Not conclusively. Studies show that AI coding assistants can help developers complete more tasks or report time saved in some settings, but that is not the same as proving software development costs less overall. A true cost comparison also needs to count tool fees, setup and training, review, rework, quality, and maintenance.
What the evidence says—and what it measures
The strongest answer is conditional: AI can improve productivity in some teams and tasks, but the available studies do not establish a general reduction in fully loaded software-development costs. Their outcomes are different, so task throughput, completion time, survey responses, and accepted code suggestions should not be treated as interchangeable measures.
| Study | People and setting | Reported result | What the result measures |
|---|---|---|---|
| Microsoft Research, June 2025 | Randomized field experiments at Microsoft, Accenture, and an anonymous Fortune 100 company; 4,867 developers | 26.08% increase in completed tasks on average; standard error 10.3% | Task throughput in those business settings, not net cost savings |
| METR, July 2025 | 16 experienced open-source developers; 246 tasks in mature projects, randomized to allow or disallow early-2025 AI tools | Task completion time increased by 19% when AI was allowed | Completion time for this small, specific sample and task setting |
| UK Government Digital Service, 2025 | Three-month trial, November 2024 to February 2025; main survey analysis included 424 responses from users in 31 departments | Respondents reported an average of 56 minutes saved per working day | Survey-reported time savings, not audited financial savings |
| DORA, 2025 | Global research with more than 100 hours of qualitative data and nearly 5,000 technology-professional survey responses | Characterizes AI as an amplifier of existing organizational strengths and weaknesses | Organizational context, not a universal cost estimate |
These findings do not cancel one another out. They studied different people, tasks, tools, and work environments and measured different outcomes. Microsoft Research’s pooled estimate is evidence of higher task throughput in its field experiments; METR’s result warns that experienced developers working in familiar, mature repositories may not get faster with the tools tested. Neither answers whether a company’s total cost fell.
Why AI can help one developer and slow another
AI assistance is not a single intervention. Its value depends on whether the task is clearly defined, how well the codebase and requirements are understood, how much review generated code needs, and whether the workflow makes it easy to use suggestions productively.
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Experience and task familiarity
Microsoft Research reported that less experienced developers adopted its assistant more and had greater productivity gains. In contrast, METR studied developers averaging five years of experience in the specific repositories they worked on. Participants forecast that AI would reduce completion time by 24% and, after the study, estimated that it had reduced time by 20%; measured completion time instead rose by 19%. That gap is a reminder that perceived speed and measured task duration can differ.
METR’s February 2026 update says its later experiment had selection effects and time-measurement difficulties, making those follow-up results an unreliable signal of the current productivity effect. The update gives the early-2025 estimate a confidence interval of 2% to 39% longer task time and says the follow-up is weak evidence about how much the effect may have changed. It therefore does not establish a quantified current speedup.
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DORA’s 2025 report puts emphasis on the organization around the tool: “AI’s primary role in software development is that of an amplifier. It magnifies the strengths of high-performing organizations and the dysfunctions of struggling ones.” Its summary says the greatest returns on AI investment come from strategic focus on the underlying organizational system, rather than the tools alone. Clear priorities, sound delivery practices, effective review, and appropriate workflows affect whether assistance translates into useful output.
How to interpret the UK trial’s time-saving figure
In the UK public-sector trial, respondents reported an average of 56 minutes saved per working day. That figure reflects survey responses, not an independent audit of net financial savings. The distinction matters because an estimate of time saved does not, by itself, show that a team produced more valuable work or reduced its spending.
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- GitHub Copilot telemetry showed an average 15.8% acceptance rate for suggested code lines.
- Separately, 39% of users said they had committed code suggested by the assistant.
- Of survey respondents, 73% said they had at least five years of coding experience.
The acceptance rate and the share reporting a commit describe different things; neither is a measure of saved labor cost. The trial is useful evidence of reported experience and tool use, but it should not be read as a financial return calculation.
Why productivity gains are not automatically cost savings
A team can complete more tasks without spending less, and it can spend less time on one step while adding effort elsewhere. To assess whether AI made development cheaper, a team needs a consistent definition of cost and a useful outcome to compare against it.
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- Direct tool costs: subscriptions, model usage, and any infrastructure required.
- Adoption costs: evaluation, setup, onboarding, and training.
- People’s time: prompting, supervision, reviewing suggestions, integrating code, and explaining or correcting generated changes.
- Quality and risk: testing, debugging, rework, defect handling, and security remediation.
- Lifecycle costs: maintenance and future work created by the code or its dependencies.
The sources covered here do not provide a representative, independently measured net-cost reduction across software teams after these factors are counted. The conclusion is not that AI never saves money; it is that the evidence does not support saying that it has made software development cheaper overall.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How a team can measure its own result
To evaluate a local deployment, compare a stable set of similar tasks under the same quality standards, with and without the assistant. Track the full work involved rather than counting suggestions or coding time alone.
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- Choose comparable work: define task types, repository context, and acceptance criteria before comparing results.
- Record total effort: include implementation, prompting, review, testing, integration, debugging, and rework.
- Count all relevant costs: include tool fees and the time spent on adoption and training.
- Measure useful outcomes: track accepted work and quality, including defects or follow-up fixes, rather than raw lines or suggestions.
- Use an appropriate time horizon: include maintenance and later corrections when those costs can reasonably be observed.
This approach will not guarantee a saving. It gives a team a better basis for deciding whether its own workflow produces more useful software for less total cost.
How to read vendor productivity claims
GitHub’s economic-impact post reports that a quantitative study found developers completed tasks 55% faster with GitHub Copilot and that users accepted nearly 30% of suggestions on average during the product’s first year. These are vendor-published figures, not direct calculations of net development cost.
The same post projects a potential global GDP boost of more than $1.5 trillion, using an assumed 30% productivity enhancement and a projection of 45 million professional developers in 2030. That is a conditional scenario, not an observed saving or a direct estimate of software-development costs.
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