2026-04-08 23:48:32
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I can’t get over how weird the AI bubble has become.
Hyperscalers are planning to spend over $600 billion on data center construction and GPUs predominantly bought from NVIDIA, the largest company on the stock market, all to power generative AI, a technology that’s so powerful that none of them will discuss how much it’s making them, or what it is we’re all meant to be so excited.
To make matters weirder, Microsoft, a company that spent $37.5 billion in capital expenditures in its last quarter on AI, recently updated the terms and conditions of its LLM-powered “Copilot” service to say that it was “for entertainment purposes only,” discussing a product that apparently has 15 million users as part of enterprise Microsoft 365 subscriptions, and is sold to both local and national governments overseas, including the US federal government.
That’s so weird! What’re you doing Microsoft? What do you mean it’s for entertainment purposes? You’re building massive data centers to drive this!
Well, okay, you’re building them at some point. As I discussed a few weeks ago, despite everybody talking about the hundreds of gigawatts of data centers being built “to power AI,” only 5GW are actually “under construction,” with “under construction” meaning anything from “we’ve got some scaffolding up” to “we’re about to hand over the keys to the customer.”
But isn’t it weird we’re even building those data centers to begin with? Why? What is it that AI does that makes it so essential — or, rather, entertaining — that we keep funding and building these things? Every day we hear about “the power of AI,” we’re beaten over the head with scary propaganda saying “AI will take our jobs,” but nobody can really explain — outside of outright falsehoods about “AI replacing all software engineers” — what it is that makes any of this worthy of taking up any oxygen let alone essential or a justification for so many billions of dollars of investment.
Instead of providing an actual answer of some sort, AI boosters respond by saying it’s “just like the dot com bubble” — another weird thing to do considering 168,000 people lost their jobs as the NASDAQ dropped by 80% in two years, and only 16% of the world even used the internet, and those that did in America had an average internet speed of 50 kilobits per second (and only 52% of them had access in 2000 anyway). Conversely, to quote myself:
Global internet access has never been higher or cheaper, and for the most part, billions of people can access a connection fast enough to use generative AI. There is very little stopping anyone from using an LLM — ChatGPT is free, ChatGPT’s cheaper “Go” subscription has now spread to the global south, Gemini is free, Perplexity is free, and Meta’s LLM is free — where the dot com bubble was made up of stupid businesses and a lack of fundamental infrastructure to give most people the opportunity to access a reliable internet experience, basically anybody can get reliable access to generative AI.
And with that incredibly easy access, only 3% of households pay for AI. Boosters will again use this talking point to say that “we’re in the early days,” but that’s only true if you think that “early days” means “people aren’t really using it yet.”
Yet the “early days” argument is inherently deceptive.
While the Large Language Model hype cycle might have only begun in 2022, the entirety of the media and markets have focused their attention on AI, along with hundreds of billions of dollars of venture capital and nearly a trillion dollars of hyperscale capex investment. AI progress isn’t hampered by a lack of access, talent, resources, novel approaches, or industry buy-in, but by a single-minded focus on Large Language Models, a technology that has been so obviously-limited from the very beginning that Gary Marcus was able to call it in 2022.
Saying it’s “the early days” also doesn’t really make sense when faced with the rotten and incredibly unprofitable economics of AI. The early days of the internet were not unprofitable due to the underlying technology of serving websites, but the incredibly shitty businesses that people were building. Pets.com spent $400 per customer in customer acquisition costs, millions of dollars on advertising, and had hundreds of employees for a business with a little over $600,000 in quarterly revenue — and as a result, nothing about its failure was about “the early days of the internet” at all, as was the case with Kozmo, or any number of other dot com flameouts.
Similarly, internet infrastructure companies like Winstar collapsed because they tried to grow too fast and signed stupid deals rather than anything about the underlying technology’s flaws.
For example, in 1998, Lucent Technologies signed its largest deal — a $2 billion “equipment and finance agreement” — with telecommunications company Winstar, which promised to bring in “$100 million in new business over the next five years” and build a giant wireless broadband network, along with expanding Winstar’s optical networking.
Eager math-heads in the audience will be able to see the issue of borrowing $2 billion to make $100 million over five years, as will eager news-heads laugh at WIRED magazine in 1999 saying that Winstar’s “small white dish antennas…[heralded] a new era and new mind-set in telecommunications.” Winstar died two years later because its business was built to grow at a rate that its underlying product couldn’t support.
In the end, microwave internet (high-speed internet delivered via radio waves) has become an $8 billion-a-year industry, despite everybody’s excitement.
In any case, anytime that somebody tells you that we’re in “the early days of AI” has either been conned or is in the process of conning you, as they’re using it to deflect from issues of efficacy or underlying economic weakness.
In fact, that’s a great place to go next.
Probably the weirdest thing about this entire era is how nobody wants to talk about the fact that AI isn’t actually doing very much, and that AI agents are just chatbots plugged into an API.
Per Redpoint Ventures’ Reflections on the State of the Software and AI Market, “the agent maturity curve is still early, but the TAM implications are enormous,” with agents able to “...run discretely for minutes, [and] execute end-to-end tasks with some oversight.”
What tasks, exactly? Who knows! Truly, nobody seems able to say. To paraphrase Steven Levy at WIRED, 2025 was meant to be the year of AI agents, but turned out to be the year of talking about AI agents. Agents were/are meant to be autonomous pieces of software that go off and do distinct tasks.
In reality, it’s kind of hard to say what those tasks are. “AI agent” now refers to literally anything anybody wants it to, but ultimately means “chatbot that has access to some systems.”
The New York Times’ Ezra Klein recently talked to the entity currently inhabiting former journalist and Anthropic co-founder Jack Clark recently about “how fast AI agents would rip through the economy,” but despite speaking for over an hour, the closest we got was “it wrote up a predator-prey simulation (a complex-sounding but extremely-common kind of webgame that Anthropic likely ingested through its training material)” and “chatbots that talk to each other about tasks,” and if you think I’m kidding, this is how he described it:
But I’ve seen colleagues who write what you might think of as a version of Claude that runs other Claudes. So they’re like: I’ve got my five agents, and they’re being minded over by this other agent, which is monitoring what they do.
Anyway, this is all bad, because multiple papers have now shown that, and I quote, agents are “...incapable of carrying out computational and agentic tasks beyond a certain complexity,” with Futurism adding that said complexity was pretty low.
The word “agent” is meant to make you think of powerful autonomous systems that carry out complex and minute tasks, when in reality it’s…a chatbot. It’s always a fucking chatbot. It might be a chatbot with API access or a chatbot that generates a plan that another chatbot looks at and says something about, but it’s still chatbots talking to chatbots.
When you strip away the puffery, nobody seems to actually talk about what AI does.
Let’s take a look at CNBC’s piece on Goldman Sachs’ supposed contract with Anthropic to build “autonomous systems for time-intensive, high-volume back-office work”:
The bank has, for the past six months, been working with embedded Anthropic engineers to co-develop autonomous agents in at least two specific areas: accounting for trades and transactions, and client vetting and onboarding, according to Marco Argenti, Goldman’s chief information officer.
The firm is “in the early stages” of developing agents based on Anthropic’s Claude model that will collapse the amount of time these essential functions take, Argenti said. He expects to launch the agents “soon,” though he declined to provide a specific date.
…okay, but like, what does it do?
Argenti said the firm was “surprised” at how capable Claude was at tasks besides coding, especially in areas like accounting and compliance that combine the need to parse large amounts of data and documents while applying rules and judgment, he said.
Right, brilliant. Great. Love it. What tasks? What is the thing you’re paying for?
Now, the view within Goldman is that “there are these other areas of the firm where we could expect the same level of automation and the same level of results that we’re seeing on the coding side,” he said.
Goldman could next develop agents for tasks like employee surveillance or making investment banking pitchbooks, he said.
While the bank employs thousands of people in the compliance and accounting functions where AI agents will soon operate, Argenti said that it was “premature” to expect that the technology will lead to job losses for those workers.
Okay, great, we have two things it might do in the future, and that’s “employee surveillance” (?) and making pitchbooks.
The upshot is that, with the help of the agents in development, clients will be onboarded faster and issues with trade reconciliation or other accounting matters will be solved faster, Argenti said.
Onboarding? Chatbot. “Issues with trade reconciliation”? Chatbot connected to a knowledge base, like we’ve had for years but worse and more expensive. Oh, and “other accounting matters” will be solved faster, always with the future tense with these guys.
How about Anthropic and outsourcing body shop giant InfoSys’ “AI agents for telecommunications and other regulated industries”? Let’s go through the list of tasks and say what they mean, my comments in bold:
How about OpenAI’s “Frontier” platform for businesses to “build, deploy and manage AI agents that do real work”?
Frontier gives agents the same skills people need to succeed at work: shared context, onboarding, hands-on learning with feedback, and clear permissions and boundaries. That’s how teams move beyond isolated use cases to AI coworkers that work across the business.
Shared context? Chatbot. Onboarding? Chatbot. Hands-on learning with feedback? Chatbot. Clear permissions and boundaries? Chatbot setting. Let’s check out the diagram!
Uhuh. Great. What real-world tasks? Uhhh.
Teams across the organization, technical and non-technical, can use Frontier to hire AI coworkers who take on many of the tasks people already do on a computer. Frontier gives AI coworkers the ability to reason over data and complete complex tasks, like working with files, running code, and using tools, all in a dependable, open agent execution environment. As AI coworkers operate, they build memories, turning past interactions into useful context that improves performance over time.
Reason over data? Chatbot. “Complex tasks”? No idea, it doesn’t say. “Working with files”? Doesn’t say how it works with files, but I’d bet it can analyze, summarize and create charts based on them that may or may not have errors in them, and based on my experience of trying to get these things to make charts (as a test, I’d never use them in my actual work), it doesn’t seem to be able to do that. “Evaluation and optimization loops”? Unclear, because we have no idea what the tasks are. What are the agents planning, acting, or executing on? Again, no idea.
Yet the media continues to perpetuate the myth of some sort of present or future “agentic AI” that will destroy all employment. A few weeks ago, CNBC mindlessly repeated that ServiceNow CEO Bill McDermott believed that agents would send college grad unemployment over 30%. NowAssist, ServiceNow’s AI platform, is capable of — you guessed it! — summarization, conversational exchanges, content creation, code generation and search, a fucking chatbot just like the other chatbots.
A few weeks ago, The New York Times wrote about how “AI agents are fun, useful, but [not to] give them your credit card,” saying that they can “do more than just chat…they can edit files, send emails, book trips and cause trouble”:
Mr. Heyneman, the founder of a tiny tech start-up in San Francisco, hoped to give a speech at the World Economic Forum, the annual gathering of business leaders and policymakers in Davos, Switzerland. So he asked the bot to arrange it.
While he slept, the bot searched the internet for people connected with the event, sent them text messages and worked to negotiate a speaking spot — or at least arrange coffee with people he would like to meet. After one lengthy conversation with a businessman in Switzerland, it succeeded.
But when Mr. Heyneman woke up, he was in a pickle. Going against his original instructions, the bot had agreed to pay 24,000 Swiss francs — or about $31,000 — for a corporate sponsorship. He could not pay the bill.
Sure sounds like you connected a chatbot to your email there Mr. Heyneman.
The bots can gather information from across the internet, write reports, edit files or even send and receive messages through email and text — driving online conversations largely on their own. For people like Mr. Heyneman, these bots are almost like an employee that people can delegate work to at any time of day. In some cases, the employee is reliable. Other times, not so much.
Let’s go through these:
Yes, you can string together chatbots with various APIs and have the chatbot be able to activate certain systems. You could also do the same with a button you bought on Etsy connected to your computer via USB if you really wanted to. The ability to connect something to something else does not mean that anything useful happens at the end, and LLMs are extremely bad at the kind of deterministic actions that define the modern knowledge economy, especially when choosing to do them based on their interpretation of human language.
AI agents do not, as sold, actually exist. Every “AI agent” you read about is a chatbot talking to another chatbot connected to an API and a system of record, and the reason that you haven’t heard about their incredible achievements is because AI agents are, for the most part, fundamentally broken.
Even OpenClaw, which CNBC confusingly called a “ChatGPT moment,” is just a series of chatbots with the added functionality of requiring root access to your computer and access to your files and emails. Let’s see how CNBC described it back in February:
Marketed as “the AI that actually does things,” OpenClaw runs directly on users’ operating systems and applications. It can automate tasks such as managing emails and calendars, browsing the web and interacting with online services.
Hmmm interesting. I wonder if they say what that means:
Users have documented OpenClaw performing real-world tasks, including automatically browsing the web, summarizing PDFs, scheduling calendar entries, conducting agentic shopping, and sending and deleting emails on a user’s behalf.
Reading this, you might be fooled into believing that OpenClaw can actually do any of this stuff correctly, and you’d be wrong! OpenClaw is doing the same chatbot bullshit, just in a much-more-expensive and much-more convoluted way, requiring either a well-secured private space or an expensive Mac Mini to run multiple AI services and do, well, a bunch of shit very poorly.
The same goes for things like Perplexity’s “Computer,” which it describes as “an independent digital worker that completes and workflows for you,” which means, I shit you not, that it can search, generate stuff (words, code, images), and integrate with Gmail, Outlook, Github, Slack, and Notion, places where it can also drop stuff it’s generated.
Yes, all of this is dressed up with fancy terms like “persistent memory across sessions” (a document the chatbot reads and information it can access) with “authenticated integrations” (connections via API that basically any software can have). But in reality, it’s just further compute-intensive ways of trying to fit a square peg in a round hole, by which I mean having a hallucination-prone chatbot do actual work.
The only reason Jensen Huang is talking about OpenClaw is that there’s nothing else for Jensen Huang to talk about:
“OpenClaw opened the next frontier of AI to everyone and became the fastest-growing open source project in history,” said Jensen Huang, founder and CEO of NVIDIA. “Mac and Windows are the operating systems for the personal computer. OpenClaw is the operating system for personal AI. This is the moment the industry has been waiting for — the beginning of a new renaissance in software.”
That’s wild, man. That’s completely wild. What’re you talking about? What can NemoClaw or OpenClaw or whatever-the-fuck actually do? What is the actual output? That’s so fucking weird!
I can already hear the haters in my head screaming “but Ed, coding models!” and I’m kind of sick of talking about them, because nobody can actually tell me what I’m meant to be amazed or surprised by.
To be clear, LLMs can absolutely write code, and can absolutely create software, but neither of those mean that the code is good, stable or secure, or that the same can be said of the software they create. They do not have ideas, nor do they create unique concepts — everything they create is based on training data fed to it that was first scraped from Stack Overflow, Github and whatever code repositories Anthropic, OpenAI, and Google have been able to get their hands on.
It’s unclear what the actual economic or productivity effects are, other than an abundance of new code that’s making running companies harder.
When a financial services company recently began using Cursor, an artificial intelligence technology that writes computer code, the difference that it made was immediate.
The company went from producing 25,000 lines of code a month to 250,000 lines. That created a backlog of one million lines of code that needed to be reviewed, said Joni Klippert, a co-founder and the chief executive of StackHawk, a security start-up that was working with the financial services firm.
“The sheer amount of code being delivered, and the increase in vulnerabilities, is something they can’t keep up with,” she said. And as software development moved faster, that forced sales, marketing, customer support and other departments to pick up the pace, Ms. Klippert added, creating “a lot of stress.”
As I wrote a few weeks ago, LLMs are good at writing a lot of code, not good code, and the more people you allow to use them, the more code you’re going to generate, which means the more time you’re either going to need to review that code, or the more vulnerabilities you’re going to create as a result. Worse still, hyperscalers like Meta and Amazon are allowing non-technical people to ship code themselves, which is creating a crisis throughout the tech industry.
Worse still, LLMs allow shitty software engineers that would otherwise be isolated by their incompetence to feign enough intelligence to get by, leading to them actively lowering the quality of code being shipped.
Per the Times:
At the same time, there are not enough engineers to review the explosion of code for mistakes. Recruiters are increasingly looking to hire senior engineers who have experience spotting errors in code and can monitor the software for risks. Open source software projects, which anyone can contribute to, have been inundated with A.I.-enabled additions. And sometimes flaws in the code can lead to security vulnerabilities or software that crashes.
The Times also notes that because LLM coding works better on a device rather than a web interface, “...engineers are downloading their entire company’s code to their laptops, creating a security risk if the laptop goes missing.”
Speaking frankly, it appears that LLMs can write code, and create some software, but without any guarantee that said code will compile, run, be secure, performant, or easy to read and maintain. For an experienced and ethical software engineer, LLMs can likely speed them up somewhat, though not in a way that appears to be documented in any academic sense, other than it makes them slower.
And I think it’s fair to ask what any of this actually means. What’s the advantage of having an LLM write all of your code? Are you shipping faster? Is the code better? Are there many more features being shipped? What is the actual thing you can point at that has materially changed for the better?
Software engineers don’t seem happier, nor do they seem to be paid more, nor do they seem to be being replaced by AI, nor do we have any examples of truly vibe coded software companies shipping incredible, beloved products.
In fact, I can’t think of a new piece of software I’ve used in the last few years that actually impressed me outside of Flighty.
Where’s the beef? What am I meant to be looking at? What’re you shipping that’s so impressive? Why should I give a shit?
Isn’t it weird that we’re even having this conversation? Shouldn’t it be obvious by now?
This week, economist Paul Kedrosky told me on the latest episode of my show Better Offline that AI is “...nowhere to be seen yet in any really meaningful productivity data anywhere,” and only appears in the non-residential fixed investments side of America’s GDP, at (and I quote again) “...levels we last saw with the railroad build out or with rural electrification.”
That’s so fucking weird! NVIDIA is the largest company on the US stock market and has sold hundreds of billions of dollars of GPUs in the last few years, with many of them sold to the Magnificent Seven, who are building massive data centers and reopening nuclear power plants to power them, and every single one of them is losing money doing so, with revenues so putrid they refuse to talk about them!
And all that to make…what, Gemini? To power ChatGPT and Claude? What does any of this actually do that makes any of those costs actually matter? And as I’ve discussed above, what, literally, does this software do that makes any of this worth it?
Ask the average AI booster — or even member of the media — and they’ll say something about “lots of code being written by AI,” or “novel discoveries” (unrelated to LLMs) or “LLMs finding new materials (based on an economics paper with faked data)” or “people doing research,” or, of course, “that these are the fastest-growing companies of all time.”
That “growth” is only possible because all of the companies in question heavily subsidize their products, spending $3 to $15 for every dollar of revenue. Even then, only OpenAI and Anthropic seem to be able to make “billions of dollars of revenue,” a statement that I put in quotes because however many billions there might be is up for discussion.
Back in November 2025, I reported that OpenAI had made — based on its revenue share with Microsoft — $4.329 billion between January and September 2025, despite The Information reporting that it had made $4.3 billion in the first half of the year based on disclosures to shareholders.
