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More code is an intermediate output, not proof that a team has shipped more useful software. To judge whether an AI coding agent helps, track what happens after generation: whether changes are accepted, delivered reliably, maintained, and used. Studies find benefits on some bounded tasks and slower work in some mature codebases, so the answer depends on what is being measured and where the tool is used.
What counts as “more software”?
Generated lines, suggestions, or files measure activity inside a development process. Software value emerges further downstream: a change must solve a real problem, pass review and tests, reach users, remain stable, and be maintainable when requirements change. More generated code may contribute to that outcome, but it may also be revised, rejected, duplicated, or become extra complexity.
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That distinction matters because productivity claims often collapse different outcomes into one. A faster solution to a short coding exercise is not the same as more changes deployed to production; more deployments are not necessarily more stable service; and more features do not establish that users need or use them.
What the evidence says—and what it does not
The findings below concern different populations, tools, periods, and measures. They are not competing estimates of one universal “AI productivity” effect.
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| Evidence | Reported result | What it measures | How to interpret it |
|---|---|---|---|
| Microsoft Research, 2023 | Participants completed a JavaScript HTTP-server task 55.8% faster with GitHub Copilot than the control group. | Completion time on one controlled programming task. | Evidence that assistance can accelerate a bounded task; it does not establish higher production delivery or software value across other work. |
| METR, July 10, 2025 | Experienced open-source developers took 19% longer with early-2025 AI tools in a randomized trial. | Task completion while developers worked in their own repositories. | A result for this experienced population, repository context, and tool period—not a finding about every developer or current tool. |
| DORA, 2024 | For a modeled 25% increase in AI adoption, the report estimated improved documentation quality, code quality, code-review speed, approval speed, and code complexity, alongside lower delivery throughput and delivery stability. | Report estimates of relationships between AI adoption and process and delivery outcomes. | These are estimates with uncertainty intervals, not guaranteed causal effects or constants that apply to every organization. |
| NBER Working Paper 35275, 2026 | The record’s summary describes data from more than 500,000 GitHub developers and reports more new apps without increased total usage across four software marketplaces. | App creation and marketplace usage across generations of AI coding tools. | The summary supports the distinction between producing apps and gaining usage. It does not provide enough methodological detail to generalize the result beyond the stated summary. |
Why a fast task result and a slower repository result can both be true
The Microsoft Research experiment tested a bounded JavaScript task. METR studied experienced developers making changes in their own established repositories, where understanding local conventions and safely modifying existing systems are part of the work. Those settings ask different questions: how quickly can someone complete a defined task with assistance, versus how much time does assistance save on real work in a mature codebase?
Neither result cancels the other. Together they show why a single headline productivity percentage is misleading unless it specifies the task, participants, tools, and outcome being measured.
Why “more apps” need not mean “more software used”
The NBER record’s summary draws a useful line between creation and adoption: it reports more new apps without increased total usage across four marketplaces. That is consistent with a basic product constraint—additional supply does not, by itself, create user demand. Because the available record summary gives only high-level findings, the result should not be stretched into a claim about all software markets or all AI tools.
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DORA’s 2024 report estimated several gains associated with a modeled 25% increase in AI adoption: documentation quality rose 7.5%, code quality 3.4%, code-review speed 3.1%, and approval speed 1.3%, while code complexity fell 1.8%. For the same modeled adoption increase, it estimated a 1.5% decrease in delivery throughput and a 7.2% decrease in delivery stability. These are report estimates with uncertainty intervals; they should not be read as direct effects that every team will experience.
The pattern illustrates why process improvements and delivery outcomes must be tracked separately. Better documentation or faster reviews can coexist with slower overall delivery if work accumulates elsewhere—for example, in integration, rework, testing, or release coordination. DORA suggests larger change batches may help explain weaker delivery outcomes and emphasizes small batches and robust testing. That is an interpretation of a possible mechanism, not settled proof of why the estimates moved.
In its 2025 report, DORA describes AI as an amplifier of existing organizational strengths and weaknesses. The report’s evidence includes more than 100 hours of qualitative research and responses from nearly 5,000 technology professionals, as summarized by Google Research. DORA’s report page puts the idea this way: “The State of AI-assisted Software Development report reveals AI’s primary role is as an amplifier, magnifying an organization’s existing strengths and weaknesses.” This organizational evidence helps frame adoption, but it is not a randomized estimate of the effect of one individual coding agent.
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How should a team tell whether an AI agent helps it ship?
Use a measurement chain rather than a code-volume target. Keep the stages distinct so a gain at one point does not conceal a loss later.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errors- Generation: Record where AI assistance is used and what work it produces. Treat generated code volume as an activity measure, not a success metric.
- Acceptance: Track how much suggested or AI-assisted work is retained after developer review, and how much is substantially rewritten or discarded. Define the attribution method consistently.
- Delivery: Measure accepted changes that reach production, along with lead time and throughput. Include work that is rolled back or delayed rather than counting every merge as a successful delivery.
- Stability: Watch production failures, incidents, rework, and recovery alongside delivery volume. A team that deploys more changes but experiences more disruption has not shown an unqualified improvement.
- Maintainability: Examine whether changes make future work easier or harder, including complexity and the effort required to understand, test, and modify the code.
- Use and value: Check whether delivered features are adopted or solve the intended user problem. Creation and deployment alone do not establish that the result is useful.
Compare these outcomes over a defined period and against a meaningful baseline. Separate new or self-contained tasks from work in established repositories, and note which tools and workflows were in use. Otherwise, a change in task mix or team practice can be mistaken for an effect of the agent.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should teams change in practice?
Keep changes small enough to review and test
AI can make it easier to produce a larger patch quickly. That does not make a large patch easier to understand or safer to merge. Smaller batches give reviewers a clearer unit of work and make failures easier to localize. DORA emphasizes small batch sizes and robust testing; teams should treat these as delivery practices to preserve, not assume that faster code generation replaces them.
Evaluate by task type, not by a company-wide average
Results on a short, clearly specified task may not predict results in an unfamiliar codebase, a legacy system, or work that depends on subtle product requirements. Record task context when evaluating assistance. If the agent saves time on routine scaffolding but adds review or integration time elsewhere, the net effect should be visible rather than hidden by an aggregate.
Make the review burden part of the calculation
Generated code still needs to be checked for correctness, security, fit with existing architecture, and test coverage. Measure the full path from prompt or assignment to a reliable change, including correction and review time. A faster first draft is useful only if it does not create a larger downstream burden.
So, do AI coding agents help teams ship more?
Sometimes they help with particular tasks; current evidence does not establish that they universally increase delivered, stable, maintainable, or widely used software. The relevant test is not whether an agent generated more code, but whether the team’s complete delivery chain improved without trading away quality, stability, or user value.
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