[libraryofbabel]

I am saying this probably is "silly behavior by a government" and it is a milestone that points towards what the future may look like. Why can't it be both?

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.

[gpm]

> Do you think that Chinese labs will continue to release open models forever

Yes.

I think the Chinese government either already has, or will soon, grasp that if they train the models that people use they dictate what people believe (at least around the margins where that's malleable), and they will happily throw resources at that.

And simultaneously that the only way they can actually get everyone to use their models is if it's possible for us to run them on our own hardware.

(This isn't exactly a utopian view of the future)

[jychang]

This is going to age very poorly when the best Chinese labs ALREADY just started not open sourcing their models.

Qwen 3.7 is not open source; previous Qwen versions would have open source releases, but Qwen 3.7 plus does not. The second best Chinese model, Minimax M3, is testing the waters by taking longer and longer between “model release” and open sourcing it. This time, they spent 2 weeks after release before open sourcing it. There’s also a lot of rumors of GLM and Deepseek not open sourcing future models.

It’s pretty obvious that you cannot take Chinese models as open source for granted, they’ll be closed source soon.

[roenxi]

If we're measuring progress in hours and days then yes. But if we're measuring progress in months then OSS models are doing fine. You can get a state-of-the art performance in an open model if you pretend it is January 2026 instead of June.

There is no evidence here that the cutting edge labs have any durable advantage. Extrapolating current trends it seems likely that even the Europeans will be capable of meeting any given performance measure with enough time. In fact the evidence suggests that the capital required to run the models is where a moat will develop. Knowing the weights won't help much.

[csomar]

The best chinese models are deepseek (general purpose) and glm (coding) and they are both open weight and share lots of their tooling.

There are lots of AI companies and it doesn’t seem that they all have the same funding fountain or share monetization goals. I wouldn’t read much into what each one of them is doing.

[Barbing]

Had seen weeks back that the top two non-Western models on ArtificialAnalysis were both closed: https://artificialanalysis.ai/#intelligence-category-tabs

How much stock should we put into that graph, though, I'm not sure.

[sameersri2004]

Even if the models by the Chinese labs are open source or open weights even after they get to mythos level intelligence lets say, still inference and the optimization of those models to be accessed at speeds of 1000 tokens/sec in not in the hands of general public as these models have parameters more than a trillion and they can't be run on some publicly available hardware, So even after being open source it does'nt fix the problem as the general public will still pay the company for inference.

[state_less]

I'm pretty sure these large models are run on Nvidia GPUs, not some unobtainable piece of secret kit. You could go down the street and buy from AMD or a number of other vendors to push out FLOPs if you wanted or needed, but you'll need a thick wallet to shell out for a cluster of GPUs to run these models. The reason people don't run the big Chinese models at home is that they can't afford the hardware, not that it isn't publicly available. This tech is essentially a large amount of matrix multiplications afterall.

I think the larger problem is that restricting US AI companies gives the Chinese a leg up because they now have a window open where they can become the source of the most powerful models available due to government restrictions rather than on technical merits. All Anthropic customers just got a downgrade last evening, for example. While the Chinese are able to serve the world or whoever, the US corporations will be limited to the US market, or whatever the powers that be will allow. This restrictiveness could turn out to be disadvantageous to American companies since people will migrate to wherever they can get the most powerful models.

[rcxdude]

if it's open source there will be many potential providers, though.

[tmpz22]

If only we had established means of pooling community resources for the public good

[corimaith]

You know a statement like this just makes Chinese big tech look bad right?

[tw1984]

> The best chinese models are deepseek (general purpose)

DeepSeek is developed by the largest Chinese hedge fund, their models used to make them $ on the share market are very profitable, they've never ever released anything on those models.

Somehow you are claiming that those same group of people are going to totally change their very consistent long term behaviour and start promoting openness when they are in the global leading position in AI?

[hurtigioll]

selling LLMs is much more profitable than trading, and with much less risk

[fn-mote]

> much more profitable

I think you made this up.