While a few outlets wrote it up, my reporting has been outright ignored by the rest of the media. I was not reached out to by or otherwise acknowledged by any other outlets, and every outlet has continued to repeat that OpenAI “made $13 billion in 2025,” despite that being very unlikely given that it would have required it to have made $8 billion in a single quarter. While I understand why — I’m an independent, after all — these numbers directly contradict existing reporting, which, if I was a reporter, would give me a great deal of concern about the validity of my reporting and the sources that had provided it.
Similarly, when Anthropic’s CFO said in a sworn affidavit that it had only made $5 billion in its entire existence, nobody seemed particularly bothered, despite reports saying it had made $4.5 billion in 2025, and multiple “annualized revenue” reports — including Anthropic’s own — that added up to over $6.6 billion.
Though I cannot say for certain, both of these situations suggest that Anthropic and OpenAI are misleading their investors, the media and the general public. If I were a reporter who had written about Anthropic or OpenAI’s revenues previously, I would be concerned that I had published something that wasn’t true, and even if I was certain that I was correct, I would have to consider the existence of information that ran counter to my own. I would be concerned that Anthropic or OpenAI had lied to me, or that they were lying to someone else, and work diligently to try and find out what happened. I would, at the very least, publish that there was conflicting information.
The S-1 will give us the truth, I guess.
Let’s talk for a moment about margins, because they’re very important to measuring the length of a business.
Back in February in my Hater’s Guide To Anthropic, I raised concerns that Dario Amodei was using a different way to calculate margins than other companies do.
Amodei told the FT in December 2024 that he didn’t think profitability was based on how much you spent versus how much you made:
Let’s just take a hypothetical company. Let’s say you train a model in 2023. The model costs $100mn dollars. And, then, in 2024, that model generates, say, $300mn of revenue. Then, in 2024, you train the next model, which costs $1bn. And that model isn’t done yet, or it gets released near the end of 2024. Then, of course, it doesn’t generate revenue until 2025. So, if you ask “is the company profitable in 2024”, well, you made $300mn and you spent $1bn, so it doesn’t look profitable. If you ask, was each model profitable? Well, the 2023 model cost $100mn and generated several hundred million in revenue. So, the 2023 model is a profitable proposition.
He then did the same thing in an interview with John Collison in August 2025:
There's two different ways you could describe what's happening in the model business right now. So, let's say in 2023, you train a model that costs $100 million, and then you deploy it in 2024, and it makes $200 million of revenue. Meanwhile, because of the scaling laws, in 2024, you also train a model that costs $1 billion. And then in 2025, you get $2 billion of revenue from that $1 billion, and you've spent $10 billion to train the model.
So, if you look in a conventional way at the profit and loss of the company, you've lost $100 million the first year, you've lost $800 million the second year, and you've lost $8 billion in the third year, so it looks like it's getting worse and worse. If you consider each model to be a company, the model that was trained in 2023 was profitable. You paid $100 million, and then it made $200 million of revenue. There's some cost to inference with the model, but let's just assume, in this cartoonish cartoon example, that even if you add those two up, you're kind of in a good state. So, if every model was a company, the model, in this example, is actually profitable.
Almost exactly six months later on February 13, 2026’s appearance on the Dwarkesh Podcast, Dario would once again try and discuss profitability in terms other than “making more money than you’ve spent”:
Think about it this way. Again, these are stylized facts. These numbers are not exact. I’m just trying to make a toy model here. Let’s say half of your compute is for training and half of your compute is for inference. The inference has some gross margin that’s more than 50%.
So what that means is that if you were in steady-state, you build a data center and if you knew exactly the demand you were getting, you would get a certain amount of revenue. Let’s say you pay $100 billion a year for compute. On $50 billion a year you support $150 billion of revenue. The other $50 billion is used for training. Basically you’re profitable and you make $50 billion of profit. Those are the economics of the industry today, or not today but where we’re projecting forward in a year or two.
The only thing that makes that not the case is if you get less demand than $50 billion. Then you have more than 50% of your data center for research and you’re not profitable. So you train stronger models, but you’re not profitable. If you get more demand than you thought, then research gets squeezed, but you’re kind of able to support more inference and you’re more profitable.
The above quote has been used repeatedly to suggest that Anthropic has 50% gross margins and is “profitable,” which is extremely weird in and of itself as that’s not what Dario Amodei said at all. Based on The Information’s reporting from earlier in the year, Anthropic’s “gross margin” was 38%.”
Yet things have become even more confusing thanks to reporting from Eric Newcomer, who (in reporting on an investor presentation by Coatue from January) revealed that Anthropic’s gross margin was “45% in the quarter ended Sep-25,” with the crucial note that — and I quote — “Non-GAAP gross margins [are] calculated by Anthropic management…[are] unaudited, company-provided, and may not be comparable to other companies.”
This means that however Anthropic calculates its margins are not based on Generally Accepted Accounting Principles, which means that the real margins probably suck ass, because Anthropic loses billions of dollars a year, just like OpenAI.
Yet one seemingly-innocent line in there gives me even more pause: “Model payback improving significantly as revenue scales faster than R&D training costs.”
This directly matches with Dario Amodei’s bizarre idea that “...If you consider each model to be a company, the model that was trained in 2023 was profitable. You paid $100 million, and then it made $200 million of revenue.” Yes, I know it’s a “stylized fact” or whatever, but that’s what he said, and I think that their IPO might have a rude surprise in the form of a non-EBITDA margin calculation that makes even the most-ardent booster see red.
This week, The Wall Street Journal published a piece about OpenAI and Anthropic's finances that included one of the most-offensive lines in tech media history:
Strip out “compute for research,” and OpenAI is actually on track to turn a small pretax operating profit this year, as is Anthropic under its best-case scenario. Add it back in, and OpenAI doesn’t expect to break even until the 2030s. Anthropic forecasts reaching that milestone sooner.
Two thoughts:
As I said a few months ago about training costs:
Yet arguably the most dishonest part is this word “training.” When you read “training,” you’re meant to think “oh, it’s training for something, this is an R&D cost,” when “training LLMs” is as consistent a cost as inference (the creation of the output) or any other kind of maintenance.
While most people know about pretraining — the shoving of large amounts of data into a model (this is a simplification I realize) — in reality a lot of the current spate of models use post-training, which covers everything from small tweaks to model behavior to full-blown reinforcement learning where experts reward or punish particular responses to prompts.
To be clear, all of this is well-known and documented, but the nomenclature of “training” suggests that it might stop one day, versus the truth: training costs are increasing dramatically, and “training” covers anything from training new models to bug fixes on existing ones. And, more fundamentally, it’s an ongoing cost — something that’s an essential and unavoidable cost of doing business.
The Journal also adds that both Anthropic and OpenAI are showing investors two versions of their earnings — one with training costs, and one without — without adding the commentary that this is extremely deceptive or, at the very least, extremely unusual.
The more I think about it the more frustrated I get. Having two sets of earnings is extremely dodgy! Especially when the difference between them is billions of dollars. This should be immediately concerning to every financial journalist, the reddest of red flags, the biggest sign that something weird is happening…
…but because this is the AI industry, the Journal runs propaganda instead:
Venture-capital firms have stomached vast losses in part because OpenAI and Anthropic are among the fastest-growing businesses in the history of tech. Each expects to more than double revenue this year, thanks largely to business customers’ adoption of new AI tools.
That “fast-growing” part is only possible because both Anthropic and OpenAI subsidize the compute of their subscribers, allowing them to burn $3 to $15 for every dollar of subscription revenue.
And no, this is nothing like Uber or Amazon, that’s a silly comparison, click that link and read what I said and then never bring it up again.
I realize my suspicion around Anthropic’s growth has become something of a meme at this point, but I’m sorry, something is up here.
Let’s line it all up:
Anthropic was making $9 billion in annualized revenue at the end of 2025, or approximately $750 million in a 30-day period.
Per Newcomer, as of December 2025, this is how Anthropic’s revenue breaks down:
Per The Information, Anthropic also sells its models through Microsoft, Google and Amazon, and for whatever reason reports all of the revenue from their sales as its own and then takes out whatever cut it gives them as a sales and marketing expense:
Anthropic counts such revenue very differently from OpenAI. AWS, Microsoft and Google each resell Anthropic’s Claude models to their respective cloud customers, but Anthropic reports all those sales as revenue, before the cloud providers receive their share of those sales. Instead, Anthropic accounts for the cloud provider payouts in its sales and marketing expenses, as we’ve previously reported here.
The Information also adds that “...about 50% of Anthropic’s gross profits on selling its AI via Amazon has gone to Amazon,” and that “...Google typically takes a cut of somewhere between 20% and 30% of net revenue, after subtracting infrastructure costs.”
The problem here is that we don’t know what the actual amounts of revenue are that come from Amazon or Google (or Microsoft, for that matter, which started selling Anthropic’s models late last year), which makes it difficult to parse how much of a cut they’re getting. That being said, Google (per DataCenterDynamics/The Information) typically takes a cut of 20% to 30% of net revenue after subtracting the costs of serving the models.
Nevertheless, something is up with Anthropic’s revenue story.
Let’s humour Anthropic for a second and say that what it’s saying is completely true: it went from making $750 million in monthly revenue in January to $2.5 billion in monthly revenue in April 2026.
That’s remarkable growth, made even more remarkable by the fact that — based on its December breakdown — most of it appears to have come from API sales. That leap from $750 million to $1.16 billion between December and February feels, while ridiculous, not entirely impossible, but the further ratchet up to $2.5 billion is fucking weird!
But let’s try and work it out.
On February 5 2026, Anthropic launched Opus 4.6, followed by Claude Sonnet 4.6 on February 17 2026.
Based on OpenRouter token burn rates, Opus 4.5 was burning around 370 billion tokens a week. Immediately on release, Opus 4.6 started burning way, way more tokens — 524 billion in its first week, then 643 billion, then 634 billion, then 771 billion, then 822 billion, then 976 billion, eventually going over a trillion tokens burned in the final week of March.
In the weeks approaching its successor’s launch, Sonnet 4.5 burned between 500 billion and 770 billion tokens. A week after launch, 4.6 burned 636 billion tokens, then 680 billion, then 890 billion, and, by about a month in, it had burned over a trillion tokens in a single week.
Reports across Reddit suggest that these new models burn far more tokens than their predecessors with questionable levels of improvement.
The sudden burst in token burn across OpenRouter doesn’t suggest a bunch of people suddenly decided to connect to Anthropic and other services’ models, but that the model themselves had started to burn nearly twice the amount of tokens to do the same tasks.
At this point, I estimate Anthropic’s revenue split to be more in the region of 75% API and 25% subscriptions, based on its supposed $2.5 billion in annualized revenue (out of $14 billion, so a little under 18%) in February coming from “Claude Code” (read: subscribers to Claude, there’s no “Claude Code” subscription).
If that’s the case, I truly have no idea how it could’ve possibly accelerated so aggressively, and as I’ve mentioned before, there is no way to reconcile having made $5 billion in lifetime revenue as of March 9, 2026, having $14 billion in annualized revenue on February 12 2026, and having $4.5 billion in revenue for the year 2025.
Things get more confusing when you hear how Anthropic calculates its annualized revenues, per The Information:
Anthropic calculates its annualized revenue by taking the last four weeks of application programming interface revenue and multiplying it by 13, and then adding another figure: its monthly recurring chatbot subscription revenue multiplied by 12, according to a person with direct knowledge of Anthropic’s finances. The monthly figure used to calculate recurring subscriptions is based on the number of active subscriptions that day, said the person.
So, Anthropic is annualizing based on the last four weeks of API revenue times 13, a number that’s extremely easy to manipulate using, say, launches of new products.
In simpler terms, Anthropic is cherry-picking four-week windows of API spend — ones that are pumped by big announcements and new model releases — and annualizing them.
Sidenote: I have no idea why Anthropic chose to multiply API revenue by 13, and only multiplied subscription revenue by 12. Multiplying by thirteen is perfectly reasonable when you’re using 28-day (or four week) windows, as if you multiply 28 by 12 and then subtract the result from 365, you’re left with 29. In essence, there’s thirteen four-week periods in a single calendar year.
But the discrepancy between API and subscription revenue? That’s weird.
The one million token context window is a big deal, too, having been raised from 200,000 tokens in previous models. With Opus and Sonnet 4.6, Anthropic lets users use up to one million tokens of context, which means that both models can now carry a very, very large conversation history, one that includes every single output, file, or, well, anything that was generated as a result of using the model via the API.
This leads to context bloat that absolutely rinses your token budget.
To explain, the context window is the information that the model can consider at once. With 4.6, Anthropic by default allows you to load in one million tokens’ worth of information at once, which means that every single prompt or action you take has the model load one million tokens’ worth of information at once unless you actively “trim” the window through context editing.
Let’s say you’re trying to work out a billing bug in a codebase via whatever interface you’re using to code with LLMs. You load in a 350,000 token codebase, a system prompt (IE: “you are a talented software engineer,” here’s an example), a few support tickets, and a bunch of word-heavy logs to try and fix it. On your first turn (question), you ask it to find the bug, and you send all of that information through. It spits out an answer, and then you ask it how to fix the bug…but “asking it to fix the bug” also re-sends everything, including the codebase, tickets and logs. As a result, you’re burning hundreds of thousands of tokens with every single prompt.
Although this is a simplified example, it’s the case across basically any coding product, such as Claude Code or Cursor. While Cursor uses codebase indexing to selectively fetch pieces of the codebase without constantly loading it into the context window, one developer using Claude inside of Cursor watched a single tool call burn 800,000 tokens by pulling an entire database into the context window, and I imagine others have run into similar problems. To be clear, Anthropic charges at a per-million-token rate of $5 per million input and $25 per million output, which means that those casually YOLOing entire codebases into context are burning shit tons of cash (or, in the case of subscribers, hitting their rate limits faster).
if Anthropic actually made $2.5 billion in a month — we’ll find out when it files its S-1! — it likely came not from genuine growth or a surge of adoption, but in its existing products suddenly costing a shit ton more because of how they’re engineered.
The other possibility is the nebulous form of “enterprise deals” that Anthropic allegedly has, and the theory that they somehow clustered in this three-month-long period, but that just feels too convenient.
If 70% of Anthropic’s revenue is truly from API calls, this would suggest:
I don’t see much evidence of Anthropic creating custom integrations that actually matter, or — and fuck have I looked! — any real examples of businesses “doing stuff with Claude” other than making announcements about vague partnerships.
There’s also one other option: that Silicon Valley is effectively subsidizing Anthropic through an industry-wide token-burning psychosis.
And based on some recent news, there’s a chance that’s the case.
As I discussed a few weeks ago, Silicon Valley has a “tokenmaxxing” problem, where engineers are encouraged by their companies to burn as many tokens as possible, at times by their peers, and at others by their companies.
The most egregious — and honestly, worrying! — version of this came from The Information’s recent story about Meta employees competing on an internal leaderboard to see who can burn the most tokens, deliberately increasing the size of their prompts and the amount of concurrent sessions (along with unfettered and dangerous OpenClaw usage) to do so:
The rankings, set up by a Meta employee on its intranet using company data, measure how many tokens—the units of data processed by AI models—employees are burning through. Dubbed “Claudeonomics” after the flagship product of AI startup Anthropic, the leaderboard aggregates AI usage from more than 85,000 Meta employees, listing the top 250 power users.
The practice is emblematic of Silicon Valley’s newest form of conspicuous consumption, known as “tokenmaxxing,” which has turned token usage into a benchmark for productivity and a competitive measure of who is most AI native. Workers are maximizing their prompts, coding sessions and the number of agents working in parallel to climb internal rankings at Meta and other companies and demonstrate their value as AI automates functions such as coding.
The Information reports that the dashboard, called “Claudeonomics” (despite said dashboard covering other models from OpenAI, Google, and xAI), has sparked competition within Meta, with users burning a remarkable 60 trillion tokens in the space of a month, with one individual averaging around 281 billion tokens, which The Information remarks could cost millions of dollars. Meta’s company-mandated psychosis also gives achievements for particular things like using multiple models or high utilization of the cache.
Here’s one very worrying anecdote:
Some workers are instructing AI agents to carry out research for hours on end to maximize their token usage, according to two current employees.
One poster on Twitter says that there are people at Meta running loops burning tokens to rise up the leaderboards, and that Meta’s managers also measure lines of code as a success metric.
The Information says that, considering Anthropic’s current pricing for its models, that 60 trillion tokens could be as much as $900 million in the space of a month, though adds that this assumes that every token being burned was on Claude Opus 4.6 (at $15 per 1 million tokens).
I personally think this maths is a bit fucked, because it assumes that A) everybody is only using Claude Opus, B) that none of that token burn runs through the cache (which it obviously does, and the cache charges 50%, as pointed out by OpenCode co-founder Dax Radd), and C) that Meta is entirely using the API (versus paying for a $200-a-month Claude Max subscription for each user).
Digging in further, it appears that a few years ago Meta created an internal coding tool called CodeCompose, though a source at Meta tells me that developers use VSCode and an assistant called Devmate connected to models from Anthropic, OpenAI and xAI.
One engineer on Reddit — albeit an anonymous one! — had some commentary on the subject:
We literally have a leaderboard of who has cost the most in compute. Not to share too much, but there are folks north of $80k in spend. Lmao. I’ve been really skeptical about the enterprise-level LLM push. It’s 100% an amazing tool, and I’ve been using Claude and tmux as my primary driver for ~six months, but it seemed like it maybe 2x’ed output, with a lot of time wasted in reinventing the wheel and bad naive solutions. The hype seemed like it was folks who had no idea what they were doing and who had never dealt with the complexities of a large codebase.
If we assume that Meta is an enterprise customer paying API rates for its tokens, it’s reasonable to assume — at even a low $5-per-million average — that it’s spending $300 million or more a month on API calls. As Radd also added, there’s likely a discount involved. He suggested 20%, which I agree with.
Even if it’s $300 million, that’s still fucking insane. That’s still over three billion dollars a year. If this is what’s actually happening, and this is what’s contributing to Anthropic’s growth, this is not a sustainable business model, which is par for the course for Anthropic, a company that has only lost billions of dollars.
Encouraging workers to burn as many tokens as possible is incredibly irresponsible and antithetical to good business or software engineering. Writing great software is, in many cases, an exercise in efficiency and nuance, building something that runs well, is accessible and readable by future engineers working on it, and ideally uses as few resources as it can.
TokenMaxxing runs contrary to basically all good business and software practices, encouraging waste for the sake of waste, and resulting in little measurable productivity benefits or, in the case of Meta, anything user-facing that actually seems to have improved.
