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LLM coding tools do not reliably make experienced developers faster in every setting. A 2025 randomized trial found that experienced contributors to mature open-source projects took 19% longer on realistic tasks when using early-2025 AI tools. A 2026 follow-up produced results consistent with a speedup, but its researchers said selection and measurement problems made the size of any gain unreliable. The most defensible answer is conditional: impact depends on the task, repository, tool and workflow—and must be measured against quality and downstream cost, not code volume alone.
First decide what “productivity” means
A developer can type less, finish a ticket sooner, ship more features or create more value. Those are related, but they are not interchangeable:
- Speed: time to complete a defined task. This is useful for comparable bugs, features or refactors, but a fast first patch may still need extensive review or repair.
- Output: accepted work delivered over a period—such as merged changes, resolved incidents or releases. More output is not necessarily more valuable.
- Value: user or business outcomes, such as improved reliability, lower infrastructure costs or customer adoption. These may take time to emerge and can be hard to attribute to one change.
- Sustainable engineering capacity: the amount of useful, reliable software a team can deliver without accumulating defects, rework, security exposure, maintenance burden or burnout.
For an engineering organization, sustainable capacity is usually the most useful goal. METR’s 2026 survey work also distinguishes speed from value: AI may make a given task faster, enable work that otherwise would not be attempted, or do both. A task that would never have entered the backlog cannot show up as a faster ticket in a same-task experiment.
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What the 2025 METR experiment actually found
In a randomized controlled trial published July 10, 2025, METR studied 16 experienced open-source developers completing 246 real issues in repositories they had contributed to for years. The repositories averaged more than 22,000 stars and one million lines of code. Tasks included features, bug fixes and refactors, and took roughly two hours on average. Participants were assigned to work with AI allowed or disallowed; the AI condition primarily used Cursor Pro with Claude 3.5 or 3.7 Sonnet, then-current frontier tools. METR’s study and methods describe the setting and results.
The measured result was a 19% increase in task-completion time when AI was allowed, with a confidence interval consistent with roughly 2% to 39% more time. Before the tasks, developers expected AI to make them about 24% faster; afterward, they still estimated a speedup of about 20%, despite the measured slowdown.
This is important evidence, not a universal rule. It applies to a small group of experienced contributors, familiar and mature repositories, a particular set of realistic tasks, early-2025 tools and the study’s workflow and success standards. It does not establish that AI slows most developers, or that the same result applies to beginners, greenfield projects, unfamiliar codebases or later tools. METR itself cautioned against those broader conclusions.
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Why AI can slow a developer who already knows the code
The study’s result does not mean any single mechanism explains the slowdown. METR examined potential explanations and reported evidence implicating several factors; the following are mechanisms that help explain why generation speed need not become end-to-end speed.
- Repository context is expensive. A model may not know the historical reason for an abstraction, a maintainer’s unwritten convention or a dependency across modules. The developer has to supply context, identify what the model missed and repair the result.
- Verification does not disappear. Generated changes still need tests, review, type checks, linting, security inspection, compatibility checks and sometimes performance analysis. In a high-standard codebase, checking an unfamiliar patch can cost more than writing a small change directly.
- Familiarity can make direct work quicker. A maintainer may already know where a fix belongs and which edge cases matter. Describing the work, waiting for a response, reviewing it and correcting a wrong-file or wrong-assumption edit can add steps.
- Interaction has overhead. Prompt construction, retries, waiting, context switching among editor, terminal and chat, and reverting unwanted edits all consume time.
- Expectations can be miscalibrated. In this particular trial, participants expected and perceived a speedup that the task-time measure did not show. That gap is a reason to measure actual workflow outcomes, not evidence that every developer misjudges every tool.
METR also noted that its tested setup may not represent the best possible prompting, sampling or scaffolding. The result is not a ceiling on what better tools or workflows might achieve.
