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AI is changing software development by moving some work from writing code line by line toward delegating tasks to AI tools and checking their output. Adoption among surveyed professional developers is high, but that does not prove teams are universally more productive or that human developers are becoming unnecessary. The clearest changes are in workflow; the results still depend on how carefully people review, test, and secure the software.
How widely are developers using AI coding agents?
In research conducted from May through July 2026, JetBrains reported that 90% of professional developers in its survey used AI coding agents at work at least weekly, while 68% used them daily. These figures describe the surveyed professional developer population—not every programmer worldwide—and reflect a particular period. JetBrains’ adoption findings show that these tools have become a regular part of work for many respondents.
Usage is not the same as impact. A developer may use an assistant for occasional suggestions, or delegate a broader task to an agent; frequency alone says little about the quality, speed, or maintainability of the resulting software.
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What is changing in a developer’s workflow?
AI coding tools vary in how much work they take on. Some suggest code or complete a small section while the developer remains in control. More agentic workflows let a tool attempt a larger coding task, potentially across multiple steps, before a person reviews the result. The broader the task and the greater the tool’s autonomy, the more important it becomes to inspect what changed and verify that it works.
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- Task scope: suggestions and completions assist with a narrow piece of work; agents can be asked to handle a broader coding task.
- Autonomy: some tools respond to each prompt or edit, while others can carry out more steps before human review.
- Workflow fit: tools may operate inside an editor, against a repository, or as part of a wider development process.
- Verification burden: code still needs appropriate review, tests, and security checks before release.
GitHub’s discussion of advanced AI users describes orchestration, delegation, and verification as emerging parts of developer work. That is an interpretation drawn from interviews and platform observations, not evidence that coding knowledge no longer matters. To delegate well, a developer still needs to define the task, judge whether the result fits the system, and catch errors that a tool or a passing test may miss. GitHub’s discussion of developer identity in the AI era explores that shift.
Does AI make software teams faster?
Current adoption and survey responses do not establish a universal productivity gain. DORA’s 2025 report draws on nearly 5,000 technology professionals and more than 100 hours of qualitative data. It offers a broad view of how practitioners experience AI-assisted software development, but those data are not, by themselves, a controlled measurement proving that AI causes teams to ship better software faster. Google Research’s DORA report should be read as evidence about reported experience and organizational practice, not a guarantee of a particular outcome.
GitHub’s 2024 enterprise survey offers another, distinct perspective. It questioned 2,000 non-student enterprise respondents in the United States, Brazil, India, and Germany. Fielded from February 26 to March 18, 2024, it found that respondents perceived benefits while also reporting slower perceived company adoption. Those self-reported views describe the respondents’ perceptions; they do not establish a causal productivity effect across companies. GitHub’s survey details and findings provide the sample and field dates.
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These sources measure different things: reported use and perceptions, qualitative accounts, and activity on a particular platform are not interchangeable with controlled productivity measurement. The practical result depends on the task, the tool’s reliability, how well it fits existing work, and the time spent reviewing and correcting output.
Why do code, tools, and development practices change together?
AI is one part of a wider shift in software development. GitHub’s 2025 Octoverse coverage highlights AI, agents, and typed languages as important trends, including TypeScript’s rise in its language rankings. That information is a signal from GitHub’s ecosystem and repository activity, not a complete census of all software development or proof that every team is changing in the same way. GitHub’s Octoverse report describes the activity it tracks.
Tools and practices influence each other: as teams adopt assistants or agents, they may adjust how they specify work, review changes, and use their development environments. At the same time, language and repository trends can reflect many factors beyond AI. A platform’s rankings are useful context, but they should not be treated as a map of the entire industry.
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What still needs human attention?
Generated code can introduce defects, security weaknesses, or maintenance burdens. The Software Improvement Group’s summary of its 2026 State of Software report frames AI-assisted coding and agents as raising technical-debt and security questions. That framing is a reason to keep those risks in view, not a substitute for examining a team’s own code, controls, and outcomes. SIG’s report announcement describes its concerns.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problems- Review the change: understand what the tool modified and whether it matches the task and the project’s conventions.
- Test the behavior: run relevant checks and add or update tests where the change warrants it; a plausible-looking answer is not proof that code works.
- Check security and dependencies: evaluate risks appropriate to the code and system rather than assuming generated code is safe.
- Consider maintainability: ask whether another developer can understand and safely change the result later.
What is a realistic view of software’s near-term future?
The evidence points to wider use of AI assistance and a workflow in which some developers spend more time directing tools and validating their work. It does not settle how much faster software teams will become, how consistently quality will improve, or how far agent autonomy will extend. Those outcomes depend on reliability, workflow integration, review, security, and organizational practice. The clearest near-term change is therefore not the disappearance of developers, but a different balance between producing code and taking responsibility for the code that ships.
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