Venture capitalist Nick Davidov mentioned yesterday that sources at Google Cloud “started seeing billions of tokens per minute from Meta, which might now be as big as a quarter of all the token spend in Anthropic.” While I can’t verify this information (and Davidoff famously deleted his photos using Claude Cowork while attempting to reorganize his wife’s desktop), if that’s the case, Meta is a load-bearing pillar of Anthropic’s revenue — and, just as importantly, a large chunk of Anthropic’s revenue flows through Google Cloud, which means A) that Anthropic’s revenue truly hinges on Google selling its models, and B) that said revenue is heavily-inflated by the fact that Anthropic books revenue without cutting out Google’s 20%+ revenue share.
In any case, TokenMaxxing is not real demand, but an economic form of AI psychosis.
There is no rational reason to tell somebody to deliberately burn more resources without a defined output or outcome other than increasing how much of the resource is being used. I have confirmed with a source at that there is no actual metric or tracking of any return on investment involved in token burn at Meta, meaning that TokenMaxxing’s only purpose is to burn more tokens to go higher on a leaderboard, and is already creating bad habits across a company that already has decaying products and leadership.
To make matters worse, TokenMaxxing also teaches people to use Large Language Models poorly. While I think LLMs are massively-overrated and have their outcomes and potential massively overstated, anyone I know who actually uses them for coding generally has habits built around making sure token burn isn’t too ridiculous, and various ways to both do things faster without LLMs and ways to be intentional with the models you use for particular tasks. TokenMaxxing literally encourages you to do the opposite — to use whatever you want in whatever way you want to spend as much money as possible to do whatever you want because the only thing that matters is burning more tokens.
Furthermore, TokenMaxxing is exactly the kind of revenue that disappears first. Zuckerberg has reorganized his AI team four or five times already, and massively shifted Meta’s focus multiple times in the last five years, proving that at the very least he’ll move on a whim depending on external forces. After laying off tens of thousands of people in the last few years, Meta has shown it’s fully capable of dumping entire business lines or groups with a moment’s notice, and while moving on from AI might be embarrassing, that would suggest that Mark Zuckerberg experiences shame or any kind of emotion other than anger.
This is the kind of revenue that a business needs to treat with extreme caution, and if Meta is truly spending $300 million or more a month on tokens, Anthropic’s annualized revenues are aggressively and irresponsibly inflated to the point that they can’t be taken seriously, especially if said revenue travels through Google Cloud, which takes another 20% off the top at the very least.
Though the term is pretty new, the practice of encouraging your engineers to use AI as much as humanly possible is an industry-wide phenomena, especially across hyperscalers like Amazon, Microsoft and Google, all of whom until recently directly have pushed their workers to use models with few restraints. Shopify and other large companies are encouraging their workers to reflexively rely on AI, with performance reviews that include stats around your token burn and other nebulous “AI metrics” that don’t seem to connect to actual productivity.
I’m also hearing — though I’ve yet to be able to confirm it — that Anthropic and other model providers are forcing enterprise clients to start using the API directly rather than paying for monthly subscriptions.
Combined with mandates to “use as much AI as possible,” this naturally increases the cost of having software engineers, which — and I say this not wanting anyone to lose their jobs — does the literal opposite of replacing workers with AI. Instead, organizations are arbitrarily raising the cost of doing business without any real reason.
Because we’re still in the AI hype cycle, this kind of wasteful spending is both tolerated and encouraged, and the second that financial conditions worsen or stock prices drop due to increasing operating expenses, these same companies will cut back on API spend, which will overwhelmingly crush Anthropic’s glowing revenues.
I think it’s also worth asking at this point what is is we’re actually fucking doing.
We’re building — theoretically — hundreds of gigawatts of data centers, feeding hundreds of billions of dollars to NVIDIA to buy GPUs, all to build capacity for demand that doesn’t appear to exist, with only around $65 billion of revenue (not profit) for the entire generative AI industry in 2025, with much of that flowing from two companies (Anthropic and OpenAI) making money by offering their models to unprofitable AI startups that cannot survive without endless venture capital, which is also the case for both AI labs.
Said data centers make up 90% of NVIDIA’s revenue, which means that 8% or so of the S&P 500’s value comes from a company that makes money selling hardware to people that immediately lose money on installing it. That’s very weird! Even if you’re an AI booster, surely you want to know the truth, right?
The most-prominent companies in the AI industry — Anthropic and OpenAI — burn billions of dollars a year, have margins that get worse over time, and absolutely no path to profitability, yet the majority of the media act as if this is a problem that they will fix, even going as far as to make up rationalizations as to how they’ll fix it, focusing on big revenue numbers that wilt under scrutiny.
That’s extremely weird, and only made weirder by members of the media who seem to think it’s their job to defend AI companies’ bizarre and brittle businesses. It’s weird that the media’s default approach to AI has, for the most part, been to accept everything that the companies say, no matter how nonsensical it might be.
I mean, come on! It’s fucking weird that OpenAI plans to burn $121 billion in the next two years on compute for training its models, and that the media’s response is to say that somehow it will break even in 2030, even though there’s no actual explanation anywhere as to how that might happen other than vague statements about “efficiency.”
That’s weird! It’s really, really weird!
It’s also weird that we’re still having a debate about “the power of AI” and “what agents might do in the future” based on fantastical thoughts about “agents on the internet” that do not exist, cannot exist, and will never exist, and it’s fucking weird that executives and members of the media keep acting as if that’s the case. It’s also weird that people discussing agents don’t seem to want to discuss that OpenAI’s Operator Agent does not work, that AI browsers are fundamentally broken, or that agentic AI does not do anything that people discuss.
In fact, that’s one of the weirdest parts of the whole AI bubble: the possibility of something existing is enough for the media to cover it as if it exists, and a product saying that it will do something is enough for the media to believe it does it. It’s weird that somebody saying they will spend money is enough to make the media believe that something is actually happening, even if the company in question — say, Anthropic — literally can’t afford to pay for it.
It’s also weird how many outright lies are taking place, and how little the media seems to want to talk about them. Stargate was a lie! The whole time it was a lie! That time that Sam Altman and Masayoshi Son and Larry Ellison stood up at the white house and talked about a $500 billion infrastructure project was a lie! They never formed the entity! That’s so weird!
Hey, while I have you, isn’t it weird that OpenAI spent hundreds of millions of dollars to buy tech podcast TBPN “to help with comms and marketing”? It’s even weirder considering that TBPN was already a booster for OpenAI!
It’s also weird that a lot of AI data center projects don’t seem to actually exist, such as Nscale’s project to make “one of the most powerful AI computing centres ever” that is literally a pile of scaffolding, and that despite that announcement the company was able to raise $2 billion in funding.
It’s also weird that we’re all having to pretend that any of this matters. The revenues are terrible, Large Language Models are yet to provide any meaningful productivity improvements, and the only reason that they’ve been able to get as far as they have is a compliant media and a venture capital environment borne of a lack of anything else to invest in.
Coding LLMs are popular only because of their massive subsidies and corporate encouragement, and in the end will be seen as a useful-yet-incremental and way too expensive way to make the easy things easier and the harder things harder, all while filling codebases full of masses of unintentional, bloated code. If everybody was forced to pay their actual costs for LLM coding, I do not believe for a second that we’d have anywhere near the amount of mewling, submissive and desperate press around these models.
The AI bubble has every big, flashing warning sign you could ask for. Every company loses money. Seemingly every AI data center is behind schedule, and the vast majority of them aren’t even under construction. OpenAI’s CFO does not believe that it’s ready to go public in 2026, and Sam Altman’s reaction has been to have her report to somebody else other than him, the CEO. Both OpenAI and Anthropic’s margins are worse than they projected. Every AI startup has to raise hundreds of millions of dollars, and their products are so weak that they can only make millions of dollars of revenue after subsidizing the underlying cost of goods to the point of mass unprofitability.
And it’s really weird that the mainstream media has a diametric view — that all of this is totally permissible under the auspices of hypergrowth, that these companies will simply grow larger, that they will somehow become profitable in a way that nobody can actually describe, that demand for AI data centers will exist despite there being no signs of that happening.
I get it. Living in my world is weird in and of itself. If you think like I do, you have to see every announcement by Anthropic or OpenAI as suspicious — which should be the default position of every journalist, but I digress — and any promise of spending billions of dollars as impossible without infinite resources.
At the end of this era, I think we’re all going to have to have a conversation about the innate credulity of the business and tech media, and how often that was co-opted to help the rich get richer.
Until then, can we at least admit how weird this all is?
2026-04-06 22:49:40
News out of The Information's Anissa Gardizy and Amir Efrati over the weekend - OpenAI CFO Sarah Friar has apparently clashed with CEO Sam Altman over timing around OpenAI's IPO, emphasis mine:
She told some colleagues earlier this year that she didn’t believe the company would be ready to go public in 2026, because of the procedural and organizational work needed and the risks from its spending commitments, according to a person who spoke to her. She said she wasn’t sure yet whether OpenAI would need to pour so much money into obtaining AI servers in the coming years or whether its revenue growth, which has been slowing, would support the commitments, said the person who spoke to her.
I cannot express how strange this is. Generally a CFO and CEO are in lock-step over IPO timing, or at the very least the CFO has an iron grip on the actual timing because, well, CEOs love to go public and the CFO generally exists to curb their instincts.
Nevertheless, Clammy Sam Altman has clearly sidelined Friar, and as of August last year, the CFO of OpenAI doesn't report to the CEO. In fact, the person Friar reports to (Fiji Simo) just took a medical leave of absence:
In an unusual move for a large company, where CFOs almost always answer directly to the CEO, Friar stopped reporting directly to Altman in August last year and instead began reporting to Fidji Simo, who had joined as head of OpenAI’s applications business. Simo last week told staff she would take a short medical leave.
It is extremely peculiar to not have the Chief Financial Officer report to the Chief Executive Officer, but remember folks, this is OpenAI, the world's least-normal company!
Anyway, all of this seemed really weird, so I asked investor, writer and economist Paul Kedrosky for his thoughts:
Having been around this industry as an investor for decades, I cannot recall an example of the CFO of a major pre-IPO tech company (allegedly) taking issue with their own company's IPO plans. It doesn't happen.
Very cool! Paul is also a guest on this week's episode of my podcast Better Offline, by the way. Out at 12AM ET Tuesday.
Anyway, The Information's piece also adds another fun detail - that OpenAI's margins were even worse than expected in 2025:
In another sign of its financial pressures, OpenAI told investors that its gross profit margins last year were lower than projected due to the company having to buy more expensive compute at the last minute in response to higher than expected demand for its chatbots and models, according to a person with knowledge of the presentation.
Riddle me this, Batman! If your AI company always has to buy extra compute to meet demand, and said extra compute always makes margins worse, doesn't that mean that your company will either always be unprofitable or die because it buys too much compute?
Say, that reminds me of something Anthropic CEO Dario Amodei said to Dwarkesh Patel earlier in the year...
So when we go to buying data centers, again, the curve I’m looking at is: we’ve had a 10x a year increase every year. At the beginning of this year, we’re looking at $10 billion in annualized revenue. We have to decide how much compute to buy. It takes a year or two to actually build out the data centers, to reserve the data center.
Basically I’m saying, “In 2027, how much compute do I get?” I could assume that the revenue will continue growing 10x a year, so it’ll be $100 billion at the end of 2026 and $1 trillion at the end of 2027. Actually it would be $5 trillion dollars of compute because it would be $1 trillion a year for five years. I could buy $1 trillion of compute that starts at the end of 2027. If my revenue is not $1 trillion dollars, if it’s even $800 billion, there’s no force on earth, there’s no hedge on earth that could stop me from going bankrupt if I buy that much compute.
It is extremely strange that the CFO of a company doesn't report to the CEO of a company, and even more strange that the CFO is directly saying "we are not ready for IPO" as its CEO jams his foot on the accelerator. It's clear that both OpenAI and Anthropic are rushing toward a public offering so that their CEOs can cash out, and that their underlying economics are equal parts problematic and worrying.
Though I am entirely guessing here, I imagine Friar sees something within OpenAi's finances that give her pause. An S-1 - one of the filings a company makes before going public - is an audited document, and I imagine the whimsical mathematics that OpenAI engages in - such as, per The Wall Street Journal, calculating profitability without training compute - might not match up with what actual financiers crave.
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2026-04-04 05:11:26
Soundtrack — Soundgarden — Blow Up The Outside World
A lot of people try to rationalize the AI bubble by digging up the past.
Billions of dollars of waste are justified by saying “OpenAI just like Uber” (it isn’t) and “the data center buildout is just like Amazon Web Services” (it isn’t, Amazon Web Services was profitable in a decade and cost about $52 billion between 2003 and 2017, and that’s normalized for inflation) and, most egregiously, that AI is “too big to fail.”
I think that these statements are acts of cowardice if they are not backed up by direct and obvious comparisons based on historical data and actual research. They are lazy intellectual tropes borne of at best ignorance, or at worst an intellectual weakness that makes somebody willing to take flimsy information and repeat it as if it were gospel. Nobody has any proof that AI is profitable on inference, nor is there any explanation about how it will become profitable at some point, just a cult-like drone of “they’ll work it out” and “look at the growth!”
And the last argument, that AI is “too big to fail” is the most cowardly of them all, given that said statement seldom precedes the word “because,” and then an explanation of why generative AI is so economically important, and why any market correction would be so catastrophic that the bubble must continue to inflate.
Over the last few months I have worked diligently to unwind these myths. I discussed earlier in the year how the AI Bubble is much worse than the dot com bubble, and ended last year with a mythbusters (AI edition) that paired well with my free opus, How To Argue With An AI Booster.
I don’t see my detractors putting in anything approaching a comparable effort. Or any effort, really.
This isn’t a game I’m playing or some sort of competitive situation, nor do I feel compelled to “prove my detractors wrong” with any specificity. I believe time will do that for me.
My work is about actually finding out what’s going on, and I believe that explaining it is key to helping people understand the world. None of the people who supposedly believe that AI is the biggest, most hugest and most special boy of all time have done anything to counter my core points around AI economics other than glance-grade misreads of years-old pieces and repeating things like “they’re profitable on inference!”
Failing to do thorough analysis deprives the general public of the truth, and misleads investors into making bad decisions. Cynicism and skepticism is often framed as some sort of negative process — “hating” on something for the sake of being negative, or to gain some sort of cultural prestige, or as a way of performatively exhibiting one’s personal morality — when both require the courage (when done properly) to actually understand things in-depth.
I also realize many major media outlets are outright against skepticism. While they frame their coverage as “taking on big tech,” their questions are safe, their pieces are safer, their criticisms rarely attack the actual soft parts of the industries (the funding of the companies or infrastructure developments, or the functionality of the technology itself), and almost never seek to directly interrogate the actual statements made by AI leaders and investors, or the various hangers-on and boosters.
This is why I’ve been so laser-focused on the mythologies that have emerged over the past couple of years, such as when people say “it’s just like the dot com bubble" — it’s not, it’s much worse! — because if these mythologies actually withstood scrutiny, my work wouldn’t have much weight.
The Dot Com Bubble in particular grinds my gears because it’s a lazy trope used to rationalise rotten economics, all while disregarding the actual harms that took place. Unemployment spiked to 6%, venture capital funds lost 90% of their value, and hundreds of thousands of people in the tech industry lost their jobs, some of them for good.
It is utterly grotesque how many people minimize and rationalize the dot com bubble, reframing it as a positive, by saying that “things worked out afterwards,” all so that they can use that as proof that we need to keep giving startups as much money as they ask for forever and that AI is the biggest thing in the world.
Yet AI is, in reality, much smaller than people think. As I wrote up (and Bloomberg clearly were inspired by!) last week, only 5GW of AI data centers are actually under construction worldwide out of the 12GW that are supposedly meant to be delivered this year, with many of them slowed by the necessity of foreign imports of electrical equipment and, you know, the fact that construction is hard, and the power isn’t available.
Meanwhile, back in October 2025, The Wall Street Journal claimed that a “giant new AI data center is coming to the epicenter of America’s fracking boom” in a deal between Poolside AI (a company that does not appear to have released a product) and CoreWeave (an unprofitable AI data center company that I’ve written about a great deal). This was an “exclusive” report that included the following quote:
“It is not about your headline numbers of gigawatts. It’s about your ability to deliver data centers,” Eiso Kant, a co-founder of Poolside, said in an interview. The ability to build data centers quickly is “the real physical bottleneck in our industry,” he said.
Turns out Mr. Kant was correct, as it was just reported that CoreWeave and Poolside’s deal fell apart, along with Poolside’s $2 billion funding round, as Poolside was “unable to stand up the first cluster of chips to CoreWeave’s timeline,” probably because it couldn’t afford them and wasn’t building anything. The FT added that “...Poolside was unable to convince investors that it could train AI models to the same level of established competitors.” It was also unable to get Google to take over the site.
Elsewhere, troubling signs are coming from the secondary markets — the place where people sell stock in private companies like OpenAI. Those signs being that, well, nobody’s buying.
Per Bloomberg, over $600 million of OpenAI shares are sitting for sale with no interest from buyers at its current $850 billion post-money valuation, though apparently $2 billion is “ready to deploy” for private Anthropic shares at a $380 billion valuation, according to Next Round Capital (a secondary share sale site)’s Ken Smythe.
Though people will try to frame this as a case of OpenAI’s shares “being too close to what they might go public at,” one has to wonder why shares of what is supposed to be the literal most valuable company of all time aren’t being sold at what, theoretically, is a massive discount.
One might argue that it’s because people think that the stock might drop on IPO and then grow, but…that doesn’t show a great degree of faith in the company. Investors likely think that Anthropic would go public at a higher price than $380 billion, though I do need to note that the full quote was that "buyers have indicated that they have $2 billion of cash ready to deploy into Anthropic,” which is not the same thing as “will actually buy it.”
In any case, the market is no longer treating OpenAI like it’s the golden child. Poolside’s CoreWeave deal is dead. Data centers aren’t getting built. Oracle is laying off tens of thousands of people to fund AI data centers for OpenAI, a company that cannot afford to pay for them. AI demand, despite how fucking annoying everybody is being about it, does not seem to exist at the scale that makes any part of this industry make sense.
Yet people still squeal that “The Trump Administration Will Bail Out The AI Industry,” and that OpenAI is “too big to fail,” two statements that are not founded in history or analysis, but are the kinds of things that you say only when you’re either so beaten down by bad news that you’ve effectively given up or are so willfully ignorant that you’ll say stuff without knowing what it means because it makes you feel better.
As I discussed in this week’s free newsletter, there is a subprime AI crisis going on.
When the subprime mortgage crisis happened towards the end of the 2000s, millions of people built their lives around the idea that easy money would always be available, and that housing would only ever increase in value. These assumptions led to the creation of inherently dangerous mortgage products that never should have existed, and that inevitably screwed the buyers.
I talked about these in my last free newsletter. Negative amortization mortgages, for example, were a thing in the US. These were where the mortgage payments didn’t actually cover the cost of the interest, let alone the principal.
