DeepSeek continues to not only push the boundaries but also publish these incredible papers explaining how they achieved their gains - something the American labs no longer do unfortunately. Chinese labs are doing the most interesting work in AI right now.
>publish these incredible papers explaining how they achieved their gains - something the American labs no longer do unfortunately.
Google is still releasing a lot of llm architecture research. They introduced speculative decoding of LLMs in 2022[1], then released the code to perform sceculative decoding for their Gemma 4 model this year[2]
[1] https://arxiv.org/abs/2211.17192
[2] https://github.com/google-gemma/cookbook/blob/main/docs/mtp/...
Thanks for the clarification - Google does publish more than others - and I actually really appreciate the work they are doing with the Gemma models, which are truly competitive open models. I do wish they’d publish more in depth papers on their Gemma models but appreciate that they are open weights.
They weren't the first to do MTP like this, and arguably did it wrong: the MTP heads are kept in a separate file and have to be welded in by the inference engine.
Qwen 3.6 shipped with working MTP first, and had working MTP in llama.cpp first.
Given the MTP drafter is basically a separate model, keeping it separate makes more sense IMO. It's out of my wheelhouse but it seems like you could adjust the MTP drafter model separately from the main model, too.
Ultimately though the real explanation, I think, is Google doesn't care since for their own purposes (in LiteRT-LM), they do bundle them. As far as I know, anyway.
MTP models share internal state with the main model, and also refer to parameters in the model.
They are more like a single model that has two separate attention head mechanisms.
I mean just like GGUFs aren't technically necessary yet are _way_ more convenient than using Safetensors and configuring the default Jinja prompt by-hand, it makes sense to bundle the draft model too. For all intents and purposes, the only people who will train a draft model are the people who train the original model
Nvidia's Nemotron 3 Super also shipped with MTP.
Probably because American AI companies are on the hook for quite a lot of investment money. I think they are trying to find the magical moat to justify their valuation.
Revealing optimizations similar to these would pretty much reduce their competitive position.
Chinese labs are also still behind, so they’re incentivized to collaborate and have no reason to do it in private.
I suspect their tune will change if they ever take the lead..
The question is also what game they're playing. Deepseek came out of a hedge fund. I think it's no coincidence that their publications tend to have a large impact on AI stock prices.
Destroying the growth story of overvalued stocks is an interesting investment strategy. It's not even new. Shortsellers understandably get terrible rep from execs, but their actions are more often in the public interest than you'd think. Normally it's exposing fraud, but here we get the really fortunate side benefit of what could eventually amount to the most significant contribution to the general software community since Linux.
> The question is also what game they're playing. Deepseek came out of a hedge fund. I think it's no coincidence that their publications tend to have a large impact on AI stock prices.
Its revealing that they always seem to publish after some big announcement by American AI companies. But regardless, this is one of the benefits of a duopoly.
No more revealing than OpenAI, Anthropic and Google always having some new model that just so happens to be waiting in the wings whenever their competitors announce their own model bump.
The framing that Chinese labs open-source because they're behind assumes it's purely a competitive tactic. But there's a structural dimension: DeepSeek operates under a completely different funding model than US labs. They're backed by a quantitative hedge fund that views AI as infrastructure, not as a product to monetize directly. The ROI for them comes from trading alpha, not API revenue.
Chinese AI companies also face a domestic market where open-source distribution is often the only way to reach enterprise clients who won't pay SaaS premiums. The business logic aligns with openness in a way that US labs' VC-funded models don't.
> They're backed by a quantitative hedge fund that views AI as infrastructure, not as a product to monetize directly. The ROI for them comes from trading alpha, not API revenue.
That used to be true, but now they've raised ~7B$, so we'll see how / if that changes.
Yeah, they were in a tough position though. All their competitors were offering equity and they didn't.
Also, we’re seeing a classic commoditization spiral with open models rapidly closing the gap and driving prices towards the marginal cost of inference. The reality is that models themselves are general commodities and there's just not enough difference between them. A company can get ahead of others by a few months, but then the rest quickly close the gap. It's a really low margin business because there's no way to differentiate yourself.
Chinese companies understand this and they're treating models as shared infrastructure akin to Linux. The money is going to be in customization niches. Companies will charge to tune models for specific use cases and charge support for that. There's also going to be money at the bottom for hardware vendors making chips and memory. But the middle tier of generic LLMs is seeing involution where there's relentless competition driving profits towards the bottom.
Nope. It is purely a marketing and distribution strategy. Without open sourcing their models, their businesses would have never gotten off the ground. I've written about this here: https://try.works/writing-1#why-chinese-ai-labs-went-open-an...
Which is a good thing. Self-serving motives are more reliable than altruistic ones.
The world runs on incentives. Altruism/Self-serving are down stream of that.
Wikipedia is altruistic, and serves humanity quite well.
Open-source is also altruistic. If DeepSeek does become self-serving once they get the top spot, it doesn’t take away from the altruistic contributions that they made towards open models.
> Open-source is also altruistic
Contributing to it might not necessarily be. Most open source development is funded by large companies after all and from their perspective it can function as a cost saving measure. Allowing them to focus on their core products and removing the possibility of their rivals from getting a competitive advantage due to having a superior low level stack under their product.
Which is why open source is so successful in areas where software is a cost-center but mostly failed for consumer products (since spending resources on them would actually be altruistic unlike e.g. Linux kernel development)
altruism is not discernable from the outside
any altruistic act can be perceived as self serving
And ultimately the motivation for those contributions just doesn’t matter, except to those who like to anthropomorphize company and argue about their souls.
