New paper: 'bottleneck pressure' measures where AI will raise the value of work it can't yet do
soumitrashukla9 · x · 2026-11-10
Soumitra Shukla's new paper "Technology Bottlenecks" flips the usual AI exposure framing: 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.
Method and key findings:
- Maps 288 million US job postings line by line to ONET tasks, combined with GPT-4 feasibility ratings, to construct "bottleneck pressure" per task per occupation
- The same AI-infeasible task faces very different pressure depending on the occupation posting it
- 90% of matched task mentions fall outside the posting occupation's own ONET task list; 33% of mention-weighted variation in pressure comes from within-occupation tasks
- Pressure peaks in occupations split about evenly between AI-feasible and infeasible tasks; as pressure rises, interpersonal and supervisory work makes up more of the infeasible portion
Reporting bottleneck pressure alongside AI exposure shows both the scope for AI to perform work and where improving what it cannot do may pay off most.
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