Similarly, in the UK, my country of birth, many homebuyers used endowment mortgages — an interest-only mortgage where, instead of paying the principal, buyers made monthly payments into an investment savings account that (theoretically) would cover the cost of the property (and perhaps provide some extra cash) at the end of the term. If the investments did extremely well, the buyer could potentially pay off the mortgage early.
Far too often, those investments underperformed, meaning buyers were left staring at a shortfall at the end of their term.
Across the globe, the value of housing was massively overinflated by the lax standards of a mortgage industry incentivized to sign as many people as possible thanks to a lack of regulation and easily-available funding.
The value of housing — and indeed the larger housing and construction boom — was a mirage. In reality, housing wasn’t worth anywhere near what it was being sold for, and the massive demand for housing was only possible with unlimited resources, and under ideal conditions (namely, normal levels of inflation and relatively low interest rates).
Those buying houses they couldn’t afford with adjustable-rate mortgages either didn’t understand the terms, or believed members of the media and government officials that suggested housing prices would never decrease and that one could easily refinance the mortgage in question.
Similarly, AI startups products are all subsidized by venture capital, and must, in literally every case, allow users to burn tokens at a cost far in excess of their subscription fees, a business that only “works” — and I put that in quotation marks — as long as venture capital continues to fund it. While from the outside these may seem like these are functional businesses with paying users, without the hype cycle justifying endless capital, these businesses wouldn’t be possible, let alone viable, in any way shape or form.
For example, Harvey is an AI tool for lawyers that just raised $200 million at an $11 billion valuation, all while having an astonishingly small $190 million in ARR, or $15.8 million a month. It raised another $160 million in December 2025, after raising $300 million in June 2025, after raising $300 million in February 2025.
Remove even one of those venture capital rounds and Harvey dies. Much like subprime loans allowed borrowers to get mortgages they had no hope of paying, hype cycles create the illusion of viable businesses that cannot and will never survive without the subsidies.
The same goes for companies like OpenAI and Anthropic, both of whom created priority processing tiers for their enterprise customers last year, and the latter of which just added peak rate limits from 5am and 11am Pacific Time. Their customers are the subprime borrowers too — they built workflows around using these products that may or may not be possible with new rate limits, and in the case of enterprise customers using priority processing, their costs massively spiked, which is why Cursor and Replit suddenly made their products worse in the middle of 2025.
The reason that the Subprime Mortgage Crisis led to the Great Financial Crisis was that trillions of dollars were used to speculate upon its outcome, across $1.1 trillion of mortgage-backed securities. In mid-2008, per the IMF, more than 60% of all US mortgages had been securitized (as in turned into something you could both trade, speculate on the outcome of and thus buy credit default swaps against). Collateralized debt obligations — big packages of different mortgages and other kinds of debt that masked the true quality of the underlying assets — expanded to over $2 trillion by 2006, though the final writedowns were around $218 billion of losses.
By comparison, AI is pathetically small. While there were $178.5 billion in data center credit deals done in America last year, speculation and securitization remains low, and in many cases the amount of actual cash available is in tranches based on construction milestones, with most data center projects (like Aligned’s recent $2.58 billion raise) funded by “facilities” specifically to minimize risk.
As I’ve written about previously, building a data center is hard — especially when you’re building at scale. Finding land, obtaining permits (something which can be frustrated by opposition from neighbors or local governments), obtaining electricity, and then obtaining the labor, machinery, and raw materials all take time. Some components — like electrical transformers — have lead times in excess of a year.
And so, you can understand why there’s such a disparity between the dollar amount in data center credit deals, and the actual capital deployed to build said data centers.
There also isn’t quite as much wilful ignorance on the part of ratings agencies, though that isn’t to say they’re actually doing their jobs. CoreWeave is one of many data center companies that’s been able to raise billions of dollars using its counterparties’ credit ratings, with Moody’s giving the debt for an unprofitable data center company that would die without endless debt that’s insufficiently capitalized to pay it off an “A3 investment grade rating” because it was able to use Meta’s credit rating and the GPUs in question as collateral.
Nevertheless, none of this comes close to the apocalypse that the global economy faced as a result of the catastrophically dangerous bets made by the entire finance industry during the late 2000s, because those bets weren’t made on housing so much as they were made on financial instruments that were given power because of housing.
Juiced by a mortgage industry that allowed basically anybody to buy a house regardless of whether they could pay for it, by the middle of 2008, nearly $9 trillion of mortgages were outstanding in America (with around $1.1 trillion of home equity loans on top). Trillions (it’s hard to estimate due to the amount of off-balance sheet trades that happened) more were gambled on top of them as they were packaged into CDOs (collateralized debt obligations) and synthetic CDOs where somebody would buy a credit default swap (CDS, or a bet against the default) against the underlying assets, assuming (incorrectly) that the company issuing the CDS would have the funds to pay them.
As I’ll get into deeper in the piece, no such comparison exists for AI, and the asset-backed securitization of data centers and GPUs remains very small. Despite many deceptive studies that attempt to claim otherwise, the economy is relatively unaffected by AI, and while software companies might have debt, AI companies, for the most part, do not appear to, and those that do (OpenAI and Anthropic) have credit facilities rather than lump-sum loans.
In totality, the AI industry seems to have made about $65 billion in revenue (not profit!) in 2025, with I estimate about a third of that being the result of OpenAI or Anthropic feeding money to hyperscalers or neoclouds like CoreWeave, and a billions more being AI startups (funded entirely by VC) feeding money to Anthropic and OpenAI to rent their models.
Even the venture capital scale of AI startups is drastically overestimated. While (as reported by The New York Times) “AI startups” raised $297 billion in the first quarter of 2026, $188 billion of that was taken by OpenAI (which has yet to fully receive the funds!), Anthropic, xAI, and Waymo. In 2025, $425 billion was invested in startups globally, with half of that (about $212.5 billion) going to AI startups, but about half of that ($102 billion) going to Anthropic, OpenAI, xAI, Scale AI’s not-quite-acquisition by Meta, and Bezos’ Project Prometheus.
The great financial crisis was, as I’ll get into, a literal collapse of how banks, financial institutions, and property businesses operated, with their reckless speculation on a housing market that was only made possible by a craven mortgage industry incentivized to get people to sign at any cost. When people speculated that there was a bubble, articles ran saying that housing was actually cheap, that subprime lending had actually “made the mortgage market more perfect,” that the sky was not falling in the credit markets because unemployment wasn’t going to rise, that subprime mortgages wouldn’t hurt the economy, and that there was no recession coming.
Sidenote: This isn’t to say the media didn’t report on the bubble. In fact, outlets like CNBC that have been staunch supporters of the AI bubble directly reported on Buffett’s concerns about the housing bubble, with even Jim Cramer worrying that the bubble might burst as early as 2005, though he did go on to tell people not to worry about Bear Stearns just before it collapsed.
More specifically, he told people not to pull their money from Bear Stearns, saying that its low price (at the time, it was trading at $65-a-share, almost a third of its one-year high) meant it was more likely to be acquired by a competitor, and at a higher price than its market value.
In the end, it was sold to JP Morgan Chase for $10-a-share.
In any case, OpenAI, Anthropic and AI startups in general are far from “systemic risks.” They are not load-bearing. TARP and associated bailouts did not bail out the markets themselves — the S&P 500 lost around half of its value during the bear market that followed, and home prices only returned to growth in 2012.
I imagine the “systemic risk” argument is that NVIDIA makes up 7% to 8% of the value of the S&P 500, and that makes sense as long as you ignore that Exxon Mobil was around 5% of the value of S&P 500 in 2008 and saw its value tank for years following the crisis without any bailout to stop it. Microsoft, Meta, Amazon, Google, NVIDIA, Tesla, and Apple are not going bankrupt if AI dies, and anybody suggesting they will is wrong.
NVIDIA’s revenue collapsing by 50% or 80% or more would not cause a “financial crisis,” nor would said collapse be considered a “systemic risk” to the stability of the broader economy, though I admit, it would be very bad for the markets writ large.
Conversely, a similar blow at TSMC — the company that owns the literal foundries that makes many of the leading-edge semiconductors used today, including those used for data center GPUs — would be, because its collapse would massively reduce the demand for its products, which, I add, require billions of dollars of upfront investment to make.
GPUs are not critical to the global economy, nor are Large Language Models, nor is OpenAI, nor is Anthropic. Their collapse would end a hype cycle, which would make the markets drop much like they did in the dot com bust, but that is not the same as too big to fail.
Today’s premium is one of the most comprehensive analyses I’ve ever written — a rundown of what makes something “Too Big To Fail,” an explanation of the actual fundamentals of the Great Financial Crisis, and a true systemic analysis of the AI bubble writ large.
None of this is too big to fail, and in many ways its failure is necessary for us to move forward as a society.
2026-04-01 00:18:11
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Soundtrack: Metallica — …And Justice For All
Bear with me, readers. I need to do a little historical foreshadowing to fully explain what’s going on.
In the run-up to the great financial crisis, unscrupulous lenders issued around 1.9 million subprime loans, with many of them being adjustable rate mortgages (ARMs) with variable rates that, after a two-or-three-year-long introductory period, would adjust every twelve months, per CBS News in July 2006:
On a $200,000 ARM that began a few years ago, the initial rate was around 4.5 percent. When the ARM adjusts to 6.5 percent, the monthly payment will increase from $1,013 to $1,254, or a rise of almost 24 percent.
Although interest rates have increased more than 4 percentage points since 2004, most ARMs typically cap the amount of the annual rate increase to 2.5 percentage points per year. Therefore, these increases are only just the beginning and it's very likely that the people experiencing an increased ARM payment this year will see a similar rise again in 12 months.
At the time, 18% of homeowners had adjustable-rate mortgages, which also made up more than 25% of new mortgages in the first quarter of 2006, with (at the time) over $330 billion of mortgages expected to adjust upwards. Things were grimmer beneath the surface. A question on JustAnswer from 2009 showed a homeowner that was about to lose their house after being conned into a negative amortization loan — a mortgage where payments didn’t actually cover the interest, meaning that each month the balance increased. Dodgy lenders were given bonuses for selling more mortgages, whether or not the person on the other end was capable of paying, and by November 2007, around two million homeowners held $600 billion of ARMs.
Yet the myth of the subprime mortgage crisis was that it was caused entirely by low income borrowers. Per Duke’s Manuel Adelino:
We found there was no explosion of credit offered to lower-income borrowers. In fact, home ownership rates among the poorest 20 percent of Americans fell during the boom because those buyers were being priced out of the market. Instead, we found credit was expanded across the board. Everybody was playing the same game. But credit expanded most drastically in areas where house prices were rising the most, and these were markets that were beyond the reach of lower-income borrowers.
The overwhelming majority of mortgages were going to middle income and relatively high income households during the boom, just as they have always done.
Despite The Big Short’s dramatic “stripper with six properties” scene made for a vivid demonstration of the subprime problem, the reality was that everybody got taken in by teaser rate mortgages, driving up the value of properties based on a housing market that was only made possible by mortgages that were expressly built to hide the real costs as interest rates and borrower payments rose every six to 36months. I’ll add that near-prime mortgages — for borrowers with just-below-prime credit scores — were also growing, with over 1.1 million of them in 2005, when they represented nearly 32% of all loans.
Many people who bought houses that they couldn’t afford did so based on a poor understanding of the terms of their mortgage, thinking that the value of housing would continue to climb as it had for over a hundred years, and/or the belief that they’d easily be able to refinance the loans. Even as things deteriorated toward the middle of the 2000s, people came up with rationalizations as to why things would work out, such as Anthony Downs of The Brookings Institution, who in October 2007 said the following in a piece called “Credit Crisis: The Sky is not Falling”:
U.S. stock markets are gyrating on news of an apparent credit crunch generated by defaults among subprime home mortgage loans. Such frenzy has spurred Wall Street to cry capital crisis. However, there is no shortage of capital – only a shortage of confidence in some of the instruments Wall Street has invented. Much financial capital is still out there looking for a home.
As this brief describes, the facts hardly indicate a credit crisis. As of mid-2007, data show that prices of existing homes are not collapsing. Despite large declines in new home production and existing home sales, home prices are only slightly falling overall but are still rising in many markets. Default rates are rising on subprime mortgages, but these mortgages—which offer loans to borrowers with poor credit at higher interest rates—form a relatively small part of all mortgage originations. About 87 percent of residential mortgages are not subprime loans, according to the Mortgage Bankers Association’s delinquency studies.
Brookings also added that “...the vast majority of subprime mortgages are likely to remain fully paid up as long as unemployment remains as low as it is now in the U.S. economy.” At the time, US unemployment was 4.7%, but a year later it was at 6.5%, and would peak at 10% in October 2009.
In an article from the December 2004 issue of Economic Policy Review, Jonathan McCarthy and Richard W. Peach argued that there was “little basis” for concerns about housing prices, with “home prices essentially moving in line with increases in family income and declines in nominal mortgage interest rates,” and hand-waved any concerns based on vague statements about “demand”:
Our main conclusion is that the most widely cited evidence of a bubble is not persuasive because it fails to account for developments in the housing market over the past decade. In particular, significant declines in nominal mortgage interest rates and demographic forces have supported housing demand, home construction, and home values during this period. Taking these factors into account, we argue that market fundamentals are sufficiently strong to explain the recent path of home prices and support our view that a bubble does not exist.
As for the likelihood of a severe drop in home prices, our examination of historical national home prices finds no basis for concern. Even during periods of recession and high nominal interest rates, aggregate real home prices declined only moderately.
From the outside, this made it appear that the value of housing was exponential, and that the “pent-up demand” for homes necessitated a massive boom in construction, one that peaked in January 2006 with 2.27 million new homes built. A year later, this number collapsed to 1.084 million, and in January 2009, only 490,000 new homes had been built in America, the lowest it had been in history.
Denial rates for mortgages declined drastically (along with the increase in things like 40-year or 50-year mortgages), which meant that suddenly anybody was able to get a house, which made it only seem logical to build more housing. Low interest rates before 2006 allowed consumers to take on mountains of new credit card debt, rising to as high as 20% of household incomes in 2007, to the point that by the 2000s, credit card companies were making more money from credit card lending than the fees from people using the credit cards, with $65 billion of the $95 billion of the credit card industry’s revenue coming from interest on debt, with lending-related penalty fees and cash advance fees contributing another $12.4 billion, per Philadelphia Fed Economist Lukasz Drozd.
While the precise order of events is a little more complex, the general gist of the subprime mortgage crisis was straightforward: easily-available money allowed massive amounts of people — many of whom couldn’t afford to buy these houses outside of the easy money that funded the bubble — to enter the housing market, which in turn made it much easier to sell a house for a much higher price, which inflated the value of housing.
People made decisions based on fundamentally-flawed information. In January 2004, the Bush administration declared that America’s economy was on the path to recovery, with small businesses creating the majority of new jobs and the stock market booming. Debt was readily-available across the board, with commercial and industrial loans spiking along with consumer debt (including a worrying growth in subprime auto loans). The good times were rolling, as long as you didn’t think about it too hard.
But, as I said, the chain of events was simple: it was easy to borrow money to buy a house, which meant lots of people were buying houses, which meant that the value of a house seemed higher than it was outside of the easy money era. Easily-available money put lots of cash into the economy, which led to higher prices, which led to inflation, which forced the federal reserve to raise interest rates 17 times in the space of two years, which made it harder to get any kind of loan, which made it harder to get a mortgage, which made it harder to sell a house, which made people sell houses for cheaper, which lowered the value of houses, which made it harder to refinance the bad loans, which meant people foreclosed on their homes, which in turn lowered the value of housing, all as demand for housing dropped because nobody was able to buy housing.
The underlying problems were, ultimately, the illusion of value and mobility. Those borrowing at the time believed they had invested in something with a consistent (and consistently-growing) value — a house — and would always have easy access to credit (via credit cards and loans), as before-tax family income had never been higher. In the beginning of 2007, delinquencies on consumer and business loans climbed, abandoned housing developments grew, and a US economy dependent on the housing bubble (per Paul Krugman’s “That Hissing Sound” from August 2005) began to stumble. By November 2009, 23% of US consumer mortgages were underwater (meaning they were worth less than their loans).
The housing bubble was created through easily-available debt, insane valuations based on debt-fueled speculation, do-nothing regulators (like eventual Fed Chair Ben Bernanke, who said in October 2005 that there was no housing bubble) and consumers being sold an impossible, unsustainable dream by people financially incentivized to make them rationalize the irrational, and believe that nothing bad will ever happen.
In February 2005, 40% ($19 billion) of IndyMac Bancorp’s mortgage originations in a single quarter came from a “Pay-Option ARM,” which started with a 1% teaser rate which jumped in a few short months to 4% or more, with frequent adjustments. Washington Mutual CEO Kerry Killinger said in 2003 that he wanted WaMu to be “the Wal-Mart of banking,” and did so by using (to quote the New York Times) “relaxed standards,” including issuing a mortgage to a mariachi singer who claimed a six-figure income and verified it using a single photo of himself.
By the time it collapsed in September 2008, WaMu had over $52.9 billion in ARMs and $16.05 billion in subprime mortgage loans.
Had Washington Mutual and the many banks making dodgy ARM and subprime loans underwritten loans based on the actual creditworthiness of their applicants, there wouldn’t have been a housing bubble, because many of these borrowers would’ve been unable to pay their mortgages, and thus wouldn’t have been deemed creditworthy, and thus no apparent housing demand would’ve grown.
In very simple terms, the “demand” for housing was inflated by a deceitfully-priced product that undersold its actual costs, and through that deceit millions of people were misled into believing said product was viable.
Did you work out where this is going yet?
In September 2024, I raised my first concerns about a Subprime AI Crisis:
I hypothesize a kind of subprime AI crisis is brewing, where almost the entire tech industry has bought in on a technology sold at a vastly-discounted rate, heavily-centralized and subsidized by big tech. At some point, the incredible, toxic burn-rate of generative AI is going to catch up with them, which in turn will lead to price increases, or companies releasing new products and features with wildly onerous rates — like the egregious $2-a-conversation rate for Salesforce’s “Agentforce” product — that will make even stalwart enterprise customers with budget to burn unable to justify the expense.
This theory is important, and thus I’m going to give it a lot of time and love to break it down.
That starts with the parties involved, and how the economics involved get worse over time, returning to my theory of “AI’s chain of pain, and the hierarchy of how the actual AI economy works.
The AI industry has done a great job in obfuscating exactly how brittle its economics really are, and as a result, I need to explain both how money is raised, money is deployed, and where the economics begin to break down.
Generally, AI is funded from only a few places::
Some things to keep note of:
This is a crucial point, so stay with me.
AI models work by charging a per-million token rate for inputs (things you feed in) and outputs, which are either the things that the model outputs (such as an image, text or code), or the “chain of thought reasoning” many models rely upon now, where they take an input, generate a plan (which is an “output”) and then do stuff based on said plan.
AI startups, for the most part, do not have their own models, and thus must pay OpenAI or Anthropic (or other providers to a much lesser extent) to build services using them.
