AI tools are widely used by software developers, but those devs and their managers are still grappling with figuring out how exactly to best put the tools to use, with growing pains emerging along the way.
That’s the takeaway from the latest survey of 49,000 professional developers by community and information hub StackOverflow, which itself has been heavily impacted by the addition of large language models (LLMs) to developer workflows.
The survey found that four in five developers use AI tools in their workflow in 2025—a portion that has been rapidly growing in recent years. That said, “trust in the accuracy of AI has fallen from 40 percent in previous years to just 29 percent this year.”
The disparity between those two metrics illustrates the evolving and complex impact of AI tools like GitHub Copilot or Cursor on the profession. There’s relatively little debate among developers that the tools are or ought to be useful, but people are still figuring out what the best applications (and limits) are.
When asked what their top frustration with AI tools was, 45 percent of respondents said they struggled with “AI solutions that are almost right, but not quite”—the single largest reported problem. That’s because unlike outputs that are clearly wrong, these can introduce insidious bugs or other problems that are difficult to immediately identify and relatively time-consuming to troubleshoot, especially for junior developers who approached the work with a false sense of confidence thanks to their reliance on AI.
As a result, more than a third of the developers in the survey “report that some of their visits to Stack Overflow are a result of AI-related issues.” That is to say, code suggestions they accepted from an LLM-based tool introduced problems they then had to turn to other people to solve.
Even as major improvements have recently come via reasoning-optimized models, that close-but-not-quite unreliability is unlikely to ever vanish completely; it’s endemic to the very nature of how the predictive technology works.