Why benchmarks, surveys and real-world trials can disagree
These evidence types answer different questions. Treating them as competing votes on one universal productivity number creates confusion.
| Evidence | What it can tell you | What it may miss |
|---|---|---|
| Controlled human experiment | Whether a specified AI workflow changed outcomes for people doing assigned work under study conditions. | Small samples, tool-version drift, selection effects and limited representation of long-term or changing workflows. |
| Agent coding benchmark | Whether a system can solve a set of defined coding tasks under a specified scaffold and scoring rule. | Human prompting and review time, repository-specific conventions, maintainability and operational requirements. Automated pass/fail scoring is not the same as a maintainer accepting a production change. |
| Survey or anecdote | Perceived usefulness, adoption, task expansion, frustration and changes in how people work. | A reliable counterfactual. People may report typing speed as total time saved, or remember successful cases more readily than expensive failures. |
| Production telemetry | How delivery, review, quality and operations changed in a real organization over time. | Causation: staffing, task mix, release policy, seasonality and other changes can move alongside AI adoption. |
METR explicitly contrasts its real-task trial and human acceptability criteria with coding benchmarks such as SWE-Bench Verified and RE-Bench, which use algorithmic scoring and often more autonomous scaffolding. Benchmark performance can indicate model capability; it is not, by itself, a measured productivity gain for an experienced developer.
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What changed in the 2026 follow-up
METR’s February 2026 follow-up broadened the study to 57 developers, 143 repositories and more than 800 tasks, including 10 participants from the earlier experiment. The participant pool had a median of 10 years’ experience and included smaller, greener and less mature repositories. Raw estimates suggested an 18% speedup for returning participants (with an interval ranging from 38% faster to 9% slower) and a 4% speedup for newly recruited developers (interval from 15% faster to 9% slower). METR’s update explains why it did not treat these figures as a reliable estimate of current uplift.
The central problem was selection. As AI use became more common, some developers were unwilling to participate if they might have to work without it. In surveys, 30%–50% said they had avoided submitting some tasks because they did not want those tasks assigned to an AI-disallowed condition. That can change who enters a study and which work is available to assign, making the comparison less representative.
Time attribution also became harder. Some participants ran multiple agents concurrently or did other work while agents waited, making “time spent on the task” less clear. METR’s conclusion was that developers were probably more accelerated by AI in early 2026 than in early 2025, but that the follow-up supported the size of that increase only weakly. It is evidence that the 2025 slowdown may not describe newer workflows; it is not a clean reversal with a dependable percentage gain.
What the 2026 survey adds—and what it cannot prove
A METR survey conducted February–April 2026 included 349 technical workers, of whom 87 were software engineers. Respondents reported median AI-related changes in the value of their work of roughly 1.4× to 2×, and a median speed change of 3×. METR warned that speed reports likely overstate value gains. Participants retrospectively estimated a 1.3× change in work value in March 2025 and 2× in March 2026, and forecast 2.5× in March 2027. These are reported perceptions and forecasts, not controlled measurements of delivered output. See the survey results and METR’s discussion of task substitution and uplift.
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The survey is useful for understanding how workers perceive AI and how it may change the work they choose to do. It cannot establish that a team delivered twice the value, or that an individual completed equivalent tasks twice as fast. A developer might use AI to add tests, automate a migration or prototype an idea that would otherwise be deferred. That is potential value expansion, not necessarily acceleration on a preselected task.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical way to measure AI’s impact in your organization
Do not run a trial called simply “AI versus no AI.” Define the workflow, decide what success means, and track its costs as well as its benefits.
- Specify the treatment. Record the product, model and version; whether autocomplete, chat, editor or terminal agents are included; whether web search is allowed; whether multiple agents may run; permitted shell and network access; and whether AI may be used for tests, documentation and debugging. Include training and usage logging. “AI allowed” can mean very different things to different developers.
- Segment the work. At minimum, classify bugs, features, refactors and greenfield tasks; familiar versus unfamiliar subsystems; small versus cross-repository changes; test coverage; tacit knowledge; risk level; and human-led versus agent-led work. Aggregate averages can hide gains on boilerplate and losses on subtle changes.
