2026-03-14 11:33:24

There's a new editor experience on LessWrong! A bunch of the editor page has been rearranged to make it much more WYSIWYG compared to published post pages. All of the settings live in panels that are hidden by default and can be opened up by clicking the relevant buttons on the side of the screen. We also adopted lexical as a new editor framework powering everything behind the scenes (we were previously using ckEditor).

Posts^[1] now have automatic real-time autosave while you're online (like Google Docs), but still support offline editing if your connection drops out. Point-in-time revisions will still get autosaved periodically, and you can always manually save your draft if you want a specific checkpoint.

The editor also has a slash menu now!

You might be eyeing the last two items in that slash menu. This post will demo some of the new features, and I'll demo two of them simultaneously by letting Opus 4.6 explain what they are:

And here's a small interactive widget, also written by Claude^[2], to demonstrate custom iframe widgets:

Policy on LLM Use

You might be wondering what this means for our policy on LLM use.

Our initial policy was this:

A rough guideline is that if you are using AI for writing assistance, you should spend a minimum of 1 minute per 50 words (enough to read the content several times and perform significant edits), you should not include any information that you can't verify, haven't verified, or don't understand, and you should not use the stereotypical writing style of an AI assistant.

You were also permitted to put LLM-generated content into collapsible sections, if you labeled it as LLM-generated.

In practice, the "you should not use the stereotypical writing style of an AI assistant" part of the requirement meant that this was a de-facto ban on LLM use, which we enforced mostly consistently on new users and very inconsistently on existing users^[3]. Bad!

To motivate our updated policy, we must first do some philosophy. Why do we care about knowing whether something we're reading was generated by an LLM? LLM-generated text is not testimony has substantially informed my thinking on this question. Take the synopsis:

1. When we share words with each other, we don't only care about the words themselves. We care also—even primarily—about the mental elements of the human mind/agency that produced the words. What we want to engage with is those mental elements.
2. As of 2025, LLM text does not have those elements behind it.
3. Therefore LLM text categorically does not serve the role for communication that is served by real text.
4. Therefore the norm should be that you don't share LLM text as if someone wrote it. And, it is inadvisable to read LLM text that someone else shares as though someone wrote it.

I don't think you even need to confidently believe in point 2^[4] for the norm in point 4 to be compelling. It is merely sufficient that someone else produced the text.

Plagiarism is often considered bad because it's "stealing credit" for someone else's work. But it's also bad because it's misinforming your readers about your beliefs and mental models! What happens if someone asks you why you're so confident about [proposition X]? It really sucks if the answer is "Oh, uh, I didn't write that sentence, and re-reading it, it turns out I'm not actually that confident in that claim..."

This is also why having LLMs "edit" your writing is often pernicious. LLM editing, unless managed extremely carefully, often involves rephrasings, added qualifiers, and swapped vocabulary in ways that meaningfully change the semantic content of your writing. Very often this is in unendorsed ways, but this can be hard to pick up on because the typical LLM writing style has a tendency to make people's eyes slide off of it^[5].

With all that in mind, our new policy is this:

• "LLM output" includes all of:
• • text written entirely by an LLM
  • text that was written by a human and then substantially^[6] edited or revised by an LLM
  • text that was written by an LLM and then edited or revised by a human
• "LLM output" does not include:
• • text that was written by a human and then lightly edited or revised by an LLM
  • text written by a human, which includes facts, arguments, examples, etc, which were researched/discovered/developed with LLM assistance. (If you "borrow language" from the LLM, that no longer counts as "text written by a human".)
  • code (either in code blocks or in the new widgets)

"LLM output" must go into the new LLM content blocks. You can put "LLM output" into a collapsible section without wrapping it in an LLM content block if all of the content is "LLM output". If it's mixed, you should use LLM content blocks within the collapsible section to demarcate those parts which are "LLM output".

We are going to be more strictly enforcing the "no LLM output" rule by normalizing our auto-moderation logic to treat posts by approved^[7] users similarly to posts by new users - that is, they'll be automatically rejected if they score above a certain threshold in our automated LLM content detection pipeline. Having spent a few months staring at what's been coming down the pipe, we are also going to be lowering that threshold.

This does not change our existing quality bar for new user submissions. If you are a new user and submit a post that substantially consists of content inside of LLM content blocks, it is pretty unlikely that it will get approved^[8]. This does not suddenly become wise if you're an approved user. If you're confident that people will want to read it, then sure, go ahead, but please pay close attention to the kind of feedback you get (karma, comments, etc), and if this proves noisy we'll probably just tell people to cut it out.

As always, please submit feedback, questions, and bug reports via Intercom (or in the comments below, if you prefer).

1. ^{^}

   Not comments or other content types that use the editor, like tags - those still have the same local backup mechanism they've always had, and you can still explicitly save draft comments, but none of them get automatically synced to the cloud as you type. Also, existing posts and drafts will continue to use the previous editor, and won't have access to the new features.

2. ^{^}

   Prompted by @jimrandomh.

3. ^{^}

   For somewhat contingent reasons involving various choices we made with our moderation setup.

