Economists debate how to study AI's economic impact before clean identification arrives
robseamans · x · 2026-09-21
Alex Olegimas argues that while economists are trained to prize clean causal identification—instruments, parallel trends—such studies take years or decades, and AI's economic impact demands usable signals right now. Peter McCrory adds practical implications: prioritize analyses updatable with new data, work in the open, acknowledge uncertainty with falsifiable predictions, and stay earnest. Rob Seamans shared in agreement.
More from AGI Musings
- Why people want to believe AI is fake: a "marketplace of rationalizations" — xuanalogue · 2026-09-21
- Reddit debates whether the AI 2027 timeline essay will hold up as the pivotal year nears — animallover301 · 2026-09-21
- Zvi: AI Twitter awash in performative confusion over Econ 101 basics — TheZvi · 2026-09-21
- Dan Hendrycks explores moral philosophy innovation for alignment beyond EA — burny_tech · 2026-09-21
- Peer reviewer: papers feel weaker post-ChatGPT, with citation-stuffing reviews — paigeinsf · 2026-09-21
- Gary Marcus calls for moratorium on calling 2024 papers 'just published' — burny_tech · 2026-09-21