- Establish a baseline and comparable tasks. Use historical data cautiously, because teams and tools change. Where feasible, randomly assign comparable tasks or developers to workflows. A task-level randomized trial can estimate effects on similar work, but may become unrealistic if developers selectively submit AI-friendly tasks or refuse the control condition. A developer- or team-level trial captures sustained workflow better, but needs more participants and is more exposed to differences between teams.
- Measure quality-adjusted completion. Track time through review and acceptance, not just time to first patch or passing test. Include rework, requested changes, failed tests, follow-up fixes, reverts and post-release defects. Compare work that meets the same acceptance and quality bar.
- Track several kinds of outcome. Combine delivery and cycle time with review latency, escaped defects, change failures, incident recovery, maintenance burden and relevant business outcomes. Use medians and distributions as well as averages: a tool may create many quick wins and a few costly failures.
- Ask developers separately. Survey perceived usefulness, cognitive load, interruptions, trust calibration, learning and frustration. These are meaningful outcomes, but label them as perceptions rather than measured time savings. Interviews and task transcripts can reveal where context or verification costs arise.
- Log versions and concurrency. Models and products change; record the exact configuration and dates. For concurrent agents, record wall-clock time, human attention and agent activity separately where possible. Do not assume elapsed time, active developer time and total compute time are the same measure.
- Repeat the evaluation. Reassess after major model, product, training or workflow changes. Report results by task type and developer experience, not just as a single company-wide percentage.
Metrics that mislead when used alone
- Lines of code: more code may mean duplication or future maintenance, not more value.
- Pull-request count: a higher count can reflect trivial, fragmented or generated changes.
- AI-generated-code share: measures tool use, not correctness, accepted work or economic benefit.
- Self-reported time saved: useful for adoption research, but not a replacement for observed outcomes. The 2025 METR trial found a notable gap between expected/perceived speedup and measured task time.
- Benchmark scores: measure performance on specified benchmark tasks, not the full human workflow in your repositories.
- Token or agent-call volume: measures consumption, not productive output.
Useful delivery indicators may include task-completion distributions, pull-request cycle time, lead time, deployment frequency and incident recovery time. Pair them with quality indicators such as defects before and after release, reverts, hotfixes, test failures, review requests, security findings and rework. Maintainability measures can include follow-up fixes, complexity, duplication, documentation and the time a developer later spends understanding the change. Choose metrics appropriate to the work; no single dashboard can turn unlike tasks into a fair comparison.
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Where AI is more or less likely to fit
AI assistance is a stronger candidate for broad use when work is repetitive and well-scoped, the code has good tests and documentation, developers can check results quickly, and usage fits security and privacy requirements. Examples to evaluate include boilerplate, test scaffolding, documentation drafts, routine API migrations and exploration of unfamiliar libraries.
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Use tighter controls or selective assistance when requirements are ambiguous, repository knowledge is largely tacit, tests are weak, architecture is fragile, or a plausible but incorrect change has high security or operational cost. These conditions do not prove AI will hurt; they raise the cost of verification and make measurement especially important.
Agentic workflows are most plausible when work can be decomposed, execution is sandboxed, tests can run automatically, human permissions are explicit and asynchronous work offers a real advantage. They can also add coordination, merge-conflict and attribution costs. The 2026 METR follow-up’s time-accounting difficulty is a reminder that more capable agents do not automatically make productivity easier to measure.
For an adoption decision, compare the value of additional accepted work with the full cost: net ROI = value of additional accepted work − tool and training costs − review and rework − security and compliance costs − maintenance cost. Tool access is only one input. The relevant unit is a particular human-plus-tool workflow, applied to a particular mix of work under a particular quality standard.
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Conclusion
The evidence does not support either “LLMs make experienced developers faster” or “LLMs make them slower” as a universal rule. The 2025 METR trial found a real slowdown in a narrow, demanding setting; its 2026 follow-up was consistent with improvement but could not reliably size it, while survey respondents reported perceived gains that are not objective productivity measures. For engineering leaders, the sound decision is to evaluate specific tools on representative tasks and judge quality-adjusted delivery, value and lifecycle cost together.
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