Economist's year-long AI paper experiment: rigorous theory, human insight

joshgans · x · 2026-09-11

Economist Josh Gans reflects on his full experiment writing economics papers with AI since late 2024, with clear upsides and downsides:

Fast but shallow: AI let him pump out papers quickly in year one, but it pushed him toward low-quality ideas that never landed at top venues.
Referee whiplash: After pivoting to better ideas, AI excelled at churning out the extensions and robustness checks referees always demand — producing enormous papers that referees then asked to be more focused, ironically since they usually cause those extras.
The writing itself: Since agentic AI arrived last year, the prose beats the average field paper, yet Gans — a prolific referee himself — concludes referees don't reward AI-written papers, partly attribution bias, partly distaste for AI's common tropes.
Core takeaway: AI can now produce rigorous theory, but idea quality, focus selection, and insight remain entirely human. The challenge is staying self-critical with a "seductive beast."
Current practice: Back to artisanal writing — AI drafts, then heavy editing; if that fails he'll write first drafts himself and report back.

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