AI coding agents generate more code, but not more software
The introduction of AI coding agents has led to a 49 percent increase in the average review process time for pull requests, with the share of pull requests with changes requested nearly doubling and comments per pull request increasing by 35%. While 80 percent of firms used some form of AI code review by March 2026, AI agents were only responsible for 23.3 percent of review comments and 10.8 percent of pull requests, indicating that humans still bear the majority of the review burden. This suggests that increased coding speed is offset by longer human review times.
This report uniquely quantifies the trade-off of AI coding agents, showing a 49% increase in code review time and a near-doubling of change requests, unlike other studies focusing solely on code generation speed.
Time & source
Times shown in UTC
Display time zone: UTC
Local time zone unavailable; showing UTC.
PublishedOffset at this time: UTC+0Oct 9, 2026, 19:43 UTC
IngestedOffset at this time: UTC+0Oct 9, 2026, 20:00 UTC
- Published
- Oct 9, 2026, 19:43
- Ingested
- Oct 9, 2026, 20:00
- Source type
- Media
- Tier
- Press
- Source status
- Healthy
Tier is a per-source editorial setting, not a per-item score.
Discussion trend
The percentage is based on collected discussion signal, not new comments or independent people. The curve only compares the same topic across time.
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 requested nearly doubl[ing], and the number of comments per pull request increas[ing] by 35%” following the AI agent shift, the researchers write.
In response to this change, the researchers found a 14 percent increase in the share of workers performing code reviews after AI agents’ introduction. They also write that they “cannot attribute significant employment changes to AI” after looking at total active workers across Jellyfish and cross-referencing with LinkedIn data at those firms.
Pull requests need changes a lot more often in the “agentic coding” era.
Chen and Stratton
While AI could also theoretically help with this review process, the researchers found that, so far, that impact has been marginal. Although 80 percent of measured firms used some form of AI code review by March 2026, AI agents were only responsible for 23.3 percent of all review comments and 10.8 percent of all pull requests, suggesting humans were still responsible for the vast majority of this work.
AI agents are still a relatively new part of the coding world, of course, and there have been significant updates and upgrades to their output even since this study’s March 2026 data cutoff. And while 95 percent of firms in the study have implemented AI coding agents by this point, many are doubtlessly still going through a learning process regarding when and how to best deploy them. These kinds of “coding time versus review time” trade-offs could improve as software engineering teams gain more experience with the pros and cons of siccing an AI agent on particular coding problems.
For now, though, letting AI write your code looks like a double-edged sword, with increases in coding speed counteracted by similar increases in human code review time and effort. It’s the kind of result that makes us wonder if the considerable time and expense to get AI coding agents working is really worth it for most companies.