Are AI Coding Tools Actually Slowing Developers Down?
AI coding tools like GitHub Copilot, Cursor, and others have been widely celebrated for boosting developer productivity by writing boilerplate code, debugging, and enhancing software testing. These tools, powered by advanced language models from OpenAI, Google DeepMind, Anthropic, and xAI, are often touted as game-changers for programming workflows. But are they delivering the promised efficiency for everyone—especially experienced developers? A recent study by the non-profit research organization METR sheds new light on this topic. Surprisingly, the research found that using AI coding tools did not help developers work faster. In fact, developers in the study completed their tasks 19% slower when using these tools. This contradicts widespread assumptions and raises questions about the true productivity benefits of AI coding assistants.
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Study Shows AI Coding Tools May Not Boost Speed for Experts
In the controlled study by METR, 16 experienced open source developers were tasked with completing 246 real-world software engineering tasks. Each developer was randomly assigned to work with or without AI coding tools like Cursor Pro, one of the most advanced options on the market. The idea was to closely measure the actual impact these tools have on coding performance. Developers expected AI to make them 24% faster on average. Instead, the tasks completed with AI assistance took longer than those completed without it. The researchers believe that this surprising outcome may stem from developers spending more time validating AI-generated suggestions, navigating tool interfaces, or correcting subtle mistakes made by the model.
This challenges the prevailing narrative that integrating AI into developer workflows automatically boosts productivity. Although AI coding tools are undeniably useful in some scenarios, they may not be as effective for seasoned coders working on complex projects, particularly when unfamiliar tools like Cursor are introduced without long-term adaptation. It also highlights the importance of familiarity—only 56% of the developers had prior experience with Cursor, even though 94% had used other AI tools or large language models (LLMs) in web-based environments.
Context Matters: Familiarity and Use Cases Define AI Efficiency
One of the major takeaways from the METR study is that the efficiency of ai coding tools isn't universal—it heavily depends on context, developer experience, and familiarity with the tool. AI models are powerful, but they aren't perfect. In many cases, they may produce code that looks correct but contains logic errors or inefficiencies that only a skilled developer would catch. This extra layer of review can negate any time saved during the initial code generation. Cursor and similar tools often need a learning curve and deep integration into a developer’s personal workflow to be fully effective.
Moreover, when developers aren’t confident in the tool’s capabilities or lack training, they tend to second-guess AI-generated outputs, leading to slower performance. This doesn’t mean AI coding tools are ineffective—they may simply be better suited for junior developers, routine tasks, or rapid prototyping rather than deep architectural or debugging work. The research suggests that companies and individual developers alike should approach AI adoption thoughtfully, tailoring use cases to specific teams and skill levels.
AI Coding Tools Still Hold Promise, But with Realistic Expectations
Despite the study’s surprising findings, ai coding tools still offer immense potential—just not necessarily in the way they’re often marketed. For example, they can be incredibly helpful for speeding up repetitive or mundane tasks, helping new developers learn syntax faster, and assisting with documentation generation. But they may not offer the same speed gains for seasoned professionals dealing with complex codebases. The productivity gains will likely increase over time as tools become more intuitive and developers become more proficient at using them.
Ultimately, the key takeaway is that AI coding tools should be seen as collaborators—not replacements. Developers still need to bring their critical thinking, domain knowledge, and code review skills to the table. As with any technology, the goal should be to enhance human ability, not depend on automation blindly. Organizations implementing AI development tools must support their teams with adequate training, realistic timelines, and a clear understanding of what these tools can and can’t do. By doing so, they can unlock real benefits without falling for inflated promises.
The METR study serves as a timely reminder that not all ai coding tools produce the same results for every developer. While they offer real advantages, especially in beginner-friendly or repetitive scenarios, their productivity impact on experienced coders may be less than expected—or even negative. Developers and tech leaders must evaluate these tools critically, invest in proper onboarding, and adopt a realistic approach to AI integration. By aligning expectations with actual performance, we can leverage AI’s capabilities without compromising speed, quality, or workflow efficiency.
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