New paper uses 288M job postings to measure where AI raises the value of work it can't do
robseamans · x · 2026-11-05
A new paper, "Technology Bottlenecks," flips the usual AI-exposure question: instead of asking what AI can do, it measures where the marginal value of work AI cannot yet do may rise as AI speeds up the rest of the job.
- Introduces "bottleneck pressure": as AI covers more surrounding tasks in a job, AI-infeasible tasks become bottlenecks whose value rises.
- Theoretically, for an AI-infeasible task, this pressure equals the AI exposure of the occupation posting it.
- Built by mapping 288 million US job postings (2018–mid-2026) to ONET tasks with GPT-4 feasibility ratings from Eloundou et al. (2024).
- Key insight: the same task carries different bottleneck pressure in different jobs — the magic happens at the task level.
Related event: New paper proposes "bottleneck pressure": tasks AI can't do gain value(2 posts)→
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