Why AI Safety Won't Make America Lose The Race With China
Deep Dives
Explore related topics with these Wikipedia articles, rewritten for enjoyable reading:
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TSMC
12 min read
The article credits TSMC as central to America's compute advantage in the AI race. Understanding the Taiwan Semiconductor Manufacturing Company's history, global dominance in advanced chip fabrication, and geopolitical significance provides essential context for why chip production is so strategically important.
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Technological singularity
12 min read
The article discusses how belief in superintelligence and the technological singularity affects strategic calculations in the AI race. Understanding this concept—the hypothetical point where AI triggers runaway technological growth—illuminates why some view AI safety differently than China's 'practical' approach.
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Made in China 2025
10 min read
The article describes China's 'fast follow' strategy and their ten-year plan to catch up in chip production. Made in China 2025 is the actual strategic plan underlying these efforts, detailing China's industrial policy goals including semiconductor self-sufficiency.
If we worry too much about AI safety, will this make us “lose the race with China”1?
(here “AI safety” means long-term concerns about alignment and hostile superintelligence, as opposed to “AI ethics” concerns like bias or intellectual property.)
Everything has tradeoffs, regulation vs. progress is a common dichotomy, and the more important you think AI will be, the more important it is that the free world get it first. If you believe in superintelligence, the technological singularity, etc, then you think AI is maximally important, and this issue ought to be high on your mind.
But when you look at this concretely, it becomes clear that this is too small to matter - so small that even the sign is uncertain.
The State Of The Race
We can divide the AI race into three levels: compute, models, and applications2. Companies use compute - chips deployed in data centers - to train models like GPT and Claude. Then they use those models in various applications. For now, those applications are things like Internet search and image generation. In the future, they might become geopolitically relevant fields like manufacturing and weapons systems.
Compute: America is far ahead. We have better chips (thanks, NVIDIA) and can produce many more of them (thanks, TSMC). Our recent capex boom, where companies like Google and Microsoft spend hundreds of billions of dollars on data centers, has no Chinese equivalent. By the simplest measure - total FLOPs on each sides - we have 10x as much compute as China, and our advantage is growing every day. A 10x compute advantage corresponds to about a 1-2 year time advantage, or an 0.5 - 1 generation advantage (eg GPT-4 to GPT-5).
Models: The quality of foundation models - giant multi-purpose AIs like GPT or Claude - primarily depends on the amount of compute used to train them, so America’s compute advantage carries over to this level. In theory, clever training methods and advanced algorithms can make one model more or less compute-efficient than another, but this doesn’t seem to be affecting the current state of the race much - most advances by one country are quickly diffused to (or stolen by) the other. Despite some early concerns, neither DeepSeek nor Kimi K2 Chinese models provide strong evidence of a Chinese advantage in computational efficiency (1, 2).
Applications: This ...
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