--- title: AI coding agents generate more code, but not more software description: A Harvard study analyzing data from Jellyfish across 700+ software firms, 700,000+ employees, and 300 million work events found that AI coding agents increase... canonical: https://daily.dev/posts/ai-coding-agents-generate-more-code-but-not-more-software-mbkcatr6y twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: AI coding agents generate more code, but not more software | daily.dev og:description: A Harvard study analyzing data from Jellyfish across 700+ software firms, 700,000+ employees, and 300 million work events found that AI coding agents increase... og:url: https://daily.dev/posts/ai-coding-agents-generate-more-code-but-not-more-software-mbkcatr6y og:image: https://api.daily.dev/og/posts/MbkcatR6y.png og:image:alt: AI coding agents generate more code, but not more software og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # AI coding agents generate more code, but not more software **[Hacker News](https://daily.dev/sources/hn)** · 3 min read · 0 upvotes · 0 comments ## Summary A Harvard study analyzing data from Jellyfish across 700+ software firms, 700,000+ employees, and 300 million work events found that AI coding agents increase raw code output significantly: 30% more lines of code, 20% more commits, and 23% more pull requests. However, this increased output does not translate into more completed software features, as measured by Jira issue and epic resolution rates, which remained statistically unchanged. The researchers conclude that human code review acts as a bottleneck, absorbing the coding-phase efficiency gains: review cycles lengthen, pull requests need more revisions, and reviewers leave more comments after AI tool adoption. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Do AI coding agents actually increase software output at companies that adopt them? Not according to a Harvard study analyzing data from over 700 software firms and 700,000 employees via Jellyfish analytics. While AI coding agents increased lines of code by 30 percent, commits by 20 percent, and pull requests by 23 percent, the resolution rate for Jira-tracked issues and epics showed no statistically significant change after adoption. _Developers weighing AI coding agent adoption can follow emerging productivity research on daily.dev._ ### Why doesn't more AI-generated code lead to more completed software features? Human code review becomes a bottleneck that absorbs the efficiency gains from faster code generation. As AI tool usage increases, the code review process lengthens, pull requests are more likely to need revisions, and reviewers leave more comments, offsetting the time saved during the coding phase itself and leaving overall feature delivery rates unchanged. _Teams rethinking review workflows around AI-generated code can track this debate on daily.dev._ ## Community take How the wider developer community reacted, aggregated from 3 discussions and 239 comments across hackernews (as of 2026-10-13). **TL;DR:** Discussion largely agrees that code review, not coding, is the real bottleneck for AI-assisted development, but there's deep disagreement over whether traditional line-by-line review should be abandoned, automated, or replaced with something new. **Sentiment:** 15% positive · 45% mixed · 40% skeptical **The case for** - AI agents can efficiently mass-produce fixes and handle low-priority backlog items when review is calibrated to risk. - Some report success using AI to audit and rewrite bloated or low-value test suites, catching real bugs in the process. - Shifting review effort to higher-level architecture/design concerns while letting agents handle line-by-line correctness can work well for some teams. **The pushback** - Code review has become a bigger bottleneck with agentic coding, with longer cycles and more revisions. - Many argue line-by-line human review can't simply be dropped, especially for critical or high-risk code paths. - AI-generated tests are often criticized as shallow, overly whiteboxed, or theater that doesn't catch real issues. - Non-technical or less rigorous contributors submitting AI-generated PRs can create large volumes of low-quality code that still require human triage. - Skeptics note that despite velocity claims, no clear wave of dramatically faster-shipping startups or superior products has actually materialized. **By community** - hackernews (heated): A long, contentious thread arguing over whether code review should be abandoned, automated, or reinvented, with strong voices on both the 'review is theater' and 'review is essential' sides. **Hottest debate:** Whether human line-by-line code review is an obsolete bottleneck that should be dismantled or a necessary safeguard that AI coding makes even more essential. **Open questions** - What new review practice, if any, should replace traditional line-by-line review in an agentic coding era? - How do teams reliably distinguish 'impactful' changes needing careful review from routine ones, given that bugs often appear in supposedly low-risk areas? - Will startups that adopt no-human-review policies actually demonstrate superior shipping velocity and product quality over time? **Highlights** > This is from March 2026, but it does match what I've encountered professionally. Code review was always a significant bottleneck and is even more so with agentic coding. We've had discussions about leaning more on automated review, but it hasn't translated into any practical improvements so far. I think senior engineers find it very difficult to give up on line-by-line review, no matter how much better the agents have gotten. I think if there is a major change in code review culture, it's not going to happen first in established engineering teams. It'll happen in startups that adopt a no-human-review policy from jump and then maintain it as they mature. I'm sure that's happening, but it's likely invisible to most studies and surveys at the moment. If it proves successful, and such companies start shipping significantly faster, the rest of the industry will be forced to adopt. > — [staminade on hackernews · 24 comments](https://news.ycombinator.com/item?id=50060068) > As a senior engineer that is struggling to give up line-by-line reviews, I agree with your take. I've spent literal decades where one of the most valuable practices has been code reviews, for many dimensions. It is one of the best ways to ensure quality production releases. It is one of the best ways to transfer skill to junior engineers. It increases tribal knowledge throughout the engineering org by exposing others to systems they may not be working in. The number of benefits to a traditional human-centric engineering team is hard to overstate. But I'm starting to feel that instead of starting at one extreme (heavy handed reviews) and moving towards less review, it probably makes more sense to start at the other extreme (no review at all) and move towards a brand new practice tailored for the new agentic reality. It is looking increasingly likely that we don't need to modify code review for the agentic era, we need some completely new practice that hasn't yet been