inactive will be blocked by their new responses, so we require the time module. The various operational modes are important, too; I didn’t dive into the specifics, but there are several ways to run the simulation.
Speaker: You can instruct them to advance step by step, run until a certain generation number is reached, or command them to run freely. In cases where mode variation occurs, you’ll need to account for time sufficiently to synchronize tasks, otherwise, the system would break down.
Speaker: If you have everything running freely, you won’t need that complex approach. You can simply say, “Run and catch up with the rest.” Does that make sense? Good questions so far.
Speaker: So here’s the process: when you receive a down message, you conduct some bookkeeping, and then continue. The next step in the protocol requires waiting for the supervisor to restart the process.
Speaker: After that, the cell registers itself again with the cell manager, allowing you to monitor it. The cell manager maintains oversight of all cells in the system, enabling you to take the necessary actions whenever required.
Speaker: What brings processes back to life is the kickoff cell, which tells the function what to do based on the kind of simulation mode you’re in.
Speaker: This might look something like this: if it’s a stepwise simulation, you retrieve the end time from the time module and run until that point. If it’s running until a set generation number, you would instruct it directly to execute that. If it’s running freely, you just tell the cell to run.
Speaker: Although you could simplify everything to just running cells endlessly, that would probably lead to a significantly overloaded CPU after a while. If you attempt what I’ve done—running tens of thousands of processes concurrently—you’ll be utilizing all of your CPU cores, which is how Erlang operates by design.
Speaker: Now at this point, you may encounter a deadlock—a situation that arises when you ask a neighbor for a value that they cannot provide, as it’s a future value for them. They might queue the response, stating, “I can’t answer you right now,” and wait until they’ve advanced to the next step before sending a reply back.
Speaker: Yet if that cell gets killed or fails for some reason, that queued information, which is essential for the current state of the process, will be forgotten. When the process restarts, all state is lost, leaving it oblivious to the queued response it owed you regarding when it arrives at a certain time.
Speaker: If you were to implement this in practice and wanted to validate that, I built a function into the program that allows you to randomly kill a process just to observe what occurs—you’ll witness a deadlock, and everything will grind to a halt at that point.
Speaker: So how can we address this issue? While it’s tempting to think you can fix it just by tweaking the code, asynchronous protocols can be quite tricky. Instead of wrestling with it, you should consider employing a more elaborate testing strategy, which brings us to QuickCheck.
Speaker: How many of you are familiar with QuickCheck? Quite a few of you seem pleased. For those who aren’t aware, you can enhance your joy by delving into QuickCheck. My advice is to use it in an operation-by-operation fashion.
Speaker: This operation-by-operation approach ties back into the message sequence chart (MSC) you created for the protocol you’re examining. It offers clues about how to develop various steps for your QuickCheck test.
Speaker: There are some tricks to keep in mind if you want to implement QuickCheck effectively. It’s built on synchronous function calls—which means it executes a task, checks the return value, and verifies everything is functional.
Speaker: So when it comes to asynchronous protocols, you need to employ a technique called mocking within QuickCheck. That allows you to deal with challenges that arise from your design. Additionally, you cannot call your own module directly.
Speaker: You’ll remember the cells communicating with each other; since you’re calling another cell inside a module, established protocols come into play. To navigate this, you construct a protocol module, which I’ll illustrate shortly.
Speaker: Since QuickCheck is synchronous, sometimes you must synchronize with your process that operates its asynchronous functionality. This necessitates adding helper functions that allow you to maintain synchronicity during specific lifecycle moments of the processes.
Speaker: This approach will enable you to test effectively. Trust me, the alternative involves running a sprawling grid of processes and guessing whether they’ve been running long enough to achieve a correct state, accounting for all sorts of interleaving processes—something that is not feasible.
Speaker: That’s why many companies commit extensive resources to testing departments with numerous engineers. In contrast, you could invest in one QuickCheck license and have a single skilled individual dedicated to tackling the problem.
Speaker: Of course, you’ll miss out on having ten managers; that’s a different discussion altogether. The protocol module serves to query content via a dedicated module created specifically for that purpose. This allows for the necessary back-and-forth communication, which is a classic pattern for these asynchronous scenarios.
Speaker: As for syncing—recall how I previously initiated the collector process? When you synchronize with it, there exists a receive clause. One part of that receive clause in the collector loop allows for status output; that’s a common practice.
Speaker: Instead of relying purely on a synchronization function, incorporate a status function. This serves a dual purpose; not only does it facilitate testing, but it is also useful for debugging.
Speaker: While debugging might be less pivotal for the Game of Life example, it proves incredibly valuable for real-world problems, saving you time and effort in the long run.
Speaker: Regarding managing a step, here’s the QuickCheck model. You need to employ a bit of cleverness; you’ll wait for the collecting status to change from inactive to active, indicating that the collective process has been triggered.
Speaker: That represents one of the challenging aspects of the QuickCheck model. While there may be a few other tricky elements, this particular component requires careful handling, especially when dealing with asynchronous processes.
