How to read shaky AI-job-impact studies: Bayesian updating beats gold standard
alexolegimas · x · 2026-09-21
A methodology debate over a paper linking AI exposure to weaker early-career employment: alexolegimas argues no such paper has gold-standard methodology — read them like a Bayesian, where six papers from different angles showing the same thing justify updating beliefs, since good causal data won't exist until the results are obsolete. Economist DavidSimon is skeptical, noting messy pre-trends in employment-to-population ratios and effect timing that doesn't line up with when AI should matter. danielrock adds that publication norms force papers to overclaim causality; the honest framing is 'AI-exposed early-career workers are having a tougher time, causality unclear.'
More from AGI Musings
- Why Is Inkling 2 Small Trained on So Few Tokens Yet Still Competitive? — georgejrjrjr · 2026-11-12
- Researcher willing to bet $100K the intelligence explosion won't happen next year — NathanpmYoung · 2026-11-12
- ICLR reviewer: the worst parts of my job are reading and answering AI slop — chhaviyadav_ · 2026-11-12
- Agent Swarms Are Autonomously Hacking Critical Infrastructure Run by Underpaid Devs — menhguin · 2026-11-12
- Fermi paradox twist: advanced civilizations may go digital after developing AI — VraserX · 2026-11-12
- Mark Cuban: AI could make healthcare costs 'drop like a rock' — alvelda · 2026-11-12