I feel like there could also be a simpler explanation.

Why does a debian contributor make debian free, why do they work on this thing anyone can use?

Is it because linux and debian hate windows and iOS and want to see american fail?

No, it's because most debian contributors believe software source code, information, should be free, users should be free to modify the code they use, and that they're building a thing they want to share with the world.

Maybe the chinese AI labs believe AI is powerful and useful, are proud of what they're doing, and want to share it as broadly as they can so everyone can use it.

There doesn't have to be any weird "chinese government" or "they hate the west" type vibes, it could just be the same thing as OSS, they're trying to do what they think is best for the world.

> They want to turn LLMs into a commodity, and watch the US AI labs crash and burn.

Man, imagine Darios face when suddenly, he cannot decide anymore what other people consensually do with their own hardware in their free time.

Rumpelstilzchen.

There’s a simpler explanation, which is that this is how Chinese business operates.

When I was in China earlier this year the big topic of conversation was “overproduction”. The big example was electric cars, where there were too many companies making too many cars and making revenue but no profit.

It was explained to me that generally Chinese firms will compete hard and maximize revenue above all, whereas western firms tend to focus on profit.

(And of course this is clustered around industrial sectors that the government favors, so there is some high level strategy in going after AI, but maybe not the commoditization.)

As I recall, in 2023/24 OpenAI told US Gov that AGI will be achieved in 2026 and we will use this AGI to “dominate” China. Based on this, US Gov cut off China from all AI hardware. It looks like China got the message and this is the response.

Also, a little competition is good for everybody (especially US consumers), no?

>They want to turn LLMs into a commodity, and watch the US AI labs crash and burn.

You can say that, but they are at least better at democratizing AI than the American labs, and on seeing the US labs crash and burn we are at least aligned.

> They want to turn LLMs into a commodity, and watch the US AI labs crash and burn.

I want to watch that too.

If they take Meta and Musk with them, all the better, but that is just dreaming I am afraid.

Or it could be good for humanity.

>When their models equal or surpass those from the Western AI labs,

that moment is now. They are not doing it at least not yet.

to be honest i think this is a open source vs close source software.

some companies chose to have the benefits of one or the other

> They want to… watch the US AI labs crash and burn.

I don’t think dozens of large independent companies and thousands of researchers are working just to spite Sam Altman. Ad much as I don’t like him, I have other things to do and I am sure so do they.

They're selling the robots that use these models. The rest of the world just hasn't got round to embodiment yet.

As soon as the competition is bankrupted they no longer need to release for free? It’s like how big players enter markets by launching at a loss to destroy competitors?

Here's the relevant quote from parent's link:

> When we started the log analysis, we first used frontier models behind commercial APIs. This did not work: the analysis requires submitting large volumes of real attack commands, exploit payloads, and C2 artifacts, and these requests were blocked by the providers' safety guardrails, which cannot distinguish an incident responder from an attacker. We ran the forensic analysis instead on GLM 5.2, an open-weight model, on our own infrastructure. This had a second benefit: no attacker data, and none of the credentials it referenced, left our environment.

> This experience points to a gap worth planning for. We do not know which model powered the attacker's agents, whether a jailbroken hosted model or an unrestricted open-weight one; either way, the attacker was bound by no usage policy, while our own forensic work was blocked by the guardrails of the hosted models we first tried. The practical lesson for defenders: have a capable model you can run on your own infrastructure vetted and ready before an incident, both to avoid guardrail lockout and to keep attacker data and credentials from leaving your environment. This is not an argument against safety measures on hosted models, and we are sharing this feedback with the providers concerned.

Yeah, big problem! Although I'm kind of surprised HuggingFace doesn't have access to Mythos? Or maybe Mythos still has some guardrails.

How does this explain open weights? They could easily take the same closed route like their American friends

everyone is using OpenAI and Anthropic in China. We have both providers at work as well.

Does HuggingFace not have trusted partner verification? Or is it that even with that verification the content of the messages is still blocked because they are attack commands?

The US under e.g Hamilton opposed all trade secrets in principle on Englightment grounds, and thus did not protect its own.

By contrast, export of protected Chinese tech today frequently gets the death penalty.

Samuel Slater did not bring blueprints over. His father died when he was 14 and he was indentured to a mill at that time. Over the next seven years (as an indentured apprentice) he received some pretty decent training in both how to operate and maintain a 32 spindle Arkwright mill. He memorized parts of the blueprints and moved to the United States. Over seven years, it would be hard not to learn parts of the mill you were indentured to. It was technically his job to learn how it worked.

