Most developers now use AI at work. A lot of them don't trust what it gives them.
That's the headline finding from Google's 2025 DORA report, which surveyed nearly 5,000 tech professionals about how AI is actually landing on engineering teams. 90% of them use AI on the job. More than 80% say it makes them more productive. But 30% report little or no trust in the code their AI tools generate, and the qualitative interviews underneath that number are pretty grim.
Google ran a separate deep dive into 1,110 open-ended responses from its own engineers, and the same theme kept coming back. People are shipping more code, but they're not enjoying it. One engineer said reviewing AI-generated code "is so much harder than writing it," and that the tools are mostly increasing the volume of stuff that needs human eyes on it. Another said the productivity gains come "at a cost," with less time writing code and more time babysitting the model. A third put it more bluntly: the AI writes faster than they can, but the code is lower quality than what they'd write themselves.
This is the part the productivity charts miss. Output is up. So is throughput, deploy frequency, all the things companies like to talk about on earnings calls. The people doing the work, though, are increasingly stuck in a role they didn't sign up for, which is reading and patching machine output instead of building anything.
Menlo Ventures partner Tim Tully recently told Business Insider that software engineers are facing "an identity crisis bordering on depression," and the DORA data is basically the quantitative version of that quote. When the work shifts from creating to reviewing, the job feels different even when the paycheck doesn't. The trust problem is bleeding into adjacent fields too. A recent industry survey found that confidence in autonomous AI penetration testing has collapsed to 9%, down sharply from a year ago. The pattern is identical: the tools work fast, the output looks plausible, and nobody quite believes it.
Not every company is in this spot. Virgin Atlantic told OpenAI that Codex has turned two-week refactoring jobs into 30-minute ones, and that the bottleneck inside their teams has flipped from engineering capacity to project managers not writing tickets fast enough. That's the upside case, and it's real. But Virgin Atlantic also shipped its latest app with near-complete test coverage and zero P1 defects at launch, which tells you they've put a lot of structural work around the AI to make it land. Most companies haven't done that. Sonar's analysis of AI-accelerated codebases found that quality declines pretty reliably in environments where teams ship faster without rebuilding their review process around the new pace.
That's the actual gap. The companies winning with AI coding aren't the ones generating the most code. They're the ones whose workflows can absorb it.
The current AI coding debate is stuck on the wrong question. Everyone keeps asking whether the tools make developers faster, and the answer has been yes for about a year. The real question is whether the rest of the organization can keep up with that speed, because right now the answer is mostly no. Reviews pile up. Quality slips. Engineers stop feeling like engineers and start feeling like editors. The companies that figure out how to redesign the work around the tools are going to look very different from the ones that bolted AI onto the old process and called it a productivity win. The next year will sort those two groups out fast.