Speaker: You must ensure that the necessary actions have been completed before you can evaluate anything. Otherwise, QuickCheck could prematurely execute a function call, assessing the state of the system without waiting for the current process to spawn actions—hence the need for patience in assuring that everything is on track during the testing.
Speaker: When executing a step here, you’ll need to request the necessary information from neighboring cells. In QuickCheck terminology, this translates to the concept of call-outs; you’ll anticipate seeing messages sent from your process to the others. Speaker: Here you can see that the callout expected to go to our protocol module. That’s why these calls have to be mocked and placed in a separate module to execute properly. I don’t have a live demonstration, but it painfully highlights the deadlock issue. You can probably check it out on GitHub at the right point. Okay, this demonstrates the deadlock, and then you need to fix it.
Speaker: Now, how do you fix it? You let the collector loop monitor the neighboring cells. When it requests a value, it expects a response back. Given the design and the protocol, if the process it’s waiting on goes down, it won’t remember to send a response back. It’s tough luck in that scenario.
Speaker: Here’s the fix: If your neighbor goes down while you’re monitoring them, you initiate a small function to wait for the neighbor to return. When it comes back, you get a new message. The neighbor is back—it’s a new one—and you can then monitor it again. You maintain control at all times.
Speaker: Again, this pattern isn’t limited to the Game of Life. Every time you have one of these situations, if someone you’re depending on goes down, you take the down message, wait for the replacement to come online, and monitor it again. That’s how you build a robust system. I double dare you to try to implement this in C++ or with threads; that’s where you might lose your hair.
Speaker: To recap, each cell is a process. In this setup, short-lived processes for small tasks can also work in other scenarios. The key is to focus on the protocols between the processes. This is the key point that many computer science professors overlook: they aren’t teaching enough about these protocols.
Speaker: If you’re still in university, ask for classes on protocols. They’re the only thing that matters when you enter the field. Understanding a bit about protocols is a significant career benefit. If you know protocols and how to handle them, you’ll stand out; the average person will seem brilliant just because you picked the right tools to tackle complex problems.
Speaker: You’ve taken note of this, and I’ll take 50% of your bonuses moving forward. So, it’s crucial to use supervisors to restart processes and manage things effectively. Then, you have this monitoring management process on the side to get these restarted up to speed.
Speaker: In summary, think in Erlang by focusing on the protocols. I’ve emphasized this several times already: focus on the protocols; it’s fundamentally important. If you have to work with Java, keep your attention on the protocols. If you’re using C++, the same goes.
Speaker: This approach also applies to Java. Always ask yourself what could go wrong, and soon enough, you’ll realize that in Java, everything can go wrong. That’s the mindset you have to adopt in your life: always ask, “What could go wrong?” It’s a natural way to think.
Speaker: We have this supervisor mechanism and a fail-fast strategy, so please implement that. Use tools and manage numerous processes. Spawn small, short-lived processes for minor tasks, and keep things organized using supervisors. Monitor wherever necessary; that part is essential.
Speaker: Another trick, which didn’t pan out for Eagle but can work in many cases, involves naming processes and allowing for lookups. There’s a library called g-proc that can be helpful for these kinds of tasks. However, note that g-proc can struggle under heavy pressure, like when dealing with 90,000 Game of Life cells—it just can’t handle that load.
Speaker: Implementing timeouts can also be beneficial. I haven’t shown anything specific, but there are transaction logs and ledgers that you can explore through my previous presentations. Different techniques exist to solve these issues effectively.
Speaker: Remember, asynchronous protocols can be tricky, which is why many developers are hesitant to work with them. However, if you wish to build scalable and robust systems, embracing them is essential. It’s a bit of a chicken-and-egg problem: you need to accept the challenges they pose, but they are indeed challenging.
Speaker: I couldn’t generate blood running down from the things that are nasty, but you must recognize that they are. Confront them head-on because that’s where the real benefits lie. Use QuickCheck for your tests and focus on mocking calls to other processes.
Speaker: If you’re interested in the code, please visit this GitHub repository. Most of it is in the testable branch, but I’ll be merging it into master soon. Feel free to reach out if you have questions about the code or ways to improve what you see; that would be perfectly fine.
Speaker: Since we’re in Krakow, I should also mention Elixir. Otherwise, I’d be remiss as people recently asked, “Why aren’t we just using Elixir for everything?” You could consider it, but remember, it’s built on top of the Erlang VM.
Speaker: The Erlang VM is a fantastic piece of technology if you’re working with Erlang. Is Robert here? No? Well, Robert Birding is one of the creators. You should thank him for the VM if you see him. The syntax in Elixir is more Ruby-like, which appeals to some folks.
Speaker: As for macros, you can create hygienic macros and easily build domain-specific languages if that’s your preferred avenue. Elixir provides better data handling support, which is probably its standout feature. Erlang, on the other hand, is likened to a ping-pong language, created by Ericsson, who plays a lot of ping-pong in Sweden.
Speaker: In Elixir, when you send a message, it feels like playing rugby where you pass the ball, and someone can come and tackle you. So, there’s more flexibility there.
