Very happy to share a new paper, “Technology Bottlenecks: Measuring Where AI Could Raise the Value of Work It Cannot Yet Do.” AI exposure measures ask about the work AI is capable of doing. We ask instead where the marginal value of work AI cannot yet do may rise as AI speeds up the rest of the job. To do so, we employ 288 million U.S. job postings mapped line by line to their closest O*NET tasks and GPT-4 feasibility ratings from Eloundou et al. (2024) to construct a measure, “bottleneck pressure,” for each task in each occupation that posts it. Using a simple task-based framework, we derive this pressure at the task level, showing that for an AI-infeasible task it is the exposure of the occupation that posts it, so the same task can face very different bottleneck pressure in different jobs. Reporting bottleneck pressure alongside AI exposure shows both the scope for AI to perform work and where improving the work it cannot do may pay off more as capabilities expand.

🧵1/10 Given the recent reactions of mathematicians over rapid advances in AI, I think it is important to remember that technological change has historically been a very personal event for people. Over the history of technological progress, looms have fallen silent, switchboards have gone dark, and factory lines have shrunk bringing with them a human crisis of identity and what it means to be a master of one's craft.

Alex Imas told me directly that his Twitter/X account has been hacked. Please don’t click links or respond to DMs from his account until he regains access. @Support, can you help? If anyone has a contact at X, please let me know. Please share.

1/13 Despite his wealth and polish, Jay Gatsby is dismissed in Fitzgerald's novel as "Mr. Nobody from Nowhere." Biographical origins can shape social evaluation even when credentials are strong. Yesterday at Oxford, I spoke about how a sense of who "fits" can be an important, yet biased, determinant of hiring at multinationals.