YHK - yet another hacker news clone

> 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?

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

Edit: Apt domain.

Appropriate domain name

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.

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.

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?

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

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.

> 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

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.

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.

“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.

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.

The clue is: Bursting the bubble

> 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.

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.

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)

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.

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

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.

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

Because contrarily to the author's assumption, all labs, Western or not, have sufficient skills to discover the optimizations anyway, and publishing or not is not actually that important?

> It’s beneficial for them to say that because it sets the ground for these models to be restrained legally and regulatorily later on.

I'm glad people are saying this out loud, because that is what they want. Not for the good of the world, but for the good of their pockets.

I am not a fan of Anthropic but this article offers no concrete evidence that Anthropic actually ripped off Deepseek. It is all circumstantial.

In all honesty, all these distillation complaints brought forward by Anthropic etc make me enjoy the cheap Chinese models even more

too much pace

To answer the author’s question of why Chinese labs give away their work for less than cost, the answer is involution. China is struggling with overcompetition in other areas of its economy as well, such as electric cars, and perhaps ironically its labor share of income is substantially lower than the U.S.

Why do you think the Chinese labs figured this out before the western labs? No reason to believe that whatsoever.

>The new game in town is adopting Chinese labs’ advances. Note how I call this adoption instead of the more vitriol-infused “stealing” that Anthropic tends to use.

I mean, there's a pretty big difference between labs publishing their research openly and a competitor utilizing it versus a lab breaking TOS to... hmmm, what's the word? steal data from a competitor?

>That’s because, unlike the Western companies, the Chinese are pretty much giving away their recipes.

Yeah, Western AI companies have never published their research. It's crazy how the Chinese had to independently develop the foundational technology that powers LLMs because Western companies simply never publish their research (I mean, as long as you ignore stuff like this <https://arxiv.org/abs/1706.03762>).

>The latest one shamelessly copied without acknowledgement is the breakthrough in KV cache optimizations that DeepSeek has generously shared with the world.

Thank you, generous corporation. I'm sorry that other corporations don't provide you free publicity for your selfless contributions to the world.

>Now I don’t know why they would freely give away such a breakthrough, but they just did

Well I'm glad the author finally got to their point. A very insightful analysis.

>They do seem to be a little embarrassed by the copying. Hence the silent releases without much pre-announcement for both Claude Opus 5.5 and GPT-6.1 Sol.

You have to be in pretty deep to infer this kind of emotion to these kinds of corporate activities.

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

Then why write this article? Why point out these things just to have no conclusion?

This article sucks. Even if you hate US AI labs and are all aboard Chinese labs producing open models, there's nothing of substance here. This is the loose draft that you hand to your LLM to finish for you, but it seems the author just forgot to do so.

Even if you're willing to characterize US AI labs as evil and selfish and Chinese AI labs as righteous and generous (which is already completely trivializing these dynamics to the extent that anybody over the age of 14 can likely identify is lacking nuance), you can at least put some effort into producing some hypotheses about why these dynamics are occurring. Of course, odds are if the author did try to articulate some hypothesis, they'd likely quickly realize that the narrative they're painting just doesn't hold up.

Color me skeptical that OpenAI and Anthropic’s researchers had never thought to dig into these optimizations, and instead are just spending hundreds of billions on data centers and custom ASICs.

This is an extremely thin analysis that has obviously been voted to the top of the homepage because HN hates the big labs.

it's funny to me how the success/not total implosion of htese companies is predicated on profitable, revolutionary-tier success, and that it's increasingly possible that the profits will never really materialize. pretty interesting move on China's part.

It's actually a little funny that this whole thing is predicated on "beating" the Chinese, when (as they have been for the past 4 decades, and the Japanese before them) they're perfectly happy to let us do the bulk of the work and then swoop in with a svelte, cheap, user-friendly version right after. One part Apple, one part Dollar Store.

Is the thinking that the day or so between US systems achieving ASI and Chinese systems doing the same, we'll figure out a way to neutralize them indefinitely? Because otherwise, none of this makes much sense. And it only starts to swerve back to sanity if the assumption is that this isn't a race or competition, but instead a joint effort to achieve something good for humanity. But you can't really delta profit off that, can you?