People who donated to OpenAI in its early years might disagree on that.
Or if they want to do anything close to predicting what they will do in the future, like curious and interested humans tend to want to do.
No parent is right. The core root driver of the world is capitalism, open source exists downstream of that.
Software engineers need money to survive. If they exclusively work on open source stuff where are they getting money from to survive? Follow the money trail… even a donation… eventually it leads to an incentive based source or action.
> If they exclusively work on open source stuff where are they getting money from to survive?
From open source. You can earn money from open source. Open source is not opposed to capitalism, idk where you got that idea.
Young blood, allow me to explain.
I said open source is derivative to capitalism. Meaning open source cannot exist without capitalism. I never said they oppose each other.
Second I said you need to follow the money trail. Money given to people who work on open source comes from non-open source places.
> If they exclusively work on open source stuff where are they getting money from to survive?
These are orthogonal. One can have a paid job while contributing to open source for entirely altruistic reasons.
> Follow the money trail… even a donation… eventually it leads to an incentive based source or action.
BS. Humans do things for altruistic reasons devoid of individual reward all the time.
I, myself, maintain multiple OSS projects entirely for fun and with the hope that others will find it useful. That's it, that's all. I also donate entirely anonymously to charities simply because I believe others deserve support and dignity.
This form of cold, American libertarianism you espouse is pure poison in the body politic, both in this US and globally. It degrades all of human interaction to transactions. Its no wonder that the US is where sociopaths like Zuck were birthed.
Is it though? A large number of people get to experience a lot of power over others because they moderate Wikipedia. That's certainly why some of them do it, just like on Reddit
I hate to quote pithy proverbs, but "the road to hell is paved with good intentions." One can have an altruistic goal which ends up harming people too, which is where that proverb comes from. Prohibition and The War on Drugs in the US are two good examples of something that had altruistic origins[†] but ended up doing way more harm than good.
[†] Another problem with altruism: we don't all agree on whether a goal is altruistic, and what's altruistic in the enactor's eyes might not be in yours. Curating a fountain of human knowledge like Wikipedia? Probably altruistic. Protecting humanity from itself by installing your company as the stewards of frontier LLMs? Not so altruistic in my view.
> Prohibition and The War on Drugs in the US are two good examples of something that had altruistic origins
The War on Drugs had the purpose (not just in its origin but in its perpetuation) of inflicting harm on elite-disfavored subsets of the population that could not be openly targeted for Constitutional reasons, which is about as far from an altruistic reason as it possible to get.
Yes, that's my point. What looks like altruism to one person is not altruism to another, and those causes can be used by bad actors.
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This statement is factually true and you are voted down because many people lack knowledge.
Any individual that provides free labor cannot survive off of said free labor. He must work for money to survive or get donations from someone who earned that money from incentive based labor in order to even buy the food he needs to exist as a living human being. Much of the time that labor is actually closed source.
This is a logistical reality. A lot of open source advocates are unable to get their brains out of the whole mentality that open source literally cannot exist without incentive based software supporting it. Who pays for GitHub to exist? Who pays for the food swes eat? I just code for open source all day and money falls out of the sky.
My smart friend says there are jobs that pay you to work on open source exclusively. Smart guy. In this case you follow the money trail. How does that company get enough money to pay a guy to work exclusively on open source?
Free labor enables capitalism, especially if you consider labor arbitrage as a mixture of free labor and properly compensated (according to the real value) labor. From literally being born, to family culture, education, and whatever level of broad social cohesion, it’s all free labor. Without that background, money itself loses its value, since an individual cannot have reasonable confidence in trading it for something of actual tangible value. It is abstract stored value, banked into society for free. Indeed, in many cases, the free labor is in the rational self interest of a group. But stability and love and peace aren’t monetized to their true value. Otherwise, markets should be much less stable. Bubbles are only notable for the large impact of a small group of bad actors. Overall, it’s pretty amazing what free labor does. Open source is just another instance of this long and critical tradition.
Free labor is derivative to incentivized labor. Your statement here doesn’t disprove or counter what I said. Again, follow the money trail. Everything you said if you follow the origin of the money it comes from paid, incentivized labor. Parents need money to raise kids… where do they get that money?? Our economy is called capitalism for a reason there is literally zero reference to charity or altruism in the vocabulary or even standard models that describe our economy and economic theory.
Put it another away: if we removed your ability to do incentivized labor and all you can do is charity work… you would run out of money and die from starvation. If we did the opposite and we removed your ability to do charity work… you’d be fine.
All of this re-emphasizes the point of this thread: In our objective reality, the world is driven by incentive based work while altruism is a side effect of surplus wealth generated by incentive based work. That is the fundamental reality.
Go read Max Stirner. True "Alturism" doesn't exist. It's all egoism, even if and especially if you think it's not.
You mean more predictable, not more reliable.
Disagree. It’s More reliable.
Could you explain? (asking in good faith)
I don't think so. I can confidently predict that altruism will give you a very unreliable income stream in the vast majority of cases.
Very interesting take
Look at how far OpenAI has drifted from their original mission. Everything comes back to greed, so it's ideal for the world if selfish motives happen to coincide with what's good for the world, like advancements in open models
can you elaborate? the original mission was "advance digital intelligence in a way that benefits all of humanity"
I don't see an inconsistency. money is pragmatic, the mission needs money
Every company on the face of the earth has a mission statement involving some bs goal that sounds altruistic. For a good example look at googles mission statement.
