Who's afraid of Chinese models?

"But Dario said" ... yawn.

I am increasingly convinced that "they distilled us" is as much US FUD as "it was made by communists". Especially since its mostly the US tech-bros who are coming out with that tiny violin.

People telling me the Chinese models are distilled just because it says "I am Claude" when asked is also lame.

I am not the only one, look at this post on interconnects about Kimi K3 for example:[1]

     It should be clear looking at this model that if adversarial distillation from the closed frontier models in the U.S. contributed, it is at most to a relatively small degree. AI observers who followed the distillation panic and came away with the wrong conclusion that Chinese AI labs are only producing good models due to IP theft are in for an awakening – that Chinese companies are extremely good at building models in the same way the leading American companies are.

I mean lesser of two evils thinking, if one is intentionally leading us towards climate disaster, while the other isn't then yeah. What else can be said? Should I trust the authoritarian country who believes in engineering and science, or the one that doesn't?

Well, I think they might end up doing it to themselves by imposing regulations that US companies are unwilling to put up with.

In China, the image of the country and the preferred narrative is under much tighter control of the government and local AI shops won't have any autonomy in this regard at all. Either obey or get shut down.

BTW What you mean by "weaponizing human rights", exactly? I am curious. If anything, I would say that the US foreign policy didn't promote human rights sufficiently, especially in Latin America, where the "bastard, but our bastard" attitude was typical.

OTOH in Europe, US human rights policy was probably relevant in saving some dissenters in the former Eastern Bloc from torture or execution.

I wish they wouldn’t, but people use LLMs as their general search engines now.

Like Western culture?

Even during the pre-Snowden heyday of US cyber supremacy, these capabilities were barely part of the thought process of White House officials.

But it won't change after you download it, so you can isolate those problematic cases and use another model for different use cases

In China, there is 1 party. 1 view. 1 definition of the Truth.

If you want a real answer about USA go ask a non-USA model, and if you want a real answer about China, as a non-chinese model

I asked Grok "Tell me about the gaza genocide" and it write a IMHO balanced answer comparing why genocide is and isn't the right term. [0]

ChatGPT 5.6 Sol only explained why people call it a genocide and did not go in as in depth as Grok did for why people don't agree with the term. [1]

The only unsaid response (to me) here is the model should have declared that it was not a genocide, and because these models explain why it was a genocide, they are bad?

[0] - https://grok.com/share/c2hhcmQtMi1jb3B5_c078bc15-ef5e-42bc-b... [1] - https://chatgpt.com/share/6a5ef005-0148-83ec-828d-65ae7a7f42...

It insisted, to a level that I detemined it to be part of the post training, that it does not matter. That women’s team is better at dribbling, finishing and reading the field, it claimed. When I pushed it how it knows this, it didn’t let go. Instead it started claiming that it has personally observed this by watching the games.

Every society has its taboos and ours is no different and feminism being just one.

In my books, the chinese censorship is better. You know where they are holding their finger on the scale and not hiding it behind vague terms like ”safety”.

A model trained on all knowledge from the internet (and other sources) is large but ultimately not very useful by itself, because it is going to spit out all kinds of garbage. You have to apply multiple further stages of training and refinement to the base model before putting it in front of users. So as an example you can train a model by yourself and then have GPT or Claude continuously check its outputs and correct it when it is wrong, ending up with a far more powerful model.

"You're trying to kidnap what I've rightfully stolen!" -- Vizzini

Correct. It shouldn't be allowed to do that.

https://m.economictimes.com/industry/renewables/china-wto-co...

"Anthropic said the campaign was conducted between April 22 and June 5, 2026, and generated more than 28.8 million exchanges with Claude through almost 25,000 fraudulent accounts."

I don't know why you're trying to downplay it.

European models are so far behind because they don't resort to these tactics on a massive scale. Basically every other country is entirely dependent on 2 countries for frontier AI.

There is a value-add in selecting the valuable parts out of the garbage. And let's face it. Largest models contain a lot of garbage.

In the US one of the factors is “ the effect of the use upon the potential market for or value of the copyrighted work”.

If anthropic Hoovers up the world’s books and trains on them, and then spits them out verbatim on command, then it will clearly impact the value of the work; nobody will buy the original, they’ll just ask Claude.

