New paper builds 'bottleneck pressure' metric from 288M job postings to find where AI raises the value of work it can't do
soumitrashukla9 · x · 2026-10-22
Soumitra Dutta released a new paper, 'Technology Bottlenecks: Measuring Where AI Could Raise the Value of Work It Cannot Yet Do,' endorsed by Rob Seamans.
- Inverted lens: instead of asking what AI can do, it measures where the marginal value of AI-infeasible work rises as AI speeds up the rest of the 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), to construct a 'bottleneck pressure' measure per task per occupation.
- Framework: for an AI-infeasible task, bottleneck pressure derives from the AI exposure of the other tasks in that occupation.
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