A critique of two contrasting research papers on AI's impact on US employment trends, along with caveats from a Stanford study on jobs in AI-exposed fields
Some top economists claim AI is now destroying jobs for a subset of Americans. Are they right? — The debate over whether AI …
NoahpinionNoah Smith
Context & Ripple Effects
The article sits in a long-running disagreement over how to measure AI’s labor effects. Earlier coverage stressed that automation operates mainly at the task level rather than cleanly eliminating whole occupations, complicating occupation-level claims about displacement the task-versus-occupation distinction.
Its critique of two papers, alongside Stanford’s caveats on AI-exposed work, matters because it challenges how confidently observed employment changes can be attributed to AI rather than treated as a settled causal result.
First-order effects
Claims that AI is already destroying jobs for a subset of Americans face a more contested evidentiary basis; readers, researchers, and policymakers must distinguish reported correlations from conclusions supported by the underlying studies.
Stanford’s caveats make AI exposure a less definitive proxy for job loss, limiting what can be inferred immediately about workers in exposed fields.
Second-order effects
Researchers and forecasters are pushed toward finer-grained measures of tasks, worker cohorts, and alternative explanations, rather than relying on broad occupation exposure alone a research debate centered on tasks rather than occupations.
Employers and workers evaluating AI-related workforce risks get less support for one-size-fits-all conclusions: exposure can indicate where to investigate, not by itself what employment outcome will follow.
Third-order effects
If this measurement debate persists, the labor-market effects of AI will be assessed through competing empirical methods rather than a single headline metric, slowing consensus on when intervention is warranted.
The durable shift is toward treating AI’s labor impact as a question of job redesign and worker mobility as well as displacement—a framing later echoed in research on at-risk workers’ ability to find new jobs research on mobility among workers most exposed to AI.
The trend: AI employment analysis is moving from broad exposure-based predictions toward contested, task-level evidence about who is affected and why.
A second paper also finds Generative AI is reducing the number of junior people hired (while not impacting senior roles). This one compares firms across industries who have hired for at least one AI project versus those that have not. Firms using AI were hiring fewer juniors [ima…
🚨1/9 In a new WP, @LichtingerGuy and I use detailed LinkedIn résumé + job-posting data on ~285k U.S. firms (2015-2025) to study a debated question: how does generative AI adoption affect entry-level employment? [image]
AI probably explains part of this but not as much as people think. We were hiring juniors at Amazon making 300k TC during peak zirp who didn't know how to use git and actually slowed me down. Leadership realized smaller experienced teams outperformed.
Even if AI doesn't kill jobs, some people will keep pointing to any group of workers who've suffered slightly worse outcomes recently and yelling “It's AI! It's AI! It's finally coming for our jobs!!” This will go on forever. https://www.noahpinion.blog/ ...
A new paper claims that AI is destroying jobs for young workers. But many details of the paper leave me skeptical of the result. https://www.noahpinion.blog/ ...
@TonyBiasotti @mattyglesias There are occasionally some jobs that get eliminated by tech. Telephone operator, for instance. More commonly it's just a shift in the number of people doing the job. But the most common outcome is a shift in the task mix of a job.