Paper: 288M job postings reveal where AI raises the value of work it can't do
soumitrashukla9 · x · 2026-10-22
Soumitra Shukla of the Burning Glass Institute released a 104-page paper, "Technology Bottlenecks," introducing "bottleneck pressure" — a new measure asking not what AI can do, but where the marginal value of AI-infeasible work rises as AI speeds up the rest of a job.
Key points:
- Maps 288 million US job postings line-by-line to ONET tasks, combined with GPT-4 feasibility ratings from Eloundou et al. (2024).
- About 90% of labeled task mentions fall outside the posting occupation's own ONET task list, showing advertised work diverges sharply from official definitions.
- Roughly 53% of mention-weighted variation in bottleneck pressure comes from within tasks, across occupations — the same task faces very different pressure in different jobs.
- Occupation-level pressure peaks where advertised work splits evenly between AI-feasible and infeasible tasks, so rankings differ from traditional exposure measures.
- As pressure rises, interpersonal and supervisory work makes up more of the AI-infeasible work; physical work less.
The author cautions the measure captures potential pressure; whether it materializes depends on how firms integrate AI, and measuring integration bottlenecks needs more work.
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