METR's results challenge previous benchmarks suggesting AI tools increase coding efficiency, as they measure productivity through methods often unrelated to actual coding effectiveness. Many benchmarks involve synthetic tasks, complicating comparisons with real-world scenarios. Surveyed developers cited the complexity of their aging repositories, which average 10 years and contain over 1 million lines of code, as a limit to AI's help. Tacit knowledge about codebases is crucial for efficiency, suggesting AI tools are less suited for high-quality standard projects while potential for future efficacy improvements exists.
Current AI coding tools may be ill-suited to settings with high quality standards or many implicit requirements, limiting efficiency amidst nuanced programming contexts.
Many coding benchmarks focus on synthetic tasks that are difficult to compare with real-world projects, which affects the measurement of coding efficiency.
Developers noted that the complexity of their repositories, averaging 10 years old with over 1 million lines of code, hindered the effectiveness of AI tools.
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