Anyone who has even tangentially associated with computer programming knows that modern AI coding assistants and agents can be incredibly efficient at generating huge amounts of functional code. But coders making use of those tools also know better than to trust the accuracy of that code, meaning substantial effort needs to be spent reviewing any AI-generated output.
A recent study 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.”
To come to these conclusions, Harvard University researchers Fiona Chen and James Stratton made use of aggregated analytics data from Jellyfish, which measures the granular output of engineering teams. That data encompasses 300 million individual “work events” (e.g., commits and pull requests) and issue management software data across more than 700,000 employees at over 700 relevant software development firms from 2021 through March of 2026.
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 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. The share of pull requests with changes requested nearly doubles, and the number of comments per pull request increases by 35% following the AI agent shift.
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.
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, 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.
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.