It's easy to wave this aside as the current administration playing political games. But I don't think there is any reason to assume that the current era of open availability of models is going to continue indefinitely. Do you think that Chinese labs will continue to release open models forever, even why they get to the level that Mythos is at now, and beyond? And do you think that a competent US government would have no interest in regulating and restricting model access in 2 years time, assuming that model capabilities continue to improve? I think we bias towards thinking the status quo is the norm and will continue, but this news invites us to question that assumption and think about different ways the future could go.

But I think my larger points stands: even if we do see Fable access again, this is the beginning of government restriction of LLMs and we are going to see more and more of it. In fact, I would be very surprised if we ever see an open weight model with Mythos capabilities. Chinese labs have been consistently releasing open models 6-12 months behind the frontier. In 6 months we may see them go dark.

Similarly, in the US I think we can expect more and more government restrictions on the strongest LLMs, in ways that may go beyond flimsy checks like uploading a valid US passport. It may not happen this year but I think it will happen eventually.

It still surprises me sometimes that LLMs are just available for _anyone_ to use. Isn't it odd that it turned out this way? When I grew up reading sci-fi I thought AI, if I ever saw it in my lifetime, would be something locked up behind the walls of big corporations and governments. But instead we have all been able to use it for an infinity of banal purposes for $100 a month. This is a strange situation but we have got used to it. But it may not continue that way.

The real story here is that this may be the beginning of governments restricting the availability of strong LLMs to the public, to you. Fable was the strongest model on the market, and the US government has told you you can't use it (technically, only if you're not a US citizen, but in practice, even if you are). If you think the solution here is going to be open source Chinese models and / or running on your own hardware, think again. Do you think China is going to allow the strongest LLMs from companies within its borders to be open source a year from now when they have Mythos capabilities, if the US government is keeping the strongest American models back? Unlikely. These are heading in the direction of being powerful cybersecurity weapons and it will be in the interest of nation states to restrict and control them. In 2 years time, I would be surprised if the strongest LLMs are available for general use at all.

Will we be the poorer for that, or will we be safer? I think poorer, because I hate being told what technology I can and can't use, but I'm not certain. Maybe you think the government should restrict strong LLMs. Maybe you don't. But either way, this is big news and a rubicon has been crossed and a precedent set. That's true even if the motivation for this is just the government settling scores with Anthropic.

If you want an analogy, look at the history of the early railroads. Full of hype, bullshitters, scammy investments, robber-barons, unrealistic promises, and with their own legion of naysayers at the time. Yet the core technology worked and it did transform the world in the end.

1) It’s easy to think about the past in terms of what we now know, and it involves a real effort to put yourself in the shoes of the people living at the time and to imagine the “fog of war” in what they knew. In 1945 nobody had ever tested a nuclear explosion before and there was still all sorts of uncertainty about it. And as one of the other commenters pointed out, in particular there was a lot of uncertainty about how fusion worked.

2) The center of the Trinity fireball did in fact produce hotter temperatures than had ever existed on Earth before. Temperature and energy being different things.

In some sense the final experimental proof that a nuclear explosion would not set off some unanticipated new chain reaction that would destroy the earth - unlikely, but hard to completely disprove - was Trinity itself. Only after Trinity is it obvious and completely proven how the physics actually worked and obvious that there were no additional reaction pathways that got missed. That is a disturbing thought.

We know how it turned out, but the people there waiting for the test did not know how it would turn out. The bomb might not have worked. Or it might have ignited a fusion reaction in the atmosphere and destroyed the world. Hans Bethe had sat down and done the calculations on that exact scenario and said it would not, but there was always the possibility of missing something. Enrico Fermi was offering bets on it on the day of the test, as a dark joke.

In the end it worked as expected; one of the most successful and horrifying experiments in the history of science.

Of all the photos from the test the one that struck me the most looking through them today was the photograph of the plutonium core being carried into the ranch house for assembly in a little heavy box. It’s a small thing, about the size of a grapefruit, although twice as dense as lead. It looked just like a sphere of any old metal, but it was something profoundly alien, made inside nuclear reactors. And it still is so strange to me that something that small has so much energy locked up inside and that, by imploding the little sphere just right, we can let the demon out.

Trinity is one of the pivotal moments in the history of our species and eighty years on we still don’t know what the eventual consequences of it will be. The bombs are still here waiting for us and they still pose all sorts of terrifying questions for the future that most people prefer not to think about.

This is completely false. Most of the dramatic improvements in LLM quality in the last two years were due to the application of new post-training methods, especially RLVR. It’s really interesting to read about (you should!) and it is the whole secret to why LLMs did not plateau in 2024 or 2025 like many people confidently predicted. Sure, RLVR requires compute to do, but this is not just throwing more compute at 2023 LLMs.

I’ve been thinking of building myself my own frontend to HN that makes it impossible to view comments, for this reason. Yet sometimes there are still really interesting discussions and it’s hard to let go of what for me feels like the last social media I want to be part of.

There are a lot of big issues at stake here and just because a person is interested in what AI can do and curious to discuss it does not make them uncritically positive about it’s effects on society, the economy, and the world. Yet that is often the assumption and it leads to battle lines being drawn, on every AI discussion, over and over again. It means the serious discussion gets swamped and that makes me sad.

Look past the press-releasey gushing from OpenAI and there are all sorts of interesting and subtle questions here about the role for LLMs in mathematical research. I urge folks to click through to the accompanying comments from mathematicians published alongside the result. There is a really interesting discussion going on. I particularly recommend Tim Gowers’ remarks. This is really interesting stuff!

Yet the comments are just a battleground of people rehearsing the same tired arguments about LLMs from 2023, refutations of those arguments, angry counters, etc.