Right now, I don’t believe any LLM company is profitable at all.

Unless you meant “more profitable” to mean “not-as-badly-negative profit”.

[ls612]

The main reason the Chinese labs are releasing models as open weights is because they don't have the compute necessary to provide all of the inference. For the US frontier models something like 80-90% of the lifetime compute required for the model is inference rather than training. China wants to shepherd as much of their limited compute as possible towards training to keep up in the race.

[Slartie]

I think the main reason is to minimize the market for closed-source models from US companies.

China knows that doing what Anthropic/OpenAI/Google/... are doing is impossible for them. No one outside of China in any sane condition will send their data to compute farms IN CHINA like people currently do with US-based frontier models. Even if they could muster the inference power.

Hence they do the second-best thing possible to attack the dominance of the US-based corporations: reduce their moat by open-sourcing models that are not fully equal, but practically useful and good enough for easily 90% of typical tasks people use agents for in their daily lives. But way cheaper to run.

As long as this arms race in AI continues, China as "number two" will have some incentive to continue open-sourcing models. But of course the US government might force a change if they continue to enforce limited public access to new frontier models - there is no market to minimize if a model is not allowed to be publicly available.

[Al-Khwarizmi]

I'm European and I don't see sending my data to China as more risky than sending it to the US. Rather the opposite.

I think your vision of how the rest of the US sees the world is tinted by a massive bias.

[wongarsu]

As a private citizen, yes.

But at work the calculus is entirely different. There is already lots of exposure to US companies (guess where our emails and tickets life), so the increase in espionage risk from adding another American company is small. Not zero, and trust towards AI companies is limited. But adding the first Chinese company to send data to would be a major risk. One nobody would sign off on, given the general reputation of the Chinese economy for widespread espionage, disregard for copyright and producing copies of successful products using insider information

[rvnx]

Totally agree, though it is an unpopular opinion here.

It’s the same paradox as people claiming: “we are European, our data is safer in Europe” when actually your privacy is higher when your data is stored in China (or Russia) you are safer because it is out of reach from your local government.

The only thing I dislike, and that’s no matter the service, is that my data or information usage is shared with third-party.

For example, Anthropic conveniently forgets to mention Datadog has tons and tons of information about Claude users, or that your data transits through machines they don’t operate.

[dariosalvi78]

was going to say this.. open sourcing Chinese models will enforce Chinese dominance instead of reducing it. When an open Chinese model becomes the best alternative to inaccessible closed US models guess what everybody will start to use. And that same open model may embed certain narratives and values that please the Chinese government.

[nxm]

Doubtful that’s happen

[Barbing]

Ya. You know enough about China to know: would they be willing to sell users outside of China models that aren't fully pro-China (and won't deflect on tough questions)? Or would that be dirty money that they wouldn't want anyone to make?

Like if they could release Ch-ythos 6 tomorrow BUT it had Western ideals, would they take the fame, clout, attention, & profit, or stick to the party line?

(hope the monolithic brush is appropriate, considering, I mean it's an impressive system/country even if I have my own strong preferences - also I've taken as true reporting about their models deflecting etc. on sensitive topics)

[rvnx]

Sounds perfect, sell it to me.

I use LLMs for health, design and programming.

If you want to make a political or religious pamphlet it’s not a single LLM that you should base yourself on. No matter where it comes from.

[throwa356262]

Serious question: why would sending data to China be worse then the US?

[londons_explore]

With nearly everyone using inference accelerators, the pool of hardware is no longer shared between training and use.

[SubiculumCode]

No, they are open sourcing them because they don't have another play, being second/3rd tier lans

[rupx]

Kimi 2.7 and GLM 5.2 released today and are open source.

[c0rruptbytes]

Minimax M3 too, and huawei claims to be releasing non-nvidia dependent training software too. openPangu 2.0 could be a shake-up if it holds up as a good model

China may not care about open source, but they know they will personally fund AI through government investments while US relies on private investments, best way to scare private investments is a free capable alternative for everyone

Add on the fact that they actually invested in energy infrastructure and can offer AI very cheap to their citizens and you can get a population well versed in AI to reduce menial tasks and focus on more productive things (if we're to believe the claims of the technology)

[chabes]

Qwen does closed and open. This is not new.

