Common Questions

Who is Anders Humlum?

A labour economist at the University of Chicago who co-authored the NBER working paper finding that LLM adoption shifts tasks rather than destroying jobs.Source: NBER

What did the Humlum Vestergaard paper find?

LLM adoption is linked to occupational switching and task restructuring but without net changes in hours or earnings, based on large-scale administrative data.Source: NBER

Does AI cause job losses according to research?

The Humlum-Vestergaard NBER paper found AI changes tasks, not employment levels. Oxford Economics found fewer than 5% of firms report direct AI-driven reductions. Yale identified 'AI washing' in layoff claims.Source: NBER / Oxford Economics / Yale

Background

Humlum is a labour economist at the University of Chicago. His research with co-author Emilie Vestergaard used large-scale administrative data to track workers exposed to LLM tools, measuring actual employment outcomes rather than survey-based sentiment. The methodology distinguished his findings from headline layoff counts.

Anders Humlum co-authored the NBER working paper that reframed the 2026 AI/jobs debate, finding that LLM adoption is linked to occupational switching and task restructuring but without net changes in hours or earnings. The paper shifted the argument from "AI destroys jobs" to "AI changes what jobs involve," providing the empirical anchor for a debate otherwise dominated by corporate announcements.

The paper sits at the centre of a three-way empirical consensus with Oxford Economics (fewer than 5% of firms report AI-driven reductions) and Yale Budget Lab ("AI washing" in corporate restructuring). Together they established that the gap between AI layoff rhetoric and measured displacement is wide.

How the World Sees Them

Economists

The paper's use of administrative data rather than surveys gives it methodological weight that headline layoff counts lack.

Policy makers

If AI shifts tasks rather than eliminating jobs, retraining programmes matter more than job protection legislation.

Tech industry

The task-shift finding supports continued AI adoption without the political cost of a mass-unemployment narrative.

Labour advocates

Critics argue aggregate stability masks individual displacement. Workers who switch occupations may take pay cuts.