Does it make anyone else sad that the battle lines seem to have been drawn 3 years ago and we just seem to have the same fights over and over?

I wonder if we’ll still be doing this two years hence.

That said. I think it’s worth saying that “LLMs just interpolate their training data” is usually framed as a rhetorical statement motivated by emotion and the speaker’s hostility to LLMs. What they usually mean is some stronger version, which is “LLMs are just stochastically spouting stuff from their training data without having any internal model of concepts or meaning or logic.” I think that idea was already refuted by LLMs getting quite good at mathematics about a year ago (Gold on the IMO), combined with the mechanistic interpretatabilty research that was actually able to point to small sections of the network that model higher concepts, counting, etc. LLMs actually proving and disproving novel mathematical results is just the final nail in the coffin. At this point I’m not even sure how to engage with people who still deny all this. The debate has moved on and it’s not even interesting anymore.

So yes, I agree with you, and I’m even happy to say that what I say and do in life myself is in some broad sense and interpolation of the sum of my experiences and my genetic legacy. What else would it be? Creativity is maybe just fortunate remixing of existing ideas and experiences and skills with a bit of randomness and good luck thrown in (“Great artists steal”, and all that.) But that’s not usually what people mean when they say similar-sounding things about LLMs.

Movable type is an amazing invention, without which the whole history of the world would look utterly different. Everyone who has the slightest interest should try setting some movable type if you can find a printshop in your city offering classes (I did; it's fun). It's harder than you might think and you learn why skilled compositors and printers were quite well-paid by the standards of early-modern craftspeople. But you also see the enormous efficiency gains because once that type is set up, the marginal cost of producing each copy is low.

[0] https://blog.lostartpress.com/2013/06/18/strong-beer-that-he... : "My companion at the press drank every day a pint before breakfast, a pint at breakfast with his bread and cheese, a pint between breakfast and dinner, a pint at dinner; a pint in the afternoon about six o’clock, and another when he had done his day’s work. I thought it a detestable custom; but it was necessary, he supposed, to drink strong beer, that he might be strong to labour."

I suppose a concrete example in 1997 would be that a lot of companies thought the future of e-commerce was setting up a store on AOL, that people would use while sitting down at a desktop PC. Obviously it didn’t turn out quite that way. Furthermore, the Internet enabled new kinds of ways to buy things that weren’t even envisioned in the pre-Internet pre-smartphone world: think Airbnb and Uber.

Predictions are hard, especially about the future. Most predictions reflect the worldview and biases of the time in which they are made: think about all the vintage sci-fi from the 60s 70s and 80s that actually reads or looks kind of retro now. Similarly, our predictions of the future will look kind of retro and strange to someone living in the 2030s or 2040s. If studying history has any lesson to teach us, it’s really just this: that the past is an alien world with alien moods of thinking, and that our moment in time will look similarly alien to people in the future who choose to look back and analyze it closely.

This isn’t an argument that we should stop trying to make predictions. We need to, but it is an argument for humility, and also for questioning all your assumptions that you might be importing.

Some things that stand out:

* He’s really good with his historical analogies, especially looking at previous transformations like the early Internet and mobile; no surprise given that he has a history degree.

* he emphasizes over and over how we have still have no idea how all of this is going to work when the dust settles. I think that’s kind of a historian’s move as well. When you look at what people were saying during the early days of the web, for example, almost all of their predictions weren’t just wrong… in hindsight, given how the future played out, they were asking the wrong questions. The implication is that we are probably asking the wrong questions about AI too.

* Nonetheless his thesis about the commoditization of models is actually a fairly strong concrete prediction. i’m not sure if I agree with it entirely, but I do keep it in mind every time I look at the valuation of leading AI labs.

* he continually makes the point that a chat bot is barely a product and that AI labs have so far had very little success in delivering products above that layer… with the exception of coding agents, of course.

Specific examples? Specific tasks?

This is the kind of stuff people were saying in 2023. But it’s 2026 now and LLMs aren’t just trained by reading lots of text anymore. That’s “pretraining”, and it’s still the first stage, but LLMs also have a huge amount of RLVR training where they actually do solve huge numbers of mathematical and logic puzzles and update their weights in response. They don’t just learn mathematics from reading about it now. They learn it by doing it. That is why they can now solve hard problems and probe theorems.

> that does not mean the model can do actual logic.

But they do, all the time. (Please tell me you’ve at least put a frontier LLM through its paces in the last 6 months?) If you think they can’t do logic and reasoning, can you provide examples of specific math or logic problems that you think a frontier LLM can’t do?

> Text generated by an LM is not grounded in communicative intent, any model of the world, or any model of the reader’s state of mind. It can’t have been, because the training data never included sharing thoughts with a listener, nor does the machine have the ability to do that. This can seem counter-intuitive given the increasingly fluent qualities of automatically generated text, but we have to account for the fact that our perception of natural language text, regardless of how it was generated, is mediated by our own linguistic competence and our predisposition to interpret communicative acts as conveying coherent meaning and intent, whether or not they do [89, 140]. The problem is, if one side of the communication does not have meaning, then the comprehension of the implicit meaning is an illusion arising from our singular human understanding of language (independent of the model). Contrary to how it may seem when we observe its output, an LM is a system for haphazardly stitching together sequences of linguistic forms it has observed in its vast training data, according to probabilistic information about how they combine, but without any reference to meaning: a stochastic parrot.

Do you really think that claiming the output of an LLM “has no reference to meaning” is not an empirical claim? That it doesn’t attempt to place any bounds whatsoever on what LLMs can and cannot do? LLMs can solve some very difficult mathematical problems quite well now: see the article from Gowers that was on here recently. Do you think that the output in a situation like that “has no reference to meaning?” If so, you’ll have to explain why, because I don’t understand at all.