[nine_k]

The US administration restricting the use of US-trained models is one of the best gifts it could make to the Chinese LLM producers, and to the PRC government.

[dozerly]

This entire administration is a gift to everybody but the US. It’s either in service of Russia, China or whoever is willing to pay Trump the most.

[scotty79]

It's funny how the acceleration of the downfall of the US (due to trump) is a gift to everyone else. It's almost as if US didn't have as postitive impact on the world as they thought.

[gpm]

A gift to [every dictatorial regime]. It's not a gift to the common people. The hundreds of thousands of people who got aids, and wouldn't have if not for Trumps withdrawal, didn't benefit. The women of Afghanistan didn't benefit. The countries of the EU... Canada... Korea... Taiwan... Ukraine... really just about any democracy didn't benefit.

The downfall of the US benefiting bad people is not evidence that the US didn't have a positive impact.

[scotty79]

I think EU is benefitting massively from US losing the capacity to hobble it while pretending to be friendly. Ukraine is doing better than ever. And how US harmed Ukrainian efforts because they were scared of their own made up boogieman they turnd russia into during cold war is well documented.

Canda, Korea and Taiwan also benefit from US showing their true colors. Now at least they know what's real and what was fiction and can plan better for themselves.

[rvnx]

Downfall sounds exaggerated.

US is a great and respectable country with amazing nature, people tech and military, very very far a collapsed state.

If anything to be worried of, it's the state of Europe. Closer and closer to war, full of insecurity and no innovation.

US is a great country.

[scotty79]

Let's agree to disagree.

[rjzzleep]

Chinese have a nickname for Trump. 川建国. Trump the nation builder(meaning China). But Biden actually continued most of Trumps policies.

[Der_Einzige]

I won’t forgive Biden for not reversing more of trumps policies, especially immigration

Between RBJ refusing to step down, Biden not reversing immigration policy, and Biden refusing to step down in the primary until too late, he’s going to go down as a poor president in the history books - even if he wasn’t a bad dude or even bad in terms of policy.

[FpUser]

He was getting senile. What did you expect. There must be age limit for rulers

[lyu07282]

> even if he wasn’t a bad dude

Technically his material support to a genocide makes him complicit, it would not have been nearly at the scale without US support tens of thousands of women and children were murdered as a direct result of his decisions[1], if international law meant anything we would hang him for that. So no, he was a "bad dude".

[1] https://en.wikipedia.org/wiki/Gaza_genocide

[vintermann]

There's also the Meta motivation, that even if you don't get the control you would like from releasing a model, it may still be worth it to at least deny others that control. I'm sure that matters even more to China vs. the US than it mattered to Facebook vs. Google.

[close04]

You don’t need the cutting edge to influence people’s opinion. “Export LLMs” to the rescue.

[spiralpolitik]

There is no moat in the model and by making the them open, it’s hard for one to be established when the free models are “good enough”.

OpenAI and Anthropic are both hamstrung by this. Anthropic does have the better chance of surviving.

[tw1984]

> I think the Chinese government either already has, or will soon, grasp that if they train the models that people use they dictate what people believe (at least around the margins where that's malleable), and they will happily throw resources at that.

that doesn't require the model to be SOTA, it can be just a compact model capable of running on some inexpensive hardware. that is vastly different from SOTA models like Mythos which can potentially disrupt lots of things.

[strangegecko]

Of course it requires SOTA, people will always choose better models over some compact thing that is obviously more limited. You can't control the truth with models nobody wants to use.

[columnarx3]

People choose SOTA right now because of the heavily subsidised model subscriptions. People aren't going to pay 20x the price for a model that's maybe 10% better.

[ezst]

And the fact that "better" is highly subjective and domain/task/vibe-specific

[adrianN]

Why do I want the model I use for coding to know Shakespeare or vice versa?

