alt Hacker NewsWho's afraid of Chinese models?
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> because U.S. open weight model makers must follow the frontier labs’ terms of service, they (1) are worse than Chinese alternatives and (2) end up distilling the distillation, just with a detour through Chinese labs. Wouldn’t it be better if western open weight model makers could go to the source?
Is this an assertion that is backed by evidence?
From the Elon/OpenAI trial:
> On the stand in a California federal court on Thursday, Elon Musk was asked if xAI has used distillation techniques on OpenAI models to train Grok, and he asserted it was a general practice among AI companies. Asked if that meant “yes,” he said, “Partly.”
https://techcrunch.com/2026/04/30/elon-musk-testifies-that-x...
A company making a decision to allow use of chinese models is a company also choosing to send tons of various credentials to chinese model companies. These will just get scooped up, OpenAI and Anthropic can probably hack into anything at this point if they wanted to.
There is no “Chinese LLM”. Each “lab” is distinct and their models behavior is as unique as those from OpenAI and Anthropic
It's say Anthropic, Allegedly OpenAI...
Honestly, as someone from a developing country, this shift is good for us. US frontier models are too expensive for us to use regularly. Chinese open-source models/subscriptions are really good to use.
One thing I have not seen mentioned between Chinese AI vs US, population.
China has a billion+ people that their AI can "study". Plus due to China's political structure, their AI has access to everyone's chats, comments and sites, scraping everyting.
Here in the US, with 1/3 the population, the AI race was lost before it even began. Plus in the US, all companies and people are doing all they can to restrict AI from scraping sites and peoples chats.
So I believe, China will end up owing AI.
The U.S. "executive" class is so obsessed with the "exploit" part of the explore/exploit cycle that it's very clear they are prematurely closing advancement. Better a little money and power for them now than a lot of money and power for their country/humanity.
This has an element of stochastic improvement so it's hard to predict but the chance of the U.S. "winning" this "race" is pretty bleak.
You see this all the time in communities that have internalized hierarchy as a "good", little kings of shit mountain vying for less and less at a higher and higher cost.
Why hasn't someone created a disinformation benchmark to assess that part of the argument?
I am French, and I am sad to see that French models aren't being talked about and that it remains a battle between the Americans and the Chinese.
I don't understand the premise in the beginning
how is running servers supposed to be 0 cost, while running ai inferrence isn't?
I really enjoyed reading this.
This might be a simplistic take, but my biggest worry with depending on Chinese models (and, by proxy, open-weights model development) is that the US can deem them a national security risk at basically any time, and Ant/OAI have minimal interest in making frontier-level models open-weights.
Regulated companies prohibit Chinese models in anticipation of the ban-hammer from the feds, so for data-sensitive work, they're stuck with LLaMa, gpt-oss and Gemma models (which are good and serve as a good-enough base for sft, but seemingly not as good or as expensive as Chinese models)
I suppose the USG can do the same thing that China is doing and bankroll/subsidize that effort; whether they will is for fate to decide.
Nonetheless, this article made it clear that nVIDIA is the real winner in all of this. Shovel selling to the extreme.
oh wow - hadn't realized they decided to opensource Qwen 3.8 Max. That's pretty big news.
I haven't had the time to look into recent and past history, but my intuition points at failed empires having similar "elites" starting to stagnate innovation for the sake of "protectionism" - whatever that means.
Nothing changed for China. The only difference between then and now is that now China is the one selling the products, instead of western capitalists taking a cut off the COGS and selling price.
You techbros need to get off your ass and go to work.
Commenting wholesale on some folks who are asking for hard evidence. I cannot provide that either but can contribute some empirical data.
I have been working on a project with about a dozen generation tasks, each of which comes with a fixed token budget. The nature of this system requires that most tasks be completed by distinct model families.
As a result, I tested ~50 models across as many model families as I could gather, frontier and open weight, API (gateway and direct) and self-hosted. Evaluation was based on a set of cosine similarity validations that was repeated across ~50 different embedding models.
