Do AI Coding Tools Really Boost Developer Productivity? A New Study Says “Not Always”
In recent years, artificial intelligence (AI) has quickly made its way into software development, with tools like GitHub Copilot and Cursor becoming popular among developers. These tools promise to speed up coding by suggesting lines of code, fixing bugs, and automating repetitive tasks. But a new independent study raises questions about just how effective these tools really are—especially for experienced developers.
A Closer Look at Developer Productivity with AI
The non-profit AI research organization METR (Model Evaluation and Testing for Reliability) recently conducted a randomized controlled trial to test the real-world impact of AI coding tools. They recruited 16 seasoned open-source developers to perform 246 genuine programming tasks from large, active repositories that the developers were already familiar with.
To evaluate the effect of AI, METR split the tasks into two groups:
One group of tasks allowed the use of AI tools (like Cursor Pro),
The other group prohibited AI assistance entirely.
Expectations vs. Reality
Before starting, developers predicted a 24% decrease in task completion time when using AI tools. But the actual results were surprising.
“Completion time increased by 19% with AI tools,” the METR study found.
Rather than speeding things up, AI tools slowed the developers down in many cases.
Why Did AI Make Things Slower?
The study explored a few reasons for the unexpected outcome:
Prompting and waiting: Developers spent significant time crafting prompts and waiting for AI responses rather than coding directly.
Popularity: Despite some onboarding, unfamiliarity may have affected performance.
Complex codebases: The tasks came from large, real-world projects. Current AI tools can struggle to navigate and understand deeply interconnected systems.
Not the Final Word on AI Coding
In fact, they acknowledged:
AI tools are rapidly improving and today’s results might be outdated just a few months from now.
Many broader studies do show productivity gains in other settings.
AI tools have made strides in tackling complex, multi-step tasks.
Still, this research offers a valuable reminder: AI isn’t a one-size-fits-all solution—and its benefits might not be immediate for every developer or every project.
Proceed with Caution
As software teams increasingly integrate AI into their workflows, this study suggests it’s important to set realistic expectations. Developers may face a learning curve, and AI tools may require tuning to fit specific environments. Additionally, earlier research has shown that AI-generated code can sometimes introduce errors or security flaws, so oversight remains crucial.
Final Thoughts
AI coding tools hold exciting promise, but their impact varies depending on context, experience level, and the complexity of the work. For now, developers and teams should treat AI as a powerful assistant, not an instant productivity fix.
