Alvin Graylin

Alvin Graylin’s core macro warning is that the US is financing an AI arms race like the USSR financed missiles—and a correction is due. He cites 45% of US stock-market value in AI, a Buffett indicator at 240% of GDP versus roughly 120% at the internet-bubble peak, and both $1.6T and $1.7T figures for off-the-books hyperscaler debt versus Enron’s $200M. He says Anthropic’s ARR has flattened in the “$70B range,” but later also says “their ARR is at $7B”; first-half revenue was under $20B against hundreds of billions in CapEx and debt commitments.

China is deliberately playing the “hare”—good-enough AI diffused into industry—while the US hunts the AGI “stag” alone, which Graylin’s game theory calls the worst configuration. Beijing’s AI Plus Plan targets 70% of companies integrating AI in five years and 90%+ in ten, spends roughly a tenth as much as the US on data centers while reaching “97% as good,” and is “not behaving like they believe ASI is around the corner”: CAC review delays model releases, and labs were told not to buy the H200s America offered.

Export controls have slowed Chinese compute and inference but, in Graylin’s view, also backfired on several fronts. Training runs happen in international data centers and return “on a disk”; Chinese GPU startups told him “we would’ve died if it wasn’t for American policies” and may export chips within two to three years. After the US robot embargo, US robotics companies were reportedly smuggling Chinese actuators home in suitcases. Chinese open-weight models rose from 2% to 61% of OpenRouter traffic, with Qwen reaching 1 billion downloads.

The distillation panic is overblown and exposes broken frontier-AI economics. Graylin estimates Anthropic’s alleged Chinese distillation cost $2–3M in queries across three labs—and only thousands of dollars for DeepSeek. If a billion-dollar model can be duplicated for a few million, “the whole economics of frontier AI doesn’t make sense.” Meta spends $100–200M monthly on Anthropic tokens and still took years to ship a competitive model. Dave Blundin agrees the reasoning-traces issue is overblown but calls Kimi K3 “a burning match” capable of self-improvement that benchmarks are not testing.

Graylin’s paper argues that the biggest models are not the biggest threats—small open models running on a basement laptop may be. Across millions to trillions of parameters he found “no correlation between risk in the real world and size of models”: tiny chemistry and biology models already design weapons or organisms, while a Microsoft system transcribed as “M-Dash” used 100 small models and scored 95 on CyberGym versus the system transcribed as “Mythos” at 83–84. Large cloud-hosted models are easier to govern through telemetry and wrappers; policy should target precursors and synthesis machines.

His explicitly speculative Taiwan war game is that a US private-credit and data-center bubble could pop within two years, prompting Washington to ask Beijing for financial help, possibly alongside a mutually agreed peaceful arrangement and greater US restraint on Taiwan. China’s T-bill purchases helped stabilize the US system during the 2008 crisis, he argues. He rejects the idea that China would invade mainly for TSMC’s fabs: a TSMC CTO was told the US would extract 1,000 engineers, and captured fabs would eventually fail without global supplies.

For the September 24 US-China AI dialogue, success is simply agreeing to a second meeting, with non-state-actor risk as the shared priority. Graylin’s prescription for the West is high-quality open source plus an “AI Marshall Plan”: the original’s $15–18B bought decades of allies and markets, and America may need buyers for its chips “when we stop building data centers here,” if financing dries up.

🔗 Original source & video: China’s Endgame: ASI Timelines, US-China Relations, and the $1.7T AI Bubble With Alvin Graylin | 281