On June 21, 2026, I argued here that AI was coming for your kid's first job, not yours:
"The robots did not take your job. Aggregate unemployment is still hovering around 4% — historically low, boringly stable. What's vanishing isn't your rung. It's the first one. The one your kid needs."
In September 2026, two working papers built on government data tested that claim. One finds that new graduates' unemployment did not spike in summer 2026. The other finds that since ChatGPT's release, graduates of the most AI-exposed majors have been less likely than others to land a job right away — and have started at lower pay. They confirm part of what I wrote and complicate another part, and a revised Stanford study updates one number.
So, a scorecard. What holds: the unemployment rate is the wrong instrument for this. What needs updating: the Stanford figure I cited, "a 16 percent relative decline in employment" for 22-to-25-year-olds in the most AI-exposed jobs, is about 19% in the team's August 12, 2026 revision (descriptive, its authors note, not causal). What I put too simply: I wrote that "the rung is still gone, and the kid is still standing on the ground." In the second paper, the graduates are not on the ground. They "remain strongly attached to the labor market, but they adjust to weaker demand at entry." In the three tenths of majors just below the top, the pay gap faded within a year or two; the group still behind is narrower than "your kid," mostly computer science and its software cousins.
One paper counted the jobless. The other followed the paychecks.
Start with the reassuring paper, because the reassurance is real. Robert Fairlie and Jane Wu of UCLA used the Current Population Survey (the monthly household survey behind the official unemployment rate) to study 22-to-25-year-olds whose highest degree is a bachelor's and who are out of school — their stand-in for the class of 2026. Their working paper for the National Bureau of Economic Research finds that "unemployment rates did not spike in summer 2026 relative to summer months in previous years," and did not rise significantly against older graduates or young workers without a degree.
Averaged over June through August, their group's unemployment rate was 7.3% in 2026, against 6.3% to 7.8% in the four summers before — "within the normal range of variation." Counting graduates who want a job but aren't looking (the authors' "sidelined unemployed") "adds nearly two percentage points to the unemployment rate of recent college graduates" on average from 2022 to 2026, and still shows "no evidence of a statistically significant increase in summer 2026." (In summer 2026 alone, that broader rate hit 10.4%, a five-summer high, though only 0.3 points above the previous high.)
The authors add that their findings "do not rule out larger effects in the future." So far, so good. Now the second paper.
In "Graduating into Disruption," three Census Bureau researchers — Cody Orr, Lee Tucker and Lawrence Warren — used records that link graduates' majors to their later jobs, and asked not only whether graduates found work, but what work, at what pay:
"In regression-adjusted estimates, the most AI-exposed decile of college majors saw their likelihood of initial employment decline by five percentage points, while full-quarter initial earnings declined by thirteen percent."
In plain English: compare the tenth of majors the authors rate most exposed to AI with the six least-exposed tenths, before and after ChatGPT arrived in late 2022. The chance of any job in the quarter after graduation fell five percentage points. Pay in the main job two quarters out, for those who worked the full quarter, fell 13%, a drop the authors call "comparable in magnitude to the earnings losses associated with graduating into a large recession."
That top tenth is "heavily dominated by computer science, information systems, and other majors linked to software development," with math, statistics, electrical engineering and journalism also in it. (Accounting sits one tenth lower.)
Where did the pay go? About half the drop was lower pay within the same industries, "with the remainder resulting from a shift in the industry mix into lower-wage sectors such as restaurants and retail." (The records show the employer's industry, not the graduate's job, so don't picture software majors behind a register.)
Two caveats. The paper's cover notes that Census working papers "have not undergone the review accorded Census Bureau publications and no endorsement should be inferred"; the data are the government's, the conclusions the authors'. And the sample covers roughly 29% of U.S. bachelor's degrees conferred from 2016 to 2024, from schools more likely to be public, large and research-intensive, with job records ending in the third quarter of 2025. The class of 2026 is not in it.
Nor is it a lone result. In Texas records, two Dallas Fed economists found the same shape: after ChatGPT, graduates of more-exposed majors were less likely to find a Texas job within a year, and their first-year earnings fell about 5% relative to less-exposed majors.
So how can both be true?
Because they measure different things. An unemployment rate asks whether you have a job, not which job, in what industry, or for how much. A software major who wanted a developer job and took a lower-paid one elsewhere counts exactly like one who got the developer job: employed.
Writing about earlier studies, the Census authors put it in one sentence: "modest employment or unemployment effects can coexist with substantial deterioration in job quality and early-career opportunities." As I read it, that fits the UCLA paper, too. Alex Imas of the University of Chicago and Jacob Schaal, reviewing the evidence on September 29, 2026, make the connection explicit: "a sharp drop confined to a few exposed majors, as in Orr, Tucker and Warren, would barely move this average." The UCLA sample, they add, "can't rule out rises of 2–3 percentage points."