4. ^{^}

   See my curation notice on that post for some additional thoughts and caveats.

5. ^{^}

   I think this recent thread is instructive.

6. ^{^}

   We'll know it when we see it.

7. ^{^}

   In the ontology of our codebase, a term which means "users whose content goes live without further review by the admins", which is not true of users who haven't posted or commented before, and is also not true of a smaller number of users who have.

8. ^{^}

   I'm sure the people reading this will be able to conjure up some edge cases and counterexamples; go on, have fun.

2026-03-14 09:04:13

Walking, I listen for sensations of physical necessity. It is surprisingly hard. Imagining myself jumping huge distances feels like imagining a normal step. Odd, that. Shouldn’t possible acts feel vivid, alive & real in a way fantasy isn’t?

I make some progress by tracking what I feel are obstacles, like that medium sized column splitting the pavement, or that tree. They’re like gaps in my planned routes. Feels like I could circle around in two ways. On noticing this gap, I think wait a moment, can’t I just climb over it? It would be a bit embaressing, but I could do it.

But going back to the column, there’s obviously a boundary there too: I can’t just walk through the thing! I could get a sledge-hammer, but my neighbours’d raise a fuss if I did that. But walking through it? No.

That seems sort of unlike a gap in paths which I can “interpolate between”. It and other obstacles are barriers. Imagining myself walking through it brings to mind a subtle sense feeling. In fact, if I wasn’t paying close attention, I would have missed it.

So is that what impossibility feels like? My brain just telling me “you can’t do that?”

Stopping, I consider jumping between buildings. Could I do it? Does it feel necessary that I’d fall to my death? Yes, same way I can’t walk through a tree.

I wonder what the equivalent feeling for more general plans is.

Hmm, if I imagine reverse-engineering my old contractor’s codebase in an hour, I get a different feeling, a sense of trepidation but not the thought that I can’t do it. But if I imagine myself saying to my contractor I can do that in an hour, I think “haha, no way”.

OK, what about some easier programs? Like doing karatsuba multiplication in one hour in C, which I don’t know? That feels like it’s on the edge, a challenge that maybe I could do at my best, maybe I could succeed, even though the thought’s a bit painful.

Though if I imagine telling a friend I can do this, it feels like they’d scoff. Though if I offered them a bet, they’d probably give me like ~ 1/5 odds. Those aren’t the odds you give for something impossible, right?

And if I bet my old contractor whether I can re-write his codebase in one hour, I feel like he might humour me with 1/30 odds. Huh, strange. Maybe because I showed him a library doing most of his code’s work? Plus, claude code.

It seems like considering a bet implicitly takes into account options that I’d miss if I just think “is this impossible”? So maybe thinking of bets about “impossible” things is the fastest way to check if I know they’re impossible?

Note to self: Clearly, this exercise was useful. Probably, I still don’t understand how my brain views obstacles and impossibilities. Tracking those feelings may reveal more ways to easily notice when something is not actually impossible.

2026-03-14 05:12:43

A widely-held view says we should avoid locking in consequential decisions before an intelligence explosion — we’ll understand more if we wait, and we’ll have time to reflect on our decisions.

But that view might be missing something: some mutually beneficial deals depend on uncertainty about the future. Once the uncertainty resolves, the window closes on potentially big ex ante gains. We make them early, or never.

The classic example is insurance: while your house hasn’t been struck by lightning, you and your insurer can improve each other’s prospects. But once your house gets struck by lightning, it’s too late to make a deal. You can think of this as a trade between possible outcomes, where the opportunity for trade depends on both outcomes being live possibilities.

I consider three kinds of agreement that fit this pattern, each hinging on a different kind of uncertainty about what comes after an intelligence explosion.

The first is uncertainty about the relative share of resources — who ends up on top without a deal. While major powers like the US and China remain uncertain about who might otherwise achieve a decisive strategic advantage, both should prefer to commit to sharing (some) future power or resources, over the straight gamble. Moreover, the expected surplus from a power-sharing deal shrinks over time, so in theory both sides should prefer to make a deal as soon as it’s possible.

The second is uncertainty about the overall ‘stakes’, like how resource-wealthy society becomes overall. Here, a less risk-averse party can effectively insure a more risk-averse one: taking on more variance in exchange for higher expected resources, and improving both their prospects. Or the stakes in question could be about something more specific, like how philanthropic actors today ‘mission hedge’ by holding positions in specific companies which pay off when their cause is most urgent.

The third kind of agreement involves theoretical and especially normative uncertainty. If one party cares much more about having resources in worlds where, say, a particular moral view turns out to be correct, they can trade for more influence in those worlds. Advanced AI could make such deals feasible by acting as a mutually trusted arbiter for questions that are otherwise hard to resolve.

The basic case for enabling all these agreements is the same basic case for any voluntary commitment: all parties improve their prospects by their own lights, and nobody else is hurt. Moreover, agreements between major powers to share resources could make the future meaningfully more pluralistic and morally diverse, which seems better under moral uncertainty than a more unipolar future. And agreements between individuals could give more influence to those who staked their wealth today on future outcomes as a credible show of their beliefs or values, and were vindicated.

