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AI can produce more code, but it does not remove the work of deciding what to build, checking whether the result is sound, and fitting it into a reliable system. That makes “foreman” a useful lens for some changes in software engineering—not proof that every engineer’s job has become supervision.
What does the “foreman” metaphor mean?
A foreman coordinates work and checks that it meets the plan. In AI-assisted development, an engineer may increasingly describe a task to a coding assistant, inspect its proposed changes, connect them to existing systems, and validate the result. Those activities can become more prominent when a tool generates code quickly.
But this is a way to describe a possible shift in emphasis, not a settled job description. The evidence below measures task output, completion time, adoption, or perceptions; it does not establish that software engineers universally spend less time coding or have become AI supervisors.
Does AI make software developers more productive?
There is no single productivity figure that applies to all developers and tasks. A survey measures what people report; a field experiment measures outcomes in a particular workplace setup; a repository trial measures performance on a different kind of work. Their results are informative, but they are not interchangeable.
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Adoption and trust are widespread survey measures
Google’s September 23, 2025 summary of DORA’s survey reports that nearly 5,000 technology professionals around the world were surveyed. Among software development professionals, 90% reported using AI, 65% reported heavy reliance on it for software development, and 30% reported little or no trust in AI outputs. These are respondents’ reported usage and perceptions—not controlled measurements of code quality, time saved, or completed work.
Workplace experiments found more completed tasks
Microsoft Research’s June 2025 publication describes randomized controlled trials at Microsoft, Accenture, and an anonymous Fortune 100 company. Its pooled analysis of 4,867 developers found an estimated 26.08% increase in completed tasks among developers using an AI coding assistant (standard error: 10.3%). Less experienced developers had higher adoption and greater productivity gains. The result concerns completed task counts in those workplace experiments; it is not a universal estimate of time saved or a guarantee that every task was completed faster.
A trial in familiar open-source repositories found slower completion
METR’s July 10, 2025 randomized trial involved 16 experienced developers who had contributed for years to large open-source repositories. Across 246 real issues, developers took 19% longer when AI use was allowed. METR described this as a snapshot of early-2025 tools in one setting and said it does not establish that AI fails to speed up most developers.
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What the studies can—and cannot—be compared on
| Evidence | Participants and setting | What it measured | Reported result |
|---|---|---|---|
| Microsoft Research, June 2025 | 4,867 developers in pooled workplace field experiments at three companies | Completed task counts | 26.08% increase among developers using an AI coding assistant; SE: 10.3% |
| METR, July 10, 2025 | 16 experienced open-source developers working on repositories they knew well; 246 issues | Time to complete real issues, with AI use allowed versus not allowed | 19% longer with AI use allowed |
| DORA survey, summarized by Google on September 23, 2025 | Nearly 5,000 technology professionals globally | Self-reported AI adoption, reliance, and trust | 90% of software development professionals reported AI adoption; 65% heavy reliance; 30% little or no trust in outputs |
The first result is about task counts, the second about time in developers’ own repositories, and the third about what professionals said they do and trust. Differences in participants, tasks, tools, and methods mean these findings should not be combined into a single productivity score.
Why can more individual output fail to improve software delivery?
Writing code is one part of delivering software. Changes also need to work with existing systems, pass tests, avoid regressions, and reach users in a dependable way. If code arrives faster but review, testing, or integration become bottlenecks, an individual productivity gain may not translate into a stable or faster release.
DORA’s 2024 report summary describes this tension: AI adoption significantly increased individual productivity, flow, and job satisfaction, while negatively affecting software delivery stability and throughput. The summary emphasizes end-user focus, stable priorities, small batches, and robust testing as practices that matter alongside AI use.
DORA’s 2025 report frames AI primarily as an amplifier of an organization’s existing strengths and weaknesses. In practical terms, a team with clear priorities and sound delivery practices may be better positioned to use AI effectively; weak coordination or inadequate checks can also be magnified. The tool is only one part of the production system.
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Which engineering work becomes more important?
The results support taking coordination and verification seriously, but they do not directly measure how engineers divide their time. As a practical response to AI-generated changes, teams can make sure responsibility remains clear for:
- Defining the task: Specify the intended behavior, constraints, and acceptance criteria before asking a tool to implement a change.
- Reviewing the result: Check that code fits the existing design and handles the relevant cases, rather than treating a plausible-looking answer as proof of correctness.
- Testing and integration: Use robust tests and small changes to catch problems before they accumulate or reach users.
- Prioritizing user outcomes: Judge success by whether a change solves the right problem reliably—not merely by how much code was produced.
These are engineering responsibilities whether the code is written by a person, an AI assistant, or both. AI may change how quickly some implementation work is done; it does not make the surrounding decisions and checks optional.
Will AI replace software engineers?
The cited evidence does not show that AI has emptied the software factory or eliminated the need for engineering expertise. It shows strong reported adoption, a productivity gain in one set of workplace experiments, and a slowdown in a small trial of experienced developers working in familiar repositories. Those findings answer different questions and do not establish a universal change in employment or job roles.
The foreman metaphor is most useful as a prompt to watch how work is changing: when code generation becomes easier, directing, evaluating, and integrating changes can take on greater importance. It should not be mistaken for proof that engineers have stopped building software.
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