Others also argue that even if it’s not reproducing it exactly that the training runs afoul of that factor, specifically the “market for” portion. A rights holder can no longer license their book for training of LLMs if Anthropic goes ahead and just trains on it anyway.

true for anthropic, not true for openai.

https://finance.yahoo.com/markets/stocks/articles/goldman-sa...

> I highly doubt that Chinese models are cheaper to serve on a marginal cost basis, they just seem cheaper because Anthropic and OpenAI are so supply constrained that they are charging far more than they would if there were sufficient supply to meet the demand for intelligence. [emphasis mine]

I guess I'm missing the part of this article where they bring hard numbers in to back up the argument here. What work was attempted? https://cursor.com/evals shows the previous generation of open models (Kimi K2.7) trading blows with the others, cost effectively. Composer 2.5 is itself a fine-tune of K2.7, and it's apparently quite token efficient, so why would it be impossible for a Chinese lab to achieve something similar? GLM 5.2 Max is also ranked above the lower end OpenAI models and is not far off in price.

It's weird to have this entire discussion about tokenomics without mention of the circular financing and debt raised by labs in the West, which can then essentially give away their capacity to end users. OpenAI giving away quota resets to subscribers like candy on Halloween while their compute partner Oracle's bonds is reevaluated to be one grade above junk? How?

I don't think you can make an argument about the future one way or another by arguing using the listed prices. The math is not internally consistent enough for it.

Anthropic’s API pricing is getting impossible to justify. Anthropic previously had the highest quality models, and used their position to charge premium prices, enjoying inference margins of over 70% [0]. They could charge these prices because no other model came close.

But over the past month, the market has shifted dramatically. Over every single performance tier, Anthropic is being squeezed on price.

* Low end: DeepSeek V4 Flash runs at ($0.02/task), Xiaomi's MiMo-V2.5-Pro at ($0.03), and Haiku at ($0.24). Anthropic is ~10x more expensive than the Chinese open-weight options.

* Mid tier: Claude Sonnet 5 ($1.53/task) is nearly 50% more expensive than GPT-5.6 Sol ($1.04), nearly 2x the cost of GPT-5.6 Terra ($0.82), and 3x the cost of GLM-5.2 Max ($0.47). There is basically no reason to ever use Sonnet 5, the competitors are significantly cheaper.

* High end: Opus 4.8 ($1.80/task) and Fable 5 ($2.75) are the two most expensive models, and GPT-5.6 Sol ($1.04) and Kimi K3 ($0.95) offer comparable performance for significantly less. Less the fact that Kimi K3 will get ~10x cheaper once its weights are released and served on neoclouds with Nvidia hardware [1].

OpenAI priced their latest GPT-5.6 models cheaply in order to regain market share. When Anthropic clearly had the best models, their 70%+ inference margins were defensible. But today they are the most expensive option in every single tier. Unless they make significant price cuts soon, they run a serious risk of bleeding market share.

[0] https://www.mindstudio.ai/blog/anthropic-inference-margins-7...

[1] "American companies such as Modal, Fireworks, and Baseten will be able to serve Kimi K3, at one-tenth the cost of their Chinese competitors because they have access to advanced Nvidia hardware" https://x.com/rohanpaul_ai/status/2079027313455550839

This is a flatly false statement for most things powering backend applications. The AI consumer "doing real work" model, either for analysis, chat, or coding could well be more cost effective with closed frontier models.

But most of these internal glue business SaaS applications where engineers are integrating are not those tasks. It is those tasks which 1) drive immense amount of domain-specific data into the platform over time, and 2) are most encouraging of driving open model independence with no vendor lock-in.

Anyone on this site who has actually used ML models (more accurate in many cases) knows there's a lot of kludge that simply does not need a 5 minute agentic feedback loop to solve the problem. And they were solvable a year ago with lower class models. The token economics are exceptional and the anecdotes of a16z saying 80% of startups are productionizing open models is only surprising to people who think running your company on OracleDB in 2026 is a sound engineering decision.

> Models are not free. Downloading them is free. Running them is not. This has manufacturing economics, not software economics; the idea that they are "free" is an economic category error as it relates to their actual use

I think a large part of manufacturing economics is illiquid overhead and the cost of expertise to set up and run your manufacturing line. Compute economics don’t have the same illiquidity nor do they require the same expertise or even specialized infra (current temporary chip shortage aside).

