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Building software faster does not prove that people can use it successfully. Developer speed, release throughput, software stability and user task success are different outcomes—and evidence about one cannot stand in for the others. Current research points to a practical test: measure whether users can complete the tasks a feature is meant to support, while tracking the engineering outcomes that affect whether it can be delivered reliably.
What does “faster software” actually measure?
“Faster” can refer to several stages: generating code, completing a developer’s task, shipping changes, or helping a user finish a task. These measures are related, but they are not interchangeable. A developer may finish a coding task quickly while review, testing, release work or user validation remains unfinished. A high volume of changes does not by itself show that those changes are stable or useful.
- Developer task time: how long it takes an engineer to complete a defined coding task.
- Delivery throughput: how much work reaches users over time.
- Delivery stability: whether changes can be released and operated reliably.
- User task success: whether intended users can complete the task the software is supposed to support.
The last measure answers the headline question most directly. Lines of code, coding speed and developers’ impressions may help explain how work gets done, but they cannot establish that users understand an interface or reach the outcome they need.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsWhat current evidence says about AI and software work
AI’s results depend on the organization around it
DORA’s 2025 report describes AI as an amplifier of an organization’s existing strengths and weaknesses. It argues that “The greatest returns on AI investment come not from the tools themselves, but from a strategic focus on the underlying organizational system.” That is an organizational finding, not a guarantee that adopting AI will improve every team’s output or its users’ experience. DORA / Google Cloud, 2025
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Productivity can move differently from stability and throughput
DORA’s 2024 summary reports that AI adoption significantly increases individual productivity, flow and job satisfaction, while negatively affecting software delivery stability and throughput. It emphasizes small batch sizes and robust testing as ways to support better delivery outcomes. In other words, an individual feeling more productive does not automatically mean a team is shipping more reliably. DORA / Google Cloud, 2024
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. When early-2025 AI tools were allowed, measured task completion time increased by 19% in that study. The authors note that experimental artifacts cannot be entirely ruled out. The result is specific to those developers, tasks, projects and tools; it does not show that AI always slows developers or predict results with current tools in other settings. Becker et al., 2025
Developers often see benefits, especially for routine work
A Microsoft Research mixed-methods study, summarized in August 2025, drew on survey responses from more than 500 developers as well as interviews and observational research. Developers broadly viewed AI as helpful, particularly for routine tasks, but reported variation based on task complexity, personal use and team adoption. This evidence describes developer experience and perception; it does not measure whether end users succeed with AI-assisted products. Microsoft Research, 2025
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →These findings are not contradictory measurements of the same thing. The trial measures developer task time in a particular setting; the Microsoft study examines reported experiences; and DORA’s reports address organizational performance and practices. None supplies a controlled comparison of end-user task success in software built with AI versus software built without it.
Why user-centered work is part of software quality
DORA’s 2024 report says, “User-centricity is the ultimate driver of performance: Organizations that prioritize the end-user experience build higher-quality products.” It also associates a user-centric mindset with developer productivity and satisfaction and lower burnout. These are organizational research findings, not a universal causal guarantee about any single interface. Their practical implication is still important: user needs belong in the definition of success, not just in a final polish pass. DORA / Google Cloud, 2024
A feature can meet its technical specification and still leave users uncertain about what to do, unable to recover from an error, or blocked from completing the intended task. Those are questions to investigate with users and product measures—not outcomes that can be inferred from coding speed.
How to tell whether users can actually use a change
- Define the intended user outcome. State what a user should be able to accomplish and under what conditions. “Ship the feature” is a delivery milestone; it is not a user outcome.
- Establish a baseline. Record how users currently perform the task, where they struggle and what a successful completion means. DORA recommends experimental continuous improvement: establish a baseline, state a hypothesis and measure changes iteratively. DORA / Google Cloud, 2024
- Test the task, not just the implementation. Observe representative users attempting the intended task, and measure whether they finish successfully. Note confusion, errors and points where assistance is needed. This is a practical way to answer the usability question directly; the studies above do not quantify a universal user-testing threshold.
- Release in small batches and test robustly. Smaller changes make it easier to identify which change affected delivery or the user journey. DORA’s 2024 summary specifically underscores small batch sizes and robust testing alongside its findings about AI’s delivery tradeoffs. DORA / Google Cloud, 2024
- Evaluate tools in the actual work context. Compare relevant outcomes for the tasks your team does, including developer time, delivery stability and user task success. Do not assume a result from routine coding, a mature open-source project or another organization will transfer unchanged to your team.
What a responsible claim about faster development can say
If a team reports that AI or another workflow change made coding faster, the claim should identify what was measured, for which work and under what conditions. It should not imply better software or happier users unless those outcomes were measured too. A clear account keeps the measures separate: development speed describes work at the engineering stage; stability and throughput describe delivery; user task success describes what happens for the people using the product.
The evidence supports neither a blanket promise that AI makes software better nor a blanket claim that it makes developers slower. It supports a more useful conclusion: effects vary by task and organization, and user success must be assessed on its own terms.
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