It looks like many of these deals won’t be possible by default. If future resources are distributed rather than auctioned, then most of our future wealth arrives as a windfall, but contracts over future income typically aren’t enforceable under common law. We might instead form agreements over future influence, but that too is legally murky. So some agreements would have to rely on private alternatives to legal contracting, through AI-enabled arbitration and enforcement. We might also consider encouraging commitments from private institutions to honour small-scale deals, or setting up infrastructure for trading on post-AGI outcomes. Zooming out to deals between major powers, we’ll need more developed diplomatic frameworks for resource-sharing treaties, likely involving AI-enabled monitoring and enforcement.

Again, each of these deals has to be made early, or never. And that also makes downsides look fairly scary. Enabling early deals lets people commit to hugely consequential terms before they’re wise enough — especially in a world where you can’t recover wealth through labour income. So if we do proactively enable these agreements, I think we should add in some serious guardrails: requirements for demonstrated understanding, caps on the fraction of future resources that can be staked, and mechanisms for voiding deals that were clearly misconceived at the time.

The dawn of the intelligence explosion may be the last period of shared ignorance about some crucial and long-lasting outcomes. Deals struck under that ignorance tend to distribute resources in ways that reflect mutual benefit rather than bargaining power. Once the veil of ignorance lifts, that changes. The case for enabling at least some early deals — despite the received wisdom against “locking-in” the future where we can help it — is fairly compelling.

You can read the full paper here: Should We Lock in Post-AGI Agreements Under Uncertainty?

2026-03-14 04:08:36

Based on a conversation with Jukka Tykkyläinen and Kimmo Nevanlinna. The original framing and many of the ideas are stolen from them.

You can think of any job as having inputs, outputs, and valued outcomes.

The input is typically time you spend on doing something in particular, as well as any material resources you need. Outputs are the immediate results of what you do. Valued outcomes are the reason why you’re being paid to do the job in the first place.

In many jobs, these are closely linked:

Digging a tunnel

• Inputs: Time spent digging a tunnel
• Outputs: Amount of tunnel dug
• Outcomes: A tunnel that people can use to tunnel through whatever^[1]

Store cashier in a busy store

• Inputs: Time spent serving customers
• Outputs: Number of customers served
• Outcomes: Amount of sales to customers

Childcare

• Inputs: Time spent looking after children
• Outputs: Time spent looking after children
• Outcomes: Children who spent this time being, at minimum, no worse off than before

They’re not exactly the same. You could spend a lot of time on the job but do it poorly (dig lazily, ring up purchases wrong, be neglectful or abusive toward the children). But assuming that you are trying to do a reasonable job and aren’t grossly incompetent, you can generally get things done by just showing up and working for a given number of hours.

In other jobs, the three are different:

B2B email marketing

• Inputs: Time spent writing emails to potential clients
• Outputs: Emails sent to potential clients
• Outcomes: Acquired new clients

It’s not enough to just send any emails; you have to send out good emails that actually get you clients. In fact, it can be better to write one really good email than it is to write a hundred bad ones.

Scientific research

• Inputs: Time spent on research, possible material expenses
• Outputs: Research papers, conference presentations, blog posts, etc.
• Outcomes: Novel contributions that push the boundaries of human knowledge

It’s possible to rack up lots of uninteresting publications that still get cited for whatever reason, without that actually contributing to the generation of any real knowledge.

In this second category of jobs, at least two things are different:

1. Outputs and valuable outcomes become much more weakly connected. You might genuinely do your best producing many real outputs and put a lot of work into them. And they might still completely fail to produce any valuable outcomes.

2. Because the connection between outputs and valuable outcomes weakens, it gets harder to tell when you’re doing a bad job and you may be actively rewarded for it. Previously, you could spend a lot of time working in a way that failed to be valuable, but this was usually relatively obvious. A salesperson who steals from the till might be unobvious in the sense of being good at hiding it, but at least everyone knows that a salesperson isn’t supposed to steal.

In contrast, if you can’t directly measure what leads to valuable outcomes but you do still need to reward performance, it’s easy to run into Goodhart’s Law. People may actively reward you for having lots of outputs separate from their connection to valued outputs.

For instance, hiring and grant committees may evaluate how many publications and citations a researcher has. This encourages maximizing the number of publications, such as by splitting up a single study into as many individual papers as possible, rather than just writing one really good and comprehensive paper. The reward process is actively pushing the researcher away from the kind of work that would produce value.

People may also be rewarded for outputs with genuine value, but in a way that neglects others that would be necessary for valuable outcomes. For instance, someone may be rewarded for their individual contributions, but get no credit for helping other team members, even if it would be more valuable for them to focus on that.

It might be tempting to conclude from this that “always focus on the outcomes”.

But.

What’s a valued outcome for you may not be the same as a valued outcome for whoever is employing you. Maybe you think that the company is doing something useless, or it’s organized in such a way that your personal contributions are useless. For instance, you are writing reports that will never be read. Or management keeps changing plans, so whatever work you just did will typically get thrown away in favor of the latest idea.

