The converted
Despite these issues, though, there’s probably no turning back. “Odds are that writing every line of code on a keyboard by hand—those days are quickly slipping behind us,” says Kyle Daigle, chief operating officer at the Microsoft-owned code-hosting platform GitHub, which produces a popular AI-powered tool called Copilot (not to be confused with the Microsoft product of the same name).
The Stack Overflow report found that despite growing distrust in the technology, usage has increased rapidly and consistently over the past three years. Erin Yepis, a senior analyst at Stack Overflow, says this suggests that engineers are taking advantage of the tools with a clear-eyed view of the risks. The report also found that frequent users tend to be more enthusiastic and more than half of developers are not using the latest coding agents, perhaps explaining why many remain underwhelmed by the technology.
Those latest tools can be a revelation. Trevor Dilley, CTO at the software development agency Twenty20 Ideas, says he had found some value in AI editors’ autocomplete functions, but when he tried anything more complex it would “fail catastrophically.” Then in March, while on vacation with his family, he set the newly released Claude Code to work on one of his hobby projects. It completed a four-hour task in two minutes, and the code was better than what he would have written.
“I was like, Whoa,” he says. “That, for me, was the moment, really. There’s no going back from here.” Dilley has since cofounded a startup called DevSwarm, which is creating software that can marshal multiple agents to work in parallel on a piece of software.
The challenge, says Armin Ronacher, a prominent open-source developer, is that the learning curve for these tools is shallow but long. Until March he’d remained unimpressed by AI tools, but after leaving his job at the software company Sentry in April to launch a startup, he started experimenting with agents. “I basically spent a lot of months doing nothing but this,” he says. “Now, 90% of the code that I write is AI-generated.”
Getting to that point involved extensive trial and error, to figure out which problems tend to trip the tools up and which they can handle efficiently. Today’s models can tackle most coding tasks with the right guardrails, says Ronacher, but these can be very task and project specific.
To get the most out of these tools, developers must surrender control over individual lines of code and focus on the overall software architecture, says Nico Westerdale, chief technology officer at the veterinary staffing company IndeVets. He recently built a data science platform 100,000 lines of code long almost exclusively by prompting models rather than writing the code himself.
Westerdale’s process starts with an extended conversation with the modelagent to develop a detailed plan for what to build and how. He then guides it through each step. It rarely gets things right on the first try and needs constant wrangling, but if you force it to stick to well-defined design patterns, the models can produce high-quality, easily maintainable code, says Westerdale. He reviews every line, and the code is as good as anything he’s ever produced, he says: “I’ve just found it absolutely revolutionary,. It’s also frustrating, difficult, a different way of thinking, and we’re only just getting used to it.”