A random accusation of western lab using DeepSeek caching technique being top of HackerNews, this article feels more awkward than the AI race for me. It's a shame for all HackerNews viewers.

https://liorsinai.github.io/machine-learning/2025/02/22/mla.... I wrote an article on MLA last year. In short, I found the main idea of MLA as very innovative but it was documented in a strange paper with other ideas I couldn't comprehend as being useful, and they hadn't properly reported the positives or negatives of MLA.

The whole confusion expressed by this article is resolved by refusing the "arms race" framing. Chinese AI labs are acting really normal for researchers. Researchers in academia collaborate and share breakthroughs. This was until very recently the norm in American ML/AI research as well. The hawk-brained reasoning that this is some kind of "fate of the world" style arms race is as far as I can tell a narrative entirely pushed by American megacorps (and their cultural orbiters) who want to hold on to a business model of proprietary control of technology at all costs, and this is lapped up by political actors who clearly mostly just want to keep public perception in a cold war framing, which also seems mostly in the interest of consolidating power through the classic FUD method. If we think of this as normal research and development on a normal technology, it makes a lot more sense. I already see little reason to use proprietary models, but these companies insisting that they're in a war about which their supposed opponents have not seemingly gotten that memo makes me want to do so even less.

> All this must mean the Western AI companies are now extremely inference-margin positive.

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

That inference wasn't profitable is a widespread myth.

Analysis based on Kimi K3 suggests that OpenAI and Anthropic have margins well north of 95%: https://inferencex.semianalysis.com/run/kimi-k3-on-b200

Over the last months I have seen news that OpenAI made breakthroughs in inference efficiency multiple times.

I have no reason to believe that the leading US labs don't have their own optimizations, or that they learned of this particular optimization from DeepSeek.

Did private frontier models use sparse embedding and ngram first? The article claims sparse attention was copied from open weight but we can't know that. We could just as easily argue that OAI and ANT had these improvements for years and decided to slash their margins only now to stay competitive with open weight neoclouds.

Second, sparse attention is an old area of active research. Offloaded N-gram tables are the next big open weight technological leap.

Wow this thread is filled to the brim with anti-Chinese sentiment based purely on… “China bad”

I started using a zero data retention(at least claimed) deepseek v4.1 flash this month

I still use claude and openai right now, but I can see that not long in the future I won't bother with them, still waiting for a model good enough with computer use and a good enough computer use agent

Hmm. This piece makes a pretty strong claim with little evidence: that the recent drops in cache read pricing for new models from OpenAI (6.1 Sol) and Anthropic (Opus 5.5) are because those labs "shamelessly copied without acknowledgement" Deepseek's published KV cache optimizations.

Certainly anyone who knows something about inference is going to speculate, looking at the change in token pricing (and particularly how the % drop in cache read pricing is much larger than the % drops in pricing for other token types), that there is some kind of KV cache optimization behind these newer models. But even if is true, I don't think anyone can say with certainty what it may be. It may be the labs making their own innovations (they have some very smart people, and this is probably an area where having unlimited pre-release access to frontier LLMs like Fable and Astra gives an additional research edge), it may indeed be the direct application of Chinese labs' methods, or it may be some combination of the two. Sure, it is fun to speculate about, but beyond the facts of the token pricing changes and the increased inference speed, it's just speculation. The certainty the author displays here is not very helpful.

The author also seems to have a bit of an axe to grind agains the US labs, judging by the tone. I think that detracts from the discussion too.

They also seem confused about why Chinese labs have released these optimizations recently. Well, you have to release them (with or without explanation) if you are going to release an open weight architecture, and that is what the Chinese labs have been doing for a long time. Sure, there are reasons behind that to discuss too, but this isn't exactly new.

So, this is an interesting topic to think about, and the Chinese labs do indeed deserve credit for some very clever new attention and inference techniques, but I would read it with a skeptical eye.

Why did this thread suddenly massively down ranked? It was at the very top 15 mins ago, now it's on page 4 and dropping fast despite very high engagement... @dang

Once again, the irony of "Hacker" news full of so many people carrying water for the IP rights of companies worth trillions. Phrack magazine must be rolling over in its grave...