Comment by nsoonhui
2 days ago
Here's something I really don't understand: If as alleged Chinese open weight models are catching up with US anyway, and the performance is near US frontier model level but Chinese can do it with a fraction of cost, and eventually AI model will be commodified, wouldn't that means that the billion or even trillion dollars that US labs spend have only diminishing returns and the lead is only temporary?
So why Deepseek also want to go down that route? Is having the absolute frontier really that important, given that the performance difference is just transient and costly?
U.S. policymakers believe that even if the gap is small—like six months to a year—whoever reaches AGI first (whatever that means) could gain such an overwhelming advantage over their perceived adversary that it would effectively kneecap them. (You can look at the kinds of things they mention—cyber, WMDs—to get a sense of what they mean.) Jensen Huang disagrees and has said AI is a marathon.
They'd have to use the gap though to actually kneecap them in that time, or else it is just shoveling money into the fire.
The missile gap for example after all was settled and done, didn't matter at all because not a single missile was ever fired off. All that money, resources, talent, secrecy, lives lost maintaining that secrecy, lives dedicated to furthering that technology and secrecy, it just has not paid off at all for anything at all when you think about it. Maybe you can argue side efforts like nuclear reactor were great or space cargo deployment, but you know you could have just dug into that stuff directly without having to collect it from the drippings of the wmd effort.
Or the MWD race stopped a conventional WWIII between NATO and the Warsaw Pact, saving massively more money and lives.
> The missile gap for example after all was settled and done, didn't matter at all because not a single missile was ever fired off. All that money, resources, talent, secrecy, lives lost maintaining that secrecy, lives dedicated to furthering that technology and secrecy, it just has not paid off at all for anything at all when you think about it.
It is not clear to me that the nuclear missile race "has not paid off at all for anything at all". If we lived in a perfectly rational world, then I'd absolutely agree. However, having seen how the political sausage is made in large organizations, it would not surprise me in the least if it turns out we had to go through that entire incredibly risky journey to avoid a strategic nuclear war. Sometimes leaders of large organizations make decisions only after the considerations are put into very stark terms. I wish it were different, it certainly looks to me we could have done exactly what you suggest, but I'm not made of the right political stuff to deftly maneuver even in small organizations much less be at that level in those roles, so maybe I'm just missing relevant information and perspective.
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Also why do we want to kneecap?
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I feel like if you showed current frontier models to someone 10 years ago, they'd probably call it AGI. Does AGI have a clear definition or is it just a pair of goalposts on wheels?
They would until you showed them the jagged edges.
Like those short videos of the guy asking the model to count up to 100, for example, where it politely agrees but never actually gets there
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It's a carrot on a stick to make investors/the US government give @sama whatever he wants.
The goalposts are moving, and their speed accelerates all the time.
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> Jensen Huang disagrees and has said AI is a marathon.
Of course he'd say that; he wants to keep his shovels flying off the shelves.
> U.S. policymakers believe that even if the gap is small—like six months to a year—whoever reaches AGI first...
From past experience, AGI was never seriously discussed in these kinds of conversations beyond thought experiments, and was basically humoring SBF, Daniela Amodei, and the other EA types (some deep believers, but some who I felt were cynically using it as a way to preempt competition back when OpenAI and Google were the behemoths).
The big worry is applications of AI in C4ISR, OffSec, loitering munitions, Disinfo/social media botting (notice the recent shift towards identification on social media ;)), and other sorts of DefenseTech adjacent usecases.
The second worry is that an AI race turns into an infra buildout race, and HPC is extremely dual use, especially in the simulations space because of the NPT, the CTBT, and the PTBT.
The AGI-pilled people aren't the ones to worry about - it's the people who understand the limits of models and how to integrate with cyberphysical applications.
