Study Reveals AI Coding Tools Slow Down Experienced Developers in Complex Projects
13 Sources
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Study finds AI tools made open source software developers 19 percent slower
When it comes to concrete use cases for large language models, AI companies love to point out the ways coders and software developers can use these models to increase their productivity and overall efficiency in creating computer code. However, a new randomized controlled trial has found that
[2]
AI coding tools may not speed up every developer, study shows | TechCrunch
Software engineer workflows have been transformed in recent years by an influx of AI coding tools like Cursor and GitHub Copilot, which promise to enhance productivity by automatically writing lines of code, fixing bugs, and testing changes. The tools are powered by AI models from OpenAI, Google
[3]
Need to Code Faster? AI Might Be Slowing You Down
Much has been made of how AI tools like Cursor Pro or Anthropic's Claude will revolutionize coders' day-to-day experience, potentially shaving tens of hours off their working weeks -- but research has now cast doubt on those assumptions. A new study assessing the performance of 16 experienced
[4]
AI coding tools make developers slower, study finds
Artificial intelligence coding tools are supposed to make software development faster, but researchers who tested these tools in a randomized, controlled trial found the opposite. Computer scientists with Model Evaluation & Threat Research (METR), a non-profit research group, have published a
[5]
AI slows down some experienced software developers, study finds
SAN FRANCISCO, July 10 (Reuters) - Contrary to popular belief, using cutting-edge artificial intelligence tools slowed down experienced software developers when they were working in codebases familiar to them, rather than supercharging their work, a new study found. AI research nonprofit METR
[6]
Study shows AI coding assistants actually slow down experienced developers
Serving tech enthusiasts for over 25 years. TechSpot means tech analysis and advice you can trust. Cutting corners: In a surprising turn for the fast-evolving world of artificial intelligence, a new study has found that AI-powered coding assistants may actually hinder productivity among seasoned
[7]
Why "use AI" may not be the answer to boosting software productivity
Driving the news: The study by METR, a nonprofit independent research outfit, looked at experienced programmers working on large, established open-source projects. * It found that these developers believed that using AI tools helped them perform 20% faster -- but they actually worked 19%
[8]
AI Promised Faster Coding. This Study Disagrees
In just the last couple of years, AI has totally transformed the world of software engineering. Writing your own code (from scratch, at least,) has become quaint. Now, with tools like Cursor and Copilot, human developers can marshal AI to write code for them. The human role is now to understand
[9]
What Actually Happens When Programmers Use AI Is Hilarious, According to a New Study
AI has taken the programming world by storm, with a flurry of speculation about the tech replacing human coders, and Google's CEO recently claiming that 25 percent of the company's code is now AI-generated. But it's possible that in practice, AI is actually hindering efficient software
[10]
Report - AI tools slow down experienced developers by 19%. A wake up call for industry hype?
A new study from Model Evaluation & Threat Research (METR) has delivered findings that contradict much of what technology vendors have been telling their customers about AI's impact on developer productivity for the past few years. The research, which measured the real-world impact of early-2025 AI
[11]
Experienced Developers are Slower with AI Than Without It, Says METR | AIM
Developers may need to rethink their approach to using AI, and evaluate if it is helping them or not. A new study conducted by Model Evaluation & Threat Research (METR) reveals that experienced open-source developers were slower when using generative AI tools, which was contrary to their initial
[12]
AI Might Be Slowing Down Some Employees' Work, a Study Says
This is a little technical, but it's a fascinating result that cuts through some of the hype surrounding this very buzzy technology. Essentially while coding experts thought AI would help them in their work, the hard data shows it actually slowed down the coders who used it. As Marcus put it, "if
[13]
AI slows down some experienced software developers, study finds - The Economic Times
Contrary to popular belief, using cutting-edge artificial intelligence tools slowed down experienced software developers when they were working in codebases familiar to them, rather than supercharging their work, a new study found. AI research nonprofit METR conducted the in-depth study on a group
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A new study by METR finds that AI coding tools unexpectedly increased task completion time by 19% for experienced open-source developers, contradicting expectations of increased efficiency.
AI Coding Tools Unexpectedly Slow Down Experienced Developers
A groundbreaking study conducted by Model Evaluation and Threat Research (METR) has revealed that AI coding tools, contrary to popular belief, can significantly slow down experienced software developers working on complex projects. The research, which involved 16 seasoned open-source developers completing 246 real-world tasks, found that using AI tools increased task completion time by 19%
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Source: Inc.
Study Methodology and Surprising Results
The randomized controlled trial focused on developers with multiple years of experience working on specific open-source repositories. Tasks included bug fixes, feature implementations, and refactoring work. Half of the tasks were completed using AI tools like Cursor Pro or Anthropic's Claude, while the other half were done without AI assistance
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.Surprisingly, developers initially expected AI tools to reduce task completion time by 24%. Even after completing the tasks, they believed the AI had made them 20% faster. However, the actual results showed a 19% increase in completion time when using AI tools
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.Factors Contributing to the Slowdown
Source: TIME
The study identified several factors contributing to the unexpected slowdown:
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Time spent reviewing AI outputs: Developers accepted less than 44% of AI-generated code without modification, spending significant time reviewing and correcting suggestions
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Prompting and waiting: Considerable time was spent crafting prompts for AI systems and waiting for responses.
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Complexity of existing codebases: The AI tools struggled with large, mature repositories averaging 10 years of age and over 1 million lines of code
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High quality standards: The repositories had very high quality bars for code contributions, limiting AI's effectiveness
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Lack of contextual understanding: AI tools couldn't utilize important tacit knowledge or context about the codebase that human developers possessed
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Implications and Future Outlook
The study's findings challenge the widespread belief that AI tools universally enhance coding productivity. However, the researchers caution against broad generalizations, noting that the results may not apply to all software development scenarios
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Source: Ars Technica
The authors remain optimistic about the future of AI in coding. They suggest that improvements in reliability, latency, and output relevance could lead to efficiency gains. There is already preliminary evidence that newer AI models, such as Claude 3.7, show promise in correctly implementing core functionality for some of the studied repositories
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.Broader Context and Industry Impact
This study adds to a growing body of research examining the real-world impact of AI tools on productivity. While some studies have shown significant gains in coding efficiency, others have found that AI can introduce mistakes and even security vulnerabilities
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.The findings may have implications for companies investing heavily in AI-powered coding tools and for predictions about AI's impact on the job market. However, it's important to note that the study focused on a specific scenario involving experienced developers working on complex, established codebases
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