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Writing code faster does not guarantee that users get a reliable change sooner. A change still has to move through review, testing, integration, deployment, and feedback—and any of those stages can become the bottleneck. To find out whether AI or another coding improvement is making delivery faster, measure the time and reliability of changes reaching users, not just how quickly code is produced.
Why hasn’t AI made us faster?
AI can speed up implementation while leaving total delivery time unchanged—or adding work downstream. If code arrives for review faster than a team can review it, the queue grows. Larger changes may also take longer to test, integrate, and diagnose when something fails. The relevant outcome is not code generated, but a useful, dependable change delivered to users.
DORA’s 2024 report found that AI adoption was associated with increased individual productivity, flow, and job satisfaction, while negatively affecting software delivery stability and throughput. This is a research-level finding, not a prediction that every team adopting AI will deliver more slowly. DORA highlights small batches and robust testing as important fundamentals. Read DORA’s 2024 report summary.
Where delivery time goes
A change moves through a system: implementation, review, testing, integration, deployment, and feedback. Work can wait at each stage. Faster implementation only shortens the total time if it helps work move through the whole system without creating a larger queue or more rework elsewhere.
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For example, a team might complete implementation sooner but wait longer for review, then discover integration problems late. In that case, coding time improved, but the time from starting a change to deploying it did not. Review and test feedback, integration delays, and batch size help reveal where work is waiting.
How to measure software delivery performance
DORA’s five software delivery performance metrics cover both the speed of change and the cost of instability. Apply them to a particular application or service and follow that service’s results over time.
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| Dimension | Metric | What it helps you understand |
|---|---|---|
| Throughput | Change lead time | How long a change takes to reach production. |
| Throughput | Deployment frequency | How often the service is deployed. |
| Throughput | Failed deployment recovery time | How long recovery takes after a deployment causes a failure. |
| Instability | Change fail rate | How often a deployment results in a failure requiring intervention. |
| Instability | Deployment rework rate | How much deployment activity is unplanned rework. |
These measures work as a set: deployment frequency alone, for example, cannot show whether more frequent releases also bring more failures or rework. DORA describes its metrics as “Leading indicators for organizational performance and employee well-being; Lagging indicators for software development and delivery practices.” DORA’s metrics guide explains how to use them.
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Compare a service with its own baseline rather than ranking unlike applications or treating a metric as an individual performance target. Different services have different delivery contexts, and blunt targets can encourage gaming rather than improvement. Use review time, test feedback, and batch size as diagnostic measures when they help explain a constraint—not as substitutes for delivery outcomes.
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Keep changes small enough to review and verify
Small, independent changes make it easier to get feedback, understand what changed, and recover if something goes wrong. DORA recommends reducing batch size; it also warns that large AI-generated changes can be difficult to review, test, and integrate safely. Where possible, make each work unit independently deployable and verifiable. DORA’s guide to working in small batches offers further guidance.
- Slice work into changes that deliver a clear, testable part of the intended value.
- Run tests and seek review while a change is still small enough to reason about.
- Track whether changes move through the full delivery path, not only whether implementation finishes sooner.
- Pair delivery measures with reliability measures so a faster release pace does not conceal failures or rework.
What the METR study does—and doesn’t—show
METR’s randomized 2025 study found that the 16 participating experienced open-source developers took 19% longer on tasks when allowed to use the AI tools studied. The experiment covered 246 issues in repositories familiar to those developers. It is evidence about that specific task setting, not a universal estimate for all developers, AI tools, or software work. Read METR’s 2025 study summary.
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In a February 2026 update, METR said its later experiment was not a reliable estimate of current productivity impact. Selection effects likely excluded some developers and tasks with high expected AI uplift, while concurrent agent use made time measurement unreliable. METR also reported that 30% to 50% of developers in that later experiment said they had chosen not to submit some tasks because they did not want to do them without AI. That is a response from this study, not a profession-wide prevalence estimate. Read METR’s February 2026 update.
Together, these results are a reason to measure your own delivery outcomes rather than assume that faster coding—or a result from one study—predicts your team’s performance.
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Run a delivery experiment on one service
- Choose a service and baseline it. Record its DORA delivery metrics over a period that reflects its normal work and release pattern.
- Map where work waits. Look at review, testing, integration, and deployment alongside batch size to identify the service’s main constraint.
- Change one thing. For example, make changes smaller or improve the way tests provide feedback. Keep the experiment focused so the result is interpretable.
- Measure again and discuss the result. Compare delivery speed and instability with the baseline. Involve the cross-functional team responsible for prioritizing, building, delivering, and operating the service, and use retrospectives to decide what to adjust next.
This approach follows DORA’s advice to use measures for learning and improvement, not as league tables. As David Farley puts it in DORA’s metrics guide, “…the real trade-off, over long periods of time, is between better software faster and worse software slower.”
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