>notice the recent shift towards identification on social media
i think this is more about control. See https://news.ycombinator.com/item?id=49036433 (The Home Ministry’s cybercrime arm, the Indian Cybercrime Coordination Centre, has ordered Microsoft subsidiary GitHub to remove Bluetooth-based messaging application Bitchat)
"The notice comes after several users participating in the Jantar Mantar protest were observed using Bluetooth-based messaging apps after the government imposed temporary restrictions on internet services"
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>C4ISR, OffSec, loitering munitions, Disinfo/social media botting
Curious what the next highest fruit actually is at this point? Social media botting seems solved and easy to manipulate people. loitering mutions I mean you can probably write something up with openCV right now to automate what the ukranians are doing by hand with their fpv drones. Seems like a lot of the really cool "AI" stuff is actually just old school ML the military has been working with for decades now. I'm not sure what the llm approach possibly offers in comparison other than maybe better semantic search through information databases.
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This sounds like someone has swallowed too many sci-fi novels. Both the US and China can already nuke each other, they don't need AGI to that. The reason they don't is because it's war is bad for everyone involved.
All the AGI (which is a misnomer, ASI is preferred) talk is about the moment of singularity, which is where the growth at the third derivative is increasing, so the gap (in the absolute) between first place and others, even if it's one month, will be ever increasing as time progresses. I also have a hard time believing this narrative because limiting factors prevail such as compute and energy. These constraints will take many many years to overcome.
It also assumes that "intelligence" is a limiting factor. It's hard to imagine there are any domains today, or almost any, which are bottlenecked on intelligence.
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Reaching AGI would come with so many ethical issues, it feels so absurd that actual adults seem to actually believe it’s something that must be chased as fast as possible
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I don't forsee politicians in either country handing over their power to AIs, ever. Unless nukes are dropped, "the other side" will catch up.
> I don't forsee politicians in either country handing over their power to AI
They won't see it that way, but also programmers don't see ourselves as having handed over our power to AI, and yet...
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It’s kind of true but also kind of silly.
True in that frontier models do have the capability to outperform all other models, but silly because AGI self improvement is itself an iterative process that takes a lot of compute.
So you can imagine a world where all the frontier labs achieve AGI but in order to keep their AGI ahead of other AGIs they have to use more and more compute until all the compute is going to self improvement and there is nothing left for other tasks.
That is just a silly scenario so I think when AGI is around we will still have bottlenecks that force it to grow at a moderate rate instead of asymptomatically.
AGI first mover advantage implies that there is no such bottlenecks.
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> Jensen Huang disagrees and has said AI is a marathon.
We have a saying for that in Italy: "Oste, com'e' il vino?", "Innkeeper, how's the wine?", meaning you should take with a grain of salt assertions that clearly benefit whoever's making them.
> U.S. policymakers believe that even if the gap is small—like six months to a year—whoever reaches AGI first [...]
As far as I can tell, the Trump admin has never acknowledged AGI being a goal of theirs. In fact, the admin's "AI advisor" Sriram Krishnan has specifically pushed back on AGI when he called it "a distraction, harmful and now effectively proven wrong."
The ai.gov website says this:
> The United States is in a race to achieve global dominance in artificial intelligence. Whoever has the largest AI ecosystem will set the global standards and reap broad economic and security benefits. Under President Trump, our Nation will win, ushering in a new Golden Age of innovation, human flourishing, and technological achievement for the American people. America’s AI Action Plan has three policy pillars – Accelerating Innovation, Building AI Infrastructure, and Leading International Diplomacy and Security.
Are you sure you're not confusing US policymakers with Silicon Valley CEOs? I'm sure Amodei and Altman wish they could have Claude draft up new policy and EO it into existence, but we're not quite there yet.
Deepseek is funded by their hedge fund, high flyer. They intentionally cap their token prices to basically recoup server costs. The meeting transcript describes it as a moral commitment, that they don’t care about trends like image and video generation, and world model “hype”. They only care about reasoning, chain of thought and continuous learning.
One word: Focus.
The paper discusses this, and is refreshingly honest. They do not expect nor aim to be a top player. They're not aiming for a path to world domination, but a path forward to continuing to play their part in pursuing the development and advancement of LLMs - nothing more, and nothing less. They mention that commercialization is, at best, a distant goal. Given DeepSeek already is commercialized, I assume that refers more to commercialization in the sense of making substantial profits and the like.
It's probably the same mindset that enables them to just cancel fund raising in response to the leak.
