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AI coding tools can make developers feel faster without making a particular task finish sooner. In a 2025 randomized trial of experienced developers working in familiar, mature open-source projects, AI availability was associated with a 19% increase in task completion time—even though participants estimated after the study that it had cut their time by 20%. That result exposes a productivity gap, not a universal verdict on enterprise AI: other studies, using different tasks and measures, found gains.
What the productivity illusion actually means
“Productivity” can mean several different things: how long one task takes, how many tasks a developer completes, how much faster they believe they are, or how much usable, reviewed code reaches delivery. A result for one measure cannot be substituted for another.
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The apparent illusion is a mismatch between perceived speed and measured completion time in one defined setting. It does not show that AI coding tools always slow developers down, nor that a reported increase in completed tasks proves each task takes less time.
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Becker, Rush, Barnes, and Rein’s July 2025 randomized study assigned AI access or no AI access across 246 tasks for 16 experienced developers working in mature open-source projects they knew well. Participants averaged five years of prior experience in the projects. The tools used were primarily Cursor Pro with Claude 3.5 or 3.7 Sonnet, available during February–June 2025. The measured outcome was time to complete assigned tasks. The study found that AI availability increased completion time by 19% in this setting.
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Before the study, participants forecast a 24% time reduction; afterward, they estimated that AI had reduced their time by 20%. Those estimates capture participants’ expectations and perceptions, while the 19% increase is the study’s measured completion-time result. The authors cautioned that experimental artifacts could not be entirely ruled out, but wrote that the consistency of the slowdown across analyses made it unlikely to be primarily a product of the experimental design. Read the METR study.
The result is narrow by design: it concerns experienced developers, familiar repositories, assigned real issue work, and early-2025 tools. It cannot establish the effect for every enterprise team, codebase, task, or later tool version. Carnegie Mellon’s summary of the METR dataset provides a separate overview of the data and study context. See the Carnegie Mellon dataset summary.
Why other studies report productivity gains
Other findings are not direct contradictions unless they measure the same outcome under comparable conditions. Enterprise studies used different participants, tools, tasks, and measures.
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Google: less time on one complex task
A Google enterprise-based randomized trial involving 96 full-time software engineers estimated that internal AI features reduced time on a complex enterprise-grade task by about 21%. The paper reports a large confidence interval and cautions against generalizing its result across the broader ecosystem. It also found that engineers who spent more hours per day on code-related activity were faster with AI in the study. This is a task-time result from an internal-tool experiment conducted in summer 2024, not a general estimate for every workplace or current assistant. Read the Google trial preprint.
Three companies: more completed tasks
A 2026 analysis of field experiments at Microsoft, Accenture, and an anonymous Fortune 100 company combined data from 4,867 developers. Developers offered an AI code assistant completed 26.08% more tasks on average, with a standard error of 10.3%. Results varied across the three experiments. Less experienced developers showed higher adoption and greater productivity gains.
That estimate concerns completed-task counts, not elapsed time per task or developers’ impressions of their speed. The combined average also does not mean each company saw an identical result. Read the *Management Science* study.
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IBM: user experience is not a causal productivity estimate
An IBM Research case study of watsonx Code Assistant, published for CHI 2025, surveyed two cohorts totaling 669 users and ran unmoderated usability tests with 15 participants. It examined perceived productivity and developer experience, and reported that benefits were not experienced by all users. It also raised questions about code ownership and responsibility. This kind of case study helps describe user experience; it is not a randomized estimate of organization-wide productivity gains. Read the IBM Research case study.
How to interpret the numbers
| Study | Setting and participants | Outcome and finding | Key limit |
|---|---|---|---|
| METR, July 2025 | 16 experienced developers; 246 tasks in familiar, mature open-source projects | AI availability increased completion time by 19%; participants estimated a 20% reduction afterward and had forecast a 24% reduction before the study. | Early-2025 tools and a specific experienced-developer setting; not an estimate for all enterprise work. |
| Google, October 2024 preprint | 96 full-time Google engineers; one complex enterprise-grade task using internal AI features | About 21% less time on the task; the paper reports a large confidence interval. | One task and internal tools; the authors caution against broad generalization. |
| Three company field experiments, online February 2026 | 4,867 developers across Microsoft, Accenture, and an anonymous Fortune 100 company | 26.08% more completed tasks among developers offered the tool; standard error 10.3%. | Task counts rather than time per task; results varied across experiments. |
| IBM Research, CHI 2025 | watsonx Code Assistant; surveys with 669 participants and usability tests with 15 | Examined perceived productivity and experience; reported that benefits were not universal. | Case study of perceptions and experience, not a randomized causal estimate. |
These percentages should not be averaged or ranked as though they measured the same thing. One is a change in time to complete assigned work, another is time on a particular task, and another is the number of tasks completed. The IBM study describes reported experience rather than a causal productivity effect.
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Why results may differ between teams
The studies do not prove a single explanation for their different results. They do show why the setting and measurement need to be stated alongside any productivity claim.
- Experience and familiarity: METR studied experienced developers in projects they knew well; the company field experiments found higher adoption and gains among less experienced developers. A tool may fit a newcomer’s needs differently from a maintainer’s workflow.
- Task definition: Assigned issue work, one complex enterprise task, and the everyday tasks counted in a field experiment are distinct workloads. Task scope and how completion is defined can affect the outcome.
- Tool and integration: The studies evaluated different tools and features at different dates. METR’s early-2025 tools and Google’s summer-2024 internal tooling are not interchangeable interventions.
- What gets counted: Perceived speed, elapsed task time, task totals, quality, review effort, and downstream delivery answer different questions. A gain in one does not automatically demonstrate a gain in the others.
- Time horizon: Immediate task completion does not settle longer-term effects involving learning, maintenance, review, or organizational throughput. The cited studies do not answer every long-run question.
How an enterprise can evaluate its own results
For a useful internal assessment, define what “more productive” means before looking at the results. A team can then compare similar work with and without the assistant where feasible, while tracking the costs that a simple speed estimate can miss.
Quick Recap
- Choose the outcome first. Decide whether the question is task completion time, completed work, perceived usefulness, or delivery. Do not report one as though it were another.
- Compare like with like. Group work by task type, complexity, codebase familiarity, and developer experience. Record which assistant and model period were used.
- Include quality and follow-up work. Track review, revisions, rework, and whether the result is accepted and usable—not just the first implementation or a task count.
- Segment the findings. Report results by experience level and task category as well as the overall average, because an average can conceal who benefits or loses time.
- Show uncertainty and limits. State the sample, period, measurement, and variation in results. A team’s own estimate applies to its observed workflow, not automatically to another organization.
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