--- title: Measuring engineering productivity is harder than ever description: AI coding tools have broken traditional engineering productivity metrics by decoupling activity from value. Engineers using tools like Codex open 70% more pull... canonical: https://daily.dev/posts/measuring-engineering-productivity-is-harder-than-ever-aityuv2va twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Measuring engineering productivity is harder than ever | daily.dev og:description: AI coding tools have broken traditional engineering productivity metrics by decoupling activity from value. Engineers using tools like Codex open 70% more pull... og:url: https://daily.dev/posts/measuring-engineering-productivity-is-harder-than-ever-aityuv2va og:image: https://api.daily.dev/og/posts/AITyUv2vA.png og:image:alt: Measuring engineering productivity is harder than ever 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. # Measuring engineering productivity is harder than ever **[LeadDev](https://daily.dev/sources/leaddev)** · 12 min read · 0 upvotes · 0 comments ## Summary AI coding tools have broken traditional engineering productivity metrics by decoupling activity from value. Engineers using tools like Codex open 70% more pull requests, but that number alone reveals nothing about quality or business impact. Traditional proxies — commits, story points, PR counts — were always imperfect, but AI makes them actively misleading. The real shift is that high-value engineering work (architecture decisions, AI output review, guardrail design, mentoring) is increasingly invisible to dashboards. Data from Dropbox, Webflow, Microsoft, and industry benchmarks shows PR volume rising while incident rates climb 23.5% and change failure rates increase ~30%. The recommended fix: sort metrics into activity vs. outcome piles, retire one activity metric per quarter in favor of an outcome metric, and ask senior engineers where their best work doesn't appear in Git history. Frameworks like DORA and SPACE remain useful but must be paired with business outcome signals to reflect how humans and AI now operate as a single delivery system. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Why do pull request counts go up but incidents also rise when engineers use AI coding tools heavily? Pull request volume increases because AI tools like Codex let engineers generate and merge more changes, but quality does not automatically follow. Industry data shows pull requests per author up roughly 20% year-over-year while incidents per pull request rose 23.5% and change failure rates increased about 30%, meaning raw PR counts alone mask a quality decline that only shows up when paired with outcome metrics like change failure rate. _Anyone weighing AI coding tool adoption against quality risk can track this debate on daily.dev._ ### How is Dropbox measuring the impact of AI coding tools on its engineering team? Dropbox reports that engineers who regularly use AI ship about 20% more pull requests, but it never presents that figure alone, always pairing it with change failure rate, which actually fell rather than rose. This pairing is what makes the productivity gain credible, since pull request volume by itself can be a misleading signal of real value. _Engineers building a case for AI tooling ROI can follow how companies like Dropbox measure it on daily.dev._ ### What are the DORA AI Capabilities Model's seven organizational capabilities for AI adoption? DORA's 2025 report lists seven capabilities that determine whether AI adoption compounds or backfires: a clear communicated AI policy, a healthy data ecosystem, AI-accessible internal data, strong version-control discipline, working in small batches, a user-centric focus, and a quality internal platform. These function as a maturity audit rather than metrics for a dashboard. _Leaders auditing AI readiness before rolling out coding assistants can track this framework on daily.dev._ ## Similar posts on daily.dev - [It’s time to rethink how we measure engineering productivity](https://daily.dev/posts/it-s-time-to-rethink-how-we-measure-engineering-productivity-nobwkqmxy) · LeadDev · 0 upvotes · 0 comments - [How To Measure Developer Productivity?](https://daily.dev/posts/how-to-measure-developer-productivity--0wzq4dgre) · Itamar Gilad · 94 upvotes · 18 comments - [AI-Assisted Engineering: Measure Outcomes, Not Activity](https://daily.dev/posts/ai-assisted-engineering-measure-outcomes-not-activity-kgafc31vm) · Thomas Thornton · 33 upvotes · 5 comments - [The current impact of AI on engineering velocity](https://daily.dev/posts/the-current-impact-of-ai-on-engineering-velocity-lic0nalsa) · Software Engineering Research · 0 upvotes · 0 comments --- Tags: [#leadership](https://daily.dev/tags/leadership), [#ai-coding](https://daily.dev/tags/ai-coding), [#dora-metrics](https://daily.dev/tags/dora-metrics) [View this post on daily.dev](https://daily.dev/posts/measuring-engineering-productivity-is-harder-than-ever-aityuv2va) ```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":"Measuring engineering productivity is harder than ever","url":"https://daily.dev/posts/measuring-engineering-productivity-is-harder-than-ever-aityuv2va","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/measuring-engineering-productivity-is-harder-than-ever-aityuv2va"},"datePublished":"2026-07-21T11:57:59.957Z","dateModified":"2026-09-13T19:16:23.671Z","description":"AI coding tools have broken traditional engineering productivity metrics by decoupling activity from value. 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