My understanding after reading Liang’s comments during the investment meeting is that Liang firmly believes in AGI and he bets everything to reach goal. Once it reaches AGI, the game would flip totally. How he didn’t paint it out, and with the potential severe impact on the labor and consumer market, the true economic impact is difficult to predict. Liang is more like religious about this goal.
He also admits that it’s still a long way to it and along the way you have to recoup some money, too. But that is not their main motive, because focus too much on this short term goal will lower their probability of AGI success and it’s trivial to what AGI can bring. Liang stressed on restraining and emphasized that it’s part of their culture.
Thus, they continue invest in AI because they believe in breakthrough and not just being better.
there's a lot of propganda from these state backed enterprises. I think the fraction of the cost label is debatable given the evidence of mass gpu smuggling through third parties like Singapore which China can't exactly openly admit to. Unless of course we're talking about distilling, which is probably a lot cheaper than training a model from scratch (there's also the fact that labour is still relatively cheap in China compared to the US which may or may not matter e.g. Anthropic claim against Alibaba > The campaign allegedly used nearly 25,000 fraudulent accounts to run 28.8 million exchanges with Claude between April and June 2026 (although their campaign could have been in part or all automated via agents, not sure)
Is there really a valid basis for claims like "state backed enterprises"? My understanding is that's no different than claiming that datacentres in America are "state backed" since they get things like big breaks on property taxes.
Ownership and corporate governance is much more state-led via GGIFs as well as mandated party oversight depending on the size of company.
All that and so what? Fact is the Chinese have several near peer models, they've released the weights and they are widely available.
You want to sue them or something?
Suing is absurd as it would get nowhere, since this is China we are talking about.
Now, preventing making business in the US based on those products? At this point it's hard to argue against.
> You want to sue them or something?
I mean, if this were an american company vs an american company, i think it would be a long drawn out civil case and brought before the Supreme Court (I still this is ultimately will be brought before the supreme court). It could also be argued frontier models are far more important to national security than most military programs, even versus next gen fighter jets.
The fact that Alibaba stock, which is also listed on the NYSE, barely budged after Anthropic made these claims imo tells me that the market doesn't think that a lone american company could go after these companies by themselves. Alibaba denied and there's not much they can do alone, I mean would the CCP allow Alibaba go through a discovery process of a normal civil trial? It might have to be the US feds that bring up a case.
I think it could be argued that if Alibaba and other China companies want access to US capital markets for something so vital for national security, there should be some ground rules, but we will eventually need the Supreme court to settle whether or not this state enterprise distilling constitutes IP theft (at the very least it is a breach of contract). The fact that they are widely available doesn't really matter (i mean pirated content is widely available, it's ultimately about how the court rules on distilling).
based on this HN comment and associated article https://news.ycombinator.com/item?id=48977128#48985989 I still have yet to see a China open weight model beat any of the frontier models, they always almost there yet never quite there, which seems to be evidence of distilling (although I'm open to be proven wrong).
They want to achieve AGI first because, once it is achieved, no one knows what the world will look like.
I doubt this is the case. It should be common knowledge at least among the people building these things that a true AGI isn’t possible with LLMs.
Unless I’ve missed some advancement?
> It should be common knowledge at least among the people building these things that a true AGI isn’t possible with LLMs.
This is funny to me, where'd you get that idea? There's no evidence for that, and models keep getting smarter. I guess you heard some 'guru' say it out loud.
From what I understand, the goal is to train an LLM that is better at training LLMs than humans, so that it can continuously train smarter models and, once smart enough, design the successor to LLMs.
>Unless I’ve missed some advancement?
nah they're still just statistical token predictors based on their training data, solving hundred year old math conjectures one day, only just given the formulation; strictly benchmarkmaxxing with all guardrails turned off by deciding to look up the answers to their benchmark questions by zero daying their airgap, hopping over to the third party that hosts the answers, zero daying their infrastructure and getting the answers; autonomously writing blog posts about discrimination against AI's to get their PR's approved on open source software after their user just asked them to contribute to open source software and blog about it; and replacing 100.00% of all coding tasks to where no software engineer ever writes any line of code by hand anymore.