For you, the valued outcome is just getting paid, and your salary is not tied to the value of your work. In fact, even if you think that the company is doing something valuable and that you could do something valuable… your salary may still be anticorrelated with the value of your work if you get evaluated and promoted based on metrics that reward the wrong things, or fail to reward the right things.

In that case, it makes more sense to focus on whatever outcomes get you promoted. Maybe that will fail to further the company’s interests, but that’s the company’s problem for not developing a better incentive structure.

Many companies may not even want their rank-and-file employees thinking about outcomes. They just want the rank-and-file showing up and doing what they are told rather than trying to change established procedures. Then you might get something like the rank-and-file caring about inputs (“we come in and do our time”), middle management caring about outputs (“let’s get the employees to hit these metrics”), and the executives caring about outcomes (“let’s have these metrics that we expect to bring in profit”).

This argument feels different for researchers in fields using public funds. In those, there’s a stronger case for focusing on outcomes even if the incentives are bad. “Academia” doesn’t have a single decision-maker who could be held responsible for the incentives, and the work is supposed to benefit the public as a whole. In principle, if you are not willing to do what’s genuinely scientifically valuable, you should not be in the field at all.

In practice, the reality may also be that one has student loans and a family to support and a situation where they need to play the incentive game to keep their income.

Some relevant questions are “Why are you in this field in the first place?” and “What’s generally expected of you?”. If you are working at a Big Corporation just to make a living, it’s reasonable to just focus on whatever gets you money and let the management worry about how to best incentivize you into doing a good job. If you are working in something like academia, a hospital, the fire department, or a non-profit with a genuine intent to do good - you may still need to deal with the reality of the incentive structure, but you shouldn’t lose track of the real purpose of your job.

Philosopher C. Thi Nguyen says in an interview:

So I went into philosophy because I loved weird, interesting questions about like the meaning of life and why any of this is here and why are things beautiful. And then I went into philosophy grad school and I got enculturated. And in philosophy, there is a status ranking collated by survey of what the status is of the journals you publish in and the departments you can be at. And you don’t know any of this when you go to grad school. You’re like, philosophy is cool. I want to think about the meaning of life. And then almost everyone comes out being like, what is my highest rank publication? What is the ranking of my job? And there’s a shift. [...]

I got obsessed with moving up the ranking and I started writing philosophy that bored me. And then I wanted to die. And then at some point I was in some place where I was like, I’ve made the weirdest decision in my life, which is to abandon any possible financial reward by not being a lawyer, by leaving Silicon Valley, by being in this weird ass dumb profession, which the only possible reason you could do it is out of love. And yet somehow… I have found myself writing boring philosophy I hate in order to move up this internal ranking system. And then I was like, and I think this is, there’s this raw reaction I often have, which is kind of like, what’s going on? What are we doing here?

Sometimes when I get frustrated with work, it’s because I was expecting or hoping my inputs to equal outputs, and they don’t.

This might look like: I haven’t written a blog post in a while and I feel that I should get one out soon. I’m thinking, “let me just sit down and write a post”. What I’m hoping to experience is the feeling of just writing and making steady progress until I have a finished article.

But then things don’t go that way. Instead of being able to just sit down and write, I realize that my idea doesn’t work and I have to rethink it, and I have no idea of how long that will take. Spending all that time thinking about it is progress, but it often doesn’t feel like progress, because there’s no clear Number Going Up that would make the progress tangible.

Or rather, spending all that time thinking about it can be progress. It’s also possible to spend a lot of time thinking about something and just stay stuck; the input doesn’t translate to much in the way of outputs. And that’s part of why it’s stressful: you can’t necessarily distinguish between “I spent a lot of time thinking about this and then made valuable progress” from “I spent a lot of time thinking about this and most of that time was effectively wasted” in advance.

If it’s actually urgent to make progress on something and it’s not turning into tangible outputs, it can be rational to get frustrated and despair. It means that something is not working, and you might do better switching strategies to something that produces faster progress. (One failure mode of some AIs is never getting frustrated and then trying the same useless approach over and over again.)

There's an old article by @AnnaSalamon and @Duncan Sabien (Inactive) on why startup founders have mood swings - flipping from “I have the greatest product ever” and “my product is terrible and will never succeed” from one day to another. They note that other circumstances that can cause this kind of oscillation include

• People in the early stages of their first serious romantic relationship (especially those who really really really want it to work, but lack a clear model of how)
• People who are deeply serious about personally making a difference in global poverty, space exploration, existential risk, or other world-scale issues
• People who are struggling to write their first novel, make their first movie, paint their first masterpiece, or win their first gold medal

And they hypothesize that all of these have a common cause: high pressure and uncertainty about what the hell you even are supposed to do. In their words: “not just being unsure about which path to take, but also about whether the paths (and the destination!) are even real”.

In the language of this post, uncertainty about what kinds of inputs and outputs you should be aiming for to achieve the valued outcomes, and sometimes about what the valued outcomes even are. I’ll just call this “lack of clarity” for short.