Speaker: Elixir is like rugby compared to Erlang’s ping-pong. However, you can’t fully grasp Elixir without understanding the Erlang programming model. Embrace the golden trinity of Erlang to effectively work on either language: share nothing, message passing, fail fast, link, monitor, and test asynchronously.
Speaker: Thank you! One question: what about managing a single cell? Yes, you could keep just one; you can only retain the last one and then move forward. In real-world applications, you typically wouldn’t keep the entire history around within the process.
Speaker: If there’s important history to maintain in a live program, you start offloading some of that data to disk. You retrieve it when necessary, evaluating what to retain in memory versus what to archive for the future.
Speaker: These are the trade-offs you make in a real system. You might implement a snapshot technique to manage that.
Speaker: It’s an astute observation. You wouldn’t normally keep all history alive in a typical system. That’s also why I mention ledgers, which serve as a checkpoint. You can establish an agreement at a synchronous point, record it, and then fast-forward to that time in the future.
Speaker: There are scenarios where a more serious approach is required.
Speaker: More questions? How do you handle a supervisor going down? That’s a great question because it addresses the challenge of ensuring reliability when everything isn’t going smoothly.
Speaker: What you do is acknowledge that you can’t continually protect everything. Many might say, “I can’t use the Erlang supervisor; I’ll create my own.” But that’s not wise; a supervisor is simple and designed to restart processes.
Speaker: The catch is that a supervisor itself may die if it keeps trying to restart its children. You must have a higher-level process that decides how to handle things. This is exemplified by the cell manager you saw here.
Speaker: You might have another process managing business-specific logic for when a supervisor goes down, because the supervisor above will just keep restarting until it runs out of attempts.
Speaker: The key point is to only protect the cells with that extra side process. They are central to my system. If things go awry to the point that you exhaust restarts, the whole program may be better off failing.
Speaker: Thankfully, this scenario is rare, but it can happen. Don’t aim to shield every supervisor from failure; you have to make trade-offs here. The beauty of Erlang, unlike Java, is that you’re not solely dependent on one exception to crash your entire system.
Speaker: You can prioritize which issues to fix, setting rules for restarts, but avoid attempting to cover for everything.
Speaker: The supervisor logic is separate from the more enjoyable coding part that you do passionately. This process occurs outside your main coding path.
Speaker: Nevertheless, it’s a trade-off you need to consider, even in Erlang. It’s not a one-size-fits-all solution for every problem.
Speaker: This relates to guarantees of message delivery. In Erlang, you get no guarantees of message delivery whatsoever. This decision makes sense when operating in potentially distributed systems.
Speaker: You can’t always know, and to ensure guarantees, the effort required is enormous, often leading to solutions that simply won’t operate effectively. With asynchronous message passing, you know that if you send a message to a process and it enters that process’s mailbox, it will remain there until the process either retrieves it or terminates, along with its mailbox.
Speaker: Thus, the message will only be delivered once the process is alive. However, there’s no guarantee it’ll reach if that process is down. You possess the process identifier and can send it a message—but if that process is already inactive, you won’t receive any alert.
Speaker: Yes, that’s why timeouts and monitoring are essential; they give you control over the situation. But much of this monitoring is handled outside standard code and consists of additional error-handling logic you might develop later.
Speaker: You’re absolutely correct.
Speaker: Scaling timeouts? That depends on your system because if you’re communicating across distributed machines, evaluate the latency of your network. Perform some calculations for reasonable timeout values, often established through rules of thumb.
Speaker: For instance, you might decide, “If I don’t hear back in five seconds” — and five seconds is a lengthy interval — “then something must be wrong, and I need to take actions like terminating my process.”
Speaker: You have that approach in Erlang; if things are malfunctioning, the solution is to kill the process.
Speaker: Overall, my perspective on actor-based programming is positive. One advantage of actor-based systems is the separation of execution threads. It allows you to conceal many of the complications inherent in programming languages, fostering a more focused message-passing approach to communication.
Speaker: If it’s not traditional message passing like in Erlang, it’s conceptually similar. Actor-based programming is powerful and advantageous. Just remember, you may not find the same level of happiness in other languages compared to Erlang.
Speaker: You must implement proper error handling. Even if forced to do so, derive ideas from Erlang and apply them elsewhere, but be aware it’s not the real thing. Other languages have certain merits, but the fail-fast and supervision concepts you gain in Erlang aren’t easily replicated.
Speaker: There’s a reason companies like WhatsApp built their entire infrastructure using Erlang: the kind of issues addressed here arise frequently. Errors inevitably occur when managing systems with 500 machines.
Speaker: A gambling company like Bet365, which moves millions of pounds every second, relies on Erlang for their infrastructure. When processes fail, it’s crucial to resolve issues quickly, as the worst thing that can happen for such a company is a disruption in the flow of money through their system.
Speaker: If a process malfunctions but you can maintain flow by managing that one process, you’re still profitable. If a Java exception crashes your whole site, the entire cash flow could stop dead. Speaker: If you’re moving millions of pounds through the system every minute, you definitely don’t want that system to go down.
Speaker: So, yes, okay, I think it’s done. Thank you.