When you pay for access to an AI startup’s service — which, of course, includes OpenAI and Anthropic — you do so for a monthly fee, such as $20, $100 or $200-a-month in the case of Anthropic’s Claude, Perplexity’s $20 or $200-a-month plan, or OpenAI’s $8, $20, or $200-a-month subscriptions. In some enterprise use cases, you’re given “credits” for certain units of work, such as how Lovable allows users “100 monthly credits” in its $25-a-month subscription, as well as $25 (until the end of Q1 2026) of cloud hosting, with rollovers of credits between months.
When you use these services, the company in question then pays for access to the AI models in question, either at a per-million-token rate to an AI lab, or (in the case of Anthropic and OpenAI) whatever cloud provider is renting them the GPUs to run the models. A token is basically ¾ of a word.
As a user, you do not experience token burn, just the process of inputs and outputs. AI labs obfuscate the cost of services by using “tokens” or “messages” or 5-hour-rate limits with percentage gauges, and you, as the user, do not really know how much any of it costs. On the back end, AI startups are annihilating cash, with up until recently Anthropic allowing you to burn upwards of $8 in compute for every dollar of your subscription. OpenAI allows you to do the same, though it’s hard to gauge by how much.
This is where the economic problem has begun. When the AI bubble started, venture capitalists flooded AI startups with cash, encouraging them to create hypergrowth businesses using, for the most part, monthly subscription costs that didn’t come close to covering the costs.
As a result, many AI companies have experienced rapid growth selling a product that can only exist with infinite resources.
The problem is fairly simple: providing AI services is very expensive, and costs can vary wildly depending on the customer, input and output, the latter of which can change dramatically depending on the prompt and the model itself. A coding model relies heavily on chain-of-thought reasoning, which means that despite the cost of tokens coming down (which does not mean the price of providing them has decreased, it’s a marketing move), models are using far, far more tokens, increasing costs across the board.
And consumers crave new models. They demand them. A service that doesn’t provide access to a new model cannot compete with those that do, and because the costs of models have been mostly hidden from users, the expectation is always the newest models provided at the same price.
As a result, there really isn’t any way that these services make sense at a monthly rate, and every single AI company loses incredible amounts of money, all while failing to make that much revenue in the first place.
For example, Harvey is an AI tool for lawyers that just raised $200 million at an $11 billion valuation, all while having an astonishingly small $190 million in ARR, or $15.8 million a month. It raised another $160 million in December 2025, after raising $300 million in June 2025, after raising $300 million in February 2025.
Cursor is an AI coding tool that raised $160 million in 2024 (As of December 2024, it had $48 million ARR, or around $4 million of monthly revenue), $900 million ($500 million ARR/$41.6 million) in June 2025, and $2.3 billion in November 2025 ($1 billion ARR/$83 million). As of March 2, 2026, Cursor was at $2 billion annualized revenue, or $166 million in monthly revenue.
I’ll get to Cursor in a little bit, because it’s crucial to the Subprime AI Crisis.
The Subprime AI Crisis is what happens when somebody actually needs to start making money, or, put another way, stop losing quite so much, revealing how every link in the chain was funded based on questionable assumptions and deadly short-term thinking.
Here’s the order of events as I see them.
The entire generative AI industry is based on unprofitable, unsustainable economics, rationalized and funded by venture capitalists and bankers speculating on the theoretical value of Large Language Model-based services. This naturally incentivized developers to price their subscriptions at rates that attracted users rather than reflecting the actual economics of the services.
Sidenote: This is what worked in the past, if you squint hard enough. In reality, there are no historical comparisons to the AI bubble’s economics in the entire history of tech — no business has been this bad, no software has ever cost this much, and no solution exists other than charging prices that are 10x higher or reducing rate limits to the point that users want to kill you.
Venture capitalists are also part of the subprime AI crisis, sitting on “billions of dollars” of AI companies that lose hundreds of millions of dollars, their companies built on top of AI models owned by OpenAI and Anthropic with little differentiation and no path to profitability. Nobody is going public! Nobody is getting acquired! As I discussed back in AI Is A Money Trap, there really is no liquidity mechanism for the billions of dollars sunk into most AI companies. Going public also reveals the ugly financial condition of these startups. MiniMax, for example, made a pathetic $79 million in revenue in 2025, and somehow lost $250.9 million in the process.
Much like the houses in the great financial crisis, AI startups only retain their value as long as there is a market, or at least the perception that these companies could theoretically go public or be acquired. It only takes one failed exit or firesale to break the illusion.
At least you can live in a house. Every AI company will be a problem child that burns money on inference, bereft of intellectual property thanks to their dependence on OpenAI and Anthropic. What use is Perplexity without an eternal subsidy? The value of having Aravind Srivinas sitting around your office all day? I’d rather start my car in the garage.
“Fast-growing” AI companies only grew because they were allowed to burn as much money as they wanted selling services that are entirely unsustainable, raising more venture capital money with every burst of user growth, which they use to aggressively market to new users and grow further to raise another bump of venture capital.
As a result, AI labs and AI startups have created negative habits with their users in two ways:
To grow their user bases as fast as possible, AI startups (and AI labs) allowed their users to burn incredible amounts of tokens, I assume because they believed at some point things would become profitable or they’d always have access to easy venture capital. This created an entire industry of AI startups that disconnected their users from the raw economics of the product, creating a race to the bottom where every single AI startup must have every AI model and every AI feature and do every AI thing, all at an incredible cost that only ever seems to increase.
Another fun feature is that just about every product gives some sort of “free” access period for new (and expensive!) models, like when Cursor had a free access period for GPT 5’s launch. It’s unclear who shoulders the burden here, but somebody is paying those costs.
In any case, nowhere are the subsidies higher than those of Anthropic and OpenAI, who use their tens of billions of dollars of funding to allow users to burn anywhere from $3 to $13 per every dollar of subscription revenue to outpace their competition.
The Subprime AI Crisis is when the largest parties are finally forced to reckon with their rotten economics, and the downstream consequences that follow.
As I reported in July 2025, starting in June last year, both OpenAI and Anthropic launched “priority service tiers,” jacking up the price on their enterprise customers (who pay for model access via their API to provide models in their software) for guaranteed uptime and less throttling of their services while also requiring an up-front (3-12 month) guarantee of token throughput.
Anthropic’s changes immediately increased the costs on AI startups like Lovable, Replit, Augment Code, and Anthropic’s largest customer, Cursor, which was forced to dramatically change its pricing from a per-request model to a bizarre pricing model where you pay model pricing with a 20% fee, but also receive A) at least as much as you pay in your subscription fee in tokens and B) “generous included usage” of Cursor’s Composer model:
What’s crazy is that even with this pricing, Cursor still gives away 16 cents for every dollar on its $60-a-month plan and $1 for every dollar on its $200-a-month plan, and that’s before “generous usage” of other models.
I’ll also add that Anthropic has already turned the screws on its subscription customers too, adding weekly limits to Claude subscribers on July 28, 2025, a few weeks after quietly tightening other limits.
Over the next few months, just about every AI startup had to institute some form of austerity. Replit shifted to something called “effort-based” pricing in June 2025, and then launched something called “Agent 3” in September 2025 that burned through users’ limits even faster — and, to be clear, Replit’s pricing gives you your subscription price in credits every single month on top of the cloud hosting necessary to get them online, meaning that a $20-a-month subscriber likely burns at least $25 a month, and Replit remains unprofitable.
Coding platform Augment Code was forced to change its pricing in October 2025 on a per-message basis, which meant that any message you sent cost the same amount no matter how complex the required response. In one case, a user spent $15,000 in tokens on a $250-a-month plan. Since then, Augment Code has moved to a confusing “credit” based model where they claim you use about 293 credits per Claude Sonnet 4.5 task, and users absolutely hate it because Augment Code was too cowardly to charge users based on the actual model pricing, because doing so would scare them away.
Now Augment Code is planning to remove its auto-complete and next edit features, claiming that their global usage was in decline and saying that developers “...are no longer working primarily at the level of individual lines of code; instead, they are orchestrating fleets of agents across tasks.”
Elsewhere, Notion bumped its Business Plan from $15 to $20-a-month per user thanks to its new “AI features,” which I imagine sucked for previous business subscribers who didn’t want “AI agents” or any of that crap but did want things like Single Sign On and Premium Integrations. The result? Profit margins dropped by 10%. Great job everybody!
In February 2026, Perplexity users noticed that rate limits had been aggressively trimmed from even its January 2026 limits, with $20-a-month subscribers now limited to arbitrary “average use weekly limits” on searches, and “monthly limits” on research queries (that one user worked out dropped them from 600 deep research queries a month to 20), down from 300+ searches a day and generous deep research limits.
Price hikes and product changes are likely to accelerate in the next few months as things get desperate. But now for a quick intermission…
I have been training with with Nik Suresh, author of I Will Fucking Piledrive You If You Mention AI Again, and while I’m kidding, I want to be clear that if you don’t stop bringing up Uber and AWS as examples of why AI will work out I may react poorly as I’m fucking tired of this point because it’s stupid and wrong. I will put you in the embrace of God, I swear.
The AI bubble and its representative companies do not and have never represented the buildout of Amazon Web Services or the growth and burnrate of Uber. If you are still saying this you are wrong, ignorant and potentially a big fucking liar.
As I discussed about a month ago, Amazon Web Services cost around $52 billion (adjusted for inflation!) between 2003 (when it was first used internally) through two years after it hit profitability (2017). OpenAI raised $42 billion last year. Anthropic raised $30 billion in February. You are full of shit if you keep saying this.
As I discussed a few weeks ago, Uber’s economics are absolutely nothing like generative AI. Uber did not have capex, and burned those billions on R&D and marketing (making it more similar to Groupon in the end):
“But Ed, What About Uber?”
What about Uber? Uber is a completely different business to Anthropic and OpenAI or any other AI company. It lost about $30 billion in the last decade or so, and turned a weird kind of profitable through a combination of cutting multiple markets and business lines (EG: autonomous cars), all while gouging customers and paying drivers less.
The economics are also completely different. Uber does not pay for its drivers’ gas, nor their cars, nor does it own any vehicles. Its PP&E has been between $1.5 billion and $2.1 billion since it was founded. Uber’s revenue does not increase with acquisitions of PP&E, nor does its business become significantly more expensive based on how far a driver drives, how many passengers they might have in a day, or how many meals they might deliver. Uber is, effectively, a digital marketplace for getting stuff or people moved from one place to another, and its losses are attributed to the constant need to market itself to customers for fear that other rideshare (Lyft) or delivery companies (DoorDash, Seamless) might take its cash.
Also: Uber’s primary business model was on a ride-by-ride basis, not a monthly subscription. Users may have been paying less, but they were still thinking about each transaction with Uber in terms that made sense when prices were raised (though it briefly tried an unlimited ride pass option in 2016).
Here’re some other myths I’m tired of hearing about:
Yet the most obvious one that I hear is the funniest: that Anthropic and OpenAI can just raise their prices!
As both OpenAI and Anthropic aggressively stumble toward their respective attempts to take their awful businesses public, both are making moves to try and become “respectable businesses,” by which I mean “businesses that still lose billions of dollars but in less-annoying ways.” Last week, OpenAI killed Sora — both the app and the model — along with a $1 billion investment from Disney, with the Wall Street Journal reporting it was burning a million dollars a day, but Forbes estimating the number was closer to $15 million.
OpenAI will frame this as part of its "refocus" on a “Superapp” (per the WSJ) that combines ChatGPT, coding app codex, and its dangerously shit browser into one rat king of LLM toys that nobody can work out a real business model for. All of this is part of a supposed internal effort to “prioritize coding and business customers” that we’ve heard some version of for months. Meanwhile, OpenAI’s attempts to bring advertising to its users have been a little embarrassing, with a two-month-long trial involving “less than 20%” of ChatGPT users resulting in “$100 million in annualized revenue,” better known as about $8.3 million in a month from what was meant to be a business line that brought in “low billions” in 2026 according to the Financial Times.
Timing confusingly with this “refocus” is OpenAI’s plan to nearly double its workforce from 4,500 to 8,000 people by the end of 2026. In fact, writing all this down makes it feel like OpenAI doesn’t really have much of a focus beyond “buy more stuff” and saying “superapp!” every six months. Hey, whatever happened to OpenAI’s plan to be “the interface to the internet” that Alex Heath reported would happen by the first half of 2025? Did that happen? Did I miss it?
In any case, OpenAI’s other strategy is to absolutely jam the gas pedal on its Codex coding product — for example, one user I found was able to burn $2,192 in tokens on a $200-a-month ChatGPT plan, and another was able to burn $1,461 in three days on the same subscription.
Meanwhile, Anthropic has been in the midst of a months-long rugpull following an all-out media campaign through December and January, pushing Claude Code on tech and business reporters who don’t bother to think too hard about things, per my Hater’s Guide to Anthropic:
On December 3 2025, the Financial Times would report that an Anthropic IPO would be happening as soon as 2026, while also revealing that the company was already working on another funding round valuing it at $300 billion.
Around this point, something strange started happening. Posts started appearing claiming that Claude Code was the best thing ever. Software development was “now boring” because of how good it was. Even Dario Amodei, a person directly incentivized to lie about it, said that an indeterminate number of coders at Anthropic no longer wrote any code. Even the creator of Claude Code said it did all his coding. One blogger said it was getting “too good.” Twitter flooded with obtuse stories about how Claude Code was doing all the work and they were scared about how good it was making them, all without really explaining what that meant.
In the last week of December, Anthropic would push a promotion doubling the rate limits on all of its monthly plans from December 25 to December 31, 2025.
By January 5, 2026, users were complaining about punishing new rate limits, with one user claiming that there had been a 60% reduction in token usage. Anthropic claimed that this was simply the expiration of holiday rate limits, but in reality, this is all part of Anthropic’s continual manipulation of rate limits to con customers into buying Claude subscriptions that decay in value.
In the end, Anthropic got what it wanted. The Verge would claim that Claude Code was “having a moment,” with word-of-mouth exposure spiking by 13% points compared to the prior 30-day period between December 29th and January 26th, likely because of all the fucking media coverage and astroturfing. Despite there not really being a thing that anybody could point at, Claude Code was apparently the biggest thing ever, terrifying competitors and changing lives in some indeterminate way that was very cool, possibly.
The media campaign worked, and Anthropic was able to close a $30 billion round on February 12, 2026.
On February 18, 2026, Anthropic started banning anybody who used multiple Claude Max accounts, something that had never been an issue before it needed everybody to talk about Claude Code non-stop. The same day, Anthropic “cleared up” its Claude Code policies, saying that you can’t connect your Claude account to external services, meaning that all of those people who have been spinning up OpenClaw instances and buying $10,000 worth of Mac Minis are going to find that they’re suddenly having to pay for their API calls.
Around a month later, Anthropic would start a two-week-long 2x-rate limit promotion for off-peak usage that ended on March 27, 2026.
A day before on March 26 2026, Anthropic would announce that it was starting “peak hours,” with Claude users maxing out their sessions faster between the hours of 5am and 11PM pacific time Monday to Friday, with a spokesperson limply adding that “efficiency wins” will “offset this” and only “7% of users will hit the limits.” All of this was sold as a result of “managing the growing demand for Claude.”
If I’m honest, this might be Anthropic’s most-egregious swindle yet. By pumping off-peak usage and then immediately cutting it just before introducing peak hours, Anthropic further muddies the water of how much actual access you get to their products. Peak hours appear to have become aggressively restricted, and I imagine off peak feels…something like the regular peak hours used to.
Users almost immediately started hitting limits regardless of what time or day they were using it.
One user on the $100-a-month Max plan complained about hitting 61% of his session limit after four prompts (which cost $10.26 in tokens). Another said that they hit 63% of their rate limit on their $200-a-month plan in the space of a day, and another hit 95% after 20 minutes of using their Max plan (I’m gonna guess $100-a-month). This person hit their Max limit after “two or three things.” This one vowed to cancel their $200-a-month subscription after hitting their weekly limit in the space of a day, saying that they (and I’m going off of a translation, so forgive me) “expected a premium experience for $200, and what they got was constant limit stress.” This guy is scared to use Claude Code because of the limits. This guy blew 28% of his limits in less than an hour. This guy “can’t even do basic work on a 20x Max plan.” This guy hit his limits “in a few prompts” on Anthropic’s $20-a-month Pro plan, and the same prompts would have (apparently) consumed 5% of the limits “normally” (I assume last week), and while Thariq from Anthropic assured him that this was abnormal, he didn’t bother to respond to this guy in the thread who said he ran out of usage on the Max plan in 15 minutes.
While Anthropic Technical Staff Member Lydia Hallie posted that Anthropic was “aware people are hitting usage limits in Claude Code way faster than expected” and that some investigation was taking place, it’s hard to imagine that Anthropic had no idea that these limits were so severe or that any of this was a surprise.
Naturally, OpenAI had already reset limits on its Codex coding model the second that these reports begun, claiming that they “wanted people to experiment with the magnificent plugins they launched” rather than saying something more-truthful like “we’re lowering limits so that the hogs braying with anger at Anthropic start paying OpenAI instead.”
While an eager Redditor claimed that these rate limits were a result of a cache bug on Claude Code, Anthropic quickly said that this wasn’t the reason, nor did they say anything about there being a reason or that anything was wrong.
Meanwhile, users are complaining about the reduced quality of outputs from its Claude Opus 4.6 model, with some saying it acts like cheaper models, and another noting that it might be because of Anthropic’s upcoming Mythos model, which was leaked when Fortune mysteriously somehow discovered an openly-accessible “data cache” that included 3000 assets but somehow no actual information about the model other than it would be a “step change” and its cybersecurity powers were too much to release at once, the tech equivalent of deliberately dropping a magnum condom out of your wallet in front of a woman, or Dril’s “I was just buying ear medication for my sick uncle…who’s a model by the way” post.
I’m gonna be honest I just don’t give a shit about Mythos or Capybara or any blatant leaks intended to spook cybersecurity stocks, especially as these models are also meant to be much more compute-intensive, and thus, vastly more expensive to run.
How will that work with these rate limits, exactly?
I think there’re a few ways this goes:
I wager that this is just the first of a few major belt-tightening operations from both Anthropic and OpenAI as they desperately shoulder-barge each other to file the world’s worst S-1. Both companies lose billions of dollars, both companies have no path to profitability, and both companies sell products — both to consumers and businesses — that simply do not work when users are forced to pay something approaching a sustainable cost.
Even with these egregious limits, a user I previously linked to was allowed to burn $10 in tokens in four prompts on a $100-a-month plan. Even in the world of Amodei’s Stylized Facts, that would still be $5 of prompts every 5 hours, which over the course of a month will absolutely be over $100.
Yet the sheer fury of Anthropic’s customers only proves the fundamental weakness of Anthropic’s business model, and the impossibility of ever finding any kind of profitability.