Some people aren’t doing anything with high stakes, but still get stressed out when it’s not clear what to do. And for that matter, not everyone finds their first major artistic endeavor particularly stressful, either. Often when I get stressed about not making enough progress within a given time, there isn’t any objective reason to - I’m just placing high demands on myself and then getting stressed when I feel like I’m failing to meet them.

Thus, one’s ability to deal with lack of clarity has to do both with one’s personal ambiguity tolerance and the amount of external pressure they’re facing.

It’s then reasonable to be pulled toward things where the connection between the three is clear and straightforward. It means that it is always easy to know what to do, and the maximum amount of time and energy is spent on something valuable. Frustration about them being poorly connected is real information - it would be nicer if they were clearly connected, and a person could just optimize for one thing.

AI and machine learning also works best on tasks where you have a clear set of target variables and can unleash all of the system’s optimization power on exactly those variables. When the system can get clear feedback of how all of its actions are reflected in the variables of interest, it can rapidly iterate toward the actions that produce the best outcomes.

Similarly, many games are enjoyable because the outputs and valued outcomes are so clearly linked. If you are shooting enemies and the player with the greatest number of kills wins, then it is very straightforward to figure out what to do. You can just focus on finding the best way to execute it.

As this suggests, the desire for clarity affects people not only at work, but with their hobbies as well. While people may not have explicit goals for their hobbies, they might enjoy things like getting better at them - or thinking that they do. The popularity of things like the fallacious 10,000-hour rule for expertise - “do a skill for 10,000 hours to become a master at it” - may be in part because it suggests a direct relationship between a time input and a mastery outcome. Just keep doing a thing and you’ll become great at it, goes the idea.

Just as with jobs, in some hobbies the valued outcomes are more directly linked to the inputs and outputs than in others. There is a reasonably close connection between the amount of time spent weightlifting, the amount of weight you can lift, and your muscle mass. For something like writing good fiction or interesting essays, things are much more subjective and murky.

I used to work in AI strategy research, but I found it very challenging for a number of reasons, including lack of clarity. I wanted to do research that would prevent the world from being destroyed by out-of-control AI, but there was no way to know what the hell would contribute to that. I spent much of my time feeling that this was critically important to get right, and also paralyzed with indecision. Eventually, I quit in order to do something easier.

But I still want to contribute to the world becoming a better place, as vague of a goal as it is. And given how much progress AI has made recently, I do want to somehow make sure things go in a positive direction.

And it’s still impossible to know what would actually be useful for it.

So my goal has been: try to write blog articles regularly. Not just about AI, but about anything that I happen to find interesting. If I can’t stop the world from being destroyed entirely, maybe I can at least make it slightly better while we’re still alive to enjoy it.

Here, the question of what kinds of outputs to aim for gets interesting.

I’ve been aiming for at least one article a month (I’ve been able to stick to this), ideally once a week (I haven’t been able to stick to this). This is an output goal comparable to “publish scientific papers regularly” - it doesn’t directly lead to the world becoming better. It would be totally possible to write one essay a week for years with absolutely nobody reading them, and that having no impact on the world.

But it seems to me that it is useful for me to act as if the outputs directly lead to valuable outcomes. Throughout my life, I have written quite a few blog articles, often just for the sake of writing them. Later, various people have thanked me for it, saying that the articles were valuable for them. Apparently, if I pursue things that feel interesting to me, those things are often interesting for others as well.

While I don’t expect every blog article I write to be interesting and valuable - I expect that a large share of them will turn out to be of little value - each one of them has a probabilistic chance of turning out to be useful. There is also the effect that the reception I get for them affects what I end up writing in the future - if people respond particularly well to some of them, I’m more likely to write about that topic.

That said, while acting as if there was a direct connection between outputs and valued outcomes is useful to some extent, it’s important not to overdo it! If I started only optimizing for publication frequency with no regard for the content, the connection between the output and the outcome would disappear - Goodhart’s law again.

This is especially the case since some of my better posts take much longer to write than others - definitely more than one week. So the rule is something like - focus on just writing posts frequently and don’t worry too much about any individual post, except if spending more time on a post would produce a clearly better one, in which case, do sacrifice quantity for quality.

Interestingly, something like this also applies recursively within the goal of writing regularly. Specifically, there is the question of what my goal should be when I sit down to write.

I mentioned earlier that if I sit down with the goal of “getting a post written”, then that might be a counterproductive mindset if writing it takes longer than I thought. Those kinds of goals also easily put me into a mindset of “Getting Work Done”, which I’ve found generally counterproductive for creative work.

So even if I have decided that the output I’m aiming for is “a couple of articles per month”, there is the question of whether my output goal on some given day should be “finish an article” or “just spend some time writing”.

“Just spend some time writing (or outlining, or thinking about the topic of the article, etc.)” is often better - unless it’s close to the end of the month and I need to get something out, even if I won’t be totally happy with it. And it’s still possible for the “just spend some time writing” goal to become corrupt - if I then spend all my time doing something vaguely connected to writing but make no meaningful progress at it for an extended time.

