Paper: "bottleneck pressure" metric shows where AI-infeasible work gains value as AI speeds up the rest
soumitrashukla9 · x · 2026-10-28
A new paper by Soumitra Shukla (Burning Glass Institute) introduces "bottleneck pressure": the rising marginal value of work AI cannot yet do as AI speeds up the rest of the job.
- Built from 288M US job postings mapped line-by-line to ONET tasks, combined with GPT-4 feasibility ratings from Eloundou et al. (2024).
- For AI-infeasible tasks, pressure depends on the exposure of the occupation posting them, so the same task faces very different pressure across jobs; occupation-level pressure = exposure × share of AI-infeasible advertised work.
- About 90% of matched task mentions fall outside the posting occupation's own ONET task list; 53% of mention-weighted pressure variation comes from within tasks, across occupations.
- Pressure peaks where advertised work is evenly split between AI-feasible and infeasible tasks, so occupation rankings differ from exposure alone. As pressure rises, interpersonal and supervisory work makes up more of the AI-infeasible work, physical work less.
- Reporting pressure alongside exposure shows both AI's scope and where improving what it can't do pays off most.
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