# AI Coding Agents Generate More Code, But Not More Software

DevFeed: [AI Coding Agents Generate More Code, But Not More Software](<https://devfeed.tech/articles/ai-coding-agents-generate-more-code-but-not-more-software-85587.md>)

Original publisher: [Read original article](<https://tech.slashdot.org/story/26/10/12/0234217/ai-coding-agents-generate-more-code-but-not-more-software>)

Author: BeauHD

Published: 2026-10-12T17:00:00Z

Content type: news

Language: en

Sources: [Slashdot](<https://devfeed.tech/sources/slashdot.md>)

Topics: [AI-assisted coding](<https://devfeed.tech/topics/ai-assisted-coding.md>), [Code review](<https://devfeed.tech/topics/code-review.md>), [Pull Request](<https://devfeed.tech/topics/pull-request.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-code-review](<https://devfeed.tech/tags/ai-code-review.md>), [ai-coding-agents](<https://devfeed.tech/tags/ai-coding-agents.md>), [code-review](<https://devfeed.tech/tags/code-review.md>), [developers](<https://devfeed.tech/tags/developers.md>), [pull-request](<https://devfeed.tech/tags/pull-request.md>), [software](<https://devfeed.tech/tags/software.md>)

## AI overview

A study of more than 700 software firms found that AI coding agents increased code output, commits, and pull requests without a statistically significant increase in issue resolution. Pull request review time rose by an average of 49 percent, with more requested changes and reviewer comments. The report attributes the gap between coding activity and shipped software to downstream review constraints.

## Source excerpt

A study of more than 700 software firms found that AI coding agents significantly increase the amount of code developers produce, but not the amount of software companies actually ship. Ars Technica reports: A recent study (PDF) of actual coding practices across hundreds of firms finds that human code review forms a significant "bottleneck" for the overall efficiency of AI coding tools, resulting in "little evidence that firms increase software output or reduce employment" by using them. Any efficiency increased during the actual coding phase, the study authors find, is "absorbed by downstream constraints in the production process"; as "the code review process significantly increases in length, pull requests are more likely to require revisions, and reviewers leave more comments." [...] In terms of raw code being produced, the results are clear and stark. The introduction of AI coding agents at a firm leads to a 30 percent increase in total lines of code generated, a 20 percent rise in the number of total commits, and a 23 percent increase in pull requests on average, the researchers write. But all that extra code doesn't translate directly into improved software output on the firm level. On the contrary, the resolution rate for Issues and Epics (i.e. wholesale software features) tracked by tools like Jira did not change in a statistically significant way after AI tools were introduced (the researchers also found no "compositional shift" in the size or complexity of those Jira-tracked issues across the AI introduction). The reason for that discrepancy can be found directly in the code review process, which takes markedly longer on average after the introduction of AI coding agents. Overall, the average "review process" time between a pull request getting submitted and it being merged into the codebase balloons 49 percent on average after AI agents are introduced. That effect can be seen in more granular data, too, with "the share of pull requests with changes reques