Economists at Anthropic (which makes the Claude chatbot) warned in March 2026 that "slowed hiring may not necessarily manifest as increased unemployment": many young entrants "may exit the labor force rather than appear as unemployed."
As I put it in July, the top-line rate is "measuring the lake, not your dock." The Census paper measured a dock.
Is it the chatbots? The skeptics' best case
So is it AI? The skeptics deserve a fair hearing.
First, timing. In the Census data, "real earnings in 2022 among more exposed graduates had already begun to decline in comparison to those of less exposed graduates," which the authors call "congruent with an underlying pandemic recovery explanation for the observed pre-trends." The odds of a first job, by contrast, break sharply at ChatGPT's arrival. Another team's January 2026 preprint found that unemployment "risk rose in AI-exposed occupations beginning in early 2022, months before ChatGPT," though it tracks occupations, not majors, and it found that graduates who took more AI-exposed coursework "had higher first-job pay and shorter job searches after ChatGPT."
Second, remote work. Writing on the New York Fed's Liberty Street Economics blog, three researchers estimate that "remote work can explain 64 percent of the recent increase in unemployment among young college graduates." But that is an argument about unemployment, not pay, and the Census estimates "are robust to controlling for differences across majors in their work-from-home intensity."
Third, the paper's own caveats. Taken at face value, the authors write, rising self-employment and graduate-school enrollment may explain up to half of the five-point employment drop, though they add that this "need not imply that our baseline estimates overstate AI's effects, in particular on earnings." All told, they conclude, the evidence points "to a substantial role for generative AI in shaping the labor market entry of recent graduates," while conceding their design cannot rule out other shocks to particular fields arriving at the same time.
Where does that leave the June piece's verdict, "circumstantial — early, converging, but not yet conclusive"? I think September firmed up more of the what than the why: graduates of the most exposed majors are measurably worse off, and whether AI is the main reason is still argued, in good faith.
Denmark's still water, and the current underneath
Denmark comes at the question from the other end. Anders Humlum of the University of Chicago and Emilie Vestergaard of the University of Copenhagen, working with Statistics Denmark, linked surveys of chatbot adoption to administrative labor-market records: 25,000 workers in the late-2024 round, in 11 highly exposed occupations from accountants to software developers, tracked through December 2024. (First circulated in 2025 as Large Language Models, Small Labor Market Effects, the paper is now titled "Still Waters, Rapid Currents," the same water image I reached for in July.)
Comparing adopters with non-adopters, they find "precise null effects on earnings and recorded hours at both the worker and workplace levels, ruling out effects larger than 2% two years after the launch of ChatGPT." What moves instead, they write, "is the structure of work."
Case closed? Not quite. Young Danes lost ground too: the early-career share of employment in exposed occupations (ages 22 to 25) slipped about 0.6 percentage points from a baseline of about 8% between October 2022 and December 2024. But the authors' analysis "reveals that the declines are not driven by firms adopting AI chatbots"; "changes in the macroeconomic environment or anticipatory hiring freezes unrelated to actual adoption" may be behind them.
So look at what each design can see. Denmark's design measures what using AI did to the people and firms using it. The Census paper fixes exposure by major, before anyone is hired. Its authors note that effects on early-career workers "seem to precede widespread AI adoption by" firms, and float a reason: AI "may also affect the information available to employers when hiring new workers," blurring signals like "grades, assignments, and work samples." As I read the two designs, that channel would barely register as a gap between adopters and non-adopters, since every employer reading a transcript faces it. Small effects on people already inside the building; a large one at the door. Both can be true.
Neither paper has been through formal review. And Denmark is less exotic than it sounds; the authors call its labor market "highly flexible, with low hiring and firing costs—similar to those of the U.S.," though union density there is 65%, and they say only that their insights "may extend" to America.
The class of 2027's first offer
Fairlie and Wu close with a conditional warning: if AI use at work keeps intensifying, "the graduating classes of 2027 and later might be more affected than the class of 2026." So imagine it. To be clear, what follows is my extrapolation, not anyone's finding.
Suppose you graduate in May 2027 from a top-tenth major. In late summer, a new paper finds your age group's jobless rate inside the usual range again, and headlines declare that AI still hasn't hurt new graduates. You do have a job: in an industry you didn't train for, at a salary your predecessors would have turned down, landed through a paid trial project because employers no longer trust grades and work samples to show who can do the work without a chatbot. None of that moves the number on the news.