The real mission statement for most companies is to make as much money as possible.
It's a standard take since it is how markets tend to work. They aren't powered by altruism, it is a big system for turning greed into good results. We don't have all this stuff because people suddenly woke up one morning and decided to be nice.
Yes but there's more to the world than markets.
On aggregate mainly because humans often tend to behave “irrationally” due to various reasons though
I don't understand what is interesting about it: it's the default.
Markets don't run on altruism.
And humans don't run on markets.
Mostly they kind of do since we do live in an utopian society of unlimited abundance. Extremely few people can afford to (or want to) spend a very large number of working hours without ever getting anything directly in return for it.
I think you made a typo of saying do instead of don't and totally reversed your argument
Neither on altruism.
The standard is applied very inconsistently. Nobody accuses the local bakery of being motivated by profit, and that they don't bake bread for you out of altruism.
Isn't it the entire basis of capitalism?
They are focused on the things you do when you are not over-capitalized and you can’t get unlimited nvidia hardware to train on. And the results speak for themselves.
Meanwhile we in the US are blocked from buying Huawei GPUs and retirees are boasting about the nvidia in their portfolios.
> Chinese labs are also still behind, so they’re incentivized to collaborate and have no reason to do it in private.
US labs in Google, Meta and SpaceX are not leading, none of them managed to build something on par with GLM 5.2.
Care to explain to me why they still don't collaborate and still choose to do it in private?
I'm not sure I'd put Google in that list, but either way: Because they think they have enough capital that they can catch up and don't need the reputational boost of this.
As good as Gemini's visual intelligence is, it's a terrible agent.
Google at least still releases open source models to the public.
Aren't they only open weights, not true open source?
The concept of open source doesn't really apply to AI models since their behavior is mostly controlled by the data they were trained on and the complex ways they are trained. Having the source code of the model by itself wouldn't help you.
From a practical POV having all the training data, training infrastructure, and training know-how wouldn't help you either unless you could afford to spend the millions of dollars (hundreds of millions for a SOTA model) in compute to train it each time they released a new training set, in which case you're only talking about the big commercial companies. "open source for the people" just does not apply.
Thank Apple?
Those are mostly for embedded devices and the current "sponsor" is Apple.
Gemini 3.1 is still up there, though? If Google started to compete on price they could be very successful.
Wait, are you claiming that these companies haven't contributed to the ecosystem via research and open source?
No idea I don’t work there.
Also, historically, China has always viewed intellectual property as public property. Similar to open source.
Not everyone is motivated by greed
What do you think is the underlaying motivation?
You ask me what I think. So far deepseek has been very consistently trying to advance state of the art research in a transplant and public way by writing papers and publishing working code. They are also not at the mercy of the stock market in the same way many Americans companies are. Before anyone assumes too much, I live in Europe.
> Chinese labs are also still behind, so they’re incentivized to collaborate and have no reason to do it in private.
Even if they're ahead they don't have enough GPUs to scale. Open sourcing is hence a good strategy to at least get market share (even if not $).
True!
Projection is a funny thing. It causes people to misread situations all the time. Southern slaveowners feared violent retribution from freed slaves, for example [1]. It was pure projection and said more about the South than it did the slaves. The reality was there was no violent retribution. It was the opposite where the former slaveowners continued to inflict violence on the formerly enslaved.
I say this because we see the same thing used as an argument against China. "If they overtake us, they'll do imperialism (like us)." Again, it says more about us than them.
A better reading (IMHO) Of the situation is that China believes that AI shouldn't be used simply to mint a few more trillionaires but the benefits should be shared with society. Why do I say this? Because we now have 70+ years of China doing exactly that. The transformation in China all the way from rural villages to Tier 1 cities has been utterly astounding. China has lifted ~800M people out of extreme poverty.
In some ways we're at a similar point to the late 1990s and 2000s when Microsoft execs complained that Linux, being free, destroyed intellectual property value. Linux should be a perfect example of how people can and do act altruistically, or at least not in a way to bait-and-switch to enrich themselves.
[1]: https://www.reddit.com/r/AskHistory/comments/1d26grm/in_the_...
It's even worse than that. China publishes stacks upon stacks of policy documents in which they explain clearly what they will do and why. This includes why they do poverty alleviation and why they believe big monopolies that own everything are bad. But almost no western observers care to read those documents. Instead, western observers, including HN, speculate endlessly about China's intentions, and "it would be naive to believe they would not do X" or drawing equivalences to Soviet Union or whatever. And the "journalists" sell this notion that Chinese state intentions are "untransparent" and "unknowable" while pretending the policy documents don't exist.
Meanwhile, Xi Jinping has published his 5th book on how governance in China works and what they're after. These are not books written for a western audience: they're compilations of speeches that he already gave to the Chinese party and state apparatus, so the contents are not sanitized for foreign audiences. But there are no English reviews of summaries of this 5th book at all by the usual China experts that distribute what western audience know about China.
This extends to beyond the government. Even though "for the people but only against the government" is an often-heard mantra, nobody seems to listen to what Chinese AI companies themselves say about why they publish open models. DeepSeek and GLM have said multiple times publicly what their motivations are, yet people on HN still speculate like they usually do.
Truly mind-boggling. I get that a lot of people don't like China. But setting aside the question of whether their dislike is justified, it would at least be rational to properly understand China, even if it's to defeat it. And listening to what China says themselves is absolutely essential for proper understanding. But people don't bother to? And they seem mostly happy with sticking to speculations that match preconceived notions, even if that hurts their chances of defeating China.
