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Building software faster does not prove that people can use it successfully. Developer speed, delivery throughput, release stability, product quality and user task success are different outcomes—and evidence about one cannot stand in for evidence about the others.
What does “faster software” actually measure?
A faster coding task is a process measure: it says something about how quickly a developer completed work. It does not, by itself, show that a change was released safely, improved the product or helped a user finish a task.
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It helps to separate the stages and outcomes:
- Code generation: how quickly code or a suggested change is produced.
- Developer task time: how long it takes to complete a defined piece of work.
- Delivery throughput and stability: how much work reaches users and whether releases remain reliable.
- Product quality: whether the resulting product meets its intended needs.
- User task success: whether people can understand and use the software to accomplish what they came to do.
Teams may accelerate one stage while validation, release work or understanding user needs remains a bottleneck. That is why “we shipped more code” is not an adequate answer to “can users use it?”
What does the evidence say about AI and development speed?
There is no single result that applies to every team or task. The available studies measure different things, use different methods and point to context as an important factor.
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Organization-wide findings show tradeoffs
DORA’s 2024 report says AI adoption significantly increases individual productivity, flow and job satisfaction, while negatively affecting software delivery stability and throughput. Its summary emphasizes small batch sizes and robust testing as ways to manage delivery. These are organizational findings, not proof that every AI-assisted change is faster or less stable. Read DORA’s 2024 report.
DORA’s 2025 report describes AI as an amplifier of an organization’s existing strengths and weaknesses. Its central implication is that results depend on the system around the tools, not just on access to the tools. Read DORA’s 2025 report.
A controlled trial found slower completion in a narrow setting
A 2025 randomized controlled trial by Becker, Rush, Barnes and Rein involved 16 experienced open-source developers completing 246 tasks in mature repositories. In that setting, allowing early-2025 AI tools increased task completion time by 19%. The authors note that experimental artifacts cannot be entirely ruled out. This result is specific to those developers, projects, tasks and tools; it does not establish that AI always slows software development. Read the trial paper.
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Developer perceptions vary by task and adoption
A Microsoft Research mixed-methods study, published in August 2025, surveyed over 500 developers and also included qualitative research. Developers broadly viewed AI as helpful, particularly for routine tasks, but reported variation according to task complexity, personal use and team adoption. These findings describe developer experience and perceptions; they are not a direct measure of whether end users can use the resulting software. Read Microsoft Research’s study summary.
These findings are not contradictory measurements of the same thing. The controlled trial measured task time in mature projects; the Microsoft work studied developers’ experiences across work contexts; DORA reports organizational patterns. None supplies a universal productivity multiplier.
Does faster coding mean better software?
No. Faster coding may help a team move work forward, but code speed alone cannot establish product quality or usability. DORA’s 2024 report says organizations that prioritize end-user experience build higher-quality products, and associates a user-centric mindset with developer productivity, satisfaction and lower burnout. Those organizational findings support prioritizing users; they are not a guarantee about any particular interface or a direct comparison of AI-built and conventionally built products. See DORA’s 2024 findings.
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DORA’s research archive describes its Core Model as an evolving practitioner guide, rather than a fixed recipe for every organization. That is consistent with treating software improvement as a process of learning and measurement rather than assuming a tool produces the same result everywhere. Explore the DORA research archive.
How can a team tell whether users can use a new feature?
The direct way to answer a usability question is to observe whether intended users can complete the relevant tasks, not to infer success from lines of code or developer confidence. A practical evaluation starts with the user outcome and compares results as a change is introduced.
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- Define the user task. State what the person is trying to accomplish and what counts as successful completion.
- Establish a baseline. Record how the existing experience performs before the change, using measures suited to the task, such as completion or errors.
- State a hypothesis. Specify what the change is expected to improve and for whom.
- Release and assess in small batches. Keep changes reviewable, with robust testing and checks for delivery stability.
- Measure the result iteratively. Compare the new experience with the baseline, investigate failures, and adjust before expanding the change.
DORA’s 2024 guidance recommends experimental continuous improvement: establish a baseline, state hypotheses and measure changes iteratively. A user-task measure is a more direct answer to “can users use it?” than developer task time, though the reviewed studies do not quantify a universal usability metric for AI-assisted products.
What remains unknown?
The cited evidence does not directly compare end-user task success in software developed with AI assistance against software developed without it. DORA reports organizational outcomes and user-centricity findings; the 2025 trial measures developer completion time in a specific setting; and the Microsoft Research study focuses on developer experience. They should not be combined into a claim that AI-built products are more or less usable.
The defensible conclusion is narrower and more useful: development speed is worth measuring, but it is not a substitute for testing delivery reliability, product quality and whether users can accomplish their tasks.
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