Interestingly, frontier models did worse on the tasks than open weight models. However, when it came to costs, the picture was reversed: frontier models were much, much more token-efficient. In fact, almost no open-weight model was able to meet the initial token budget, while almost all frontier models did. Moreover, open weight models struggled massively with reasoning, in terms of latency and token consumption.
I also found that the latest models did not perform better than older models. And any a priori benchmarking data was utterly useless.
So, I ended up using a set of open weight models without reasoning, as it turned out reasoning as well as frontier negatively correlated with the tasks. However, before I knew this, I had spent a lot of time running each available reasoning level for each model.
Lastly, as an aside, when it came to embedding models, size (dims as well as model size) did not correlate with quality, once a hurdle figure (~2k dims) was met. In fact, sweet spot was 3-5K, and for my (text-based) set of tasks, dense models tended to outperform MoE ones.
Someone (anyone!) get David Sacks on the horn and tell him to read this.
The future is SLMs and China is going there..
The fact that Anthropic has a model like Mythos means that counterpart countries like Russia and China are not far behind, if they haven't already developed something similar or better.
It really is amazing that China went from the country that hacked Google out of its market to a trusted source of AI in the tech world.
All the article relies on the premise that selling tokens is profitable. I don't see any indication for this, and it makes the whole house of card crumble.
the leader of China praised Open Source in a speech. Crazy times
So happy that we have finally 2 countries playing the competitive game. No more secret deals between competitors. No real competition. A race to the bottom is always a good thing for consumers.
Related:
Ben Thompson is wrong: US frontier labs are right to be panicking
While intelligence is said to be a replaceable commodity, oil and copper can be used in nearly the same way even if you change suppliers as long as the quality grade is matched. However, I question whether two models that produce the same benchmark answers are actually interchangeable in real world use.
Personally, I think models will increasingly become specialized in different areas, some good at X, others good at Y, and we might see workflows that mix multiple models.
> Second, intelligence isn’t in fact a perfect commodity
We have got very far from Cicero's coining of the word 'intelligentia' (from inter legere, a 'reading between' and hence discernment) when people talk about 'intelligence' as a commodity
People have been decrying the 'cheapening' of the word intelligence for over a century now, going back to Psychology's adoption of the word and coining of nonsenses like "Intelligence Quotient". "Artificial Intelligence" is just the latest degradation of the original humanistic meaning, and now people aren't ever bothering to prepend 'artificial' to their idiotic use of the word
Is this article written by AI ? It looks so.
The options are to use LLMs from a country run by a psychopathic regime or alternatively to use Chinese LLMs.
Distilling models using more advanced LLM is not a new phenomena. It is a cost effective strategy in a highly competitive emerging field.
Also, the Hidden-Agent problem exists in every model, and is a persistent tangible risk independent of whatever team people cheer for at the games. Let us remember, every LLM nuked all of humanity 92% of the time in simulated war games. =3
Answer: US investors
My thinking is that with the current narratives out of washington we are on track for a ban on Chinese models and possibly sanctions against Chinese AI companies
I think it is the right move to protect American interests
not enough people
I'm honestly more afraid of Claude
i think most is vcs
later secondaries investors in openai/anthropic. It's like time traveling into the spacex ipo.
what a horrible article. full of misinformation and dishonesty.
1. training new base models are expensive for sure, but fine-tuning them are relatively inexpensive enough the labs can continue to do so forever. the main reason why frontier models are so good is because the massive input they generated from user usage. they are using that information to strategically build better training data. and this is why no other models can catch up, til now that is. but if chinese models are good enough, and free to host, and cheaper to use, then the consequence is the frontier labs will lost valuable user inputs and the chinese labs will gain more. as time goes by this will be a domino effect.