Extremely interesting comment, thank you. Got some links where I can download this source material? I don't read or speak the language, but will try interrogating it with an LLM
The fifth book is on Amazon. https://www.amazon.com/XI-JINPING-GOVERNANCE-CHINA-V/dp/7119... It's already an English translation.
For something shorter, you can see Arnaud Bertrand's recent review. https://arnaudbertrand.substack.com/p/the-book-the-west-refu... The review is behind a paywall, but not expensive.
If you want to read policy documents directly (primary source), try the State Council / Chinese government policy database: https://www.gov.cn/zhengce/ and https://sousuo.www.gov.cn/zcwjk/policyDocumentLibrary
They also provide official translations: https://english.www.gov.cn/policies/
For Central Party documents: https://news.cn/politics/zywj/. It lists recent Central Committee / General Office / joint Party-State documents, e.g. 2026 documents on township duty lists, Party member development rules, carbon evaluation, long-term care insurance, and SOE leadership rules.
Thanks again, this is more than enough for a clanker-assisted rabbit hole to disappear into
I 100% agree with you and want to add something.
If you simply take what the Chinese government says at face value, you will be correct way more often than 95% of Western policy wonks, media talking heads, "analysts" and so forth. Because, like you say, they tell you everything they're doing.
In the recent US-China summit, Xi Jinping just came out and used the Thucydides Trap metaphor, which tells you everything about where China thinks it is and where it sees the US going, which is to become increasingly belligerent as their power declines. Now whether or not you agree with that assessment (I do agree), it still tells you China wants to avoid open hostilities, it sees itself as continuing to rise and it fears what a declining US might do.
The Thucydides Trap mention is different from what you describe. Xi has dismissed the Thucydides Trap multiple times in the past as being hearsay and self-imposed bias (https://www.globaltimes.cn/content/944179.shtml). "We should strictly base our judgment on facts, lest we become victims to hearsay, paranoid or self-imposed bias. There is no such thing as the so-called Thucydides trap in the world. But should major countries time and again make the mistakes of strategic miscalculation, they might create such traps for themselves."
But western politicians keep raising this metaphor. So at some point they're like "okay we'll speak your language". They then used this metaphor to make the point "our rise isn't the threat, your fear of it is. If you resist it you're walking right into the trap Thucydides warned about". So your conclusion is still right, they don't want open hostilities, a stable world is in their interest.
Then western media ran away with this and were like "OMG Xi mentioned the Thucydides Trap", completely ignoring his point.
So the marketplace is working.
This is the way! Open source models will benefit, and once open source models reach the state of "good enough" the hyped up US AI companies will fear, since the availability of free, good enough, AI models will set the ceiling for how much they can charge. Then the bubble will pop.
You mean open weights, I guess? There are as far as I know very few open source models, the training data is seldom released. Sadly.
Regardless of where they are, the Chinese will always share their progress, as they're collectivist/cooperative at their core, compared to the individualistic/competitive US.
I don't really see the moat for frontier AI labs being "more efficient models" although that could help their margins - I think moats will be built by expanding the horizontal and vertical market expansion - like Anthropic is doing the most at the moment
Who is financing DeepSeek and what are they expecting in return?
Until recently, DeepSeek were self-financed (it was a spin-out from a hedge fund). They just raised ~50million RMB (US$7bn), and according to media [0] (which admittedly can be unreliable), the lead investors were:
1) The CEO himself 2) Tencent 3) CALT (the battery company) 4) NetEase (internet/media company) 5) JD.com (ecommerce) 6) Chinese investment firms
What are they expecting in return? I'd say the same thing that all those investors in OpenAI and Anthropic are expecting - profit.
[0] https://finance.sina.com.cn/stock/vcpe/2026-06-11/doc-iniazi...
I don't think this question would get to the reason. There could be one or two persons in charge who simply shape the culture of the company, including how much to publish.
They are self financed, the company that makes DeepSeek is a finance company that trades on the markets.
The CCP's approach has historically been to subsidize their companies far more than other countries do. Why would LLMs be any different?
https://www.oecd.org/en/data/dashboards/magic-database-indus...
Access to everything every American company feeds into the AI is well worth it to the CCP.
According to EU statistics, yeah
OECD isn’t the EU.
And regarding the dataset:
> Unlike most OECD databases, which rely on government data provided at country-level, the OECD MAGIC database uses firm-level data. The subsidy estimates included in the database are based on raw data obtained from firms’ annual reports, financial statements, bond prospectuses, IPO prospectuses, etc. The data are collected and verified manually by the OECD to maximise accuracy, consistency, and comparability. In some cases, additional information is also obtained from government databases, either to verify the firm-level information or to complement it. Care is taken to avoid double-counting where the data mix corporate and government sources.
Even the latest World Bank report, the defacto neoliberal institution, recognized a couple of months ago that leaving the industries focus be dictaed by purely capital decisions was bad, as in _really_ bad.
Does that figure hold up when we look at Silicon Valley financing? Uber alone was subsidized to the tune of billions. Let alone the recent batch where we're into hundreds of billions.
Even if they were fully self-financed, which isn’t the case, they would expect something in return.
You can give them money by using their api. Just because their model is open, doesn’t mean they are a non profit.
