US AI Dominance Is Over: Here's Why | Stop Guessing Whether a Cheaper Model Can Do the Job

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• Core argument: rejects both extremes in the Chinese-model debate — treating one good DeepSeek/Qwen/GLM/Kimi/MiniMax answer as proof the American frontier has been “caught,” or one censorship failure as proof the whole Chinese stack is unusable — and argues for systematic bakeoff testing over ideology or anecdote.
• Named metric: “cost per accepted result” — token pricing alone misleads because it excludes review time; a model that’s cheaper per token can still be more expensive once verification labour is counted.
• The Ringer experiment: a 34-task job run across GLM-5.2, GPT-5.5, Grok 4.5, and Composer 2.5 Fast produced 213 verified quotations, 13 of which were fabricated — total run cost ~$8.
• Covers mixture-of-experts architecture and hardware/deployment tradeoffs (API vs third-party hosting vs self-hosting) — Chinese models suit bounded, high-volume, checkable tasks but remain risky where errors are costly.
• Practical takeaway: ships a bakeoff toolkit (validator, manifest, score sheet, test fixtures) for running your own model evaluation rather than deciding by geography or benchmark headlines; Nate reports using Chinese models “aggressively” in some workflows while keeping job, model, and deployment decisions separate.