Stop being skeptical about AI for development

Charity Majors and The Pragmatic Engineer approach AI-assisted software development as a practical engineering question rather than a culture-war test. The conversation begins with an old problem that becomes sharper with AI: can anyone measure an individual engineer’s productivity? The answer is necessarily incomplete. Metrics can illuminate some dimensions, but they cannot substitute for understanding a person’s role, the team’s output, or the quality of the resulting system.

Speed is not the definition of better

The guests note that delivery speed is tempting because it is easy to count. Yet shipping more changes does not automatically create better functionality or happier customers. AI pushes organizations to ask more demanding questions: what does good look like, what does productivity mean, and what outcomes should a team optimize for? Those questions matter more than a headline count of generated code or deployment frequency.

Trust has to be earned with evidence

A central idea is that code written by AI should be treated as code produced by an unreliable actor. That is not wholly new: operations and reliability teams already build confidence in complex systems through tests, deterministic simulation, evaluation, guardrails, observability, and feedback from production. If engineers increasingly ship code they did not read line by line, the lost trust must be replenished somewhere else in the delivery system.

Two camps can both be seeing reality

Majors is sympathetic to both enthusiastic adopters and people alarmed by AI’s effects. One group sees practical wins and faster iteration; another sees slop, outages, extra work, and declining reliability. Neither perspective has to be fabricated for the other to be wrong. The useful response is to share concrete evidence about both gains and costs, then improve the system around the tool instead of arguing from identity.

Agency matters for careers

The discussion also turns to engineers, managers, and changing career paths. The durable advantage is not passive certainty about the future but staying close to real work, learning the new tools, and retaining judgment about system behavior and user value. The closing message is deliberately active: anxiety and excitement may feel similar, but agency changes what people can do with either feeling.