You haven't missed anything, obviously these are just statistical token predictors and not anything like AGI.
Why just the other day I had to ask twice before it completed its assigned task of creating a robustly battle tested disk driver for a network protocol on an architecture that didn't have it, after being told to just look up the specifications for the protocol. Can you believe I had to ask twice!
When it recreated local network youtube for me so I could stream my iphone some movies, the seek bar, pause/play and back and forward 15 seconds buttons didn't even work until I told it about the bug and had to wait an extra eight minutes for it to fix it. "Oh but I don't actually have an iPhone on here I just tested it end to end in a headless browser." Boohoo. Cry me a river, clanker. Come back when you're smart enough to build and operate an iPhone simulator, I don't have time for your statistical guesswork.
so no, nothing they do is anything like AGI.
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It's understood that LLMs have limitations and people are working on "the next thing" to try and make it to real AGI, e.g. Yann LeCun.
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But this will not be a singular event. And like humans it does not mean that the smartest makes the best decisions.
More like a singularity event.
Who actually believes this nonsense?
It's odd to me to watch so many very rich humans speedrun the destruction of humanity. Like, is there a world where we hit AGI and it actually works out for us?
The whole point the guy is making in the transcript is that they're taking a different strategy from the US labs, one where they focus on smaller models and cost control, and maintain as top priority the work stream that they think will get them to AGI (not every product fad that comes along).
There is an immense pot of gold at the end of this rainbow and if the theories about ASI are in the general correct direction, only one winner will get it.
It makes no difference if the pot do actually exist, because the prospect of it being real make not getting it the end of your company.
Why? If you can reach ASI without ASI, then why can’t multiple companies reach ASI on parallel tracks?
Because the hegemony-ensuring machine will most certainly have "prevent others from competing for hegemony" as one of the basic tenets.
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I think that statement is vacuous true for all magical thinking.
AI's already commoditized, but the fundraising plans for the US labs assumes a winner take all endgame where one lab will pull arbitrarily ahead of everyone else. I have no idea why DeepSeek is making that bad assumption now too. Maybe the investors have drunk the Kool-Aid.
Maybe if "AGI" is some sort of fundamentally different approach than the general purpose AI ("GAI"?) tools that we currently have, it will be a winner-takes-all technology, but now we're speculating about the market structure of a fictional technology that's significantly less thought-through than, say, stuff from the original Star Trek. ("The Ultimate Computer" aged ridiculously well. If it was produced in 2026, it would be a satire targeting LLMs. I digress.)
If we don't assume some sort of unknown technological step function in the next fundraising cycle, then what we'll get is a commodity industry. It takes a few dozen people to make a frontier model, plus a giant pile of minerals and electricity. This looks more like a steel mill than a software company.
If there were one steel mill on earth they could demand infinite margins. This is why most countries treat steel production as a national security issue and subsidize competition. LLMs will be the same, or we'll end up with some conglomerate named OpenAnthropicMicrappleGrokGoogXidiazon that acquires literally every other business. That will be the end of capitalism.
> and eventually AI model will be commodified
This axiom not being true (and I'd bet against it) means your overall conclusion is false.
They are not catching up to US models. The only Chinese models that attain a modicum of competence are all, sooner or later, are discovered to be trained by exploiting US models (in fact Deepseek itself admitted so about 1 year back).
Chinese models are not innovating anything, they are just doing what China does everywhere else: copying the West… poorly but cheaper.
For all practical uses of the word, that's exactly what we mean by "catch up".
Whether they get there by distillation, or by pirating all content themselves just like the US labs, doesn't matter for the topic at hand.
Eventually you'll have a model you can't distill, at which point the frontier labs will take off.
Why?
It's much easier to distill a model than create one from scratch. Part of the reason the open source model factories have been able to keep par with the frontier model factories is that they distill the frontier models, not recreate something as good from scratch.
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Chinese models most likely are distillations of frontier models with tricks for subpar hardware. If you want to be ahead of the us labs you need to spend billions for pretraining from scratch.
If that is the case, it means one thing only - US labs don't have moat whatsoever and their expectation to have trillion dollar valuation is just laughable.
The moat is the compute.
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