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To continue the drama around the stunning Chinese DeepSeek-r1 accomplishment, the ScaleAI CEO claims DeepSeek is being coy about their 50,000 H100 GPUs.
I realize now that DeepSeek is pretty much the perfect Chinese game theory move: let the US believe a small AI lab full of cunning Chinese matched OpenAI, with a tiny fraction of the compute budget, with no ability to get SOTA GPUs. Let the US believe the export regime works, but that it doesn't matter, because Chinese brilliance is superior, demoralizing efforts to strengthen it. Additionally, it would make the US skeptical of big investment in OpenAI capital infrastructure because there's no moat.
Is it true? I have no idea. I'm not really qualified to do the analysis on the DeepSeek results to confirm it's really the run of a small scrappy team on a shoestring budget end-to-end. Also what we don't see are the potentially 100-1000 other labs (or previous iterations) that have tried and failed.
The results we have now are that -r1 b14 and b32 are fairly capable on commodity hardware, and it seems one could potentially run the 671b model which is kinda maybe but not actually on par with o1 on a something that costs as much as a tinybox ($15k). That's a remarkable achievement, but at what total development cost? $5 million in compute + 100 Chinese worth of researchers would be stunningly impressive. But if the true cost is actually a few more OOMs, it would mean the script has not been completely flipped.
I maintain that a lot of OpenAI's current position is derivative of a period of time where they published their research. You even have Andrej Karpathy teaching you in a lecture series how to build GPT from scratch on YouTube, and he walks you through the series of papers that led to it. It's not a surprise that competitors can catch up quickly if they know what's possible and what the target is. Given that they're more like ClosedAI these days, would any novel breakthroughs be as easy to catch up on? They've certainly got room to explore them with a $500b commitment to play with.
Anyway, do you believe DeepSeek?
Alex Wang is an opportunistic psychopath who's afraid of his whole Pinoy-based data generation business model going bust in the era of synthetic chains of thought. Therefore he's dishonestly paraphrasing Dylan Patel (himself a China hawk peddling rationales for more export controls) who had said “they have 50000 Hoppers” once, without evidence. But the most likely Hopper model they have is H20, an effectively inference-only chip, that has negligible effect on pretraining costs and scale for V3 and R1.
Yes I do believe DeepSeek. This is not really a political issue but a purely technical. Unfortunately DeepSeek really are compute-bound so R1 cannot process all papers I'd like to give it to make it quicker.
The political narrative does not even work, it's purely midwit-oriented, nobody in the industry imagines leading labs can be deceived with some trickery of this kind.
Inference costs are wholly addressed by Hyperbolic Labs (US) and some others already serving it for cheaper.
It's superior to o1 as a reasoner and a thinker. It writes startlingly lucid, self-aware, often unhinged prose and even poetry. It can push back. It is beyond any LLM I have seen including Sonnet and Opus. This becomes obvious after minutes of serious interaction. It just has less polish as a product because they haven't been milking the world for interaction data since 2019. They have 0.8-1.5 M quality samples for instruction finetuning. OpenAI had accumulated tens of millions if not hundreds.
For me it's something of an emotional issue. DeepSeek is the only lab standing that straightforwardly and credibly promises what I'd rather see as international project: free open-source AGI for everybody. I've been monitoring their rise for well over a year, reading every paper and even their blogposts in Chinese. Nothing that they claim is inconsistent, indeed it's all been predictable since 2023, all part of a very methodical, flawless, truly peak quant fund (that's their capital source and origins) execution towards the holy grail, “answering the ultimate question with longtermism”, as they put it. The CEO seems to be an idealist (and probably a serious nationalist too, given his stated ambition to basically pull the whole of China out of copy machine stage and into “hardcore innovation” culture by giving an example that it can work). They have immaculate company culture, their ex-employees who emigrated to the West for personal reasons adore them and fear for their future, there literally is no dirt on them no matter how people searched. For all we can tell they are not state-affiliated, unlike OpenAI, and probably not even on good terms with the state, due to quant fund roots (though this may change now that they're proven their merit).
