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Yes, with precise limits. Evidence published in 2026 links exposure to generative AI with weaker early-career outcomes for recent graduates in the most exposed majors, and with fewer hires of 22- to 24-year-olds in the most exposed industry-and-state groups. None of these studies establishes that AI alone caused the broader slowdown in graduate hiring. They use different populations, places, time windows and outcomes, so they describe a pattern rather than a single count of jobs lost.
Is AI taking entry-level jobs?
The clearest evidence concerns hiring rather than layoffs. A 2026 Census Bureau working paper links employer and employee administrative records to compare hiring of people aged 22 to 24 across industry-and-state groups that differ in AI exposure. After ChatGPT’s introduction, hires fell immediately in the most exposed groups, and the decline persisted. The authors conclude that this drop in hiring is the main reason for large employment declines in those groups over the following period.
That is a narrower finding than the headline question implies. It describes who gets hired into a first job, not whether employers are cutting junior staff. The studies discussed here do not measure layoffs of early-career workers, so they cannot be read as evidence about that.
The same working paper also tests a rival explanation. Its historical decomposition attributes part of the broader early-career employment decline to monetary-policy shocks through 2023. Those shocks may account for up to a quarter of the relative employment decline measured through the second quarter of 2025. According to the paper, they do not explain the rapid drop in hires at the most exposed firms. Because this is a single working paper rather than a settled consensus, its result is an important finding, not a final verdict.
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Is AI making it harder for recent college graduates to find work?
Two sources look at graduates directly, grouping them by college major. Both rank majors by how exposed their typical jobs are to AI, and both find that outcomes diverged for the most exposed majors after late 2022. They differ in where they look and what they measure, so their estimates should be read side by side, not merged into one number.
National major-level estimates (Census Bureau, September 2026)
A Census Bureau working paper published in September 2026 relates exposure rankings for college majors to what graduates did after leaving school. Its headline estimates concern the most AI-exposed tenth of majors. In regression-adjusted estimates after ChatGPT’s release, graduates in that group had a lower likelihood of initial employment, and their full-quarter initial earnings were lower. The effects shrink as time passes since labor-market entry, but they remain substantial for the most exposed majors. Job-switching patterns also diverged after late 2022.
The paper attributes roughly half of the earnings decline to lower earnings within the industries where graduates were employed. The remainder reflects a shift into lower-wage sectors such as restaurants and retail.
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Texas graduates (Federal Reserve Bank of Dallas, September 22, 2026)
The Dallas Fed analysis uses Texas administrative education and wage records for four-year university graduates. Each major is scored by the AI exposure of the occupations in job advertisements that have historically asked for that major. A 10-percentage-point higher share of automatable tasks in a major is associated with a 1.7-percentage-point relative decline in employment within the first year. Among graduates who found work in Texas, first-year earnings in more-exposed majors fell about 5% from 2021 to 2024, relative to less-exposed majors.
The event study compares more- and less-exposed majors around ChatGPT’s release. The gap in employment rates between the two groups was stable before the release, so a change afterward is easier to read as a break from an existing pattern rather than a continuation of one. The analysis covers Texas only, and its earnings figure counts only graduates who found work in Texas, so it does not describe graduates who left the state or who did not find work.
Which college majors are most exposed to AI?
In the Dallas Fed ranking, a major’s exposure reflects the occupations it feeds, not its coursework alone. Whether a major ranks high depends on how much of the work its graduates are hired to do can be automated, so comparing fields by actual task exposure and employer demand is more useful than a simple “safe major” label.
- Most exposed: computer science, computer engineering, and languages.
- Least exposed: nursing, education, and psychology.
How to read the numbers side by side
Before comparing estimates, check four things: the population, the place, the outcome and the time window. The table below sets out the design of each source. It does not restate their results.