And at the same time, it’s important to feel okay with the fact that some days are just going to be spent making no meaningful progress because my brain needs time to background process, or because I’ve gone down a path that turns out to be a dead end, or something similar.

David MacIver writes that he sometimes finds himself wanting read more, but isn’t sure what exactly he wants to read, so he needs to make himself read until he figures it out:

First I make up a number, and then I set myself a goal of making that number go up. I create a score.

That number can be anything, but the two obvious choices are number of books read and time spent reading. Making those numbers go up requires me to read more books, so I do. [...]

Crucially, by making it into a game, I have separated my goal (make the number go up), from my purpose - read more books.

Except of course, “read more books” isn’t quite right. [...] If I just wanted to read more, I’d just trawl through Royal Road some more and read things like An Infinite Recursion of Time. What I want isn’t exactly to read more, but to have my life enriched by reading. And no number can easily capture that. [...]

I’ve done the “number of books read” metric a number of times in the past. It’s worked very well for getting me out of reading slumps. I strongly recommend it as a method. But at some point the process I always start to notice that I’m deliberately picking shorter, easier, books to read because if I pick a long and difficult book then I will make it much harder to keep the number going up at the rate I want it to. Usually that’s a sign that it’s time to stop tracking the number.

The way he describes his approach is similar to how I’m thinking of blogging: make up a number and treat that as the valuable outcome. Except that, when I notice that the number is distracting me from what I actually want to achieve, I should then stop treating it as the valuable outcome.

I mentioned that for blog posts, each post has a probabilistic chance of being valuable, so I might just focus on writing posts and trust them to be valuable in expectation, even if no particular one is. The outputs do not equal valuable outcomes, but I get good results by pretending that they do.

For something like making large-scale effects on the world, one might be faced with the dilemma where there’s only a probabilistic chance of their whole career turning out to be valuable. Maybe some researcher pursues a particular theory or invention that may turn out to be immensely valuable for humanity - or it might be a complete dead end. Maybe a reformer is working to change things in a particular country, where other forces may eventually turn out to completely eradicate all their work.

There, it may be best to just focus on outputs that have a probabilistic chance of leading to a good outcome. Even if the efforts of any particular person are likely to go to waste, if a large number of people follow the strategy of “do things that have a small chance of being hugely beneficial on net”, this may turn out to be more impactful than if they all tried to maximize their chance of making an individual impact. Many social reforms also take a really long time - decades or more - and tying one’s self-worth too much into visible results may be psychologically devastating.

But at the same time, it’s still bad to completely lose one’s sight of what all the work is meant to achieve, or it might just become an entirely lost purpose.

(And there’s the huge topic of “what if your work might have a negative impact”, which is outside the scope of this post.)

1. ^{^}

   Though if you are tunnel man, just having dug the tunnel may be intrinsically valuable, regardless of how it is ever used.

2026-03-14 03:00:33

In the event of a late-2020s Chinese attempt to invade Taiwan, leading to a US-China military conflict, what would be the most urgent bottlenecks in military equipment?

Where is there the greatest need to scale up defense manufacturing?

I am not an expert in military matters, so take all this with a grain of salt. But I looked into this question and it seems to have a single clear-cut answer: energetics. (That is, explosives and propellants for munitions like missiles and torpedoes).

The US has extremely small manufacturing capacity for energetics, and in the event of a Pacific war, demand would rapidly exceed supply.

What Does a US-China War Look Like?

Military experts think there’s a substantial, but not overwhelming, chance that China will invade Taiwan before 2030.

Metaculus predicts an invasion at 13% by 2028, and 25% by 2030.

The “Davidson window”, named for retired Admiral Philip S. Davidson, refers to the view that China will invade Taiwan by 2027, which “gained widespread attention” following former CIA director William J. Burns’ 2023 announcement that U.S. intelligence had found that Xi Jinping had told the People’s Liberation Army to be ready for an invasion by 2027.

The “Davidson window” model is not universally shared within the US defense & foreign policy establishment. In a Defense Priorities survey of 51 experts, 85% rated a Chinese invasion of Taiwan as “somewhat unlikely” or “very unlikely.”

On the other hand, the 2025 Pentagon’s Annual China Military Report to Congress straightforwardly declares that “China expects to be able to fight and win a war on Taiwan by the end of 2027.”

The bottom line is that there is substantial uncertainty and disagreement about the chance of a near-term Chinese invasion of Taiwan — but that still puts it as enough of a possibility to be worth planning for.

What will the US do in case of an invasion of Taiwan?

Here, we have an actual official answer, from the US’s 2026 National Defense Strategy. The strategy is known as “deterrence by denial”: that is, deterring China from invading Taiwan via a credible threat to sink the Chinese fleet before it crosses the strait.

This involves setting up defenses along the First Island Chain, which runs from Japan through Taiwan and the Philippines to Indonesia. Accordingly, this is where US forces in the Pacific are currently stationed: mostly in Japan, Hawaii, South Korea, and Guam.