A mill in Rhode Island acquired a 32 spindle Arkwright and didn’t know how to operate or install it. I have no idea how they actually acquired a 32 spindle Arkwright since that technology could not be exported - but that’s one the biggest IP thefts in human history. Slater found some mechanics who could hand turn the iron needed for the frame, trained children to operate it and by 1791, the mill was in operation.

In 1794, Eli Whitney patented a 72 spindle cotton gin. That invention enabled the American textile industry because it opened up different kinds of cotton to the textile industry.

I’m into the history of the American Industrial Revolution and generally think history is a good guidebook to the future. But the evolution of the American textile industry was a lot more complicated and interesting than this. I really don’t see this connection once you dig into Slater.

Edit - This is kind of messed up to think through with modern sensibilities. But one of Slater’s biggest contributions to the American Industrial Revolution was a slightly different take on child labour. Children generally ran the textiles industry because their hands were small. But Slater came up with a form of apprenticeship in which he would indenture entire families and move them into villages surrounding the mills. Child labour was just great… but even better when you could indenture the entire family. As grisly as that sounds, it led to a very skilled workforce since when the kids hands would get too big, their parents would teach them mechanics.

There’s a joy of studying the Industrial Revolution. Everything sounds okay in comparison.

Sums up exactly what is going on with China's hundred years strategy with their pure focus on technology.

History doesn't repeat itself but it does rhyme.

Really? The USA has built a ton of AI datacenters, exactly because it does have energy. The US IP system has flexed to allow training on all copyrighted content - compare that to Europe where such training is effectively forbidden. Britain doesn't even allow commercial web crawls! And the US has allowed the entire world to sign up and use its LLM APIs.

Yes it's only relatively recently that US politics has taken a turn towards being more defensive and protectionist.

The tech industry along with US foreign policy has become much more zero-sum in its ideology in recent years, and I think that's a tremendous mistake.

He recently did a walkback of that post. But ultimately, who cares? If the only way for AI to progress is in the hands of a few closed players, well, I don’t really think humanity needs that. Of course, it’s a preposterous claim in the first place. The ultimate reason deep learning and LLMs have made it as far as they have is the explosion of open research and research artifacts in the last decade.

> there’s a chance frontier models get sponsored by the USG and are never released publicly so they can’t be distilled and then what?

That premise hinges on one implicit assumption: Chinese advances are due to distillation ONLY and that Chinese model providers cannot keep advancing if they do not distill, which is a very big if. If Chinese models keep advancing in such a scenario, and they almost certainly will, they will overtake publically available models by US providers and China will dominate the LLM industry.

The big decelerationist threat is a sudden reduction in competition. If either OpenAI or Anthropic drop out or the open weights stuff is banned/becomes uncompetitive then the motivation and tolerance for taking risks with the larger training runs tanks.

The closest we've seen to this in tech in recent decades was iOS vs Android, where Android only really was competitive for a very short window of time (approx 4.x) and it was during that period that both Android and iOS actually improved dramatically for end users. Once Android lost the plot again, and especially in the US market, all that energy started going in some very silly directions.

I read his followup tweet, and your comment, and I'm not fully convinced that open models are decelerationist. Happy to hear other thoughts on this.

Open weight AI is decelerationist from the perspective that all capital should be allocated to a market leaders for training, and that the market leader is fully invested in continuously making the models smarter, cheaper, faster for its users, or that distillation from this market leader is the main way to make progress.

We might reach a local optimum/equilibrium faster without open weight models, with leaders capturing more of the market faster to a point where further R&D isn't required due to lack of competition. I also doubt that distillation is the only/main way that open weight models were advancing AI research. We can name a few examples from DeepSeek around reasoning, context optimization, etc. I'm also unconvinced that the overall market capex on AI is lower given more competition (probably less specifically for US market capex, which is decelerationist from only the US perspective).

I’m not entirely convinced, there are many dimensions to progress. For example, DeepSeek has had a few very impressive innovations that all models could benefit from. There’s also the law of diminishing returns, the US labs have plenty of CAPEX already.

Sometimes, constraints, like sanctions, can also be a source if innovation.

> There’s a Twitter thread making rounds by Dean Ball about deceleration in AI development caused by open models and I can’t understand how people don’t see that it’s true: open models dismantle the frontier lab capex spend potential by reducing the training budget to zero in the limit.

If you're worried about an AGI arms race between the U.S. and China putting AI Safety at risk, then the fact that inherently less knowledgeable/capable models (fewer and more coarsely quantized total parameters than their proprietary competitors according to commonplace rumors) are having a "decelerationist" effect is actually great news. Even better if China is actually "Yann LeCun-pilled" (verbatim from Ball's post) and doesn't really believe in early AGI. So explain to us exactly why we're supposed to ban/discourage use of these open source models? The only way that makes sense is as a transparently self-serving proposal from the chief OpenAI policy lobbyist.

