OpenAI's Trask debates 'economic FOOM': job displacement takeoff much slower than classic RSI

OpenAI researcher iamtrask (Andrew Trask) held a brief debate with Tom Davidson and Brad Carson on recursive self-improvement (RSI) risks and the timescale of AI takeoff. Trask contrasted two takeoff narratives: the classic RSI fear of algorithms building better algorithms in a cascading chain, ending with 'everyone gets hacked in a day and it's all over'; versus Davidson et al.'s 'economic FOOM' path—there is a threshold beyond which AI's intelligence translates into revenue, and once crossed, AI displaces large numbers of legitimate jobs, creating a recursive feedback loop at the economic level.

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Why it matters

The debate touches on a key fault line in AI safety and policy discussions: is takeoff a fast, algorithm-driven 'hard takeoff' or slow, economically driven gradual substitution? The two assumptions imply very different governance tools—with slow takeoff, labor organizations, regulation, and other social mechanisms have a window to intervene; with hard algorithmic RSI, those mechanisms become meaningless. Trask's position offers a clear framework for distinguishing the two timescale hypotheses when assessing RSI risk.

2026-11-10 ~ 2026-11-10 · 7 related posts

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