The Economist counted about a million American jobs created by AI since 2023, and that count is solid. But look at what those jobs actually are: electricians, HVAC techs, equipment makers, data-centre technicians. People building and running the buildings, not people using the software. Only about one per cent of professional jobs are AI jobs. That matters, because a number like this tracks how much money companies are spending, not how good the technology is. If the models stopped improving tomorrow, hiring would keep going for a year because the contracts are already signed. If the models kept improving but three big spenders cut their budgets, the hiring would stop cold. So the figure is a spending indicator, not proof the technology works. And the people using it to say there’s no problem are missing that the office jobs going quiet and the construction jobs booming are two different labour markets with two different sets of people in them.

A staffing manager walks into a café in downtown San Francisco and hands out small packages. Inside each one is an elastic headband with a phone mount. The instructions are simple: strap it to your forehead, go home, and film yourself doing the dishes. Roughly two hours of usable footage pays about $80. One longtime gig worker, who has delivered food and hung Christmas lights for a living, summed up the appeal without much ceremony — he has to do the chores anyway, so he may as well be paid for them.

Every few months the same headline resurfaces: DevOps is dead. In 2026, the claim has more surface credibility than before — job boards are brutal, the title is disappearing from org charts, and AI is eating the work that once defined the role. None of that means the function is gone. It means the floor dropped out and the ceiling went up.

The mechanics are straightforward. AI systems built into cloud platforms now handle what junior and mid-level DevOps engineers spent most of their time on — infrastructure provisioning, pipeline configuration, alert triage, rollback decisions. That work isn’t being done by fewer people out of choice. It’s being done by machines out of necessity, because the scale modern systems operate at makes human-in-the-loop intervention a bottleneck. Netflix deployed 4,000 times daily last quarter. Amazon pushes a change every 11.7 seconds across global infrastructure. No headcount solves that. Automation does.

We are likely in the early stages of another productivity boom.

You can feel it before you can fully measure it. Not in the official statistics yet, not neatly captured in GDP releases or quarterly productivity reports, but in the way work itself is beginning to change. Tasks that used to take hours now collapse into minutes. Layers of friction around research, drafting, analysis, coordination, and execution are starting to thin out. The shift is still uneven, still messy, but it is becoming difficult to ignore.