And the AI industry has nobody to blame but itself.
While it’s really easy to make fun of people obsessed with LLMs, I want to be clear that Anthropic and OpenAI are inherently abusive companies that have built businesses on theft, deception and exploitation.
Anybody who’s spent more than a few minutes in one of the many AI Subreddits has read story after story of models mysteriously “becoming dumb,” or rate limits that seem to expand and contract at random. Even the concept of “rate limits” only serves to further deceive the customer. Outside of intentionally asking the model, users are entirely unaware of their “token burn,” or at the very least have built habits around rate limits that, as of right now, are entirely different to even a month ago.
A user who bought a $200-a-month Claude Pro subscription in December 2025, a mere three months later, now very likely cannot do the same things they did on Claude Code when they decided to subscribe, and those who use these subscriptions for their day jobs are now having to sit on their hands waiting for the rate limits to pass, and have no clarity into whether they’ll be able to work at the same rate they did even a month ago, let alone when they subscribed.
All of this is a direct result of Anthropic, OpenAI, and other AI startups intentionally deceiving customers through obtuse pricing so that people would subscribe believing that the product would continue providing the same value, and I’d argue that annual subscriptions to these services amount to, if not fraud, a level of consumer deception that deserves legal action and regulatory involvement.
To be clear, no AI company should have ever sold a monthly subscription, as there was never a point at which the economics made sense. Yet had these companies actually charged their real costs, nobody would have bothered with AI, because even with these highly-subsidized subscriptions, AI still hasn’t delivered meaningful productivity benefits, other than a legion of people who email me saying “it’s changed my life as a programmer!” without explaining to me what that means or why it matters or what the actual result is at the end.
Isn’t it kind of weird that we have these LLM subscriptions to products that arbitrarily become less-accessible or less-performant in a way that’s impossible to really measure, and labs never seem to address? We don’t know the actual rate limits on Claude (other than via CCusage or Shellac’s research), or ChatGPT, or any of these products by design, because if we did, it would be blatantly obvious how unsustainable and ridiculous these products were.
And the magical part about Large Language Models is that your most engaged customers are also your most-expensive, and the more-intensive the work, the more expensive the outputs become.
If you’re about to say “well they’ll just raise the prices,” perhaps you should check Twitter or Reddit, and notice that Anthropic’s customers are screaming like they’re being stung to death by bees because of new rate limits that only let them burn $10 of compute in five hours. Do you think these people would be comfortable with a $130-a-month, $1,300-a-month or $2,500-a-month subscription? One that performs the same way (if not worse) as their $20, $100 or $200-a-month subscription did?
Or do you think they’ll do Aaron Sorkin speeches about Anthropic’s greed and immediately jump to ChatGPT in the hopes that the exact same thing doesn’t happen a few months later?
Much as homeowners were assured that they’d simply be able to refinance their homes before the adjustable rates hit, AI fans repeatedly switch subscriptions to whichever provider is currently offering the best deal, in some cases paying for multiple subscriptions under the explicit knowledge that rate limits existed and would become increasingly-punishing.
Based on the reactions of their users, I don’t really see how the AI labs — or AI startups, for that matter — fix this problem.
On one hand, AI subscribers are acting like babies, crying that their product won’t let them use $2500 of tokens for $200. This was an obvious con, a blatant subsidy, and a party that wouldn’t last forever.
On the other, AI labs and AI startups have never, ever acted with any degree of honesty or clarity with regards to their costs, instead choosing to add “exciting” new features that often burn more tokens without charging the end user more, which sounds nice until you remember that things cost money and money is not unlimited.
The very foundation of every AI startup is economically broken. The majority of them sell some sort of “deep research” report feature that costs several dollars to generate at a time, and many sell some form of expensive coding or “computer use” product, tool-based web search features, and many other products that exist to keep a user engaged while burning tokens, all without explaining to the user “yeah, we’re spending way more than we make off of you, this is an introductory rate.”
This intentional, blatant and industry-wide deception set the terms for the Subprime AI Crisis. By selling AI services at $20 or $50 or even $200-a-month, AI startups and labs created the terms for their own destruction, with users trained for years to expect relatively unlimited access sold at a flat rate for a service powered by Large Language Models that burn tokens at arbitrary rates based on their inference of the user’s prompt, making costs near-impossible to moderate.
And when these companies make changes to slightly bring costs under control, their users act with revulsion, because rate limits aren’t price increases, but direct changes to the functionality of the product. Imagine if a subscription to a car service was $200-a-month, and let you go 50 miles, or 25 miles, or 100 miles, or 4 miles, or 12 miles depending on the day, and never at any point told you how many miles you had left beyond a percentage-based rate limit. To make matters worse, sometimes the car would arbitrarily take a different route, driving you five miles in the opposite direction, or decide to park on the side of the curb, charging you for every mile.
This is the reality of using an AI product in the year of our lord 2026. A Claude Code or OpenAI Codex user cannot with any clarity say that in three months their current workload or workflow will be possible based on their current subscription. Somebody buying an annual subscription to any AI product is immediately sacrificing themselves to the whims of startup CEOs that intentionally decided to deceive users for years as a means of juicing growth.
And when these limits decay, does it eventually make the ways in which some of these users work with Claude Code impossible? At what point do these rate limit shifts start changing how reliable the experience is and how much one can get done in a day? What use is a tool that gets more unreliable to access and expensive over time? Even if this week’s rate limits are an overcorrection, one has to imagine they resemble the future of Anthropic’s products, and are indicative of a larger pattern of decay in the value of its subscriptions.
I’m going to be as blunt as possible: every bit of AI demand — and barely $65 billion of it existed in 2025 — that exists only exists due to subsidies, and if these companies were to charge a sustainable rate, said demand would evaporate.
There is no righting this ship. There is no pricing that makes sense that customers will pay at scale, nor is there a magical technological breakthrough waiting in the wings that will reduce costs. Vera Rubin will not save AI, nor will some sort of “too big to fail” scenario, because “too big to fail” was based on the fact that banks would have stopped providing dollars to people and insurance companies would have stopped issuing insurance.
Despite NVIDIA’s load-bearing valuation and the constant discussion of companies like OpenAI and Anthropic, their actual economic footprint is quite small in comparison to the trillions of dollars of CDOs and trillion plus dollars of mortgages involved in the great financial crisis. The death of the AI industry would be cataclysmic to venture capitalists, bring about the end of the hypergrowth era for the Magnificent Seven, and may very well kill Oracle, but — seriously — that is nothing in comparison to the scale of the Great Financial Crisis. This isn’t me minimizing the chaos to follow, but trying to express how thoroughly fucked everything was in 2008.
On Friday I’m going to get into this more in the premium. This wasn’t an intentional ad, I just realized as I wrote that sentence that that was what I have to do.
Anyway, I’ll close with a grim thought.
What’s funny about the comparison to the subprime mortgage crisis is that there are, in all honesty, multiple different versions of the Stripper With Five Houses from The Big Short:
All of these entities are acting based on a misplaced belief that the world will cater to them, and that nothing will ever change. While there might be different levels of cynicism — people that know there’re subsidies but assume they’ll be fine once they arrive, or people like Sam Altman that are already rich and don’t give a shit — I think everybody in the AI industry has deluded themselves into believing they have the mandate of Heaven.
Back in August 2024, I named several pale horses of the AIpocalypse, and after absolutely fucking nailing the call two years early on OpenAI’s “big, stupid magic trick” of launching Sora to the public, I think it’s time to update them:
Anyway, thanks for reading this piece.
2026-03-28 01:21:30
I’m turning 40 in a month or so, and at 40 years young, I’m old enough to remember as far back as December 11 2025, when Disney and OpenAI “reached an agreement” to “bring beloved characters from across Disney’s brands to Sora.” As part of the deal, Disney would “become a major customer of OpenAI,” use its API “to build new products, tools and experiences (as well as showing Sora videos in Disney+),” and “deploy ChatGPT for its employees,” as well as making a $1 billion equity investment in OpenAI.
Just one small detail: none of this appears to have actually happened.
Despite an alleged $1 billion equity investment, neither Disney’s FY2025 annual report nor its February 2, 2026 Q1 FY2026 report mention OpenAI or any kind of equity investment. Disney+ does not show any Sora videos, and searching for “Sora” brings up “So Random,” a musical comedy sketch show from 2011 with a remarkably long Wikipedia page that spun off from another show called “Sonny With A Chance” after Demi Lovato went into rehab.
It doesn’t appear that investment ever happened, likely because — as was reported earlier this week by The Information and the Wall Street Journal — OpenAI is killing Sora. Shortly after the news was reported, The Hollywood Reporter confirmed that the deal with Disney was also dead.
Per The Journal, emphasis mine:
CEO Sam Altman announced the changes to staff on Tuesday, writing that the company would wind down products that use its video models. In addition to the consumer app, OpenAI is also discontinuing a version of Sora for developers and won’t support video functionality inside ChatGPT, either.
Oh, okay! The app that CNBC said was “challenging Hollywood” and “freaking out the movie industry” and The Hollywood Report would suggest could somehow challenge Pixar and was Sam Altman successfully “playing Hollywood” and that The Ankler said was OpenAI “going to war with Hollywood” as it “shook the industry” and that Deadline said made Hollywood “sore” and that Boardroom said was in a standoff with Hollywood and that the LA Times said was “deepening a battle between Hollywood and OpenAI” and “igniting a firestorm in Hollywood” and that Puck said had “Hollywood panicking” and TechnoLlama said was “the end of copyright as we know it” and that Slate said was a case of AI "crushing Hollywood as it we’ve known it” is completely dead a little more than five months after everybody claimed it was changing everything.
It’s almost as if everybody making these proclamations was instinctually printing whatever marketing copy had been imagined by the AI labs to promote compute-intensive vaporware, and absolutely nobody is going to apologize to the people working in the entertainment industry for scaring the fuck out of them with ghost stories! Every single person who blindly repeated that Sora existed and was changing everything should be forced to apologize to their readers!
I cannot express the sheer amount of panic that spread through every single part of the entertainment industry as a result of these specious, poorly-founded mythologies spread by people that didn’t give enough of a shit to understand what was actually going on. Sora 2 was always an act of desperation — an attempt to create a marketing cycle to prop up a tool that burned as much as $15 million a day that most of the mainstream media bought into because they believe everything OpenAI says and are willing to extrapolate the destruction of an entire industry from a fucking facade.
Thanks to everyone who participated in this grotesque scare-campaign, everybody I know in the film industry has been freaking out because every third headline about Sora 2 said that it would quickly replace actors and directors. The majority of coverage of Sora 2 acted as if we were mere minutes from it replacing all entertainment and all video-based social media, even though the videos themselves were only a few seconds long and looked like shit!
Sora 2 was never “challenging Hollywood” or “a threat to actors and directors,” it was a way to barf out videos that looked very much like Sora 2’s training data, and the reason you could only generate a few seconds at a time was these models started hallucinating stuff very quickly, because that’s what Large Language Models do.
Yet this is what the AI bubble is — poorly-substantiated media-driven hype cycles that exploit a total lack of awareness or willingness to scrutinize the powerful. Sora 2 was always a dog, it always looked like shit, it never challenged Hollywood, it never actually threatened the livelihoods of actors or directors or DPs or screenwriters outside of the tiny brains of studio executives that don’t watch or care about movies. Anybody that published a scary story about the power of Sora 2 helped needlessly spread panic through the performing arts, and should feel deep, unbridled shame.
You have genuinely harmed people I know and love, and need to wise up and do your fucking job.
I know, I know, you’re going to say you were “just reporting what was happening,” and that “OpenAI seemed unstoppable,” but none of that was ever true other than in your mind and the minds of venture capitalists and AI boosters. No, Sora 2 was never actually replacing anyone, that’s just not true, you made it up or had it made up for you.
But that, my friends, is the AI bubble. Five months can pass and an app can go from The End of Hollywood that apparently raised $1 billion to “discontinued via Twitter post that reads exactly like the collapse of a failed social network from 2013” and “didn’t actually raise anything.” It doesn’t matter if stuff actually exists, because it’ll be reported as if it does as long as a company says it’ll happen.
Perhaps I sound a little deranged, but isn’t anybody more concerned that a billion dollars that was meant to move from one company to another simply didn’t happen? Or, for that matter, that this keeps happening, again and again and again?
I’m serious! As I discussed in last year’s Enshittifinancial Crisis, OpenAI has had multiple deals that seem to be entirely fictional:
That’s just the AI bubble, baby! We don’t need actual stuff to happen! Just announce it and we’ll write it up! No problem, man! It doesn’t matter that one of the largest entertainment companies in the world simply didn’t give the most-notable startup in the world one billion dollars, much as it’s not a big deal that the entire media flew like Yogi Bear lured with a delicious pie toward every single talking point about OpenAI destroying Hollywood, much like it’s not a problem that Broadcom, AMD, SK Hynix, and Samsung all have misled their investors and the media about deals that range from threadbare to theoretical.
Except it is a problem, man! As I covered in this week’s free newsletter, I estimate that only around 3GW of actual IT load (so around 3.9GW of power) came online last year, and as Sightline reported, only 5GW of data center construction is actually in progress globally at this time, despite somewhere between 190GW and 240GW supposedly being in progress. In reality, data centers take forever to build (and obtaining the power even longer than that), but nobody needs to harsh their flow by looking into what’s actually happening.
In reality, the AI industry is pumped full of theoretical deals, obfuscations of revenues, promises that never lead anywhere, and mysterious hundreds of millions or billions of dollars that never seem to appear.
Beneath the surface, very little actual economic value is being created by AI, other than the single-most-annoying conversations in history pushed by people who will believe and repeat literally anything they are told by a startup or public company.
No, really. The two largest consumers of AI compute have made — at most, and I have serious questions about OpenAI — a combined $25 billion since the beginning of the AI bubble, and beneath them lies a labyrinth of different companies trying to use annualized revenues to obfuscate their meager cashflow and brutal burn-rate.
To make matters worse, almost every single data center announcement you’ve read for the last four years is effectively theoretical, their nigh-on-conceptual “AI buildouts” laundered through major media outlets to give the appearance of activity where little actually exists.
The AI industry is grifting the finance and media industry, exploiting a global intelligence crisis where the people with some of the largest audiences and pocketbooks have fundamentally disconnected themselves from reality.
I don’t like being misled, and I don’t like seeing others get rich doing so.
It’s time to get to the bottom of this.
2026-03-25 01:25:52
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The entire AI bubble is built on a vague sense of inevitability — that if everybody just believes hard enough that none of this can ever, ever go wrong that at some point all of the very obvious problems will just go away.
Sadly, one cannot beat physics.
Last week, economist Paul Kedrosky put out an excellent piece centered around a chart that showed new data center capacity additions (as in additions to the pipeline, not brought online) halved in the fourth quarter of 2025 (per data from Wood Mackenzie):
Wood Mackenzie’s report framed it in harsh terms:
US data-centre capacity additions halved from Q3 to Q4 2025 as load-queue challenges persisted. The decline underscores the difficulties of the current development environment and signals a resulting focus on existing pipeline projects. While Texas extended its pipeline capacity lead in Q4 2025, New Mexico, Indiana and Wyoming saw greater relative growth. Planned capacity continues to be weighted by new developers with a small number of massive, speculative projects, targeting in particular the South and Southwest. New Mexico owes its growth to a single, massive, speculative project by New Era Energy & Digital in Lea County.
As I said above, this refers only to capacity that’s been announced rather than stuff that’s actually been brought online, and Kedrosky missed arguably the craziest chart — that of the 241GW of disclosed data center capacity, only 33% of it is actually under active development:
The report also adds that the majority of committed power (58%) is for “wires-only utilities,” which means the utility provider is only responsible for getting power to the facility, not generating the power itself, which is a big problem when you’re building entire campuses made up of power-hungry AI servers.
WoodMac also adds that PJM, one of the largest utility providers in America, “...remains in trouble, with utility large load commitments three times as large as the accredited capacity in PJM’s risked generation queue,” which is a complex way of saying “it doesn’t have enough power.”
This means that fifty eight god damn percent of data centers need to work out their own power somehow. WoodMac also adds there is around $948 billion in capex being spent in totality on US-based data centers, but capex growth decelerated for the first time since 2023. Kedrosky adds:
The total announced pipeline looks huge at 241 GW — about twice US peak electricity demand — but most of it is not real. Only a third is under construction, with the rest a mix of hopeful permits, speculative land deals, and projects that assume power sources nobody has actually built yet. In particular, much of it assumes on-site gas plants, a fraught assumption given current geopolitics.
The most serious problem is in the mid-Atlantic. Regional grid operator PJM has made power commitments to data centers at roughly three times the rate that new generation is actually coming online. Someone is going to be waiting a very long time, or paying a lot more than they expected, or both.
Let’s simplify:
The term you’re looking for there is data center absorption, which is (to quote Data Center Dynamics) “...the net growth in occupied, revenue-producing IT load,” which grew in America’s primary markets from 1.8GW in new capacity in 2024 to 2.5GW of new capacity in 2025 according to CBRE.
Definition sidenote! “Colocation” space refers to data center space built that is then rented out to somebody else, versus data centers explicitly built for a company (such as Microsoft’s Fairwater data centers). What’s interesting is that it appears that some — such as Avison Young — count Crusoe’s developments (such as Stargate Abilene) as colocation construction, which makes the collocation numbers I’ll get to shortly much more indicative of the greater picture.
The problem is, this number doesn’t actually express newly-turned-on data centers. Somebody expanding a project to take on another 50MW still counts as “new absorption.”
Things get more confusing when you add in other reports. Avison Young’s reports about data center absorption found 700MW of new capacity in Q1 2025, 1.173GW in Q2, a little over 1.5GW in Q3 and 2.033GW in Q4 (I cannot find its Q3 report anywhere), for a total of 5.44GW, entirely in “colocation,” meaning buildings built to be leased to others.
Yet there’s another problem with that methodology: these are facilities that have been “delivered” or have a “committed tenant.” “Delivered” could mean “the facility has been turned over to the client, but it’s literally a powered shell (a warehouse) waiting for installation,” or it could mean “the client is up and running.” A “committed tenant” could mean anything from “we’ve signed a contract and we’re raising funds” (such as is the case with Nebius raising money off of a Meta contract to build data centers at some point in the future).
We can get a little closer by using the definitions from DataCenterHawk (from whichAvison Young gets its data), which defines absorption as follows:
To measure demand, we want to know how much capacity was leased up by customers over a specific period of time. At datacenterHawk we calculate this quarterly. The resulting number is what’s called absorption.
Let’s say DC#1 has 10 MW commissioned. 9 MW are currently leased and 1 MW is available. Over the course of a quarter, DC#1 leases up that last MW to a few tenants. Their absorption for the quarter would be 1 MW. It can get a little more complicated but that’s the basic concept.