2. nvidia is not only the player in the hardware scene. amd mi350p is getting popular, and huawei is pumping SuperPoDs. what does this mean for us? chinese models will surely use chinese hardware, and optimize for them. the other people will pick amd because compare to nvidia they are cheaper. with open weight models and open source inference stacks, they are freely to experiment and improve the stack, thus further lower the inference cost and nvidia dependency. and they even plan to build their own inference hardware, too. and nvidia loses market share meaning all the fund it gives to openai or anthropic will be cut, too.
and you say there is nothing to afraid?
Gemini Pro has become so bad for my purposes -- copy & paste Go programming and code analysis with the web GUI -- that I'll take any model with equivalent capabilities at the same price or lower. I don't care where it comes from, I'm not dealing with state secrets and, frankly speaking, US corporations have an abysmal track record regarding safety and surveillance.
> To that end, here’s an even more interesting question around distillation: why exactly is it bad? After all, what are large language models but the distillation of all of the knowledge on the open Internet, scraped by the frontier labs and distilled into the models that are themselves being distilled? Who is exactly being wronged here?
Frontier labs that thought they could Rupoor[0] the entire creative class, transferring the coercion premium of copyright ownership from Hollywood to themselves. In their eyes, copyright should not apply to them, but also their models should have exactly the same value as a copyrighted work.
Stratechery also argues the US should explicitly make training fair use and forbid terms of service that prohibit distillation. I'm in support of the latter, but NOT the former, even though I normally hate copyright. My reasoning is primarily that copyright is one of the few legal paths available for a rando to go and put the work of an AI frontier lab in legal jeopardy. In the EU and Japan, such legal action has already been foreclosed by similar law. And while free distillation would obviously be preferable, it's also much more of a legal long-shot. Getting America to do anything that even smells like taking property away from the powerful is impossible[1] - it's our zeroth amendment. But we can at least hack the property laws that currently exist to cause problems for the frontier labs.
And, to be clear, if distillation is OK but training is not fair use, distillation is still OK. The output of an AI model is never copyrightable, because copyright only protects the human element. Essentially, this would say "don't train on humans, but absolutely rip off and steal the shit out of other AI labs and give it to the rest of us."
[0] In the Legend of Zelda series, Rupoor is anti-money - collecting it decreases the amount of money you own. I am using it to mean "turn someone's asset into a liability".
[1] Given that America was literally created to protect a wealthy land/slave owner class from disenfranchisement, either from above or below, and the last time we did this we literally had to fight a civil war against that same owner class that installed a new owner class that has largely remained today
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Kellogg School of Business -- he said -- token as a commodity and therefore Open AI is constrained .. ha ha ha hee hee ha .. Well... you build a better mousetrap, and DeepSeek, K3, and ByteDance are just that -- just as good and fit to purpose -- What is needed is to build on top of -- not paniteir (invade privacy and kill people with the information) -- not USMC AI -- use PI's as overwatch killer drones -- but how can I make harder steel, longer-lasting, seawater-resistant concrete, faster time to build housing, better enforcement of USDA rules and FDA adverse enforcement, and better EPA water cleanup, a better FTC for consumer goods -- that is, if I buy an item, that item is safe and built to purpose -- ANYONE not talking about public protection of consumer rights usng AI, is wasting your time
People with 401k’s and retirees?
Looks like authored by AI. With quite some reasoning, but no real data to back up main point.
But it’s a problem to be dependent on China. The U.S. should pass a law that (1) makes explicit that collecting data for training models is fair use, and (2) bars terms of service that forbid distillation, for U.S. companies at a minimum.
I'm amazed that no one is talking about proposals that are surely being discussed in Washington and pushed by SV lobbyists to restrict Chinese models on national security grounds, or other some other basis.
The belief that Bytedance could engineer a finger on the algorithmic scales to serve the interests of the Chinese Communist Party led to a lot of debate in Washington, and ultimately resulted in TikTok being divested from its Chinese owners. Huawei is shut out from the U.S. market, which limits its business even in markets where it's not banned because it's effectively stamped with a scarlet letter.
IMHO, Chinese models are headed for a similar fate or at least a showdown in Washington or the courts because they are supported and/or controlled by entities which ultimately serve the CCP.