New "Bottleneck Pressure" Metric Uses 288M Job Postings to Measure Where AI Raises Value of Work It Can't Do

Soumitra Shukla, a researcher at the Burning Glass Institute, released a 104-page paper on October 22 titled "Technology Bottlenecks: Measuring Where AI Could Raise the Value of Work It Cannot Yet Do," proposing a new metric complementary to the mainstream "AI exposure" measure: "bottleneck pressure." The key finding: as AI accelerates workflows, it is the bottleneck tasks AI cannot yet perform that determine where value flows—and their value will rise accordingly.

Confirmed

• The paper is authored by Soumitra Shukla of the Burning Glass Institute, published October 22
• Full title: "Technology Bottlenecks: Measuring Where AI Could Raise the Value of Work It Cannot Yet Do," 104 pages
• The metric is built on 288 million job postings
• It is positioned as complementary to, not a replacement for, traditional "AI exposure" metrics: exposure measures how much a job is affected by AI, while bottleneck pressure measures where AI's sticking points most increase the value of work

Why It Matters

• While mainstream discussion focuses on "which jobs will be replaced by AI," this paper shifts to "how much more valuable the parts AI can't do will become," offering a fresh lens for assessing AI's impact on the labor market
• A measurement method based on 288 million job postings can give policymakers, businesses, and researchers a finer-grained, task-level value analysis tool

The sourcerefs correspond to the original publishers' posts.

2026-10-22 ~ 2026-10-22 · 5 related posts

• [source] Paper: 288M job postings reveal where AI raises the value of work it can't do — soumitrashukla9 · 2026-10-22
• [source] New Paper Uses 288M Job Postings to Measure Where AI Bottlenecks Raise Work Value — soumitrashukla9 · 2026-10-22

3 near-duplicate retellings: soumitrashukla9 · soumitrashukla9 · soumitrashukla9