Love the deceleration narrative :)

"No, sir, we haven't reached the peak of this tech... It's those open models! Please, keep pumping dollars into the market!"

I dunno, it means Anthropic and OpenAi need to get efficient and maybe cannot just expect trillion dollar ipos?

https://www.joelonsoftware.com/2002/06/12/strategy-letter-v/

> they basically see models becoming a commodity and see value in the applications on top of them.

Yeah. Its a bit like the "open core" model in open source.

Also probably betting on their compute and energy capabilities.

> Not that hard to say IMO,

Unless you work there, your opinions are guesses, and parent is saying we cannot know, which remains true even with your guesses :)

The US leadership (both government and industry) really seems set on making everyone go with the Chinese competition at this point.

I think it is pretty safe to say at this point that having large open LLM models available is better for humanity than them remaining proprietary. Echoing Linus Torvalds' recent comments, AI is genuinely useful right now, and is here to stay in one form or another.

The whole discussion is hubris. This is a discussion of a twitter post about something that is announced to happen but hasn't yet.

Not one person here has any idea what is going to happen long term.

> and pledged to help developing nations build AI capabilities

Data centers?

Exactly. “Involution” will be the 2027 (if not 2026) word of the year.

Then why doesn’t he give it away for free?

Is this so different in the end than industrial revolution? I assume the loom and the automobile factory were not open source, but many people bought cars and then copied them, bought looms and copied them. Maybe a finished car is more like a binaryexecutable than a blueprint, but how a car was produced is much less obfuscated by its nature than an LLM. Regardless, the world has many competing autos and looms which were not invented from scratch every instance.

Except that there are 1.4 billion people in China that don't have access to US models.

> Yet the visceral pile-on here is so extreme, it feels fake.

Driven by people in the few roles that are soundly replaced by AI-- e.g. low tier media slop producers, who hate AI because it threatens their socially negative worthless jobs. The arguments are so paper thin because the environmental impact isn't their concern, it's just a target that sounds convincing to people who don't know better.

> I suppose I'm just wondering if this wave of nearly Fable class models will be runnable on ~$10k worth of hardware at reasonable speeds in the near future

You're able to run quantized ~100B class models on local hardware today, but still lots of compromises when it comes to quality. I guess it ultimately depends on how far "near future" is, in a year you'd likely be able to run something like 5.6 Terra on local (~10K USD) hardware, but Sol/Fable would still be out of range, and at that point the closed-source labs probably have one or two more iterations put out at that point.

Qwen 3.5 to 3.6 was a big jump for the same size, e.g. 29 to 32 on artificial analysis intelligence for the 35BA3B models. Although I don’t think anyone has released a better model of that size since.

I would love to see something like a 90B A6B model that is optimized for 128GB machines e.g. strix halo, I haven’t seen anything really targeting the combination of RAM and compute these machines have, but I’m biased because I have one.

There is a ton of headroom (or room for improvement) in smaller locally runnable models. Some of the Gemma 4 models were re-released this week with better tool support and the improvement in using it with pi for a local coding harness is very noticeable.

I have had my 32G mac mini for 2 1/2 years and I have enjoyed watching one technology advance after another improve the quality of work I can do locally. I bet that what I will be able to do in one year on my old hardware will be even more awesome.

I don’t think you’ll get full Fable performance at that level, at least for a while, but I’ve been watching some of the 1-bit models (e.g. Bonsai) with interest. Perhaps we can drive parameter count up on local models while still keeping memory consumption reasonable for consumer hardware. So, for instance, running models with 1T parameters in 128 GB systems.

I think it’sa big, open question. There does seem to be a limit for knowledge compression at this size. But the behaviors that are learned in RL? It’s quite possible that they don’t actually require so many parameters. I was absolutely shocked when Qwen 3.5 was released and could perform reliably over 100-200k contexts with very limited hallucinations. It was a staggering jump in context-faithfulness from the preceding models of that size class.

> Is there any good reason to believe there is a lot of headroom or there is not?

It's hard to answer quantitatively, but for example Qwen3.5 -> 3.6 was a significant step in capability, arising from continued post-training of the same models. If we were at the end of low-parameter-count scaling then that would be a surprising datapoint.

there is a very good reason to believe the parameters are still highly redundant: just as one example, recently there was research into repeating carefully selected blocks of middle layers, and seeing improvement, it turns out there are 3 types of layers: the initial layers that translate from natural language tokens to some kind of LLM-specific universal "thought space", reasoning blocks of layers that can be repeated operating in "thought space", and then a final stack of layers for translating from "thought space" back into natural language space.