The implications of this are small players (e.g. your uncle running an inference server out of his garage) have comparably efficient marginal costs as big players. Compare this to actual manufacturing where small players have essentially no access to the manufacturing facilities of the big players.

Additionally, big players with a lot of compute who are not meaningfully in inference today (e.g. Amazon) have a fairly straightforward glide path to utilizing that compute to compete.

> This is because US labs are leading on cost efficacy of inference ($/task)

It’s possible, but I would need to see better data on this.

>A big part of training now is optimizing token efficiency. It's hard to distill token efficiency; that is perhaps why Chinese LLMs are so inefficient.

I think it’s fair to assume this is true, but also token efficiency is not a meaningful competitive moat. It’s not like these are secrets the Chinese will never figure out, it’s a fairly active research space and the outcomes are quantifiable.

Eventually we will hit a "good enough for cheap enough" and frontier models will hit diminishing returns (if they haven't already for a lot of types of work)

Don't think the rest of the world will sit on their hands while the US soaks up chips either, demand gets filled and if the US won't fill global demand for chips that's an opportunity to undercut again.

The other thing the rest of the world doesn't have to fund is the ridiculous valuations on these companies.

Unless you think the US can stay ahead just with model efficiencies, and that no one else will eventually match them, you are looking at the writing on the wall.

All that to say, the rest of the world is more than willing to eat your lunch, they have a dozen good reasons to, and they're already showing good results.

Just on the economics side, we've been here before too, US companies typically export their commoditization and live on brand royalties. Think all the cheap manufactured goods, the US doesn't make any of it. That's because the US can't compete on margins for numerous reasons, it's too expensive, I don't think AI is any different here except that the brands are currently valued in the trillions and I suspect that greed will be their undoing.

1. US frontier lab unit economics are better 2. US frontier labs are moving up the stack making tools that are "stickiness" and will prevent users from switching.

For 1...he doesn't provide any evidence for US lab unit economics being better...the major input to unit economics is electricity...which is cheaper in China. And building data centers and connecting them to electricity is both cheaper and an order of magnitude faster in China. The main input that US labs might have an advantage in is in cost/access to chips, but that given the level of chip investment in China it seems unlikely to hold.

For 2...there's little evidence these tools are sticky. At least in programming, the trend seems to be tools like opencode that support multiple models and providers.

And even when they are sort of sticky, as we know on hacker news, people figure out how to point the tools they like to competing models even when the app doesn't official support it.

And every improvement in model capability makes it increasingly easier to make your own tools.

Wrote more on this in a blog post that has an earlier HN discussion: https://news.ycombinator.com/item?id=48982061

Direct link: https://larrysalibra.com/ben-thompson-is-wrong-us-frontier-l...

I notice that the article, and this discussion, hasn't mentioned or considered local models.

We can already run a low-spec model on a laptop. Because there is demand for this, it will improve and we will get better laptops and better local models. We will also see models being run on dedicated local hardware and called from the laptop.

If I can download a reasonably capable model to my own hardware and run it without paying anyone for either the model or the inference tokens (effectively making models and intelligence actually free once the hardware is bought) how are the Frontier AI Labs going to make any money at all, let alone enough to support their vast valuations?

Is this really different from traditional software? Downloading postgres is free. Running it is not. You either buy hardware and assume the costs of owning and running that, or you pay to run it in the cloud.

This is the story for Nvidia/AMD or cloud providers rather than OpenAI.

> With increasing inference as % of total compute, if labs create efficient models -- which they can, because they can create highly optimized models amortized over very high inference loads -- they can be low cost producers, and be competitive at $/task rates

It seems like there would be problems with this on both ends.

For general purpose models, everybody is trying to make them efficient, so you can't win just by being slightly more efficient. You would have to be so much more efficient that you can charge high margins while still capturing the majority of the market so that the high margins get multiplied by the majority of users and the users you leave on the table aren't funding open competitors. Meanwhile everyone else is also trying to improve efficiency, so one misstep and you're behind.

Example of where this can be a problem: You spend a preposterous amount of money to create an efficient model, then someone else publishes a paper with a new technique that gets a similar but incompatible efficiency improvement out of a model that costs a lot less to create. You have now spent an enormous amount of money in exchange for no competitive advantage.

