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Is AI taking entry-level jobs? The evidence shows real pressure on some early-career groups, including weaker hiring and first-job outcomes in recent U.S. studies. But it does not show that AI has broadly eliminated entry-level work. The clearest signal so far is uneven: hiring flows and job content may be changing before an overall employment effect can be established, and the studies cannot cleanly separate AI from other economic forces.
What the strongest U.S. studies found
Two 2026 U.S. Census Bureau working papers find weaker outcomes for groups defined in different ways. One tracks workers by age and the AI exposure of their industry-state cells; the other examines college graduates by the AI exposure of their majors. Neither measures every entry-level worker, and exposure is a proxy—not proof that an employer adopted AI or replaced a worker with it.
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Workers aged 22–24: fewer hires in the most-exposed cells
In an April 2026 working paper, Lee C. Tucker reports that employment among workers aged 22–24 in the most AI-exposed quintile of U.S. industry-state cells fell 12% over the 10 quarters after ChatGPT’s introduction. The decline was primarily associated with fewer hires. The hiring rate largely recovered by early 2025, but from a smaller employment base. Tucker also notes signs of earlier shifts around the start of the COVID pandemic.
“The rate of hiring largely recovered by early 2025, attributable to a smaller employment base.”
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That is language from the paper’s abstract. It captures an important distinction: a hiring rate can recover without the employment lost during the earlier slowdown immediately returning.
Recent college graduates: weaker initial employment and earnings
A separate Census Bureau working paper, published in September 2026, estimates that the most AI-exposed decile of college majors had a five-percentage-point fall in the likelihood of initial employment and a 13% decline in full-quarter initial earnings in adjusted estimates. About half of the earnings decline was linked to lower earnings within industries employing these graduates; the rest was linked to movement into lower-wage sectors such as restaurants and retail. The authors say the effects attenuate as graduates move further from labor-market entry.
These results describe initial outcomes for graduates grouped by major exposure, not a measured count of jobs that AI directly replaced. The earnings result also reflects both pay within industries and where graduates found work.
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What other hiring and employment measures show
The figures below answer different questions. They should not be combined into a single estimate of AI-related job losses: some measure observed employment, others hiring plans, postings, or employer expectations.
| Source and population | Measure and reported result | What it does—and does not—show |
|---|---|---|
| UK government, June 2026 snapshot; 38 tracked entry-level occupations | Overall UK hiring was down 14% year-on-year in April 2026; 30 of 38 occupations declined. Accounting, graphic design, and software engineering were among the steepest declines, while sales and customer-facing roles were growing. | A hiring trend across the tracked occupations, not a finding that AI caused the declines. The department says more research is needed to assign a cause. |
| NACE Spring Update; U.S. Class of 2026 | Projected 5.6% more graduate hiring; 10.5% of entry-level job posts required AI skills. | The projection and the posting share are separate measures, not contradictory estimates of the same outcome. The update had 185 respondents, including 142 employer members. The hiring projection was initially collected in August–September 2025 and updated using a survey fielded February–March 2026. |
| Statistics Canada; workers across occupations, November 2022–December 2025 | Employment generally grew regardless of occupational AI exposure, while younger workers generally had weaker growth. | A national employment pattern that does not isolate AI’s effect. Statistics Canada cautions that pandemic-related adjustments, demographics, trade tensions, and other economic changes may also matter. |
| Federal Reserve Board, 2026 analysis; computer programmers | Coder employment continued to grow after ChatGPT’s introduction, but more slowly than before 2022. An industry-shock control suggested the slowdown was occupation-specific rather than caused by exposure to slowing industries. | Evidence about one exposed occupation, not entry-level work as a whole; it does not by itself establish that AI caused the slowdown. |
What employer surveys say about AI and junior work
Expectations are mixed, not observed job losses
Strada surveyed nearly 1,500 U.S. executives and senior talent leaders. More respondents expected AI to increase rather than reduce entry-level hiring in 2026, while many said AI was changing the tasks junior employees perform. Those responses describe expectations and reported task changes; they do not establish what hiring or employment actually did because of AI.
North Carolina offers a local counterpoint
A 2026 North Carolina employer survey found that 30% of employers currently used AI and 43% planned to start or expand use. Among employers using AI, nearly all reported no change in total employment due to AI in the previous year, and 73% expected no change in demand for entry-level or lower-skilled workers. These are local survey findings, not a national estimate or a forecast proven by later hiring outcomes.
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Why the evidence does not settle causation
Several findings are consistent with AI-related pressure: the Census studies show weaker outcomes in groups with higher measured exposure, and the UK snapshot records declines across many tracked entry-level occupations. But consistency with an AI effect is not proof that AI caused those outcomes. Exposure measures classify industries, majors, or occupations; they do not verify that a specific workplace introduced AI or that a particular role disappeared because of it.
- The measures differ. Employment counts the workers in jobs; hiring rates capture flows into jobs; initial-employment probability and earnings describe graduates’ first outcomes; postings show stated requirements; surveys report expectations or recollections.
- The populations differ. Workers aged 22–24 are not interchangeable with recent college graduates, entry-level occupations, programmers, or all young workers.
- Other changes overlap the timeline. The early-career Census paper notes earlier shifts around COVID, and Canada’s statistical agency says its recent patterns cannot be separated from pandemic adjustments, demographic change, trade tensions, and other factors.
- Sector movement matters. In the graduate study, some of the initial earnings decline was associated with graduates moving into lower-wage sectors, not just earnings changing within industries.
The result is a credible warning signal, not a clean estimate of jobs lost to AI. A broader, global count of entry-level jobs specifically eliminated by AI is not established by these findings.
What this means for people starting a career
The studies point to an uneven market rather than one uniform outcome. Some routes into work may be tightening, while others are growing; in the U.S. graduate study, outcomes also differ by major and sector. At the same time, employer reports suggest that junior work may be changing in content even where employers do not expect headcount to change.
For a job seeker, treat AI requirements in postings as a signal about the tools or tasks an employer may value—not evidence that every entry-level role requires advanced AI expertise. Pay attention to the actual responsibilities, required skills, and experience level in each posting. For employers and educators, changing task composition matters alongside hiring totals: an entry-level role can be reshaped without disappearing, although that does not guarantee that the same number of first jobs will remain available.
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