Artificial intelligence is supposed to help us work faster, code better, and build smarter. But what if, for some developers, it’s doing the exact opposite?
A recent study has stirred the tech world by revealing a surprising truth: for experienced software engineers working on familiar projects, AI coding assistants might actually be slowing things down.
🚦 When AI Becomes a Speed Bump — Not a Shortcut
The study, conducted by nonprofit research group METR, found that seasoned developers using Cursor—an AI-powered coding assistant—took 19% longer to complete tasks than those who didn’t use any AI at all.
That’s not just surprising—it’s the opposite of what developers expected. Before the experiment, participants predicted the AI would save them 24% of their time. The results highlight a growing disconnect between AI hype and practical outcomes.
Why the slowdown? The answer lies in the extra time required to review, debug, and adjust AI-generated code. While tools like Cursor are directionally helpful, the code often lacks the precision required for complex, established codebases.
🧠 AI for Beginners vs Pros: Why Context Matters
Interestingly, not all developers experience this slowdown. In fact, other studies—like one involving Google’s own engineers—found that AI copilots boosted productivity by as much as 21%. The difference? Experience level and project familiarity.
For junior developers or engineers exploring new codebases, AI suggestions can serve as a valuable guide. But for experienced developers who already know their way around the code, AI’s “help” sometimes just adds another layer to sift through.
✨ Still Useful: The Hidden Benefits of AI Coding Tools
Even if AI isn’t always faster, it can still make the coding experience more enjoyable. Many developers say that AI reduces their mental fatigue. Instead of staring at a blank editor, they’re editing and refining AI-generated code, which feels more like collaboration than creation.
AI also acts as a brainstorming partner. It may not always give the perfect solution, but it often presents new approaches that can spark better ideas.
📊 What This Means for Dev Teams and Tech Leaders
This study is a reality check: AI tools are not plug-and-play productivity boosters. Their effectiveness depends on how, when, and who uses them.
For managers and engineering leads, this means:
- Don’t force AI adoption across the board.
- Train your team to understand when AI is useful—and when it’s not.
- Regularly evaluate productivity outcomes to ensure AI tools are actually delivering value.
AI is a powerful tool—but only when used in the right hands, for the right tasks.
❓FAQ: What Developers Are Asking
Q: Why would AI slow down experienced developers?
Because it often generates generic or imprecise suggestions that require manual correction—especially in mature codebases where precision is key.
Q: Should we stop using AI copilots?
Not at all. AI still offers value—especially for ideation, reducing mental strain, and aiding less experienced developers.
Q: When is AI most helpful?
AI tools shine when developers are learning a new codebase, prototyping, or working on documentation and repetitive coding tasks.
Q: How can teams make smarter use of AI tools?
Start by understanding your team’s workflows. Use AI for what it’s best at—then build processes around reviewing, validating, and improving its output.
🔍 Final Thought
AI in coding isn’t a magic bullet—it’s more like an intern with potential. Sometimes helpful, sometimes in the way, always needing supervision.
As we move into an AI-assisted future, developers who understand when to use these tools—and when to trust their own skills—will have the upper hand.
Sources Reuters
