Twitter/X

Ryan Fedasiuk (tweet, 2026-05-11) argues U.S. export controls on AI chips are…

2026-05-11 · 18:41 UTC ·@RyanFedasiuk ·1 min read

Brief

Ryan Fedasiuk argues U.S. export controls on AI chips are aimed at limiting compute, not tied to AGI, because compute scarcity in 2026 is the main barrier preventing Chinese labs like DeepSeek, Qwen, and Moonshot from hosting competitive inference services; global memory shortages (e.g., M3 Ultra Mac Studio sellouts) and strained supply chains favor U.S. labs with hyperscaler and allied manufacturer ties.

Why it matters

Ryan Fedasiuk (tweet, 2026-05-11) argues U.S. export controls on AI chips are intended to restrict compute availability — not because of beliefs about AGI or first‑mover advantage — and that compute is the singular bottleneck blocking labs like DeepSeek, Qwen, and Moonshot from hosting competitive inference services against OpenAI, Anthropic, and Google.

Key details

  • Compute scarcity in 2026 — including global memory shortages and Mac Studio M3 Ultra sellouts — is forcing Chinese labs to push compute costs onto consumers, and this supply‑chain strain advantages U.S. labs that have stronger partnerships with U.S. hyperscalers and allied manufacturers.
Source evidence

Without a doubt, the biggest misconception about U.S. export controls on AI chips is that they must be predicated on some belief in "AGI" or a first-mover advantage in an AI capabilities race.

Control over computational power has nothing to do with belief in AGI. The fact is that compute availability is constraining global deployment of AI services, period. A lack of compute is the singular bottleneck preventing any developer — including DeepSeek, Qwen, Moonshot, and other big Chinese labs — from hosting AI services and selling them to global publics in a manner competitive with OpenAI, Anthropic, and Google. Compute is the substrate of the global AI diffusion race.

"But Ryan," you say, "China's models are open-source! The point is for people to run them locally." Yeah. Here's what you're missing: Many people who would go out and run a mid-sized Qwen model simply don't have a computer capable of doing so. By all means, I encourage you to try! You literally can't even buy a Mac Studio with an M3 Ultra any more. They aren't making them, because the world is completely sold out of memory.

Chinese AI labs face a scaled-up version of this problem. Because they don't have large clusters needed to host demanding inference services, they have effectively been forced to push compute costs onto consumers. In 2026, when every compute-adjacent supply chain — down to the last mom-and-pop epoxy resin manufacturer in Japan — is stretched to its limits, this is great for American AI labs (who have more reliable access to compute, thanks to strong partnerships with U.S. hyperscalers and allied manufacturers), and bad news for pretty much everyone else.

No set of talking points about export controls' second-order effects (e.g. "accelerating innovation"), no matter how clever, was going to obfuscate the fact that AI still needs compute to function during this critical period of global infrastructure build-outs.

Everyone, including and especially China, will need boatloads of compute — much more than has ever been previously installed.