That’s great! Except Avison Young has chosen to define absorption in an entirely different way — that a data center (in whatever state of construction it’s in) has been leased, or “delivered,” which means “a fully ready-to-go data center” or “an empty warehouse with power in it.”
CBRE, on the other hand, defines absorption as “net growth in occupied, revenue-producing IT load,” and is inclusive of hyperscaler data centers. Its report also includes smaller markets like Charlotte, Seattle and Minneapolis, adding a further 216MW in absorption of actual new, existing, revenue-generating capacity.
So that’s about 2.716GW of actual, new data centers brought online. It doesn’t include areas like Southern Virginia or Columbus, Ohio — two massive hotspots from Avison Young’s report — and I cannot find a single bit of actual evidence of significant revenue-generating, turned-on, real data center capacity being stood up at scale. DataCenterMap shows 134 data centers in Columbus, but as of August 2025, the Columbus area had around 506MW in total according to the Columbus Dispatch, though Cushman and Wakefield claimed in February 2026 that it had 1.8GW.
Things get even more confusing when you read that Cushman and Wakefield estimates that around 4GW of new colocation supply was “delivered” in 2025, a term it does not define in its actual report, and for whatever reason lacks absorption numbers. Its H1 2025 report, however, includes absorption numbers that add up to around 1.95GW of capacity…without defining absorption, leaving us in exactly the same problem we have with Avison Young.
Nevertheless, based on these data points, I’m comfortable estimating that North American data center absorption — as the IT load of data centers actually turned on and in operation — was at around 3GW for 2025, which would work out to about 3.9GW of total power.
And that number is a fucking disaster.
Earlier in the year, TD Cowen’s Jerome Darling told me that GPUs and their associated hardware cost about $30 million a megawatt. 3GW of IT load (as in the GPUs and their associated gear’s power draw) works out to around $90 billion of NVIDIA GPUs and the associated hardware, which would be covered under NVIDIA’s “data center” revenue segment:
America makes up about 69.2% of NVIDIA’s revenue, or around $149.6 billion in FY2026 (which runs, annoyingly, from February 2025 to January 2026). NVIDIA’s overall data center segment revenue was $195.7 billion, which puts America’s data center purchases at around $135 billion, leaving around $44 billion of GPUs and associated technology uninstalled.
With the acceleration of NVIDIA’s GPU sales, it now takes about 6 months to install and operationalize a single quarter’s worth of sales. Because these are Blackwell (and I imagine some of the new next generation Vera Rubin) GPUs, they are more than likely going to new builds thanks to their greater power and cooling requirements, and while some could in theory be going to old builds retrofitted to fit them, NVIDIA’s increasingly-centralized (as in focused on a few very large customers) revenue heavily suggests the presence of large resellers like Dell or Supermicro (which I’ll get to in a bit) or the Taiwanese ODMs like Foxconn and Quanta who manufacture massive amounts of servers for hyperscaler buildouts.
I should also add that it’s commonplace for hyperscalers to buy the GPUs for their colocation partners to install, which is why Nebius and Nscale and other partners never raise more than a few billion dollars to cover construction costs.
It’s becoming very obvious that data center construction is dramatically slower than NVIDIA’s GPU sales, which continue to accelerate dramatically every single quarter.
Even if you think AI is the biggest most hugest and most special boy: what’s the fucking point of buying these things two to four years in advance? Jensen Huang is announcing a new GPU every year!
By the time they actually get all the Blackwells in Vera Rubin will be two years old! And by the time we install those Vera Rubins, some other new GPU will be beating it!
Before we go any further, I want to be clear how difficult it is to answer the question “how long does a data center take to build?”. You can’t really say “[time] per megawatt” because things become ever-more complicated with every 100MW or so. As I’ll get into, it’s taken Stargate Abilene two years to hit 200MW of power.
Not IT load. Power.
Anyway, the question of “how much data center capacity came online?” is pretty annoying too.
Sightline’s research — which estimated that “almost 6GW of [global data center power] capacity came online last year” — found that while 16GW of capacity was slated to come online in 2026 across 140 projects, only 5GW is currently under construction, and somehow doesn’t say that “maybe everybody is lying about timelines.”
Sightline believes that half of 2026’s supposed data center pipeline may never materialize, with 11GW of capacity in the “announced” stage with “...no visible construction progress despite typical build timelines of 12-18 months.” “Under construction” also can mean anything from “a single steel beam” to “nearly finished.”
These numbers also are based on 5GW of capacity, meaning about 3.84GW of IT load, or about $111.5 billion in GPUs and associated gear, or roughly 57.5% of NVIDIA’s FY2026 revenue that’s actually getting built.
Sightline (and basically everyone else) argues that there’s a power bottleneck holding back data center development, and Camus explains that the biggest problem is a lack of transmission capacity (the amount of power that can be moved) and power generation (creating the power itself):
The biggest driver of delay is simple: our power system doesn’t have enough extra transmission capacity and generation to serve dozens of gigawatts of new, high-utilization demand 100% of the time. Data centers require round-the-clock power at levels that rival or exceed the needs of small cities, and building new transmission infrastructure and generation requires years of permitting, land acquisition, supply chain management, and construction.
Camus adds that America also isn’t really prepared to add this much power at once:
Inside utilities, planners and engineers are working diligently to connect new loads. But the tools available to planners were built for extending power lines to new neighborhoods or upgrading equipment as communities grow. They weren’t designed to analyze 50 new service requests of 100 MW each, all while new generation applications pile up.
As a result, planners and engineers are overwhelmed; they’re stuck working to review new applications while simultaneously configuring new tools that are better equipped for the scale of this challenge. And unlike generation interconnection, which has well-defined steps across most ISOs and utilities, the process for evaluating large loads is often much more ad hoc. This makes adopting the right tools much more difficult too. In fact, the majority of utilities and ISO/RTOs are still developing formal study procedures.
Nevertheless, I also think there’s another more-obvious reason: it takes way longer to build a data center than anybody is letting on, as evidenced by the fact that we only added 3GW or so of actual capacity in America in 2025. NVIDIA is selling GPUs years into the future, and its ability to grow, or even just maintain its current revenues, depends wholly on its ability to convince people that this is somehow rational.
Let me give you an example. OpenAI and Oracle’s Stargate Abilene data center project was first announced in July 2024 as a 200MW data center. In October 2024, the joint venture between Crusoe, Blue Owl and Primary Digital Infrastructure raised $3.4 billion, with the 200MW of capacity due to be delivered “in 2025.” A mid-2025 presentation from land developer Lancium said it would have “1.2GW online by YE2025.” In a May 2025 announcement, Crusoe, Blue Owl, and Primary Digital Infrastructure announced the creation of a $15 billion joint vehicle, and said that Abilene would now be 8 buildings, with the first two buildings being energized by the “first half of 2025,” and that the rest would be “energized by mid-2026.” Each building would have 50,000 GPUs, and the total IT load is meant to be 880MW or so, with a total power draw of 1.2GW.
I’m not interested in discussing OpenAI not taking the supposedly-planned extensions to Abilene because it never existed and was never going to happen.
In December 2025, Oracle stated that it had “delivered” 96,000 GPUs, and in February, Oracle was still only referring to two buildings, likely because that’s all that’s been finished. My sources in Abilene tell me that Building Three is nearly done, but…this thing is meant to be turned on in mid-2026. Developer Mortensen claims the entire project will be completed by October 2026, which it obviously, blatantly won’t.
I hate to speak in conspiratorial terms, but this feels like a blatant coverup with the active participation of the press. CNBC reported in September 2025 that “the first data center in $500 billion Stargate project is open in Texas,” referring to a data center with an eighth of its IT load operational as “online” and “up and running,” with Crusoe adding two weeks later that it was “live,” “up and running” and “continuing to progress rapidly,” all so that readers and viewers would think “wow, Stargate Abilene is up and running” despite it being months if not years behind schedule.
At its current rate of construction, Stargate Abilene will be fully built sometime in late 2027. Oracle’s Port Washington Data Center, as of March 6 2026, consisted of a single steel beam. Stargate Shackelford Texas broke ground on December 15 2025, and as of December 2025, construction barely appears to have begun in Stargate New Mexico. Meta’s 1GW data center campus in Indiana only started construction in February 2026.
And, despite Microsoft trying to mislead everybody that its Wisconsin data center had ‘arrived” and “been built,” looking even an inch deeper suggests very little has actually come online” — and, considering the first data center was $3.3 billion (remember: $14 million a megawatt just for construction), I imagine Microsoft has successfully brought online about 235MW of power for Fairwater.
What Microsoft wants you to think is it brought online gigawatts of power (always referred to in the future tense), because Microsoft, like everybody else, is building data centers at a glacial pace, because construction takes forever, even if you have the power, which nobody does!
The concept of a hundred-megawatt data center is barely a few years old, and I cannot actually find a built, in-service gigawatt data center of any kind, just vague promises about theoretical Stargate campuses built for OpenAI, a company that cannot afford to pay its bills.
Everybody keeps yammering on about “what if data centers don’t have power” when they should be thinking about whether data centers are actually getting built. Microsoft proudly boasted in September 2025 about its intent to build “the UK’s largest supercomputer” in Loughton, England with Nscale, and as of March 2026, it’s literally a scaffolding yard full of pylons and scrap metal. Stargate Abilene has been stuck at two buildings for upwards of six months.
Here’s what’s actually happening: data center deals are being funded by eager private credit gargoyles that don’t know shit about fuck. These deals are announced, usually by overly-eager reporters that don’t bother to check whether the previous data centers ever got built, as massive “multi-gigawatt deals,” and then nobody follows up to check whether anything actually happened.
All that anybody needs to fund one of these projects is an eager-enough financier and a connection to NVIDIA. All Nebius had to do to raise $3.75 billion in debt was to sign a deal with Meta for data center capacity that doesn’t exist and will likely take three to four years to build (it’s never happening). Nebius has yet to finish its Vineland, New Jersey data center for Microsoft, which was meant to be “at 100MW” by the end of 2025, but appears to have only had 50MW (the first phase) available as of February 2026.
I’m just gonna come out and say it: I think a lot of these data center deals are trash, will never get built, and thus will never get paid. The tech industry has taken advantage of an understandable lack of knowledge about construction or power timelines in the media to pump out endless stories about “data center capacity in progress” as a means of obfuscating an ever-growing scandal: that hundreds of billions of NVIDIA GPUs got sold to go in projects that may never be built.
These things aren’t getting built, or if they’re getting built, it’s taking way, way longer than expected, which means that interest on that debt is piling up. The longer it takes, the less rational it becomes to buy further NVIDIA GPUs — after all, if data centers are taking anywhere from 18 months to three years to build, why would you be buying more of them? Where are you going to put them, Jensen?
This also seriously brings into question the appetite that private credit and other financiers have for funding these projects, because much of the economic potential comes from the idea that these projects get built and have stable tenants. Furthermore, if the supply of AI compute is a bottleneck, this suggests that when (or if) that bottleneck is ever cleared, there will suddenly be a massive supply glut, lowering the overall value of the data centers in progress…which are, by the way, all filled with Blackwell GPUs, which will be two or three-years-old by the time the data centers are finally turned on.
That’s before you get to the fact that the ruinous debt behind AI data centers makes them all remarkably unprofitable, or that their customers are AI startups that lose hundreds of millions or billions of dollars a year, or that NVIDIA is the largest company on the stock market, and said valuation is a result of a data center construction boom that appears to be decelerating and even if it wasn’t operating at a glacial pace compared to NVIDIA’s sales.
Not to sound unprofessional or nothing, but what the fuck is going on? We have 241GW of “planned” capacity in America, of which only 79.5GW of which is “under active development,” but when you dig deeper, only 5GW of capacity is actually under construction?
The entire AI bubble is a god damn mirage. Every single “multi-gigawatt” data center you hear about is a pipedream, little more than a few contracts and some guys with their hands on their hips saying “brother we’re gonna be so fuckin’ rich!” as they siphon money from private credit — and, by extension, you, because where does private credit get its capital from? That’s right. A lot comes from pension funds and insurance companies.
Here’s the reality: data centers take forever. Every hyperscaler and neocloud talking about “contracted compute” or “planned capacity” may as well be telling you about their planned dinners with The Grinch and Godot. The insanity of the AI buildout will be seen as one of the largest wastes of capital of all time (to paraphrase JustDario), and I anticipate that the majority of the data center deals you’re reading about simply never get built.
The fact that there’s so much data about data center construction and so little data about completed construction suggests that those preparing the reports are in on the con. I give credit to CBRE, Sightline and Wood Mackenzie for having the courage to even lightly push back on the narrative, even if they do so by obfuscating terms like “capacity” or “power” in ways that reporters and other analysts are sure to misinterpret.
Hundreds of billions of dollars have been sunk into buying GPUs, in some cases years in advance, to put into data centers that are being built at a rate that means that NVIDIA’s 2025 and 2026 revenues will take until 2028 to 2029 to actually operationalize, and that’s making the big assumption that any of it actually gets built.
I think it’s also fair to ask where the money is actually going. 2025’s $178.5 billion in US-based data center deals doesn’t appear to be resulting in any immediate (or even future) benefit to anybody involved.
I also wonder whether the demand actually exists to make any of this worthwhile, or what people are actually paying for this compute.
If we assume 3GW of IT load capacity was brought online in America, that should (theoretically) mean tens of billions of dollars of revenue thanks to the “insatiable demand for AI” — except nobody appears to be showing massive amounts of revenue from these data centers.
Applied Digital only had $144 million in revenue in FY2025 (and lost $231 million making it). CoreWeave, which claimed to have “850MW of active power (or around 653MW of IT load)” at the end of 2025 (up from 420MW in Q1 FY2025, or 323MW of IT load), made $5.13 billion of revenue (and lost $1.2 billion before tax) in FY2025.
Nebius? $228 million, for a loss of $122.9 million on 170MW of active power (or around 130MW of IT load). Iren lost $155.4 million on $184.7 million last quarter, and that’s with a release of deferred tax liabilities of $182.5 million. Equinix made about $9.2 billion in revenue in its last fiscal year, and while it made a profit, it’s unclear how much of that came from its large and already-existent data center portfolio, though it’s likely a lot considering Equinix is boasting about its “multi-megawatt” data center plans with no discussion of its actual capacity.
And, of course, Google, Amazon, and Microsoft refuse to break out their AI revenues. Based on my reporting from last year, OpenAI spent about $8.67 billion on Azure through September 2025, and Anthropic around $2.66 billion in the same period on Amazon Web Services. As the two largest consumers of AI compute, this heavily suggests that the actual demand for AI services is pretty weak, and mostly taken up by a few companies (or hyperscalers running their own services.)
At some point reality will set in and spending on NVIDIA GPUs will have to decline. It’s truly insane how much has been invested so many years in the future, and it’s remarkable that nobody else seems this concerned.
Simple questions like “where are the GPUs going?” and “how many actual GPUs have been installed?” are left unanswered as article after article gets written about massive, multi-billion dollar compute deals for data centers that won’t be built before, at this rate, 2030.
And I’d argue it’s convenient to blame this solely on power issues, when the reality is clearly based on construction timelines that never made any sense to begin with. If it was just a power issue, more data centers would be near or at the finish line, waiting for power to be turned on. Instead, well-known projects like Stargate Abilene are built at a glacial pace as eager reporters claim that a quarter of the buildings being functional nearly a year after they were meant to be turned on is some sort of achievement.
Then there’s the very, very obvious scandal that NVIDIA, the largest company on the stock market, is making hundreds of billions of dollars of revenue on chips that aren’t being installed. It’s fucking strange, and I simply do not understand how it keeps beating and raising expectations every quarter given the fact that the majority of its customers are likely going to be able to use their current purchases in the next decade.
Assuming that Vera Rubin actually ships in 2026, it’s reasonable to believe that people will be installing these things well into 2028, if not further, and that’s assuming everything doesn’t collapse by then. Why would you bother? What’s the point, especially if you’re sitting on a pile of Blackwell GPUs?
Why are we doing any of this?
Last week also featured a truly bonkers story about Supermicro, a reseller of GPUs used by CoreWeave and Crusoe, where co-founder Wally Liaw and several other co-conspirators were arrested for selling hundreds of millions of dollars of NVIDIA GPUs to China, with the intent to sell billions more.
Liaw, one of Supermicro’s co-founders, previously resigned in a 2018 accounting scandal where Supermicro couldn’t file its annual reports, only to be (per Hindenburg Research’s excellent report) rehired in 2021 as a consultant, and restored to the board in 2023, per a filed 8K.
Mere days before his arrest, Liaw was parading around NVIDIA’s GTC conference, pouring unnamed liquids in ice luges and standing two people away from NVIDIA CEO Jensen Huang. Liaw was also seen congratulating the CEO of Lambda on its new CFO appointment on LinkedIn, as well as shaking hands (along with Supermicro CEO Charles Liang, who has not been arrested or indicted) with Crusoe (the company building OpenAI’s Abilene data center) CEO Chase Lochmiller.
Supermicro isn’t named in the indictment for reasons I imagine are perfectly normal and not related to keeping the AI party going. Nevertheless, Liaw and his co-conspirators are accused of shipping hundreds of millions of dollars’ worth of NVIDIA GPUs to China through a web of counterparties and brokers, with over $510 million of them shipped between April and mid-May 2025. While the indictment isn’t specific as to the breakdown, it confirms that some Blackwell GPUs made it to China, and I’d wager quite a few.
The mainstream media has already stopped thinking about this story, despite Supermicro being a huge reseller of NVIDIA gear, contributing billions of dollars of revenue, with at least $500 million of that apparently going to China. The fact that Supermicro wasn’t specifically named in the case is enough to erase the entire tale from their minds, along with any wonder about how NVIDIA, and specifically Jensen Huang, didn’t know.
This also isn’t even close to the only time this has happened. Late last year, Bloomberg reported on Singapore-based Megaspeed — a (to quote Bloomberg) “once-obscure spinoff of a Chinese gaming enterprise [that] evolved into the single largest Southeast Asian buyer of NVIDIA chips” — and highlighted odd signs that suggest it might be operating as a front for China.
As a neocloud, Megaspeed rents out AI compute capacity like CoreWeave, and while NVIDIA (and Megaspeed) both deny any of their GPUs are going to China, Megaspeed, to quote Bloomberg, has “something of a Chinese corporate twin”:
This firm used similar presentation materials to Megaspeed’s, had a nearly identical website to a Megaspeed sub-brand and claimed Megaspeed’s Southeast Asia employees as its own. It’s also posted job ads at and near the Shanghai data center whose rendering was used in Megaspeed’s investor deck — including for engineering work on restricted Nvidia GPUs.