[Jare]

Because you communicate with it using natural language and real-world references and descriptions of what you want, you use emotion and emphasis (especially when re-prompting), you use examples and illustrative stories and common expressions. Understanding and interpreting all of that and replying in kind, to some degree, requires a large body of non-computation, cultural knowledge, or else the prompts are just meaningless words, and the replies will look like compiler output.

[adrianN]

That sounds intuitively true, but I’m not convinced that it is actually the case. I don’t think we know enough about neural network training to say what training and how many parameters are necessary for what kind of performance on which tasks. To me it looks like we currently guess that more is better and try to throw as much compute and data at the problem as is economically feasible. There is little incentive for companies to invest into small model research since their moat is huge models that require special hardware to run.

[Der_Einzige]

This is why: https://www.emergent-misalignment.com/

[rjzzleep]

Small models are the future.

[baq]

> > Do you think that Chinese labs will continue to release open models forever

> Yes.

holy shit the naivete of HN nowadays.

[deanishe]

> Why can't it be both?

Is the government going to fund all further development? Hard to imagine investors continuing to throw billions at products they aren't allowed to sell.

[CraftingLinks]

Why wouldn't they? They see this technology as a military asset now.

[VBprogrammer]

Honestly, with the caliber of people who currently comprise the US administration; leaving the whole thing to Openclaw and some new fancy model might not be the worst idea.

[layer8]

Trump and friends are only interested in investments they can personally make money from.

[JohnBooty]

Yeah, there’s been a lot of debate about this on r/localllama — will there be a steady supply of new free/open models in the future?

And if not, can we simply keep augmenting “stale” models with new knowledge to keep them useful?

I’m on the pessimistic side of things on both questions.

As for the second question, obviously stale models can be augmented to an extent but it’s nowhere near a substitute for new knowledge being fully baked directly into its training.

[locknitpicker]

> I am saying this probably is "silly behavior by a government" and it is a milestone that points towards what the future may look like. Why can't it be both?

Here is why it's unlikely this is anything other than "silly behavior by a government":

- some benchmarks show GPT-5.5, Gemini 3.1, and even Claude Opus outperforming Claude Fable, and yet it's Fable which is restricted.

- some benchmarks still show the likes of Kimi 2.5 outperforming any Claude model, and DeepSeek is getting equivalent scores (a few tenths of a percent difference)

> Do you think that Chinese labs will continue to release open models forever (...)

That's immaterial to the discussion. Even if China forced Chinese labs to restrict access to all models, the truth of the matter is that Trump's administration to restrict access to US-based models does not prevent others from having access to models that are as capable or even better.

So what's exactly the point of this?

[solumunus]

You’re completely overrating these benchmarks and it’s landing you at a nonsense opinion. Just actually use the models and you will see that the gap is significant.

[irthomasthomas]

It should be easy for a company like Anthropic to prove this beyond a doubt. Why don't they? Why don't they have a collection of prompts and side-by-side comparisons with other models showing how far ahead they are?

[largbae]

I think it's mainly because the difference in models at the frontier isn't "response to prompt X", but rather "coherence with 500K tokens of context and instructions in play"

[viking123]

Good morning to the Anthropic office good sir

[dagss]

I got to try using Fable for a day... it was a clear and definite shift in quality and how independent it is.

It was almost like having another human using and shepherding Opus for me, instead of herding Opus directly myself.

[rileyphone]

All that says is some benchmarks aren’t worth the tokens it takes to evaluate them. Mythos is clearly capable of finding zero days other models can’t, and Fable is close enough to be lumped with it.

[mullingitover]

> Mythos is clearly capable of finding zero days other models can’t

I'm unconvinced that this is anything more than proof of work and marginal improvement that other models will catch up with, perhaps as early as to next week. Lots of other current-gen models will find vulns that can be chained together if you're willing to burn enough tokens on the task, and Fable is an absolute token incinerator.

[kolinko]

Did you use the models yourself?