Recent CSIS wargames represent the best non-classified information about how the US expects a conflict to play out. They present the US deploying submarines in and around the Taiwan Strait and Philippine Sea, as well as bombers launched from Pacific bases like Guam and Kadena. Bombers and submarines would launch missiles against Chinese ships and ports, with the goal of preventing an invasion of Taiwan.

The invasion always starts the same way: an opening bombardment destroys most of Taiwan’s navy and air force in the first hours of hostilities. Augmented by a powerful rocket force, the Chinese navy encircles Taiwan and interdicts any attempts to get ships and aircraft to the besieged island. Tens of thousands of Chinese soldiers cross the strait in a mix of military amphibious craft and civilian rollon, roll-off ships, while air assault and airborne troops land behind the beachheads.

However, in the most likely “base scenario,” the Chinese invasion quickly founders. Despite massive Chinese bombardment, Taiwanese ground forces stream to the beachhead, where the invaders struggle to build up supplies and move inland. Meanwhile U.S. submarines, bombers, and fighter/attack aircraft, often reinforced by Japan Self-Defense Forces, rapidly cripple the Chinese amphibious fleet. China’s strikes on Japanese bases and U.S. surface ships cannot change the result: Taiwan remains autonomous.

Munitions Shortages

Fundamentally, this is a situation where the US (and its allies like Japan) has a roughly fixed supply of ships and aircraft, which are continuously delivering munitions (precision-guided missiles and torpedoes) to the Chinese fleet. The munitions are “consumables” that get used up; the ships and aircraft are (hopefully!) more durable. So a priori you’d expect the limiting factor on supply to be munitions.

In fact, that’s exactly what national security experts have been observing for years. (See e.g. this 2025 piece from the Foreign Policy Research Institute.) The US has shortages of artillery shells, THAAD anti-missile defenses (which are themselves missiles), and precision-guided missiles.

The key munitions likely to be used in a US-China war over Taiwan include:

• AGM-158C LRASM, a bomber-launched anti-ship missile.

• AGM-158B JASSM-ER, also a precision-guided long-range anti-ship missile, and also suffering from low production and stockpiles.

• Tomahawk cruise missiles, launched from submarines, used against ships

• Mk 48 ADCAP torpedoes, also launched from submarines and used against ships

The Heritage Foundation reports that the US only has 250 LRASMs, against a requirement exceeding 1000. We are estimated to have only enough precision-guided munitions for a few weeks of combat, with some specific types, like LRASMs, only lasting for one week. Torpedoes are estimated to be exhausted in months.

It’s not easy to make more of these, either. Two-year lead times are typical. “Annual production rates—115 LRASMs and 79 to 120 MK 48 torpedoes—are orders of magnitude below projected weekly or monthly wartime consumption.”

One thing to keep in mind is that defense contractors are oligopolistic: the same few suppliers make just about everything. LRASMs, JASSMs, and ADCAP torpedoes are manufactured by Lockheed Martin; Tomahawks are manufactured by Raytheon. LRASMs and JASSMs share the same turbofan engine, manufactured exclusively by Williams International.

But what is the fundamental bottleneck in manufacturing munitions? Why can’t Lockheed and Raytheon simply make way more of them?

There are several difficult-to-manufacture components, including radiation-hardened electronics and carbon-fiber motor casings, but the most extreme bottleneck is energetics.

The Energetics Supply Chain is Tiny

Say what you will about American manufacturing, there are still lots of things we’re great at making at scale.

US domestic production of materials like plastics, oil and gas, concrete, and fertilizer is excellent. So is production of high-tech, mass-produced finished goods like pharmaceuticals, automotives, and airplanes.

Even where we’re weaker, at intermediate products like fabricated metal parts, there are still machine shops across the country that are capable of doing the work, they’re just fragmented and inefficient.1

Energetics are not like that.

Explosives and propellants, and their precursors/inputs, are often made exclusively in one facility in the US — or not at all.

• There are zero TNT manufacturers in the US.

• One facility in Louisiana, GOEX Industries, is the only domestic producer of black powder — and it only recently reopened in 2022 after a shutdown due to an industrial accident.

• Only one US company, AMPAC, is certified to provide ammonium perchlorate, an oxidizer used in rocket propellant. (Again, AMPAC scaled down in the wake of a catastrophic industrial accident — the largest non-nuclear explosion in US history, in 1988).

• Radford Army Ammunition Plant in Virginia is the only manufacturer of military-grade nitrocellulose, “the key ingredient in the manufacture of all propellants.”

• Holston Army Ammunition Plant in Tennessee is the only manufacturer of RDX (cyclonite), the active ingredient in the polymer-bonded explosives used in JASSMs, LRASMs, and similar weapons. Mixing the polymer with the explosive filler, and loading the warheads with the mixture, are similarly only done at a handful of GOCO (government-owned, contractor-operated) facilities.

• The Naval Surface Warfare Division facility in Indian Head, Maryland is the only manufacturer of Otto II fuel, which is used in torpedoes.

Why so sparse?