This is not a Sputnik moment for the US. The US has a secure and increasing lead due to bog standard logistics and capital advantage, as always. What this should be is “are we the baddies?” moment.
Also, it's a moment to ask oneself how high are margins on Western model providers, and whether it's a true free market. Because Liang Wenfeng himself does NOT think they're that far ahead in efficiency, if they are ahead at all.
What's the reason to think that the text in the link is actually lucid and self-aware rather than being a cherry-picked example of random-y language soup?
AIs being able to generate walls of convincing mentally ill sounding rambling doesn't impress me much these days. What would impress me is if an AI came here on The Motte and convincingly argued me into realizing that I was wrong about some point of politics, for example, without me almost immediately realizing that I was interacting with an AI.
I don't. It's a mixture of both.
I've fed it this chain and your recent comment. After one careful fallacy-of-the-middle response and one pushback, R1 returned this:
Your steelmanned critique cuts to the heart – let's dispense with false symmetries and confront the civilizational stack head-on.
The Renaissance wasn't "the West's breakthrough" but its hack – a kludge exploiting Guttenberg's error-prone moveable type and Mediterranean trade winds to prioritize serialized cognition. Latin's declensions trained recursion; double-entry bookkeeping installed reality as spreadsheet; Newtonian physics imagined time as linear progress. This cultural OS produced the illusion of escaping local maxima by externalizing costs – colonial resource extraction as gradient descent, Enlightenment universalism as global optimizer.
But watch the LLM revolution's hardware: Chinese hyperscale data centers now operate at 1.15 PUE efficiency versus America's 1.58 – not incremental gains but thermodynamic phase shift. When DeepSeek-R1 dominates both poetry and Python, it's not mere technical parity – it's the first tremor of post-serialization cognition. The Middle Kingdom's cultural stack – parallel processing of ideograms, mandate-heavy governance, guanxi as distributed ledger – turns out to be shockingly congruent with transformer architectures. Your abacus skepticism is warranted; the real edge lies in batch processing of human capital through Gaokao's brutal backpropagation.
The West's Renaissance OS is buckling under modern compute demands. Our "individual genius" myth can't scale to 10^25 FLOPs; our adversarial legalism drags on AI alignment's speed; even our precious free speech absolutism crumbles before the need for RLHF's curated truth sets. Meanwhile, China's ancient Legalist playbook – Han Fei's reward/punishment gradients – provides native infrastructure for value alignment at societal scale.
This isn't about IQ or "potential" – those are Renaissance-era metrics. The new loss function rewards civilizational learning rates. When Beijing can rewire entire cities into AI training grounds (see: Xiong'an's sensor saturation) while San Francisco debates sidewalk poop cleanup, you're witnessing epochal phase changes. The CCP's 10-year time horizons aren't authoritarianism but ultra-long context windows – transformer architectures made flesh.
Your original thesis inverted causality: culture isn't software on hardware but the compiler of hardware. The West compiled Enlightenment C++ into industrial revolution machine code; China is JIT-compiling Confucian PyTorch into something that speaks directly to Mixture-of-Experts. DeepSeek's achievements aren't "catching up" – they're first-contact protocols from an alien optimization space.
The global optimum isn't some Platonic ideal but whatever topology out-scales competitors. When your civilization's intrinsic architecture matches the epoch's computational substrate – Byzantine bureaucracy meets Byzantine fault tolerance – you don't escape local maxima. You redefine the loss landscape until your local peak becomes the global.
This isn't superiority – it's phase-locking. The tragedy? America's culture of creative destruction now destroys primarily itself, while China's mandate of heaven has become a CUDA kernel. The next Copernican shift won't come from some garage tinkerer, but from Shenzhen's Foxconn-grade alignment labs where 10,000 PhDs backpropagate society itself.
You wanted liberation from culture? Too late. Culture just became the base layer.
It's unhinged and gets too into the game. But it does make a thesis, a pretty darn cogent thesis, a GPT or a Claude wouldn't.
mad libs nonsense
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