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| Source and date | Population | Place | Exposure measure | Outcomes | Time window |
|---|---|---|---|---|---|
| Census Bureau major-outcomes working paper, September 2026 | Recent graduates grouped by college major | Not stated (Census Bureau, September 2026) | Major-level exposure ranking | Initial employment, full-quarter initial earnings, job switching | Outcomes after late 2022 |
| Federal Reserve Bank of Dallas analysis, September 22, 2026 | Four-year university graduates; earnings only for those who found work in Texas | Texas | Occupations in job advertisements that historically demand the major | First-year employment rate, first-year earnings | Event study around ChatGPT’s release; earnings for 2021 to 2024 |
| Census Bureau early-career hiring working paper, 2026 | People aged 22 to 24 | Industry-by-state groups | Industry-state AI exposure | Hires and employment decomposition | Hiring after ChatGPT’s introduction; decomposition through 2025 Q2 |
| Bureau of Labor Statistics, released April 22, 2025 | Recent bachelor’s recipients aged 20 to 29 who earned degrees January to October 2024 | National (United States) | No AI measure | Employment, unemployment, enrollment | October 2024 snapshot |
| New York Fed recent-graduate series | Bachelor’s degree holders who are recent graduates | National | Whether the job typically requires a degree, using O*NET data | Unemployment and underemployment | 1990 to present; updated quarterly, with majors updated annually |
| Federal Reserve Board report on 2025 household well-being (2026) | Workers aged 18 to 29 | Not stated (Federal Reserve Board, 2026) | No AI measure; self-reported survey | Generative AI use, worry about replacement, expected career effect | Use in the month before the survey; reported for 2025 |
| NACE Class of 2026 employer outlook, April 2026 | Employers hiring the Class of 2026 | Not stated (NACE, April 2026) | No AI measure; employer projections | Projected hiring change, skills employers seek | Projection for the Class of 2026 |
- Association, not proof of cause. Each design compares more-exposed with less-exposed groups. That supports a link to AI exposure, but it cannot rule out other forces that hit exposed fields at the same time.
- Different units. Percentage points, percentage changes in earnings, hire flows and population shares are not interchangeable. The 13% figure in the Census major-level estimates is a change in full-quarter initial earnings, not a change in the number of jobs, so it does not mean AI eliminated 13% of jobs in a field.
- Averages. Every estimate describes a typical result across majors or firms. It does not mean that every graduate in a named field faces the same outcome.
- Fixed windows. The Dallas earnings comparison ends in 2024, and the hiring decomposition ends in the second quarter of 2025. Later data may revise both.
National baselines: BLS and the New York Fed
The Bureau of Labor Statistics snapshot covers recent graduates aged 20 to 29 who earned degrees between January and October 2024. Among recent bachelor’s recipients in that group, 69.6% were employed, 15.3% were unemployed, and 25.2% were still enrolled in school. Enrollment is part of the picture: about one in four in this group was still in school, so the employment rate describes a different population from the first-job cohorts in the AI studies.
The New York Fed’s series is the longer national view. It classifies a recent college graduate as underemployed when working in a job that typically does not require a college degree, using degree-requirement data from the U.S. Department of Labor’s O*NET Education and Training Questionnaire. The series covers bachelor’s degree holders from 1990 to the present, is generally updated quarterly, and reports outcomes by major annually. Underemployment is not the same as unemployment, and the series does not isolate AI’s effect.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Counter-signals: hiring projections and worker attitudes
Two sources complicate a purely negative picture. Neither shows that AI is creating net jobs, and neither measures realized outcomes for graduates.
Employer projections (NACE, April 2026)
NACE’s April 2026 outlook projects a 5.6% increase in hiring for the Class of 2026. Growth is uneven by sector. Information, engineering services, wholesale trade, construction, and miscellaneous professional services are among the industries increasing hiring, while utilities and several manufacturing categories are decreasing. These are employer projections, not placements that have already happened.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteWorker attitudes (Federal Reserve Board, 2026)
The Federal Reserve Board’s 2026 report on household well-being in 2025 shows adoption and anxiety side by side. Overall, 25% of workers used generative AI at work in the prior month. Among workers aged 18 to 29, 20% did so, 23% worried that AI would replace their job, and 19% said AI would improve their career. These are self-reported answers about use and attitudes, not measurements of displacement.
What skills do employers want from new graduates now?
NACE’s April 2026 summary names three skills employers look for on Class of 2026 candidates’ résumés:
- Teamwork
- Problem-solving
- Communication
The same summary reports that employers increasingly expect AI-related capabilities from early-career talent. No study cited here tests whether learning a particular AI tool improves a graduate’s chances of being hired, so the evidence does not support a tool-specific promise.
Quick Recap
What a recent graduate can do with this evidence
- Read entry-level postings in your target field and list the tasks they describe. Exposure measures track the tasks a role involves, not only its title. The Dallas Fed method works the same way, using job advertisements to link occupations to majors.
- Track hiring in the industries and states you are targeting. The hiring evidence is measured at the industry-and-state level, and national totals average over the differences that matter for your search.
- Keep a record of work that shows judgment. Note projects where you made a decision, resolved a disagreement in a team, or explained results to a non-specialist. These map onto the skills listed above, and they are easier to show in an interview than a list of tools.
- Quote figures with their population and date. In a cover letter or interview, say that a 2026 Census Bureau working paper estimated a hiring decline for people aged 22 to 24 in exposed industry-state groups, rather than that AI has cut graduate jobs by a given percentage.
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