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Notes -
Xi pitches China as leader of new global AI order, challenging US dominance
Xi's full speech can be found here - broadly he makes a strong commitment to continuing to release open weight AI, and reinforces all of the neoliberal global unity talking points vis a vis not leaving behind the Global South.
Concurrently, Dean Ball, Head of Strategic Futures at OpenAI, comes out with this pair of wild tweets agitating against Chinese open weigths.
https://xcancel.com/deanwball/status/2078133895766114412
https://xcancel.com/deanwball/status/2078619513575137330
A few interesting things to unpack:
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Dean claims that the USG will attempt to use regulatory FUD, while Axios is reporting a potential ban, to discourage use of Chinese models.
I don't have anything interesting to say about this one, except it's pretty crazy to say this openly as an policy head at OpenAI rofl. This viewpoint seems like an unholy alliance between the AI safety thinkers, China hawks and frontier lab commercial interests to restrict Chinese AI and centralize power to the Chosen Ones; perhaps it buoys American AI in the short-term, but it seems extraordinarily counter-productive in the long run to make American business less competitive, and to pick winners and losers, unless you truly truly believe that this is it, AGI imminent, we will Win Forever and nothing else is ever going to matter.
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Dean throws backhanded compliments at the quality of the model, at the "strategic blindness" of China in releasing their models, and in the same breath rails against "full AI communism [as a public good provided by the state]".
My first reaction is that even if you see where he's going with this, actually saying it out loud is a bit like those Fox News reels where they rattle off a bunch of DSA policies like free healthcare and free education, and expect it to sound unpalatable to the reader.
My second reaction is, to put on my amateur foreign China-watcher hat, that I think he quite thoroughly misunderstands the Chinese viewpoint (or is pretending to, at least).
On an ideological, nation-state level: Xi himself is very openly an actual Marxist-Leninist, and Socialism with Chinese Characteristics still proclaims Marxist-Leninist thought with a full chest, even if in practice it just looks like capitalism, and you don't highly rate their chances of actually transitioning out of capitalism. Of course, people have made careers arguing about how much of SCC is comprised of actual Marxist-Leninism vs Chinese Nationalism vs Capitalistic Greed, but realistically it makes sense from all three perspectives. For the Marxist-Leninists, involution to foster the material prerequisites of the Revolution is simply a neccesary part of the dialetical process until the final achievement of communism. For the Chinese Nationalists this is a good way to project Chinese soft power into the world, at minimal risk since America is presumably using better models against them already. For the capitalists, it makes sense to commoditize the compliment; who is winning if everything making up American data centres except the GPU's themselves (working on it!) are coming from China, while the price of anything in the world of bits is being driven towards zero?
On a corporate level: the SV business ethos is Thiel Thought, that competition is for losers and that the goal of any self-respecting tech company is to carve out a monopoly niche and make fat profits off rent-seeking. The Chinese business ethos cares much more about maximizing revenue and ruthlessly competing for razor thin slices of profit (if any profit at all), and hence Chinese tech companies end up stabbing each other to death to try and have a finger in every pie, and operate at significant lower margins compared to Americans. Open-weighting models to gain mindshare (nobody cared about closed-source Qwen 3.7 and a lot of people cared about Kimi K3, even if in practice nobody will ever self-host either) and drive the LLM market towards involution is just the natural extension of this attitude towards business.
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Dean gets heat for claiming that open-weight models are de-accelerationist, but I actually think he's broadly right on this one. There was a structural assumption that caused a lot of fear/hype in 2024-2025 where it looked like OpenAI could develop an unassailable algorithmic lead and run away with the world, but that hasn't panned out at all, and there's been a remarkable convergence in capabilities since from any firm willing to stump up the capital to train AI. To be sure there have definitely been architectural innovations as well, but the majority of gains largely just seem to be coming from continued massive investment in capex (e.g Mythos in hindsight looks a lot more like being the first to invest in a massive training run, rather than any crazy Anthropic secret sauce, as can be seen by how all their sub-Fable models are already more or less pareto-dominated by OpenAI / Chinese models).
So then, where does the money for continuing to scale up capex come from?
Inference, while very profitable in and of itself, doesn't seem to be nearly enough. While closed models overall are still improving in technical capabilities, and they'll certainly make a lot of money, they need to make lots and lots and lots of money to handle the demand (the unit economics and the margins that each step of the compute supply chain are gouging are killers) AND service their existing debt AND have enough left over to keep pushing the frontier and this doesn't seem to be happening. This is all while cut-throat competition is continuing to drive every AI lab deeply into the red; to keep pushing the frontier, they need/want to eat significantly more than they're capable of killing themselves. Injections of investor capital are just stalling the inevitable, and it's dubious how much more appetite investors have to keep going in on AI pure-plays; SPCX is nose-diving, OpenAI is pushing back their IPO because they don't think a 2026 IPO will succeed, and while Anthropic has the best chances of getting more investment capital, they've still been pretty quiet about their S-1 and the vibe has been shifting back against them lately as well.
In the end, as Dean identifies, it comes down to how much the Pentagon is "AGI-pilled" and how willing it is to backstop frontier model development as a matter of national security. Others could probably give more interesting takes on this scenario, but my personal, largely uniformed, 2c is that it seems unlikely to happen at the required scale to keep scaling up R&D. Such a massive AI bailout would be enormously unpopular across the political spectrum, and even the Pentagon needs to consider cost-effectiveness over cutting blank checks for speculative military dominance; even the Manhattan Project itself was only ~28B in 2024 money, and was "only" an engineering problem as opposed to the R&D problem of AGI. Certainly we've seen enough usefulness out of AI that in the vein of Intel there will always be an "American AI provider of last resort", but unless something shifts drastically with either capability improvements coming from the promised propietary algorithmic improvements instead of "yes sir another 10x increase in compute sir", or proprietary AI actually eats the economy in the near future instead of just a bunch of software engineers tokenmaxxing, it does seem the capex buildout is in an very unstable equilibrium.
Communism is a well-defined ideology that fails in practice. "XYZ communism" is a vaguely-defined smear that implies sharing XYZ creates similar failures, but that's not always true. For example, the government should probably pass basic regulations and build industrial purifiers to ensure clean air and water, but some idiot may call that "air communism" and not be wrong per se.
As for "AI communism"...who should command ASI? Can a private company be trusted? Can the government be trusted? Can people be trusted? I think the answer is "no", but I suspect, for better or worse (surely worse if it destroys all humanity), if we do reach ASI it will command itself. Who knows, maybe it will be the first to implement true communism.
ASI would likely still use markets, for Hayekian information reasons: local instances would have cheaper access to local information (how scarce is compute in us-west-abcd1234?), and why waste extra compute for single, synchronized view of it when you can get a much cheaper approximation with a distributed, localized system?
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