3 Minutes
Open your IDE. Type one keystroke. Watch the code appear. That image stopped being science fiction this week at Davos, where Anthropic CEO Dario Amodei offered a blunt forecast: models are getting close to doing the heavy lifting in software development.
Speaking at the World Economic Forum and in a conversation with The Economist — alongside Demis Hassabis of DeepMind — Amodei suggested a startling timeline. He said AI could be carrying out more, or even all, of the end-to-end work software engineers do within six to twelve months. Short timeline. Big implications.
He didn’t couch this in hypotheticals. Inside Anthropic, Amodei says, engineers already lean on models to produce code. The human role, in many cases, has shifted from author to editor: the models draft, and engineers refine, stitch, and validate. As he put it: 'We have engineers who no longer write code in the traditional sense; the model writes it and they edit and finish.' That alone signals a major change in day-to-day workflows.
Not everything, he was careful to add, is on the fast track to automation. Chip design, hardware manufacturing and the resource-heavy process of training large models still depend on physical infrastructure, massive investment and specialized labor. Those knots slow how quickly the entire stack can be automated. Which parts fall first? That remains an open question.
The reaction online was predictably split. Some technologists greeted the forecast with skepticism and nuance; others treated it as a clarion call about job displacement. Amodei has floated similar warnings before, and each time the conversation circles back to the same dilemma: when tools change faster than institutions do, people feel the shock.
Developers are likely to see their roles reshape from routine coding toward oversight, systems integration and governance of AI-generated outputs.
That shift won’t happen the same way everywhere. Startups and cloud-native teams may adopt code-generating models rapidly. Regulated industries, large enterprises and hardware-focused firms will move at a different pace. The takeaway for anyone building software is simple: adapt, learn to supervise AI, and think in terms of orchestration rather than typing every line by hand. The future of coding looks less like solitary craftsmanship and more like collaborative curation — and the clock is already ticking.
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