The AI Race Just Got Awkward

amelius

> So the Chinese labs have thrown a lifeline to the Western loss-making labs, and I just have no clue as to why. Any ideas?

Handy-Man

Just assumptions, nothing backing it. So maybe I'd sit out calling others out. Edit: Apt domain.

nba456_

Appropriate domain name

eggbrain

Performance optimizations don't just help the western labs, they also help with running more powerful/useful LLMs locally. If local LLMs get "good" enough, people will soon paying for subscriptions to ChatGPT and Claude, which hurts their revenue.

slowin

I'm also grateful to the Chinese labs for providing workarounds for the walled gardens that the US based AI companies are attempting to create. Does anyone know if there are any distillation datasets available? I'd love to see these distributed on BitTorrent. I think it's critical that AI be democratized and not isolated in the hands of a few private companies.

emtel

As far as I can tell, neither of the frontier US labs have referred to distillation as "stealing", but someone please provide a link if I'm wrong. They do claim that it violates their ToS, which we can assume is simply correct, since they get to put whatever they want in their ToS. Given all that, I don't know what the fuss is. Are they supposed to not use the advances that were openly published by Chinese labs? The entire industry is built on a discovery made at Google, which was published openly. Should Chinese labs therefore not use transformers? Should US labs not try to prevent distillation of their models?

adamrezich

OpenAI is jobbing (in professional wresting terminology) hard right now.

reedf1

I've been running Qwen 3.8 27b (an opus 4.6 tier model), locally on a 5090 for just over two weeks @ 170 tokens/s. That's a frontier model from 9 months ago running on consumer hardware. Who knows where distillation and pruning gets us in another year.

open592

> If you read the news headlines these days, you would be forgiven for thinking that the Western labs are getting spawn-camped by Chinese labs en masse. Ah brings back Halo 2 memories

LogicFailsMe

Watch any interview with the Chinese AI leaders and compare it to the unending doomer word salads from America's mightiest paper billionaires. We're losing because we have a loser late stage capitalist scarcity mindset. They're winning because they're sharing notes and one-upping each other just like we used to until 2015 or so. They have a healthy ecosystem of competing small AI startups. We have two bloated unprofitable pigs both striving to be too big to fail. My money's on China for the immediate future.

cmiles8

Why is it a problem that the Chinese labs are just distilling down Anthropic’s models? Aren’t Anthropic’s models not just distilling down other people’s work? Feels like Anthropic crying do as I say not as I do.

reticulates

“So the Chinese labs have thrown a lifeline to the Western loss-making labs, and I just have no clue as to why.” I don’t think it is intentional but this is actually quite bad for the western labs. The entire booster narrative has been “look at how their revenue is growing! $10bn to $100bn ARR in under a year! This’ll be a multi-trillion IPO!” and the extrapolated future growth from $100bn to $500bn and $500bn to $1tn justified future investment… but that revenue was just because inference was expensive. The revenue growth story is all that matters pre-IPO. If revenue falls from $100bn to $50bn that’s very very bad optics for OpenAI and Anthropic even if they are now profitable, it completely destroys the growth narrative.

Reptur

Open releases are just the obvious move when you're not the incumbent. You commoditize the thing your competitors charge for and get distribution you could never buy.

LunicLynx

The clue is: Bursting the bubble

rglover

> So the Chinese labs have thrown a lifeline to the Western loss-making labs, and I just have no clue as to why. "Thus the expert in battle moves the enemy, and is not moved by him." They figured out a clever method for avoiding excessive training costs via distillation. That forces the hand of frontier labs to move faster, produce better models, etc. (to avoid embarrassment and 'falling behind'—all the while shouldering most of the cost), which they can just keep distilling—or applying other techniques against—much to the dismay of said frontier labs. Checkmate.

impossiblefork

Yeah, and Anthropic probably got inspired to this new fast read-in thing for making agentic stuff make more sense from the latest DeepSeek model. Maybe it was in the pipeline, but it clearly has the same effect and DeepSeek had published it by the point Anthropic dropped their prices for reading tokens in, so they may well have copied it.

bwest87

The best explanation is that it's a goal of the CCP to generally commodotize LLMs, because LLMs will ultimately be a compliment to manufacturing (which China dominates), and you always want to "commodotize your compliments". I think this explains why they are open sourcing broadly. It's not to be nice. It's a strategic play by the Chinese government to help ensure there are many players in this race and not too much power accumulates to American labs (even if American labs benefit in the process)

listless

I'm beyond thankful that Chinese AI models are so good. I desperately want us to cure the myriad of maladies that humans suffer needlessly with on a daily basis. We're going to need more powerful models than we have now if we're gonna do that and the Chinese are providing the competition needed to push this thing as fast as we can. I realize "going as fast as we can" is not the most popular position atm. But I'm far more interested in what good we can do than 10% apocalypse scenarios. I volunteer with a charity for childhood brain cancer and I do not want to see another 4 year old die. I'm willing to risk anything to stop this.

sigbottle

This is insanely cool, what the hell. How co-designed are these optimizations with the model itself? I'd imagine you can't just stick post-training adapters onto existing architectures for these things, or am I wrong? I really want to explore the inference space, but it seems like many of the inference optimizations are coming from model-hardware codesign. I don't seem to recall many generic "inference engine" optimizations since prefill/decode disagg a year ago. This matters for me since I want to break in but the bar seems to be understanding the actual theory of the training process now too given the codesign happening, and I'm not the richest guy on the block lol

wren6991

The doublethink required to simultaneously believe "our safeguards prevent our models from doing unsanctioned cybersecurity tasks" and "distillation is why Chinese models are getting better at cybersecurity tasks" is genuinely quite funny.