Not everyone has the American “fuck you got mine” zero sum game attitude. Also they’re making some of the American and European AI companies look bad which they can leverage with their trades if they wanted to.
IMHO to promote that China believes in free markets and making the technology available to all.
Which will likely help them bolster the sales of the MANY new AI chips in development/use in China to international markets. Dislodging Nvidia.
Kinda the opposite of what Jensen Huang (Nvidia) thinks US is doing: https://www.youtube.com/shorts/u3SY8nvjhQA
Edit: I'm a fan of deepseek and believe it's good to make the technology open/available. And do think that also help business - which I support as well.
Edit 2: No idea why I'm getting downvoted. That's also their official stance https://english.www.gov.cn/news/202601/08/content_WS695f1b55...
Short AI companies
???
Profit!
Not suggesting this is it, but you know, one possible angle.
I seriously am far from fear mongering and doomsday mentality, but I just can't see how OpenAI and Anthropic can have a successful IPO if the quality gap between the free and paid continues to narrow like that...
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For real. Reading old comment threads makes me sad, because the level of discourse was so much higher in the past. Although this place is still deeply appreciated, it’s clear that its culture is going monotonically towards reddit.
Is there anywhere public anymore that isn’t being overrun by lobotomized p-zombies (partisan zombies)? Is it even possible to make such a public space? Ressentiment consumes all discourse.
Yet accumulation of power by a very small elite through state and selected corporations happens to be a defining characteristic of that political regime.
you're right, full of corporate sock puppets shilling their vapor wares, idly dreaming that the world isn't what it is.
> Probably because American AI companies are on the hook for quite a lot of investment money
That's a lot of words to say it's just capitalist greed.
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Do you think that DeepSeek are building their models for free, or something? They aren't "on the hook" for anything?
What's with all the China glazing about this stuff? They release some open-source work and people act like they are suddenly the beacon of freedom and transparency.
This is incorrect binary thinking. Them releasing open source can be good, but that does not commit you to think that china or chinese companies are saints. There are many shades of grey here and one does not exclude the other (nor include it).
Are you reading the comments?
I think there are some sockpuppet accounts active but what also contributes is that many people are absolutely fed up with US technological hegemony and welcome alternatives to core technologies from elsewhere.
Not just US technological hegemony, but the USA has threatened to invade Europe (Greenland) and Canada, and has actually invaded Venezuela and Iran. China hasn't. Maybe lots of people that live in those places are now switching sides.
Over the past 2y the US also started a trade war with Europe, triggered the worst oil shock the world ever experienced for no reasons, threatened to leave NATO, tried to force Ukraine to give up its territory to the invading country, and way more
I’m think its in our best interests to lever these american ai companies to exhibit at least some degree of freedom and transparency anyway we can…
Publishing by necessity I wonder? American labs on the cutting edge pioneering the way forward, so Deepseek open sourcing what they’ve got is to help even the playing field.
Hopefully the experts here can offer insight. The above is just my hunch and I’m not a specialist in this field.
Yes, challenger Labs publish out of necessity. It is a marketing strategy. People assuming open source means giving something up, but the reality is that Z.ai has a revenue of some $100M and it would be about $0M if they never open sourced their models.
Wouldn’t that just help the American labs anyway though? Or do they assume they’ve actually already figured this stuff out and kept it secret?
It used to be the case that NSA hired the majority of all math graduates in the US, and were assumed to be years ahead in cryptography. Yet in the 90s, it became clear that they no longer were that - among other things, the cipher of the notorious Clipper chip was broken, and we can rule out that it was made weak on purpose because the whole point of Clipper was that they had a backdoor.
So, despite hiring the cream of the crop of math graduates, who could read the papers of free academia, but whose own result the free world could not access - they fell behind.
I have a theory explaining why. I think it's because science is an interactive process. NSA cryptographers could read papers, but they couldn't talk openly with the authors of those papers, because of secrecy demands - even asking question might indicate what they were working on. You can easily imagine them spending months on something they could have avoided by going to the original authors and getting told "Oh, we tried that for a long time, it doesn't work".
Whether that theory is right or not, cryptography is a concrete example of a domain where public research with fewer resources beat private research with a lot more resources.
Everyone in this thread is getting distracted by nationalism, but you hit the nail on the head. In this case for whatever reason the Chinese AI industry is collaborative and the American AI industry is not. This will result in the Chinese companies making progress faster. Full stop. This isn't a judgement on the merits of either system, only an observation of likely results.
Hasn't that been the mantra of open source for 40 years. Armies of companies, trillions of valuation, or even just Wayland, suggest that isn't always the case.
So free software can only be considered a successful strategy if every single project succeeds?
Reminds me of Dot Net in the early 2000-2012... No one collaborated
From what I gather, the Chinese are behind, but a lot of their research amounts to scrappy, clever discoveries in how to use more novel technologies (for Qwen and Deepseek, its mixture of expert models, that can do inference using a portion of the model at a time). The chinese also distill information from American models, so there’s that.
The American companies, from my impression don’t involve themselves with such lowly “hacks” because they have so much money to just push forward with doing everything on big heavy models that run on the most cutting edge nvidia chips that they can, the moment, kinda sorta get on demand (I say that in some degree of jest).
The American companies would love to develop these 'hacks' because it would make them more money, something they are in existential need of right now.
They don't develop them because they don't collaborate publicly anymore.
Where would the whole industry be if Google never allowed publishing the transformers paper?
It's not a coincidence that the American AI industry grew fastest in capability when it was the most open.
