Artificial intelligence is writing more code, but software isn't being released faster.
One of the most interesting pieces of data I've read about AI coding agents is this: more code doesn't automatically mean more software.
A study by Fiona Chen and James Stratton from Harvard analyzed data from over 700 software companies, covering over 700,000 employees and 300 million work events from 2021 to March 2026. After the introduction of coding agents, the average number of lines of code increased by 30%, the number of commits by 20%, and the number of pull requests by 23%.
But the key question is: does this translate to actual progress? There was no statistically significant change in the completion rates of bugs, tasks, and features tracked in tools like Jira.
The bottleneck is in code review. The time it takes for a pull request to be submitted and merged into the main codebase increased by 49%. The rate of pull requests requiring changes nearly doubled, the number of comments per pull request increased by 35%, and the percentage of employees involved in code review also rose by 14%. In other words, a significant portion of the speed gained in the coding phase is being lost in the review and correction process.
Companies are trying to solve this with AI: as of March 2026, 80% of the companies studied were using some form of AI-powered code review tool. However, only 23.3% of review comments came from these agents, and they only contributed to 10.8% of pull requests; the vast majority of the work is still being done by humans. There was also no significant change in the number of active employees, at least during the period studied.
It's important to note that the data ends in March 2026, and the tools have likely improved since then. But the lesson I take away is clear: to improve efficiency, it's not enough to just speed up the coding process; you also need to accelerate code review, bug fixing, and integration.
News article on Ars Technica: https://arstechnica.com/ai/2026/10/ai-coding-agents-generate-more-cod…
PDF of the study: https://fion.ac/jellyfish.pdf
Source: DonanımHaber
A study by Fiona Chen and James Stratton from Harvard analyzed data from over 700 software companies, covering over 700,000 employees and 300 million work events from 2021 to March 2026. After the introduction of coding agents, the average number of lines of code increased by 30%, the number of commits by 20%, and the number of pull requests by 23%.
But the key question is: does this translate to actual progress? There was no statistically significant change in the completion rates of bugs, tasks, and features tracked in tools like Jira.
The bottleneck is in code review. The time it takes for a pull request to be submitted and merged into the main codebase increased by 49%. The rate of pull requests requiring changes nearly doubled, the number of comments per pull request increased by 35%, and the percentage of employees involved in code review also rose by 14%. In other words, a significant portion of the speed gained in the coding phase is being lost in the review and correction process.
Companies are trying to solve this with AI: as of March 2026, 80% of the companies studied were using some form of AI-powered code review tool. However, only 23.3% of review comments came from these agents, and they only contributed to 10.8% of pull requests; the vast majority of the work is still being done by humans. There was also no significant change in the number of active employees, at least during the period studied.
It's important to note that the data ends in March 2026, and the tools have likely improved since then. But the lesson I take away is clear: to improve efficiency, it's not enough to just speed up the coding process; you also need to accelerate code review, bug fixing, and integration.
News article on Ars Technica: https://arstechnica.com/ai/2026/10/ai-coding-agents-generate-more-cod…
PDF of the study: https://fion.ac/jellyfish.pdf
Source: DonanımHaber
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