For top-tier pros, AI won't just slowly take your job—your value will explode for a minute and then fall off a cliff.
March 26, 2026
Original Paper
<div> Estimating Within-task Competence Heterogeneity (κ): </div> <div> A Cross-occupation Approach with Implications for AI Displacement </div>
SSRN · 6347218
The Takeaway
The paper finds that for cognitively complex jobs like law or medicine, the demand for human experts will rise superexponentially as AI tools make them more productive, only to suffer a discontinuous collapse the moment the AI reaches a specific competence threshold. It suggests 'Generation AI' faces an all-or-nothing employment future.
From the abstract
The parameter κ = (s max-µ)/σ governs the shape of AI-driven labor displacement in Deobhakta (2026). When κ is small, displacement follows the monotonic stepfunction of Acemoglu and Restrepo (2018). When κ is large, displacement follows a spike-cli trajectory: per-expert value rises superexponentially before discontinuous collapse. This paper provides the rst cross-occupation empirical estimates of κ, using two complementary approaches: (1) within-occupation wage percentile ratios from BLS Occup