Just a crazy catch 22, it seems
Why would they collaborate? Why not defect and just keep theirs private and implement the open ones?
I'm afraid I'm even balking at the word "pioneering" in context with US frontier labs. They are probably doing a few new things, right, but they are not blazing any trails for others to follow along, the Chinese are.
> Publishing by necessity
It's more a cultural thing. Sharing progress is just in their blood.
This is overly simplistic to the point of glazing. Plenty of Chinese companies maintain industrial secrets to gain an advantage.
Chinese papers and techniques have been very influential and copied by US labs.
Multi-head Latent Attention (MLA), Multi-Token prediction, MoE architecture are some of the most famous examples.
MoE is from Google (Noam Shazeer)
MTP is from Meta
Another DeepSeek advance that the west are copying is DeepSeek Sparse Attention (DSA)
Mixture-of-Expert (MoE) was introduced in the 1990s [1, 2], see also [3, 4]. The idea was that MoE scales up model capacity and only introduces small computation overhead. MoEs did not become viable for high-performance applications until sparse routing was integrated with modern deep networks, made possible by large-scale distributed computation. The breakthrough came with the development of sparsely gated networks [5], which showed that it is possible to maintain model accuracy while activating only a small fraction of a large parameter network during both training and inference.
[1] R. A. Jacobs, M. I. Jordan, S. J. Nowlan, G. E. Hinton, Adaptive mixtures of local experts. (1991)
[2] M. I. Jordan, R. A. Jacobs, Hierarchical mixtures of experts and the EM algorithm. (1993)
[3] L. Xu, M. Jordan, G. E. Hinton, An alternative model for mixtures of experts. (1994)
[4] S. Waterhouse, D. MacKay, A. Robinson, Bayesian methods for mixtures of experts. (1995)
[5] N. Shazeer, A. Mirhoseini, K. Maziarz, A. Davis, Q. Le, G. Hinton, J. Dean, Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. (2017)
Yes - I meant as applied to LLMs/Transformers.
Deepseek is commoditizing the performance gains US labs rely on to make their investors money.
This is so out of touch. Go to Neurips or the top AI conferences to see what is happening.
Yep. It's about time western world realized Chinese are not the "very bad guys under dictatorship"
I don't think it's very common to believe the Chinese people are bad guys. It's the government and its control of the people that's the problem. And no, I don't think the US is immune to that sort of problem either.
Honestly it's just a hierarchy difference between the two countries. In the US, tech/fin/military companies have the upper hand compared to the government (fragmented between 2 parties). Despite the sharades with Anthropic, Tech-fluencers are in control. Compared to china, the government (dictatorship) has more control over Tech companies (take any example from the past 10 years). For them, undermining the US AI supremacy is an objective, and releasing open weight models is the way, and I'm all for it.
Let's not get crazy here. You can acknowledge that the Chinese AI industry has some structural advantages right now without trying to claim anything else. China is still a brutal autocracy.
If American labs aren't publishing, it doesn't mean they aren't doing even more interesting work.
You could also come up with a cure for cancer, but if nobody knows what you’ve done then there’s not a whole lot we can say about it
I'm deep seeking for that open in OpenAI indeed. It’s clear who’s the most anthropocentric in this space.
They push the boundaries, alright. Of obtaining the results of work without doing the work themselves, which I hate to say it but this is classic Chinese machiavellianist business behavior:
https://www.cnbc.com/2026/06/24/anthropic-alibaba-distillati...
Exactly. They did not have to open up their research up and this is what happens when smart researchers are forced to squeeze performance gains out of existing hardware.
They don't have TPUs or access to the latest Vera Rubin GPUs either to get performance gains for free. All of the optimizations Deepseek have done are in software and it goes down to the PTX assembly level.
Compared to Anthropic who are celebrating in fixing a flickering issue in a terminal app which took months to fix.
> All of the optimizations Deepseek have done are in software and it goes down to the PTX assembly level
DeepSeek are still using NVIDIA (PTX) to train on, but for inference have already transitioned to Huawei Ascend chips, and inference speed is what this paper is addressing.
> Compared to Anthropic who are celebrating in fixing a flickering issue in a terminal app which took months to fix.
It's funny, because if you ran Claude Code on a slow terminal, the cause of the flicker was obvious: They kept dumping the entire history of the chat back into the terminal in a number of situations, and relied on the terminal to them end up in the correct state.
Anthropic almost certainly also has optimized software down to the assembly level, considering this take-home interview challenge they published: https://github.com/anthropics/original_performance_takehome/... which is all about instruction-level performance optimizations. That they don't prioritize UI fixes just means they consider other things more important.
Unlikely: that product is written completely by AI, of which they are not lacking.
More likely is that an AI generated codename is impossible to fix by humans, and SOTA was not able to figure it out until now.
that's pretty silly to use as a measure of what they do internally
It's pretty representative of what they do internally
All frontier labs are working down to the PTX level (and lower)
It's almost as if ... they were what OpenAI was when it started. Sad to see but glad someone is doing is.
Google and Microsoft publish more than enough and American universities are publishing the science beyond DeepSeek's engineering. That fact that you don't know about them means you're not following the science only reading hacker news.
Google hasn’t published much in depth ML work since T5 (which was hugely influential at the time) - most Gemma releases are 1-3 page model card pdfs these days with no in depth analysis. Even TurboQuant is shaking out to have basically been a rehash of previous work without proper attribution. I do think Microsoft is doing some interesting things with smaller models but haven’t read much research, interested in any refs you might have to share!