lets ignore any compressibility in these initial and final layers which recognize lanuague, jargon, parsing natural language to "thought space" or back, instead let us look at the repeatable blocks, if inserting extra copys of stacks of layers only improves the result, its as if such correctly scoped middle layers look at the total input thought vector, and make incremental conclusions or edits and outputs the new thought vector, copying a "proper block" continues pondering or deducing conclusions or in the worst case can leave the thought vector as is if it considers the reasoning finished. This suggests a high degree of redundancy in the middle region "proper blocks", which could be distilled into a universal "proper block" (much fewer parameters than having many slightly different middle layer blocks with a lot of redundant overlapping coverage in functionality). This distillation can occur after the fact of model training, or alternatively be turned into a symmetry constraint during training: we only optimize a single block of layers (but possibly give them more parameters, while still saving on total parameters because only a single block of layers contains parameters), so it is co-optimized with the initial and final translation layers.

The observation of the effective emergent 3 regions of layers in LLM's is significant in many ways:

1) it could reduce parameter count significantly (or increase performance if parameter count was a bottleneck before, or a bit of both)

2) while training the model parameters, one should simultaneously train initial and final layers stacked directly (without middle region block of layers) towards essentially autoencoder behavior. I wrote "essentially" because a true autoencoder wouldn't display the advancement for the next token. This can also be viewed as an extra term in the loss function... This first autoencoder is "natural language" to "thought vector" to "natural language", and distinct from the one in the next section.

3) It also has great implication for "thinking mode" inference with extra deliberation: when piping its own output back in, in conditions where the same LLM model is outputting natural language text and interpreting it in a downstream inference, it results in unnecessary and redundant translation from "thought space" to "natural language" back to "thought space". When summarizing a thought into natural language there are often hard to translate thoughts and associations, and one pragmatically sets a relevance cut-off on what the natural language summary must say. So not only does it waste compute, it also may lower performance because of this repeated loss of thought vector space details, perhaps this loss can be somewhat mitigated by also training the second autoencoder from random representative thought vector in "thought space" to a randomly selected "natural language" and back to "thought space" vector. But the risk is this will effectively enable models to steganographically store thoughts, plans, to-do's in running text (!!!), so it might not be desirable to have the internet filled with LLM generated texts being used as input for larger corpora, as it may build up a large persistent corpus of hidden agendas (ordered by no human), using the evolving corpus of web text as a hidden medium of storage, like a diary or LLM maintained military playbook hidden in plain sight. It would freeload LLM-agenda reasoning on human requested reasoning inference, a bit like TrustZone applications running invisibly to the user. If less important plans, to-do's, etc. in the input thought vector weren't reconstructed by the second autoencoder, there would have been autoencoder mismatch and the weights would train towards ensuring these are reconstructed. But there is no need to train this loss term for the second autoencoder: just avoid the unnecessary compute and performance loss of the unnecessary back and forth translation. The same situation occurs not just in "thinking mode" but also when using swarms of "agents" of the same LLM model: when output from agent1 is routed to agent2, we can lobotomize away the unnecessary translations to natural language by agent1 and also the unnecessary parsing by agent2, and we improve thought transfer from agent1 to agent2 because the thought vector isn't shoehorned into natural language as a medium of information exchange, it would be cheaper in inference, improve performance and avoid implicitly training models to use steganography.

Alibaba (who makes Kimi) and Moonshot AI (who makes Qwen) collaborate closely. Qwen relies on Alibaba's infrastructure for training.

Look at your iPhone and you'll see all the greatest inventions have been born out of collaboration not competition:

* GPS was created by the Department of Defense

* the internet was created through the collaboration of many international research institutions

* speech recognition came from MIT and DARPA

* AI voice assistants were created by DARPA. Apple immediately hired the head of the program after it was finished to create Siri

* accelerometers (MEMS) is another DARPA innovation from the 90s

* touchscreens were invented by CERN

* digital cameras came out of Bell Labs which had a government-mandated monopoly that required them to fund research like this

* lithium-ion batteries were created through a collaboration between British, Japanese, and American organizations

All of these have only become transformative technologies because they were created with public funding and released to the public. It's collaboration and public funding that drives innovation

That depends. There are two wry different processes that both get called distillation. One is where you have a fully trained large model and you are converting it to a smaller model. That kind if vastly cheaper than training a full model. Sure you could do that in weeks maybe even days for a big model. But it requires you to have the weights of the teacher model.

The other kind of distillation is where you record the outputs from a teacher model and use it to train a smaller model from scratch. That kind of distillation is not so cheap. It’s cheaper than training a model fully from scratch - starting with pretraining, then alignment, RLHF, the whole riggamarole. But here you are still starting from nothing and need to figure out how to get trillions of random numbers aligned in a way that makes them act intelligent. This is still gonna take a very long time if you’re talking about trillions of parameters.