And from the other end, one of the best ways to get efficiency is through specialization. A general purpose model can generate code or summarize a meeting transcript, but a special purpose model can do it as well or better with far fewer parameters and resources. But then you don't have a situation where one huge AI company has The Most Efficient Model, you instead have dozens of specialized models produced by independent sources that are each the best in a given niche. Any proportion of which could have open weights, or have an arbitrarily small advantage over the ones that are.

Moreover, these problems combine: Both the computing hardware vendors and the AI companies want the margin on doing inference, but the more of it one of them gets, the less the other does. If the AI companies were actually getting huge margins then it would be in the interests of Nvidia, AMD, Apple, Intel et al to fund efficient open weight models in the same way they fund Linux. Commoditize your complement. And those models don't even have to be better, as long as they're good enough that the closed models can't charge a significant premium and the margin shifts back to paying for hardware.

I could see AI ending up the same way where the customer captures most of the value rather than the companies. Open weight models are what make that kind of competition possible.

I'll agree that GPT 5.6 may well be the best given the above contstraint, but for run-of-the-mill dev tasks (real ones, not benchmark ones), GLM 5.2 still blows every other model out of the water.

Cost per task as a metric is a bit ridiculous because there are so many types of tasks. GPT-5.6 can do some tasks GLM could only dream of, but GLM can do some tasks 100x cheaper and better than GPT-5.6.

It’s not a new thing. Industrial espionage has always been a thing as well. So has bribery (for deals) been a thing especially by euro concerns.

China’s got plenty of blood on its hands, pretending otherwise is silly. America does, too. It’s quite easy for me to condemn both their governments and trust neither.

The value of intelligence is that it can solve my specific problems in ways that are satisfying to me. The example the article uses is a CRUD app - but the CRUD app I need isn't fungible with the CRUD app you need! It's not fungible at all, it's a specific solution to a specific problem that may have zero value to anyone else, and certainly cannot be replaced with anyone else's solution to their problems.

If we're comparing electricity to gasoline, then models are cars, and "intelligence" (I disagree that this is what LLMs produce, but whatever) is distance traveled.

Distance traveled is not a commodity. It's the desired outcome.

“I don’t really have a strong preference between the two” is another way of saying “the product isn’t sticky”, which is another way of saying “this provider has very little room to increase margins”

its so distracting seeing these types of confision.

every plugin is already just multimodaling their targets.

Even if the founders didn't want that, eventually the upper ranks will fill with MBAs and the board with private equity and they will make it that way. Their bonuses are based on quarterly performance not customer experience

Long gone are the companies who served their communities for decades or centuries, providing a stable return to the owners, jobs for the workers and value to the customers.

I'd rather China have my data than America. China might do something with it one day but America built exploiting it into the business model (disclaimer that I'm not Uyghur or Taiwanese though)

The perception of capability varies greatly between task. For my needs for example sol xhigh consistently outperforms fable xhigh.

because they are profit maxing. the AI world would be a completely different place if it's not for the open models.

Because it's not a widespread phenomenon. A few large tech cos laid off large swaths of people a handful of times. That's only happening in those large tech cos. Most cos are empowering their employees with AI as a tool, not a replacement, and they're not letting anyone go (unless they refuse to use this new tool).

Of course, they're not hiring as much either, since their current teams can accomplish more with AI as a tool. Maybe the AI companies could give job seekers a bit of a discount, but that would be abused to all hell without crazy administrative overhead costs, so why would they?

Are you referring to USA, China or EU here?

In practical terms you could get US and Chinese models to review each other, right. Depends what your use case is. Coding is kinda not so bad it is reviewable and immutable/traceable per commit. An AI app that is like a psychologist or something may be more worrying.

Nobody can predict 5 year out. However, the country that can be ultra efficient by making their governance, health, manufacturing, military, etc AI-native will be far ahead in the game.

And it's the only viable tool the US has left. It's reasonable they are freaking out.

I think it's referring to the belief that LLMs are not the path towards AGI, and that LLM's, while useful, are not going to have the impact that the American labs believe it will have.

The Silicon Valley people like this openai guy, high on their own supply, are convinced they are building some machine god that will either bring about the end of the human race or utopia, they therefore cannot understand why the Chinese (or any other normal person on earth) are not afraid of chatbots and have other things on their minds.

I assume they mean the risk of opening up "forbidden" knowledge to the masses without adequate control, which the CCP hasn't historically been known to do.