Check recent iclr acl icml neurips you will see 10-20 papers from Google Research which are not just simple model cards. they are solid reproducible research.
The idea that America is going to stay ahead of China is I think at this point clearly delusional. It's also just such silly framing. Why should 350 million people stay ahead of 1 billion people on the other side of the world? If an AI lab in China cures cancer or something do Americans lose?
So many Americans seem to (at least in theory) be ready to sign up for this ongoing confrontation with China. Does anyone think it isn't America who is poking the bear when it comes to the Thucydides trap? Why not try to get along? It occurs to me the only people more Chinese innovation would hurt are the mega cap class in the United States. Elon Musk certainly doesn't want BYD in the United States. Same story all the way down with these super capitalized AI companies. Most average Americans would probably be better off in a world where the United States and China got along. But its those Americans who will be called upon to suffer most of the burden if that trap ever springs.
By this population-only logic, you should concede that India will overtake China.
Why not talk about how China shut out American companies for decades before complaining about BYD?
As an Indian immigrant, the PRC China has engaged in conflict with almost all its neighbors and stated wars in its short history.
China is not so benevolent when they get to the #1 spot:
https://m.economictimes.com/industry/renewables/china-wto-co...
Its not population only logic, but it does underscore that it is silly to expect the United States to inevitably be ahead.
As for the rest of it:
https://youtu.be/74DAI2hr9Kk?t=159
R1 was very influential on US models development.
Thank you so much to everyone at DeepSeek who is working on this and who have the courage and generosity to open source this for humanity.
We in the United States will never forget!
For all the harm Trump does to the US at least he is helping China!
Chinese companies (and labs) operate in conjunction with the CCP so whatever they're doing, it's because it's Chinese state policy.
What became clear when DeepSeek came onto the scene was that China was seeking to commoditize LLMs. They consider it an issue of national security not to be beholden to US tech companies when it comes to AI. And I, for one, fully endorse this policy.
Another data point on this is the black market for Claude tokens in China [1]. The chat logs themselves are a commodity to train models.
I believe that OpenAI in particular is a bet on a trillion dollar pot of gold that doesn't exist. Google, Microsoft, Amazon and Meta will all be fine. Anthropic is in a far better position than OpenAI (IMHO) but if DeepSeek or some other Chinese open weight model gets as good at coding, they're in real trouble too.
[1]: https://news.ycombinator.com/item?id=48667495
I don’t see how Anthropic is in a better position. They have a slight edge in model quality right at a time when we’re getting a taste of what cheap, “good enough” AI looks like. They don’t own their own compute. And their own arrogance and lies have alienated a huge chunk of their customer base and alerted everyone to the dangers of being dependent on them.
I personally think not owning their own compute is going to be an advantage.
There is a meteor headed towards all this AI investment that I don't think has been properly accounted for and that is, what happens to all the existing hardware investments when NVidia's next architecture comes out. Blackwell (H100/H200) is the current generation. Rubin (R100, presumably R200) is the next and arrives soon. Now a lot of the investment hasn't been spent yet so will likely be spent on Rubin but at that point, what happens when the next iteration comes out and does 3-4x the compute for the same electricity input and same hardware cost?
Also, what happens when people can run way bigger models on consumer hardware in 5 years? The effective limit for useful local LLMs is currently ~31B parameter models because the RTX 5090 has 32GB of VRAM and Apple's shared memory architecture, which can keep bigger models in memory, just doesn't have the raw processing power.
Anyway, why I argue Anthropic is in a better position (than OpenAI) is that they seem to have captured a market that may well be profitable for them as a company, specifically Claude for coding. So they just haven't burnt quite as much cash as OpenAI so aren't in as deep of a hole.
While I think local models are going to improve maassively over the next few years, running them in a data center at scale is always going to be cheaper for a company. Why? Because they can amortize their costs by running 24/7 and powering them and cooling them is simply cheaper at scale when you're talking about 1000+ engineers who otherwise might only be using their hardware ~40 hours a week.
IMHO Google is in the best position here of all the US companies, even though their models aren't the best, because their data centers are ruthlessly efficient, their homegrown TPUs will eventually catch up (and thus avoid the NVidia tax) and they simply haven't bet the farm on winning AI.
I'm generally with you on all of these ideas.
However, Google probably won't catch up. Nvidia has been winning in spite of the fact that their hardware is general purpose rather than tuned for inference.
Rubin has architectural differences I don't understand that are supposed to make inference much cheaper and faster while still retaining those other more generic capabilities. Their next generation after that is going to do even better at being fast for inference and general purpose.
Google is betting that their TPUs won't depreciate faster than the markup they have to pay to Nvidia. I don't think they will be right.
Why do people who don't follow the prices of A100 talk like they know things about GPU pricing dynamics?
A100s are ~7 years old and going for more than 2 dollars an hour, significantly more expensive than even 2 years ago. This is because anything with 80gb of VRAM or more and made by Nvidia will have economically useful lifespans of like, 10 years.
I could see H100s getting 12 years.
Micheal Berry doesn't know shit about GPUs.
So I was curious about how A100s would do running DeepSeek v4. I can't find any instances of running v4 Pro on even an 8xA100 cluster. So you need to run Flash at ~284B params. A100s don't support FP8 so you're running FP16 so you're taking a hit that way. But I see estimates of 30-50tok/s for an 8xA100 cluster. They're drawing 300-400W each so you're looking at probably 3500+ Watts, which is roughly 0.01tok/W.
