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Last year it was 13 percent. Now it’s 19. Half the internet did that subtraction and filed the six points under proof that AI is wiping out entry-level jobs.
I opened the paper. That pair of numbers isn’t in it.
What Stanford measured
The paper is “Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence”, August 2026 edition. Erik Brynjolfsson wrote it with Bharat Chandar and Ruyu Chen. The data is anonymized ADP payroll, between 3.5 and 5 million workers a month, through June 2026. Exposure comes from a task-level impact measure and from the Anthropic Economic Index, which tracks how occupations use Claude at work. It is the same Stanford operation that puts out the AI Index I went through earlier this year.
Here is the finding everyone quoted about entry-level jobs. Employment for 22 to 25 year olds in the most AI-exposed occupations now sits 19 percent below where it would be if it had kept pace with their less-exposed peers.
The raw numbers behind that are worth having. In the two most exposed quintiles, employment for that age group fell roughly 11 percent between November 2022 and June 2026, while in the three least exposed quintiles it grew roughly 10 percent. That is a spread of 21 percentage points.
Experienced workers show no such gap. Whatever is happening to entry-level jobs is leaving the people inside alone.
Where the 13 percent came from
The authors are open about it, and earlier versions of the paper headlined a regression estimate that adjusted for firm-level shocks. That was the 13, on July 2025 data. On September 2025 data the same measure read 16 percent.
For this edition they dropped the regression from the headline and switched to a simple descriptive measure that needs no modelling choices. And they were straight about what that measure showed a year ago, which was fifteen percent.
So the like-for-like pair is 15 against 19, which is four points of movement where the coverage reported six.
Both figures track entry-level jobs, with a different instrument behind each.
This is not nitpicking a decimal, because the gap between “it grew half again as fast as we thought” and “it grew a bit” decides whether you are writing about a trend accelerating or a trend continuing. Ars Technica put both figures in neighbouring sentences and closed the paragraph by noting that last year the gap was “just 13 percent”. Every word of that holds up on its own. What the reader walks away with is a subtraction between two different measurement methods.
What the paper admits about itself
I went to the limitations first, because that is usually where the thing missing from the headline sits.
The authors say this is not a causal estimate. Their phrase is early descriptive indicators, which sits a long way from a verdict on entry-level jobs.
The effect weakens once you control for education, which is awkward for the headline. Some of the divergence shows up before generative AI existed. The pattern is sharper in the ADP sample than in national survey benchmarks. That sample over-represents large firms and occupations with high AI exposure. Manufacturing and wholesale also carry more weight in it than in the wider economy.
The panel is balanced, which means it is conditioned on firms surviving. Companies that went under drop out of the picture. On top of that, roughly 30 percent of records have no job title, so the occupation code gets filled in by an algorithm.
The finding on entry-level jobs survives all of that. What changes is how hard you are allowed to lean on it, which is the same problem I hit when 272 experts ranked AI risk and the ranking got quoted without its error bars.
What this means if you run security
The mechanism behind the entry-level jobs number is simple enough to check against your own team. AI substitutes for codified knowledge, the kind written down in a textbook or a procedure. It complements tacit knowledge, the kind you only get from years of practice and mentorship. The decline shows up in hiring, while separation rates did not move.
Map that onto a SOC, where tier one sits on alert triage against a runbook. That is written procedure with checkable output, and in security it is where the entry-level jobs live.
Seniors don’t fall from the sky, and your L3 analyst is somebody’s L1 from five years ago. Cut the entry-level jobs today and in five years you have nobody to promote. No model covers for you there, because tacit knowledge does not sit in your documentation.
Speaking to the Washington Post, quoted by Ars, Brynjolfsson said the entry-level effects he is measuring “are real, persistent and widening”, and that he is more worried than he was “about a labor market that keeps its overall employment level while quietly closing the on-ramp for people starting their careers”.
The on-ramp closing is in the data. The worry is his.
The rule
Before a number about entry-level jobs goes on a slide for your board, check whether last year’s number from that same paper was measured the same way. Authors do change their headline metric mid-project, and they describe the change in the paragraph the journalist never reaches.
Never trust the headline. Trust the footnote.
Source | https://arstechnica.com/ai/2026/08/ai-is-hitting-entry-level-jobs-hardest-stanford-study-finds/
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