ChatGPT is changing programming by giving developers a conversational way to ask about code, draft or refine snippets, and work through maintenance tasks. It is becoming part of how some people approach programming—not proof that every developer is faster, that generated code is reliable without review, or that programmers are being replaced.
How does ChatGPT help with coding?
Developers can use ChatGPT as an assistant for focused tasks: asking what a piece of code does, requesting a draft or revision, or getting help with repetitive work in an existing project. It can also offer options or help a developer think through a problem. The developer still needs to decide whether an answer fits the project and works as intended.
One study provides a window into ChatGPT use specifically. Ruiyin Li and coauthors’ 2025 DevChat study collected 2,547 unique ChatGPT conversation links shared on GitHub between May 2023 and June 2024. In that curated set, 43.4% of shared links appeared in Code and 32.3% in Commits. The authors describe task delegation as the leading reason for sharing, with software development and maintenance or evolution among the main activity groups.
Those figures describe public links associated with GitHub activity, not all developers or all ChatGPT conversations. Private chats are not represented by this collection, and the study does not show how often the suggested code worked or how much time it saved.
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How widely are developers trying AI coding tools?
Surveys indicate broad exposure to AI coding tools, but their populations and questions differ. They should not be treated as repeated measurements of the same group or the same kind of use.
| Source and population | Reported result | What the result measures |
|---|---|---|
| GitHub’s 2024 survey of 2,000 software-development team members in the United States, Brazil, Germany, and India | More than 97% said they had used AI coding tools at some point. | Ever-use; the survey did not measure frequency. |
| Stack Overflow’s 2025 Developer Survey | 84% of respondents were using or planning to use AI tools in development; 51% of professional developers reported daily use. | Two distinct survey measures among Stack Overflow respondents. |
The results point to substantial adoption, but neither says that every respondent uses ChatGPT specifically. “AI coding tools” includes a broader category of products.
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Does ChatGPT make programmers more productive?
There is no single productivity result that applies to every developer and task. Surveyed perceptions, a controlled task-time experiment, and country-level software activity measure different outcomes. The table shows why apparently conflicting findings can coexist.
| Evidence | Finding | Scope and limitation |
|---|---|---|
| OpenAI’s 2025 State of Enterprise AI report, drawing on a survey of 9,000 workers across almost 100 enterprises and enterprise usage data | 73% of surveyed engineers reported that AI helped them deliver code faster. | Reported experience published by the AI provider; not a randomized comparison of completion times. |
| METR’s 2025 randomized controlled trial | Sixteen experienced developers took 19% longer on 246 assigned issues when allowed to use AI tools. | The developers worked in large open-source repositories averaging more than 22,000 stars and one million lines of code. Participants chose their tools; most used Cursor Pro with Claude 3.5 or 3.7 Sonnet. This was not a ChatGPT-only test, and METR cautions against generalizing it to most developers or other settings. |
| Alexander Quispe and Rodrigo Grijalba’s working paper on ChatGPT availability and GitHub activity | The authors report increases in git pushes, repositories, and unique developers per 100,000 people, particularly for high-level, general-purpose, and shell-scripting languages. | Country-level activity analyzed using GitHub Innovation Graph data and quasi-experimental methods—not time saved by an individual or code quality. The paper was circulated in 2024; its arXiv record lists a later version dated March 22, 2026. |
METR’s result concerns experienced maintainers working on selected issues in complex repositories with the tools available in early 2025. OpenAI’s result captures what engineers said about delivery speed, while the working paper tracks aggregate activity across countries. None establishes a universal effect on individual productivity. An assistant may help with a small, well-scoped draft yet add work when its output needs extensive correction or when a developer must supply and verify missing context.
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Can you trust AI-generated code?
Generated code should be treated as a draft to inspect, not as verified software. In Stack Overflow’s 2025 Developer Survey, 46% of respondents said they actively distrust the accuracy of AI output, while 33% said they trust it. Also in that survey, 66% cited solutions that were almost right but not quite as a frustration, and 45% said debugging AI-generated code took more time.
Those are respondents’ reported attitudes and experiences, not a measurement showing that every generated answer is faulty. They do explain why a plausible-looking response is not enough evidence that a change is correct. GitHub likewise notes that generated tests need human review to check whether the relevant scenarios are covered.
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A practical review routine
- Keep the request bounded. Ask for an explanation, a specific change, or a test draft, and provide only the project context appropriate to share.
- Read the whole answer. Check assumptions, edge cases, dependencies, security implications, and how the proposed change handles project data.
- Run it in the actual project. Execute the relevant tests and inspect their coverage; do not assume generated tests cover every important scenario.
- Keep an accountable reviewer. A developer who understands the system should decide whether to accept, modify, or reject the output.
Can ChatGPT help developers learn?
In GitHub’s 2024 four-country survey, between 60% and 71% of respondents, depending on country, said AI coding tools made it easy to adopt a new programming language or understand an existing codebase. These are perceptions from surveyed software-development team members, not evidence that users retain the material or become independently more skilled over time.
That distinction matters in practice: asking for an explanation can help someone navigate unfamiliar code, but copying a solution without understanding its assumptions leaves the learner dependent on an answer they may not be able to assess. The survey supports describing AI as a perceived aid to onboarding and comprehension—not as a substitute for learning fundamentals.
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What is changing—and what remains uncertain?
Programming work is gaining a conversational assistance layer. Instead of starting every task by searching documentation or composing code from scratch, a developer can ask for an explanation, a candidate implementation, or a way to approach a maintenance problem. The usefulness of that exchange depends on the task, the available context, the tool and model in use, and the developer’s ability to evaluate the result.
Evidence about adoption or task completion does not settle what will happen to programmer employment, pay, or team size over the long term. Nor does a reported speed gain establish that a team will produce more software, deliver better outcomes, reduce staffing, or create new demand. GitHub COO Kyle Daigle has written, “AI doesn’t replace human jobs—it frees up time for human creativity.” That is GitHub’s stated position, not a measured finding about employment outcomes.
When judging claims about a coding assistant, check whether the evidence concerns ChatGPT or AI tools generally, what kind of users and work it covers, whether it measured reported usefulness or observed outcomes, and which product versions were available at the time. A result about one workflow is not a verdict on programming as a whole.
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