Now jump ahead 2 years and you seem to have a massive jump in performance [1]. The tokens/Watt goes up by at least 2 orders of magnitude. And the B100 is 3-4x that. And we're about to hit the R100 (Rubin) cliff.
That's what this is going to come down. When hyperscalar DCs are getting to Gigawatt power usage, it all comes down to power efficiency. Those A100s aren't far from being sold for scrap.
I've been looking into how different companies are handling depreciation for this. Amazon seems to be saying the life is 3-4 years, Google 4-5 and Meta is saying 8+, which I think is wildly optimistic.
[1]: https://lambda.ai/inference-models/deepseek-ai/deepseek-v4-f...
> Another data point on this is the black market for Claude tokens in China [1]. The chat logs themselves are a commodity to train models.
anyone with IQ higher than 130 (thus qualified for actual AI R&D) would be questioning something obvious here -
if they are already doing such dodgy stuff with the aim to maximize profits, why would those resellers have large amount of logs with actual American model responses to sell to those AI labs in the first place. shouldn't they just post train & customize some leading Chinese open source models to pretend to be Opus or GPT for the vast majority of their users (as classified by some models) who don't know much about expected Opus behaviours & not skilled enough to tell the differences?
that is actually the interesting bit not covered in your censored version of the story line, it is also what happens on the ground. your censored version of the story implies that those dodgy resellers using stolen credit cards, pooling accounts with stolen IDs and illegally selling very personal logs would somehow be honest enough to spend extra $ to ensure their victims (aka paying users) can actually use real Opus and GPT. LOL
dude, you failed this IQ test miserably.
You don't actually need a very high IQ to do AI R&D. More than it takes to post IQ comments on this site, maybe.
The galaxy brains in the labs putatively buying the logs wouldn't notice this? Or figure out a structure to prevent this?
resellers wouldn't be trying to sell such junk in the first place. they use faked models to avoid the cost of Opus tokens, not to double dip to scam those with arguably the highest IQ in the country.
The difference between greed and power
Its because our culture worships pieces of paper the government tells us is worth something.
Nope, people seek it out because government tells them to pay taxes _or else_.
Money is just a physical representation of the ability to get what you want. The problem is not money. It’s the fact that we live in a “me” society.
Sure, in part by "stealing" from American AI companies with Distillation attacks:
https://yipzap.com/anthropic-accuses-alibaba-of-largest-ai-d...
If your moat is “please don’t copy my outputs”, you don’t have a moat. There is no such thing as a distillation “attack”.
How very machiavellianist-libertarian of you.
Don't even try to combine it with any notion of "leadership" then, however, since distillation is literally "copying the actual leader"
How does it differ from pirating music or movies?
According to US AI labs, training on other people's output is fair use. So that's how.
AI training is considered transformational. That's how AI training gets around copyright and it's probably consistent with copyright precedent. For example, indexing the web is considered transformational, even though you can recover the full text of everything in an inverted index.
Machine-extruded text is not copyrightable, since there was no human creativity involved in producing it.
(and if you argue the US models do produce copyrighted works, then oooops - whose copyright is it huh?)
Ow my head.
That when I pay for a model, the copyright of the output belongs to me. This is as work for hire as it gets.
US AI companies trained their own models on vast amounts of copyrighted and publicly available content without obtaining permission. There's no moral high ground here.
You know what, if someone wants to downvote this guy by claiming distillation attacks are not "attacks" or don't cross some ethical bound (especially since I just posted a similar comment), then go right ahead, but if you're combining it with any notion of "leadership", that's like saying that the person in 2nd place in a bike race who is drafting behind the person actually in 1st place is exhibiting "leadership".
There's no "leader" if, absent someone whose results you're copying, you are an emperor without clothes
Besides "attack" being a ludicrous name for distillation, note how your article says "accuses", also it's mostly about Alibaba, not DeepSeek (although it's mentioned there). Both Dario Amodei and Sam Altman publicly claimed that DS used their outputs to train their models, and knowing the differences between all these models by heart, I believe they're simply lying through their teeth to sway the public opinion and/or the policy. These models are absolutely nothing alike, and distillation necessarily makes student's outputs similar to teacher's. This is very visible in Z.ai models (which were trained on Gemini outputs to the point that they repeated Google's conditional prompt injections in the CoT, and later on Claude where it started repeating their CoT as well) and certain Google models which were trained on Claude's outputs in a roundabout way. Distillation always shows up in the result.
And certainly they have no idea whether these outputs (assuming they ever existed and it wasn't made up) were used for training. The article mentions that DS made 150k requests. This isn't much and might have been just an eval or a benchmark to compare their own model against. It's really hard to believe DeepSeek had any Claude outputs anywhere in their training schedule, since it's just too different. Besides training on random vibecode of course, which is mostly written by Claude.
While I don't agree with your comment being downvoted, I don't think distillation is either an "attack" nor is it "stealing". The idea that someone else gets to decide how I use tokens that I pay for is ludicrous.
Imagine if your casio calculator would come with a ToS that says you can't use it to develop a competitor calculator or any other tools. Or that your hammer can't be used to make other tools. Or, closer to the HN crowd, imagine MS in the 90s saying that you can't use their OS to build competing services to MS. They'd be laughed at and be split immediately if they tried that.
The only thing they can do is to refuse serving tokens (and even that's debatable, if we get to tokens being commoditised). But that's gonna be a game of whack-a-mole, and they know it.