The Surprising Downsides of AI for Seasoned Developers: A 19% Slowdown

In recent years, artificial intelligence has often been hailed as a game-changer for productivity across many fields, and software development is no exception. Tools like Copilot, ChatGPT, and Cursor have generated excitement by promising to ease developers’ workloads and speed up coding. However, a recent study conducted by the nonprofit METR reveals a more nuanced reality: for experienced developers working with familiar codebases, AI coding assistants can actually slow down work by about 19%.

This finding might seem counterintuitive, especially since many perceive AI as a helpful accelerator for complex or repetitive tasks. Yet, the study shows that while developers felt they were working faster with AI assistance, the actual measured time to complete tasks was longer. The main reason? Time spent reviewing, correcting, and adapting the AI-generated code suggestions—which were often “directionally correct” but not precise—added an extra layer of work rather than reducing it.

For experienced professionals who know their codebases inside and out, writing and fixing code tends to be quick and efficient. When AI provides suggestions that aren’t perfectly accurate, it forces developers to pause, evaluate, and sometimes rework the code, introducing cognitive overhead and distractions that ultimately slow progress.

That said, this does not mean AI coding tools lack value. Other studies indicate that AI assistance can provide significant productivity boosts—sometimes speeding up tasks by over 50%—when used by less experienced developers or when working on unfamiliar or new code. In those contexts, AI helps overcome creative blocks, reduces time spent on boilerplate or routine code, and provides useful scaffolding.

This contrast highlights an important point: AI should be seen as a strategic, targeted tool rather than a universal productivity hack. For seasoned developers in mature projects, AI is unlikely to replace their expertise or speed. However, it can still ease the mental load during repetitive tasks or support more stressful phases of development, improving the overall experience.

Moreover, even when AI slows down actual task completion, many developers report that the interaction feels less mentally taxing or stressful—something that can positively impact well-being and work quality over time. This psychological benefit shouldn’t be underestimated in a field often marked by intense cognitive demands and tight deadlines.

Ultimately, this study serves as a valuable reminder to approach AI integration in software development with balanced expectations. AI is not a magic bullet that automatically boosts speed or quality, but a complementary aid that—when used thoughtfully—can enhance work outcomes. The key is understanding when and how to leverage AI tools effectively, combining their strengths with human expertise to minimize overhead.

As AI becomes increasingly embedded in development workflows, this awareness will be crucial to avoid disappointment and to harness these technologies to their fullest potential—without losing sight of the irreplaceable value of skilled, experienced developers.