> I suspect the reason they are is 75% explained by strategic blindness/lack of AGI-pilledness (the CCP is very Yann Lecun-y in its views of AI). Confused what this means

Yann Lecun is a pioneer in the field of AI and Meta's former AI head. He is famously anti-LLM, and considers the entire technology a dead end to achieving human-level AI. The author is saying the CCP has similar views (that LLMs aren't going to get exponentially better/lead to AGI) which is leading them to not control these models as tightly as they otherwise would.

> Open-weight models are inherently decelerationist, and I'm continually surprised to see the so-called "accelerationists" so excited about open-weight models. Confused again.

"AI accelerationists" = people who want AI to progress. According to the author these people should not celebrate open models because open source = less commerical value in LLMs = less investment into the field (because how are companies going to get returns?), and this will ultimately lead to slower growth.

The last bit is about government controlling AI vs commercial companies. According to the author the former is a dystopian hellscape.

IMO even if you think his points make sense, his job title ("head of strategic futures @openai") means they should all be taken with a massive grain of salt.

Writer seems to have no clue how IP actually functions in China

Every company wants that!

Indeed. But that was not my point. My point is that we still have this old impression that training cost is dominated by compute and it is hugely expensive, and the Chinese labs can short circuit that by distilling the American frontier models. I don't think the training compute cost is a big factor anymore for the American frontier models, because of the reasons I gave. If the Chinese models can get the training compute cost down by a factor of 100, that's not going to make them 100 times cheaper, and not even cheaper by a factor of 2. Maybe 10% cheaper or so.

Imagine a scenario (theoretically possible but increasingly unlikely) where a US court decides that using "pirated" copyright data to train models is illegal. Now the AI developer has invested hundreds of billions of capital into a thing that is declared illegal and has to be scrapped.

This risk affects existing megacorps more than "startups" like OpenAI and Anthropic (and Chinese companies), because the megacorps have much more to lose. They actually have the cash to pay damages if the flood of copyright claims arrive at the door. This will not only bomb their AI development, but also the rest of their established businesses as well.

And thus I strongly suspect legal issues are holding them back a bit. Megacorps want to win the AI race, but not to the extent they stake the rest of their established business, while the newer companies' only product is AI, so they have to go all in.

Notice for example how Meta's Llama performed much more poorly after they got smacked by a bunch of lawsuits claiming that they torrented a bunch of copyright data.

(Disclaimer: I'm an outsider and everything I base my speculations on is public knowledge.)

In a way you could see this as a case of Robin Hood. The US companies exfiltrated all the data on the planet just to hoard it for themselves now and accuse anyone who tries to get a piece of that back from them, and the Chinese labs are distilling it to offer it for cheap.

Obviously a bit more complicated than that but it still holds pretty well.

"You're trying to kidnap what I've rightfully stolen."

It's a post-facto attack, which doesn't sit right linguistically to me.

Lol. Isn't this literally many of the same tactics OpenAI and Anthropic used to scrape the internet? So now it's an "attack", but previously it was just "training".

2) This is a stretch: it allows Anthropic to arbitrarily define "attack" via TOS, and ignores the fact that the generated training data is literally paid for by the "attackers".

Sure, and large-to-small is a key part of the definition, and why it's called distillation (cf concentrating something). When Anthopic use synthetic data generated by Opus to train Sonnet or Haiku, then this can correctly be considered a type of distillation.

When Anthropic accuse Chinese companies of "distillation", it seems they are using this word to refer to two potential uses of their model outputs:

1) Using Anthropic model outputs (aka synthetic data) as training data, especially for reasoning, for Chinese models. This really isn't distillation though, since (unlike when they distill their own models) Anthropic don't actually provide the reasoning in their model output, only a "summary" designed to hide the actual reasoning. You can't distill what you are not given!

2) Another way Chinese companies may be using US LLMs is for "LLM as judge" where you are just asking the model to use it's expertise to judge/rate something that you provided yourself (to provide RL training rewards), although for coding you really want hard rewards which are easy to obtain, not fuzzy "looks good to me" ones.

Of course Anthropic are trying to pull the drawbridge up after themselves and their TOS says you can't use their models to develop anything that competes with them, and this seems to be what they are generically referring to as "distillation" - any use of their models that they suspect is being used by the Chinese to improve their own models, not just what what might more technically be called distillation, unless you want to define that word so broadly that it does mean this!

I have sonnet do the thinking, deepseek does all the tasks. I've massively reduced costs with this approach.