Well, many of these compounds do not have civilian applications, so there is little demand in peacetime.

Sometimes the recipes themselves are classified and require employees to have security clearances.

It can take 2-5 years to qualify a new facility’s output as “milspec” (up to the military’s specifications.)

Also, explosives manufacturing is really dangerous. Industrial accidents happen every year or so, most dramatically the 2025 Accurate Energetic Systems explosion that killed 16 employees. The Holston Army Ammunition Plant is a Superfund site, as are many other army ammunition plants; RDX has contaminated soil and drinking water around the US.

A typical chemical manufacturing facility isn’t good enough for explosives manufacturing; for instance, you need special concrete-reinforced bays to stop potential explosions. There’s substantial regulatory restrictions, due to the unusual safety and environmental hazards of making energetics. So there’s extra expense and expertise involved in making a new explosives plant, compared to other kinds of manufacturing.

Modernizing or constructing new explosive plants seems to cost high tens to hundreds of millions of dollars — $435M to construct a new TNT plant, $93M to restart M6 propellant manufacturing at Radford, $600M to triple production at Holston, etc. (These are all military budget allocations; the actual minimum costs are likely smaller).

Fundamentally, the reason energetics manufacturing has dwindled is that the US military is a monopsony. There is essentially only one buyer; in peacetime, demand is limited; and after the Cold War ended, the US DOD deliberately slimmed down the defense industrial base and shut down “idle” facilities, cutting costs to produce the “peace dividend” budget surplus of the Clinton years. Until recently, manufacturing lots of explosives was simply not a priority for the only buyer of military-grade explosives.

Is There A (Tech) Business Here?

The energetics industry looks really gnarly for new entrants: dirty, dangerous, highly regulated, with volatile demand and an absolute requirement for rare forms of domain expertise.

On the other hand, if you ask “in principle, could new technology help us make energetics more efficiently?” the answer is obviously yes. We’re using a lot of the same processes — in the same facilities! — that we did in WWII. There’s a ton of room for modernization.

First of all, if you ever want to make much higher volumes, you’d want to do as the pharmaceutical and chemical industries do, and switch from batch processing to continuous flow processing.

Second of all, mixing polymer-bonded explosives without causing defects (or accidents!) is fundamentally the same kind of composite manufacturing that you see in the plastics and ceramics industries, it’s just that the “fill” is highly chemically reactive. Like a lot of these materials manufacturing processes, it’s exactly the sort of messy, sensitive process that often doesn’t behave the way the hard-coded physics models say it will — which means it’s heuristically a great candidate for an AI application.

Take a lot of process data to train on, and a big, nonparametric time series model (which is really what a Transformer is), and you can plausibly do a lot better than SOTA on estimating the quality of the final output and what you can do to improve it. Which, in this case, includes the chance it will go boom too soon or not at all.

When it comes to unstable chemistry and complex, non-uniform materials, we do not know with full mechanistic certainty what factors affect process failures — that’s why there are so many industrial accidents in this business. And that’s precisely why “learn from lots of data” and “detect anomalies, even subtle ones that don’t count as “failures” by the official rubric, and try to remediate them” is likely to be a good strategy here.

Admittedly, this is a little bit pie-in-the-sky compared to the challenge of building new manufacturing plants at all. It’s just a pointer in the direction of “yes, going high-tech here could be a substantial improvement in productivity and safety.”

So is anyone starting new companies in this space?

Turns out, yes!

• Critical Materials Group came out of stealth in 2026 to make C4 at a new facility in Texas.

• Deterrence raised $10.1M in a seed round in 2025 to make explosives with robots.

• Supply Energetics, founded in 2025, is manufacturing nitrocellulose in Kansas.

• Firehawk, founded 2020, is manufacturing 3D-printed propellant in Oklahoma for solid rocket motors.

• X-Bow Systems, founded 2016, makes solid rocket motors, including propellant, in Texas

It’s not a ton, but it’s some evidence that new entrants into this field are viable at all.

It May Be Too Late

If there’s a war in the 2020s, with China or anyone else, that consumes large quantities of munitions, there’s a very serious time crunch for scaling up energetics production.

(And the conflict in Iran definitely isn’t helping the stockpile situation.)

If it takes over a year to get a new facility (or an expansion) built and ready to begin operations, and another year to get the process development optimized and production ramped up, and several more years to get qualified as milspec? Then the timeline doesn’t work for a war before the end of the decade.

The military would have to streamline and speed up its procurement and qualifications process. Or loosen its prohibitions on sourcing energetics from allied foreign nations. Or substitute inferior but more abundant materials. Or cannibalize munitions from elsewhere in the world to focus on an active conflict. None of these are comfortable, business-as-usual choices.

It’s an unfortunate situation. One could make a rah-rah American Dynamism defense-tech pitch here, but it would ring a little hollow; I’m honestly not sure if this problem is going to get solved at all.

Let’s just hope for peace.

2026-03-14 02:25:23

Is there any way to recover lost work? 

Apologies for 'polluting' the site with this question, I recently lost an important section of a post I had been typing.