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Is AI taking entry-level jobs? The evidence shows real pressure on some early-career workers and occupations, but it does not establish that AI has broadly eliminated entry-level work. Hiring and first-job outcomes have weakened in some exposed groups; researchers still cannot separate AI’s contribution from other forces with confidence.

What do the U.S. Census studies show?

Employment and hiring among workers aged 22–24

In an April 2026 U.S. Census Bureau working paper, Lee C. Tucker found that employment among workers aged 22–24 in the most AI-exposed quintile of industry-state cells fell 12% over the 10 quarters after ChatGPT’s introduction. The decline was primarily associated with fewer hires, rather than a reported wave of existing workers losing jobs. Tucker writes in the paper’s abstract: “The rate of hiring largely recovered by early 2025, attributable to a smaller employment base.” In other words, a recovery in the hiring rate did not erase the earlier drop in employment.

The result is an important warning about the early-career pipeline, not proof that AI caused every part of the decline. Tucker also notes signs of employment and hiring shifts around the start of the COVID pandemic, which complicates comparisons that begin near ChatGPT’s release.

Initial outcomes for college graduates

A separate Census Bureau working paper, published in September 2026, examined college graduates by major rather than young workers by age and industry. In adjusted estimates, graduates from the most AI-exposed decile of majors saw a five-percentage-point decline in the likelihood of initial employment and a 13% decline in full-quarter initial earnings. These are distinct outcomes: the first concerns whether graduates found an initial job, while the second concerns earnings for a full quarter.

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About half of the earnings decline was associated with lower earnings within industries employing these graduates. The other half was associated with graduates moving into lower-wage sectors, including restaurants and retail. The authors report that the effects attenuate as graduates move further from labor-market entry. The findings therefore point to weaker starts for a particular group of graduates, not a uniform earnings penalty for everyone entering work.

How do findings from other places and methods compare?

The studies below measure different things. A hiring trend, an employment outcome, an employer’s expectation and a job-posting requirement cannot be combined into one estimate of AI-caused job losses.

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Source and scope What it found What the measure does—and does not—show
UK government snapshot, June 2026 Overall UK hiring was down 14% year on year in April 2026. Of 38 tracked entry-level occupations, 30 declined; accounting, graphic design and software engineering were among the steepest falls, while sales and customer-facing roles grew. This is a hiring trend across tracked occupations, not a count of jobs directly replaced by AI. The department said more research is needed before attributing the pattern to AI.
NACE Spring Update, U.S. Class of 2026 NACE projected 5.6% more hiring for the graduating class; separately, 10.5% of entry-level job posts required AI skills. The first figure is an employer hiring projection, not realized hiring; the second is a share of postings, not a measure of jobs created or lost. The update had 185 respondents, including 142 employer members. The projection was first collected in August–September 2025 and updated using a survey fielded in February–March 2026.
Statistics Canada, November 2022–December 2025 Employment generally grew across occupations regardless of AI exposure, although younger workers generally had weaker growth. Labor-force outcomes provide a broader comparison, but the agency cautions that AI cannot be isolated from pandemic-related adjustments, demographic change, trade tensions and other economic forces.
Strada survey of nearly 1,500 U.S. executives and senior talent leaders More respondents expected AI to increase rather than reduce entry-level hiring in 2026. Many also said AI was changing the tasks junior employees perform. These are employer perceptions and expectations, not observed job changes or a causal estimate.
North Carolina employer survey, 2026 Thirty percent of employers said they currently used AI, and 43% planned to start or expand use. Among employers already using AI, 73% expected no change in demand for entry-level or lower-skilled workers. This is a state-level survey, not a national estimate. Expectations about future demand and human-centered skills do not establish what hiring will actually do.
Federal Reserve Board analysis of computer programmers, 2026 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 explained by exposure to industries that were slowing. This is evidence about one exposed occupation, not all entry-level roles, and it does not by itself establish that AI caused the slowdown.

Why can these results point in different directions?

“Entry-level” is not one consistent statistical category. Tucker’s first Census analysis follows workers aged 22–24 in industry-state cells; the second looks at graduates from particular college majors. The UK snapshot tracks named occupations, while the Federal Reserve analysis focuses on computer programmers. NACE’s posting figure concerns advertised skill requirements. Those groups and measures overlap only partly, so a result for one cannot be treated as a result for all new workers.

Likewise, AI exposure is a proxy for how much an industry, occupation or major may be affected by AI. It does not establish that a specific employer adopted a system, automated a particular task or replaced a particular worker. Administrative records and labor-force data observe employment outcomes, while surveys capture respondents’ reports or expectations and job postings show stated requirements. Each answers a different question.

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Timing alone does not settle causation. The Census early-career analysis finds timing consistent with a hiring effect after ChatGPT’s release, but also identifies earlier shifts around COVID. Statistics Canada explicitly says recent differences cannot be separated from other economic changes. Remote-work adjustment, macroeconomic conditions, changes in educational attainment, sector shifts and demographic trends may all influence early-career outcomes. The UK government also cautions against assigning its hiring pattern to AI without further research.

What may be changing even where job losses are unclear?

The evidence on task changes and skill requirements helps explain why an entry-level role may change without the total number of jobs immediately falling. Strada’s respondents reported that AI was changing junior employees’ tasks, and NACE found AI skills required in a portion of entry-level postings. Those findings suggest that some employers may expect new workers to use AI tools or take on different work; they do not show how many jobs those changes will create or remove.

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The Federal Reserve’s coder analysis is a useful example of why an occupation-specific slowdown deserves attention but should not be generalized: employment in that occupation still grew, albeit more slowly than before 2022. Across the evidence, hiring flows and the composition of junior work may shift before a clear aggregate employment effect can be established.

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What should a new graduate or early-career worker take from this?

Do not read these findings as proof that an entry-level job search is futile or that AI has made early-career work disappear. The more defensible conclusion is that entry routes are uneven: some exposed groups show weaker hiring or first-job outcomes, while other measures show continued hiring expectations, growing employment, or no expected near-term change. For an individual job search, use the skills named in relevant postings to guide preparation, but treat a requirement for AI skills as a signal about role expectations—not proof that the role is secure or at risk.

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