New paper uses 288M job postings to measure where AI raises the value of work it can't do
soumitrashukla9 · x · 2026-10-22
A new paper, "Technology Bottlenecks," flips the standard AI-exposure question: instead of asking what AI can do, it measures where the marginal value of AI-infeasible tasks may rise as AI speeds up the rest of a job.
- Method: 288 million US job postings mapped line by line to ONET tasks, combined with GPT-4 feasibility ratings from Eloundou et al. (2024), yield a task-level "bottleneck pressure" measure for each occupation.
- Key insight: the same task can be a bottleneck in one occupation but not another; looking inside jobs beats assigning a single exposure score per occupation.
- Economist Ronnie Chatterji highlighted the paper, noting his own team is working on similar approaches.
Related event: New Paper Uses 288M Job Postings to Measure "Bottleneck Pressure" of AI(8 posts)→
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