Import AI 459: AI oversight is difficult; scaling laws for protein folding models; and pricing the extinction risk of AI systems
Import AI reports that the U.S. AI economy is expanding at an unprecedented rate, with nominal AI GDP projected at $250 billion in 2025 and growth of roughly 2,600 % per year in quality‑adjusted terms. The paper notes that traditional GDP metrics miss this surge because AI’s main impact is in inference, where per‑unit prices fall as capability rises. It calls for new AI satellite accounts, better primary data on training versus inference compute, and inclusion of AI capacity in medium‑term projections. The newsletter also highlights challenges in automated alignment research, noting that errors may be harder to spot than human‑generated mistakes and suggesting measurement, generalization, and scalable oversight experiments to improve safety.
The Core Development
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The AI economy in the US is growing at 2,000% a year: …The more directly you measure the AI economy, the weirder and more unprecedented it seems to get… Economists with the University of Virginia* and Anthropic, and the Bank of Canada have written a paper outlining both the tremendous growth of the emerging “AI economy” in the US, and wrestling with why this growth is hard to see in aggregate GDP statistics. “The AI economy in the United States has been growing at an unprecedented rate, but this extraordinary growth is largely invisible in conventional GDP statistics,” they write. “Treating the AI sector as a coherent economic entity yields preliminary estimates of nominal AI GDP at approximately $250 billion in 2025, growing at roughly 2,600 percent per year in quality-adjusted real terms.”
Why it’s hard to see: There are a couple of factors here - one is that though the datacenter building boom is large it still isn’t quite large enough to uplift GDP significantly. By comparison, where the majority of AI’s economic impact is taking place is in AI inference - the usage of AI’s systems - but there are confounding factors here as it relates to GDP measurement: “Nominal AI revenues grow only moderately because per-unit prices for any given level of AI capability fall almost as fast as quality-adjusted output rises,” they write.
Strategic Perspective & Ecosystem Impact
If we can’t measure this, we might end up surprised in a way that’s hard to recover from: “AI is the latest in a series of fast-moving technologies that have raised measurement concerns; semiconductors and the internet generated similar debates in their time,” they write. But a key difference is that AI as a technology might have a far bigger impact on labor than these other technologies. “In the prior episodes, the rapidly improving technology was a complement to human labor at the aggregate level,” they write. “AI is the first plausible candidate for large-scale technological mismeasurement in which the rapidly improving sector may become a substitute for human labor”.
Nominal compute spending : US compute spending rose from $37 billion in 2023 to $90 billion in 2024 to $219 billion in 2025.
Raw compute capacity : Due to efficiencies in newer chips, actual capacity grows even faster than spending: “US AI computing capacity grew at more than 200 percent per year”.
Why it matters
The hidden scale of AI’s economic impact could lead to unanticipated labor‑market shifts and fiscal policy gaps, while robust oversight research is essential to safely deploy increasingly capable systems.