For years, AI industry watchers of all stripes have been warning of a coming jobs apocalypse driven by ultra-intelligent AI systems that will be able to replicate most human tasks more cheaply. Now, newly updated research from Stanford University economists suggests AI seems to be causing significant entry-level job losses for younger workers in some fields, even as older workers appear largely unaffected so far.
The August 2026 edition of “Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence” updates and revises a paper of the same name published last year with fresh data and refined statistics. In that update, the Stanford researchers find the employment trends they identified for entry-level workers last year are persisting and expanding. Specifically, employment levels for workers aged 22 to 25 in the most “AI-exposed” occupations are now 19 percent below those of their peers in fields less exposed to AI disruption.
Last year, that gap measured just 13 percent.
To determine those numbers, the researchers used a large subsample of the anonymized, high-frequency payroll data regularly aggregated by HR management company ADP. The team then rated each occupation’s “exposure” to AI disruption using both a potential labor market impact gauge established by previous researchers (we took a somewhat critical look at that previous research earlier this year) and the Anthropic Economic Index, which looks at how various occupations actually use the Claude model in their everyday work (Google released a similar report based on occupational Gemini usage last month).
When crunching the numbers economy-wide, the researchers found little to no difference in relative overall employment between the jobs judged most and least affected by AI on these metrics. When separating out workers aged 22 to 25, though, the researchers found that, since 2022, employment in the top 40 percent of “AI-impacted” jobs had fallen by about 11 percent. In the 60 percent of jobs with the least AI impact, by contrast, total employment for those young workers grew by 10 percent over the same period.
Digging deeper into the data, the researchers found that this phenomenon is mainly manifesting itself through lower hiring rates for entry-level workers in AI-impacted fields, rather than increased firings or employees quitting. They also found that the labor market effects among this age group were mostly seen in lower overall employment, rather than reduced pay rates.
But not all jobs that show potential for AI “disruption” are created equal, the researchers found. In its Economic Index, Anthropic differentiates between queries related to tasks that are “automative” (i.e., fully replacing work previously done by a human) or “augmentative” (i.e., helping human workers be more effective at tasks they are still needed for). By this measure, jobs like “accountants and auditors” and “receptionists and information clerks” were among those judged most susceptible to AI automation, while jobs like “chief executive” and “registered nurse” were among those using AI augmentation most often.
Unsurprisingly, jobs where AI automation is prevalent are the ones showing the worst relative employment levels for entry-level workers these days. “The findings are consistent with automation-oriented uses of AI substituting for labor while complementary uses are associated with flat or rising employment,” the researchers write.
The researchers also theorized that entry-level workers could be especially affected by AI’s impact on jobs requiring heavily “codified” knowledge—the kind of “formal, standardized, documented knowledge that can be taught through education, textbooks, or written procedures.” That would contrast with jobs where AI mainly complements an experienced worker’s more “tacit” knowledge, which is overwhelmingly “acquired through practice, mentorship, and repeated exposure to real situations,” the researchers write.
To test this hypothesis, the researchers used the required level of formal education in O*NET’s extensive occupational database as a proxy for how reliant that job is on codified knowledge. Breaking out the employment data, the researchers found that “occupations with higher codified knowledge have slower entry-level employment growth, while occupations with higher tacit knowledge have faster employment growth for mid-career and senior workers.”
At the same time, the researchers found that higher education might still serve as a buffer against the employment effects the Stanford researchers identified. As they write, occupations with a higher share of college graduates showed more “muted differences between more-exposed and less-exposed occupations” regarding AI. In jobs with few college graduates, on the other hand, “the least AI-exposed occupations [saw jobs] growing and the most exposed occupations [were] declining in employment.”
In a recent interview with The Washington Post, lead researcher Erik Brynjolfsson warns that the current trends suggest a near future in which jobs for those employed in the pre-AI era largely persist while many jobs for the incoming working-age cohort start disappearing. “The entry-level effects we’re measuring are real, persistent and widening,” he said, “and I’m more worried than I was about a labor market that keeps its overall employment level while quietly closing the on-ramp for people starting their careers.”