Bloomberg reported that Megaspeed imported goods “worth more than a thousand times its cash balance in 2023,” with two-thirds of its imports being NVIDIA products. The investigation got weirder when Bloomberg tried to track down specific circuit boards that NVIDIA had told the US government were in specific sites:
Data centers aren’t the only Megaspeed facilities Nvidia visited. The vast majority of Megaspeed’s $2.4 billion worth of Bianca boards, the circuit boards that house Nvidia’s top-end GB200 and GB300 semiconductors, were unaccounted for at the sites Nvidia described to Washington. After Bloomberg asked about those products, the chipmaker went to separate Megaspeed warehouses, an Nvidia official said, and confirmed the Bianca boards are there.
This person declined to specify the number observed in storage, nor where and when the chips — imported more than half a year ago — would be put to use. “Building data centers is a complex process that takes many months and involves many suppliers, contractors and approvals,” an Nvidia spokesperson said.
Things get weirder throughout the article, with a Chinese company called “Shanghai Shuoyao” having a near-identical website and investor deck (as mentioned) to Megaspeed, with several of the “computing clusters under construction” actually being in China.
Things get a lot weirder as Bloomberg digs in, including a woman called “Huang” that may or may not be both the CEO of Megaspeed and an associated company called “Shanghai Hexi,” which is also owned by the Yangtze River Delta project… who was also photographed sitting next to Jensen Huang at an event in Taipei in 2024.
While all of this is extremely weird and suspicious, I must be clear there is no declarative answer as to what’s going on, other than that NVIDIA GPUs are absolutely making it to China, somehow. I also think that it would be really tough for Jensen Huang to not know about it, or for billions of dollars of GPUs to be somewhere without NVIDIA’s knowledge.
Anyway, Supermicro CEO Charles Liang has yet to comment on Wally Liaw or his alleged co-conspirators, other than a statement from the company that says that their acts were “a contravention of the Company’s policies and compliance controls.”
Jensen Huang does not appear to have been asked if he knew anything about this — not Megaspeed, not Supermicro, or really any challenging question of any kind for the last few years of his life.
Huang did, however, say back in May 2025 that there was “no evidence of any AI chip diversion,’ and that the countries in question “monitor themselves very carefully.”
For legal reasons I am going to speak very carefully: I cannot say that Jensen is wrong, or lying, but I think it’s incredible, remarkable even, that he had no idea that any of this was going on. Really? Hundreds of millions if not billions of dollars of GPUs are making it to China — as reported by The Information in December 2025 — and Jensen Huang had no idea? I find that highly unlikely, though I obviously can’t say for sure.
In the event that NVIDIA had knowledge — which I am not saying it did, of course — this is a huge scandal that, for the most part, nobody has bothered to keep an eye on outside of a few brave souls at The Information and Bloomberg who give a shit about the truth. Has anybody bothered to ask Jensen about this? People talk to him on camera all the time.
Sidenote: Earlier today, US Senators Jim Banks and Elizabeth Warren issued a letter to Howard Lutnick, Trump' s Commerce Secretary, demanding the Department of Commerce take “all necessary and appropriate actions” to stop the flow of NVIDIA chips to China, including potentially block exports to countries believed to be intermediaries, like Malaysia, Thailand, Vietnam, and Singapore.
The arrest of Liaw has, it seems, ruffled some feathers in Washington, and I would not be shocked to see Huang sat before a congressional inquiry at some point.
I’ll also add that I am shocked that so many people are just shrugging and moving on from Supermicro, which is a major supplier of two of the major neoclouds (Crusoe and CoreWeave) and one of the minors (Lambda, which they also rents cloud capacity to). The idea that a company had no idea that several percentage points of its revenue were flowing directly to China via one of its co-founders is an utter joke.
I hope we eventually find out the truth. Nevertheless, this kind of underhanded bullshit is a sign of desperation on the part of just about everybody involved.
So, I want to explain something very clearly for you, because it’s important you understand how fucked up shit has become: hyperscalers are forcing everybody in their companies to use AI tools as much as possible, tying compensation and performance use to token burn, and actively encouraging non-technical people to vibe-code features that actually reach production.
In practice, this means that everybody is being expected to dick around with AI tools all day, with the expectation that you burn massive amounts of tokens and, in the case of designers working in some companies, actively code features without ever knowing a line of code.
“How do I know the last part? Because a trusted source told me — and I’ll leave it at that”
One might be forgiven for thinking this means that AI has taken a leap in efficacy, but the actual outcomes are a labyrinth of half-functional internal dashboards that measure random user data or convert files, spending hours to save minutes of time at some theoretical point. While non-technical workers aren’t necessarily allowed to ship directly to production, their horrifying pseudo-software, coded without any real understanding of anything, is expected to be “fixed” by actual software engineers who are also expected to do their jobs.
These tools also allow near-incompetent Business Idiot software engineers to do far more damage than they might have in the past. LLM use is relatively-unrestrained (and actively incentivized) in at least one hyperscaler, with just about anybody allowed to spin up their own OpenClaw “AI agent” (read: series of LLMs that allegedly can do stuff with your inbox or Slack for no clear benefit, other than their ability to delete all of your emails). In Meta’s case, this ended up causing a severe security breach:
According to internal Meta communications and an incident report seen by The Information, a major security alert occurred last week after a Meta software engineer used an in-house agent tool, similar to OpenClaw, to analyze a technical question that another Meta employee had posted on an internal discussion forum. After doing the analysis, the AI agent posted a response in the discussion forum to the original question, offering advice on the technical issue, according to internal communications. The agent did so without approval from the employee.
According to The Information, Meta systems storing large amounts of company and user-related data were accessible to engineers who didn’t have permission to see them, and was marked a sec-1 incident, the second highest level of severity on an internal scale that Meta uses to rank security incidents.
The incident follows multiple problems caused at Amazon by its Kiro and Q LLMs. I quote Business Insider’s Eugene Kim:
On March 2, customers across Amazon marketplaces saw incorrect delivery times when adding items to their carts. The incident led to nearly 120,000 lost orders and roughly 1.6 million website errors. Amazon's AI tool Q was one of the primary contributors that triggered the event, according to an internal review.
On March 5, another outage caused a 99% drop in orders across Amazon's North American marketplaces, resulting in 6.3 million lost orders, one of the internal documents stated. One key factor was a production change that was deployed without using a formal documentation and approval process called Modeled Change Management.
Despite the furious (and exhausting) marketing campaign around “the power of AI code,” I believe that these events are just the beginning of the true consequences of AI coding tools: the slow destruction of the tech industry’s software stack.
LLMs allow even the most incompetent dullard to do an impression of a software engineer, by which I mean you can tell it “make me software that does this” or “look at this code and fix it” and said LLM will spend the entire time saying “you got this” and “that’s a great solution.”
The problem is that while LLMs can write “all” code, that doesn’t mean the code is good, or that somebody can read the code and understand its intention (as these models do not think), or that having a lot of code is a good thing both in the present and in the future of any company built using generative code.
LLM-based code is often verbose, and rarely aligns with in-house coding guidelines and standards, guaranteeing that it’ll take far longer to chew through, which naturally means that those burdened with reviewing it will either skim-read it or feed it into another LLM to work out what the hell to do.
Worse still, LLM use is also entirely directionless. Why is anybody at Meta using an OpenClaw? What is the actual thing that OpenClaw does, other than burn an absolute fuck-ton of tokens?
Think about this very, very simply for a second: you have given every engineer in the company the explicit remit to write all their code using LLMs, and incentivized them to do so by making sure their LLM use is tracked. You have now massively increased both the operating costs of the company (through token burn costs) and the volume of code being created.
To be explicit, allowing an LLM to write all of your code means that you are no longer developing code, nor are you learning how to develop code, nor are you going to become a better software engineer as a result. This means that, across almost every major tech company, software engineers are being incentivized to stop learning how to write software or solve software architecture issues.
If you are just a person looking at code, you are only as good as the code the model makes, and as Mo Bitar recently discussed, these models are built to galvanize you, glaze you, and tell you that you’re remarkable as you barely glance at globs of overwritten code that, even if it functions, eventually grows to a whole built with no intention or purpose other than what the model generated from your prompt.
Things only get worse when you add in the fact that hyperscalers like Meta and Amazon love to lay off thousands of people at a time, which makes it even harder to work out why something was built in the way it was built, which is even harder when an LLM that lacks any thoughts or intentions builds it. Entire chunks of multi-trillion dollar market cap companies are being written with these things, prompted by engineers (and non-engineers!) who may or may not be at the company in a month or a year to explain what prompts they used.
We’re already seeing the consequences! Amazon lost hundreds of thousands of orders! Meta had a major security breach! The foundations of these companies are being rotted away through millions of lines of slop-code that, at best, occasionally gets the nod from somebody who has “software engineer” on their resume, and these people keep being fired too, raising the likelihood that somebody who knows what’s going on or why something is built a certain way will be able to stop something bad from happening.
Remember: Google, Amazon, Microsoft, and Meta all hold vast troves of personal information, intimate conversations, serious legal documents, financial information, in some cases even social security numbers, and all four of them along with a worrying chunk of the tech industry are actively encouraging their software engineers to stop giving a fuck about software.
Oh, you’re so much faster with AI code? What does that actually mean? What have you built? Do you understand how it works? Did you look at the code before it shipped, or did you assume that it was fine because it didn’t break?
This is creating a kind of biblical plague within software engineering — an entire tech industry built on reams of unmanageable and unintentional code pushed by executives and managers that don’t do any real work. LLMs allow the incompetent to feign competence and the unproductive to produce work-adjacent materials borne of a loathing for labor and craftsmanship, and lean into the worst habits of the dullards that rule Silicon Valley.
All the Valley knows is growth, and “more” is regularly conflated with “valuable.” The New York Times’ Kevin Roose — in a shocking attempt at journalism — recently wrote a piece celebrating the competition within Silicon Valley to burn more and more tokens using AI models:
An engineer at OpenAI processed 210 billion “tokens” — enough text to fill Wikipedia 33 times — through the company’s artificial intelligence models over the last week, the most of any employee. At Anthropic, a single user of the company’s A.I. coding system, Claude Code, racked up a bill of more than $150,000 in a month.
And at tech companies like Meta and Shopify, managers have started to factor A.I. use into performance reviews, rewarding workers who make heavy use of A.I. tools and chastening those who don’t.
This is the new reality for coders, some of the first white-collar workers to feel the effects of A.I. as it sweeps through the economy. A.I. was supposed to help tech companies boost productivity and cut costs. But it has also created an expensive new status game, known as “tokenmaxxing,” among A.I.-obsessed workers who are desperate to prove how productive they are.
Roose explains that both Meta and OpenAI have internal leaderboards that show how many tokens you’ve used, with one software engineer in Stockholm spending “more than his salary in tokens,” though Roose adds that his company pays for them.
Roose describes a truly sick culture, one where OpenAI gives awards to those who spend a lot of money on their tokens, adding that he spoke with several tech workers who were spending thousands of dollars a day on tokens “for what amount to bragging rights.” Roose also added one more insane detail: that one person found a loophole in Claude’s $20-a-month using a piece of software made by Figma that allowed them to burn $70,000 in tokens.
Despite all of this burn, Roose struggled to find anybody who was able to explain what they were doing beyond “maintaining large, complex pieces of software using coding agents running in parallel,” but managed to actually find one particularly useful bit of information — that all of this might be performative:
They said, by and large, that A.I. coding tools were making them more productive. But some also framed their use of A.I. as a strategic move — a way to signal, to their colleagues and bosses, that they’re keeping up with the times, as the era of human coding appears to be coming to an end.
I do give Roose one point for wondering if “...any of these tokenmaxxers [were] producing anything good, or whether they [were] merely spinning their wheels churning out useless code in an attempt to look busy.” Good job Kevin.
That being said, I find this story horrifying, and veering dangerously close to the actions of drug addicts and cult followers. Throughout this story in one of the world’s largest newspapers, Roose fails to find a single “tokenmaxxer” making something that they can actually describe, which has largely been my experience of evaluating anyone who talks nonstop about the power of “agentic coding.”
These people are sick, and are participating in a vile, poisonous culture based on needless expenses and endless consumption.
Companies incentivizing the amount of tokens you burn are actively creating a culture that trades excess for productivity, and incentivizing destructive tendencies built around constantly having to find stuff to do rather than do things with intention. They are guaranteeing that their software will be poorly-written and maintained, all in the pursuit of “doing more AI” for no reason other than that everybody else appears to be doing so.
Anybody who actually works knows that the most productive-seeming people are often also the most-useless, as they’re doing things to seem productive rather than producing anything of note. A great example of this is a recent Business Insider interview with a person who got laid off from Amazon after learning “AI” and “vibe coding,” and how surprised they were that these supposed skills didn’t make them safer from layoffs:
At the time of the October layoffs, there was debate around whether AI was the reason.
The company was encouraging us to use AI at the time, but I don't think it took my job. I wrote descriptions for internal products at Amazon, and when I used AI to help, I'd need to ask it to rewrite its output without fluff words. It didn't sound like how people talk. Despite my ethical qualms, I used AI, but, in my opinion, it was nowhere close to replacing my role. Before I was laid off, I helped build an internal site for Amazon using AI. I hadn't really coded before, but with a colleague's help, I learned how to vibe code with a lot of trial and error.
I thought using AI for this project and showcasing different skills would make me more valuable to the company, but in the end, it didn't keep me from being laid off.
To be clear, this person is a victim. They were pressured by Amazon to take up useless skills and build useless things in an expensive and inefficient way, and ended up losing their job despite taking up tools they didn’t like under duress.
Sidenote: If you read that sentence and suggest that she should’ve used AI better, you are a mark. You are being conned into an unpaid marketing job for AI companies that actively hate you.
This person was, at one point, actively part of building an internal Amazon site using AI, and had to “learn to vibe code with a lot of trial and error” and the help of a colleague. Was this a good use of her time? Was this a good use of her colleague’s time?
No! In fact, across all of these goddamn AI coding hype-beast Twitter accounts and endless proclamations about the incredible power of AI agents, I can find very few accounts of something happening other than someone saying “yeah I’m more productive I guess.”
I am certain that at some point in the near future a major big tech service is going to break in a way that isn’t immediately fixable as a result of thousands of people building software with AI coding tools, a problem compounded by the dual brain drain forces of layoffs and a culture that actively empowers people to look busy rather than actually produce useful things.
What else would you expect? You’re giving people a number that they can increase to seem better at their job, what do you think they’re going to do, try and be efficient? Or use these things as much as humanly possible, even if there really isn’t a reason to?
I haven’t even gotten to how expensive all of this must be, in part because it’s hard to fully comprehend.
But what I do know is that big tech is setting itself up for crisis after crisis, especially when Anthropic and OpenAI stop subsidizing their models to the tune of allowing people to spend $2500 or more on a $200-a-month subscription.
What happens to the people who are dependent on these models? What happens to the people who forgot how to do their jobs because they decided to let AI write all of their code? Will they even be able to do their jobs anymore?
Large Language Models are creating Silicon Valley Habsburgs — workers that are intellectually trapped at whatever point they started leaning on these models that were subsidized to the point that their bosses encouraged them to use them as much as humanly possible. While they might be able to claw their way back into the workforce, a software engineer that’s only really used LLMs for anything longer than a few months will have to relearn the basic habits of their job, and find that their skills were limited to whatever the last training run for whatever model they last used was.
I’m sure there are software engineers using these models ethically, who read all the code, who have complete industry over it and use it as a means of handling very specific units of work that they have complete industry over.
I’m also sure that there are some that are just asking it to do stuff, glancing at the code and shipping it. It’s impossible to measure how many of each camp there are, but hearing Spotify’s CEO say that its top developers are basically not writing code anymore makes me deeply worried, because this shit isn’t replacing software engineering at all — it’s mindlessly removing friction and putting the burden of “good” or “right” on a user that it’s intentionally gassing up.
Ultimately, this entire era is a test of a person’s ability to understand and appreciate friction.
Friction can be a very good thing. When I don’t understand something, I make an effort to do so, and the moment it clicks is magical. In the last three years I’ve had to teach myself a great deal about finance, accountancy, and the greater technology industry, and there have been so many moments where I’ve walked away from the page frustrated, stewed in self-doubt that I’d never understand something.
I also have the luxury of time, and sadly, many software engineers face increasingly-deranged deadlines set by bosses that don’t understand a single fucking thing, let alone what LLMs are capable of or what responsible software engineering is. The push from above to use these models because they can “write code faster than a human” is a disastrous conflation of “fast” and “good,” all because of flimsy myths peddled by venture capitalists and the media about “LLMs being able to write all code.”
Generative code is a digital ecological disaster, one that will take years to repair thanks to company remits to write as much code as fast as possible.
Every single person responsible must be held accountable, especially for the calamities to come as lazily-managed software companies see the consequences of building their software on sand.
In the end, everything about AI is built on lies.
Hundreds of gigawatts of data centers in development equate to 5GW of actual data centers in construction.
Hundreds of billions of dollars of GPU sales are mostly sitting waiting for somewhere to go.
Anthropic’s constant flow of “annualized” revenues ended up equating to literally $5 billion in revenue in four years, on $25 billion or more in salaries and compute.
Despite all of those data centers supposedly being built, nobody appears to be making a profit on renting out AI compute.
AI’s supposed ability to “write all code” really means that every major software company is filling their codebases with slop while massively increasing their operating expenses. Software engineers aren’t being replaced — they’re being laid off because the software that’s meant to replace them is too expensive, while in practice not replacing anybody at all.
Looking even an inch beneath the surface of this industry makes it blatantly obvious that we’re witnessing one of the greatest corporate failures in history. The smug, condescending army of AI boosters exists to make you look away from the harsh truth — AI makes very little revenue, lacks tangible productivity benefits, and seems to, at scale, actively harm the productivity and efficacy of the workers that are being forced to use it.
Every executive forcing their workers to use AI is a ghoul and a dullard, one that doesn’t understand what actual work looks like, likely because they’re a lazy, self-involved prick.
Every person I talk to at a big tech firm is depressed, nagged endlessly to “get on board with AI,” to ship more, to do more, all without any real definition of what “more” means or what it contributes to the greater whole, all while constantly worrying about being laid off thanks to the truly noxious cultures that are growing around these services.
AI is actively poisonous to the future of the tech industry. It’s expensive, unproductive, actively damaging to the learning and efficacy of its users, depriving them of the opportunities to learn and grow, stunting them to the point that they know less and do less because all they do is prompt. Those that celebrate it are ignorant or craven, captured or crooked, or desperate to be the person to herald the next era, even if that era sucks, even if that era is inherently illogical, even if that era is fucking impossible when you think about it for more than two seconds.
And in the end, AI is a test of your introspection. Can you tell when you truly understand something? Can you tell why you believe in something, other than that somebody told you you should, or made you feel bad for believing otherwise? Do you actually want to know stuff, or just have the ability to call up information when necessary?
How much joy do you get out of becoming a better person?If you can’t answer that question with certainty, maybe you should just use an LLM, as you don’t really give a shit about anything.
And in the end, you’re exactly the mark built for an AI industry that can’t sell itself without spinning lies about what it can (or theoretically could) do.
