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Not necessarily. An AI coding agent can help produce code or finish a narrow programming task faster without increasing the amount of reliable, useful software a team delivers. The distinction matters: code output is an intermediate measure; software value depends on changes being reviewed, integrated, stable, maintainable and used. Studies of different tools and settings report different results, so no single productivity figure settles the question for every team.
Code generated is not the same as software delivered
A code suggestion, a completed task, a merged change and a working feature are different milestones. Even a merged change may not improve an outcome if it introduces defects, makes later changes harder, or solves a problem users do not have.
It helps to treat software work as a chain of outcomes rather than one output count:
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- Acceptance and integration: whether the change is reviewed, accepted and merged into the product.
- Delivery: how much useful change reaches users, and how reliably it does so.
- Value over time: whether the software is adopted and remains understandable and safe to change.
Improvement at an earlier stage does not guarantee improvement at the next. More generated code can mean more possibilities to review and maintain, not automatically more useful capability.
#1 Best Overall
What the evidence says—and what it does not
These findings measure different things, use different populations and tools, and should not be combined into a single AI-productivity score.
| Study | Reported result | What it can tell you | Important boundary |
|---|---|---|---|
| Microsoft Research, 2023 | Participants completed a JavaScript HTTP-server programming task 55.8% faster with GitHub Copilot than the control group. | AI assistance can speed up a particular, bounded programming task in a controlled experiment. | Task completion speed is not a measure of production delivery, software quality over time or user adoption. Microsoft Research study. |
| METR, July 10, 2025 | Experienced open-source developers took 19% longer in a randomized trial using early-2025 AI tools while working in their own repositories. | Assistance may slow work in some established codebases, even for experienced developers. | This result is bounded to the participants, repositories, tools and study period; it does not establish how all developers or tasks will fare. METR research listing. |
| DORA, 2024 | For a modeled 25% increase in AI adoption, the report estimated a 1.5% decrease in delivery throughput and a 7.2% decrease in delivery stability. | Adoption can rise while delivery measures move in an unfavorable direction. | These are report estimates with uncertainty intervals, not guaranteed effects or timeless causal constants. DORA 2024 report. |
| NBER Working Paper 35275, 2026 | The NBER record summary describes data from more than 500,000 GitHub developers and reports more new apps without increased total usage across four software marketplaces. | Producing more apps is not by itself evidence that total software use has increased. | The record summary does not establish the detailed definitions or methods behind those figures. The finding should be read as a high-level result, not a broad conclusion about all AI-generated software. NBER paper record. |
Microsoft’s result and METR’s result are not contradictory: one concerns a controlled, relatively bounded task, while the other concerns experienced developers doing work in their own mature repositories. Neither alone answers whether a team ships more stable, adopted software over time.
Process can improve while delivery gets worse
DORA’s 2024 report estimated changes in several process and code measures alongside the delivery estimates. Each figure below is associated with a modeled 25% increase in AI adoption; the report presents estimates with uncertainty intervals.
| Measure | DORA 2024 estimate |
|---|---|
| Documentation quality | 7.5% increase for a modeled 25% increase in AI adoption. |
| Code quality | 3.4% increase for a modeled 25% increase in AI adoption. |
| Code-review speed | 3.1% increase for a modeled 25% increase in AI adoption. |
| Approval speed | 1.3% increase for a modeled 25% increase in AI adoption. |
| Code complexity | 1.8% decrease for a modeled 25% increase in AI adoption. |
| Delivery throughput | 1.5% decrease for a modeled 25% increase in AI adoption. |
| Delivery stability | 7.2% decrease for a modeled 25% increase in AI adoption. |
These figures describe report estimates, not an automatic forecast for an individual company. In interpreting weaker delivery outcomes, DORA suggests that larger change batches may be one explanation and emphasizes small batches and robust testing. That is an interpretation of a possible mechanism, not settled causal proof.
Why more code may not become more useful software
Review and integration still take work
Generated code must fit the existing architecture, pass tests and make sense to the people responsible for maintaining it. If an agent increases the amount of proposed code faster than a team can review and integrate it, the queue of unfinished work can grow even as code output rises.
Faster local work can create larger delivery batches
When it becomes easier to produce changes, teams may be tempted to bundle more into each release. DORA’s 2024 report offers larger batch size as a possible explanation for weaker delivery outcomes and points to small batches and robust testing as delivery fundamentals. The practical lesson is to track what reaches production and how safely—not only what was generated or approved.
Rank #4
Software supply is not the same as user demand
The NBER record summary reports more new apps without increased total usage across four marketplaces. That illustrates why counting applications or features cannot establish that people are using more software. The record’s summary does not provide enough detail to generalize the result beyond the reported setting.
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DORA’s 2025 report describes AI as an amplifier of an organization’s existing strengths and weaknesses. The report draws on more than 100 hours of qualitative data and responses from nearly 5,000 technology professionals, according to the Google Research bibliographic summary. This is organizational evidence, not a randomized estimate of what an individual coding agent will do. The implication is that clear ownership, effective review, testing and delivery practices shape whether assistance translates into useful outcomes. DORA 2025 report.
Best Value
How a team can tell whether AI is helping it ship
Measure outcomes at several points in the delivery chain, and compare like with like. A code-generation count can help explain changes in workflow, but it should not stand in for delivery or product value.
- Separate task speed from delivery: track time spent on a defined task separately from the time a change takes to reach users.
- Follow changes through review: observe what is accepted and merged, and whether review work or unfinished changes accumulate.
- Watch delivery and stability together: track throughput alongside failures or other relevant signs of instability; faster approval alone does not establish safer delivery.
- Check maintainability: look at whether teams can understand and change the resulting code, not just whether it passes its initial task.
- Look for actual use: where the software is meant for users, check whether the delivered capability is adopted rather than counting features or applications.
- Compare comparable work: separate new, bounded tasks from changes in mature repositories, and account for differences in teams, tools and work.
If task completion gets faster but review queues, delivery stability or user adoption do not improve, the evidence supports a narrower claim—AI helped with a step—not the stronger claim that it increased useful software delivered.
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