Some of it has started. Nationally, computer and information science enrollment at four-year institutions had fallen 8.4% by spring 2026, per National Student Clearinghouse figures in AP reporting. In the same report, Tracy Camp of the Computing Research Association said of employers: "If they don't change how hiring is currently happening, they're not going to have mid-level career people in a few years." Push that to 2030, and the firms that skipped junior hiring go looking for seniors nobody trained, from a pipeline of students who picked other majors. (That part is my guess, not hers.)
Same squeeze, different suspects
The Manhattan Institute's Allison Schrager told the class of 2026 in June, months before the Census paper, to blame work-from-home, not AI: "AI is not ruining your job prospects, at least not yet." AEI's Brent Orrell sees AI absorbing junior-level tasks — "people who used to assign routine work to assistants are assigning it to AI instead" — and calls it "a classic example of the tragedy of the (skills) commons."
A Stanford SIEPR brief by Neale Mahoney, Erika McEntarfer (who led the Bureau of Labor Statistics until August 2025) and Karsen Wahal finds that entry-level hiring in AI-exposed occupations "clearly declined markedly around 2022," but that it is harder to say whether that is "clear evidence of AI's impact on demand for young workers." Stanford's Erik Brynjolfsson told NPR that "AI is not the whole story, but it's part of the story and the evidence is building," while Harvard's David Deming, pointing to the timing, said, "I think it's more like remote work."
The Roosevelt Institute's Michael Madowitz argues the problem is real whatever the cause, because "higher education isn't providing the same protections for young workers as it used to."
Imas, surveying the evidence, lands about where I do: "The best current evidence supports a narrow claim: AI may already be affecting the hiring margin for junior white-collar roles most exposed to AI, but this attribution is contested, and aggregate labor-market disruption has yet to appear in the data."
What does this mean for you?
If you're choosing a major, weighing a first offer, or paying for either, here is what the evidence supports — information, not guarantees.
Look up the program, not just the field. The Education Department's College Scorecard shows "the salaries and debt of graduates" by field of study at each college. Mind the vintage: as of October 3, 2026, its field-of-study earnings are for graduates of the 2014-15 through 2019-20 award years, one year out, all before ChatGPT. For the most exposed majors, I'd read them as a ceiling, not a promise.
See where a program's graduates land. The Census Bureau's PSEO Explorer shows graduates' earnings 1, 5 and 10 years out and maps the industries where they go to work, the very margin that shifted. It covers 1,117 institutions, about 40% of graduates, as of August 27, 2026. Its public tables, as of October 3, 2026, stop at the class of 2021.
Ask about underemployment, not just unemployment. As of the second quarter of 2026, the New York Fed counted 42% of recent graduates in jobs that don't need their degree; its outcomes-by-major table, updated each February, is the place to compare majors.
Read a lower-paying first offer as a starting point, not a verdict. In the Census data, job switching rose and the pay gap narrowed; for the three tenths of majors just below the top, it all but closed within one to two years. For the top tenth, the researchers "cannot yet assess when or whether exposed graduates will catch up."
Think hard before a same-field master's. In the Texas study, new master's holders in exposed fields "experienced similar relative declines in earnings as four-year graduates," and the economists suggest that "the returns to formal upskilling within AI-exposed fields may be limited." One state, but worth knowing before you borrow.
Don't flee a major because of a headline. The worst of it is concentrated in the top tenth, and the preprint above tied AI-exposed coursework to higher first-job pay. If you're a parent, skip "Did you get a job?" Ask which job, at what pay, and what it leads to.
The lesson, as I see it
In June, I warned that if we kept watching only the unemployment rate, the robots would "take something quieter and more permanent." September's papers suggest the quieter thing is measurable: not a missing rung for everyone, but a lower one for some, concentrated in a handful of majors, with a cause still in dispute.
Policy built on unemployment rates and layoff counts will miss it. Layoff notices count people who are let go; in August, I wrote about the case these forms cannot see, a layoff too small to trigger them, and the hire that never happens is the same blind spot, wider. California's SB 951, signed September 30, 2026, at least gives it a name — "technological cessation in hiring," a permanent end to hiring for a job, caused at least partly by AI or other automation — but requires no one to report one; on hiring, it orders only a state report, due "On or before January 1, 2028." That's a start, not a measurement.
What would help? Faster data: public earnings tables that don't run years behind the market these papers describe. Job-quality measures: what new graduates earn and in which industries, not just whether they work. Then watch the New York Fed's next quarterly update, expected in November, and summer 2027.
My vote? Keep June's warning, and sharpen it. The robots didn't take your kid's first job, at least not in these data. For some majors, they (or something arriving at the same time) made it smaller and slower to come. That's harder to see than a layoff, which is exactly why it's worth counting.
Choosing a major and weighing a first offer are big decisions made on old data. If a student or a new graduate you know is facing one, bring this piece into the conversation before they decide.