invented. > — [stillpointlab on hackernews · 3 comments](https://news.ycombinator.com/item?id=50060902) > IME, the biggest source of rework cost that happens at (or after) code review is when someone notices that the code, correctly or not, implements the wrong thing. For example, the feature, as implemented, doesn’t sufficiently solve the original business problem. Or it solves it in a bad way, like because of overengineering or taking on unnecessary technical debt. I haven’t really seen much evidence of AI code review catching either of these problems. I’ve heard people complain that human code review doesn’t reliably catch them, either. And I have seen that with my own eyes. Just not universally. Teams that have a strong culture of shared ownership and collaborating on designs regularly catch these kinds of problems in code review. If they’re really good at it, they catch them even earlier. e.g., I know planning poker isn’t fashionable anymore, but when I’ve been on teams that take it seriously they tend to be pretty good at finding minimally expensive, correct designs pretty early in the process. Which makes both coding snd code review go faster because simpler designs are easier to manage. And design flaws that do make it to code review tend to be smaller and less expensive to fix. So those teams didn't tend to bottleneck on code review in the first place. I think that this is the kind of thing that led to folks like DORA observing that AI coding is something of a multiplier for the effects of both good and bad software development practice. If you’ve got a cowboy coding culture, throwing AI into the mix will probably just lead to agentic cowboy coding. > — [bunderbunder on hackernews](https://news.ycombinator.com/item?id=50062984) > > I think if there is a major change in code review culture, it's not going to happen first in established engineering teams. It'll happen in startups that adopt a no-human-review policy from jump and then maintain it as they mature. I've been hearing this for more than a year. But if these startups are all moving at superhuman speed, a month instead of a year from idea to a shipping product, where are the products? Every time I ask that, people talk about the amazing velocity gains they experience for the thing they are about to ship. It never does, unless we count throwaway hobby projects at GitHub - by that metric, AI is adding a lot of software. And I guess never-downloaded slop-games on mobile app stores. But if you actually go shopping for consumer or enterprise software that's camera-ready, the landscape is basically the same as in 2022. > — [socializer on hackernews · 3 comments](https://news.ycombinator.com/item?id=50061175) > Yup we already knew this. Google had many engineers working on a) failed projects b) people being paid to sit around. There seems to be this strange belief on here that making something more accessible to everyone will always result in better outcomes. I don’t see this being true of software. I’m very pro-tech. But nobody can point me to a product built by someone outside of the large tech firms, made by by llm’s that is a great value add to one’s life. Many of those were already created - they’re on the apple App Store for example. I’m absolutely confident coding speed is irrelevant. A person who has a real vision + strategy and hand written code will beat the team with no vision, strategy and code blasted out by llm’s. Sure llm’s could augment the former - but you’re missing the point!! > — [euiee on hackernews · 2 comments](https://news.ycombinator.com/item?id=50062121) **Source threads** - [hackernews](https://news.ycombinator.com/item?id=50059523) · 272 points · 231 comments - [hackernews](https://news.ycombinator.com/item?id=50050448) · 11 points · 2 comments - [hackernews](https://news.ycombinator.com/item?id=50030717) · 9 points · 6 comments ## Similar posts on daily.dev - [As AI helps us write more code, who’s catching the bugs?](https://daily.dev/posts/as-ai-helps-us-write-more-code-who-s-catching-the-bugs--swtlsnqbi) · LeadDev · 1 upvotes · 1 comments --- Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#github](https://daily.dev/tags/github), [#productivity](https://daily.dev/tags/productivity), [#code-review](https://daily.dev/tags/code-review) [View this post on daily.dev](https://daily.dev/posts/ai-coding-agents-generate-more-code-but-not-more-software-mbkcatr6y) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"AI coding agents generate more code, but not more software","url":"https://daily.dev/posts/ai-coding-agents-generate-more-code-but-not-more-software-mbkcatr6y","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/ai-coding-agents-generate-more-code-but-not-more-software-mbkcatr6y"},"datePublished":"2026-10-12T18:48:36.684Z","dateModified":"2026-10-13T01:34:51.102Z","description":"A Harvard study analyzing data from Jellyfish across 700+ software firms, 700,000+ employees, and 300 million work events found that AI coding agents increase...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/dd3b48596bdeb82a4a17a85b93cbd171?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/dd3b48596bdeb82a4a17a85b93cbd171?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Hacker News","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Hacker News","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/hn","url":"https://daily.dev/sources/hn"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/ai-coding-agents-generate-more-code-but-not-more-software-mbkcatr6y","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"ai-agents,github,productivity,code-review","timeRequired":"PT3M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Hacker News","item":"https://daily.dev/sources/hn"},{"@type":"ListItem","position":3,"name":"AI coding agents generate more code, but not more software"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/ai-coding-agents-generate-more-code-but-not-more-software-mbkcatr6y#faq","mainEntity":[{"@type":"Question","name":"Do AI coding agents actually increase software output at companies that adopt them?","acceptedAnswer":{"@type":"Answer","text":"Not according to a Harvard study analyzing data from over 700 software firms and 700,000 employees via Jellyfish analytics. While AI coding agents increased lines of code by 30 percent, commits by 20 percent, and pull requests by 23 percent, the resolution rate for Jira-tracked issues and epics showed no statistically significant change after adoption. Developers weighing AI coding agent adoption can follow emerging productivity research on daily.dev."}},{"@type":"Question","name":"Why doesn't more AI-generated code lead to more completed software features?","acceptedAnswer":{"@type":"Answer","text":"Human code review becomes a bottleneck that absorbs the efficiency gains from faster code generation. As AI tool usage increases, the code review process lengthens, pull requests are more likely to need revisions, and reviewers leave more comments, offsetting the time saved during the coding phase itself and leaving overall feature delivery rates unchanged. Teams rethinking review workflows around AI-generated code can track this debate on daily.dev."}}]} ```