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AI is influencing which programming languages developers use, but the available evidence does not show that AI alone caused any language to rise or fall. GitHub’s platform rankings, Stack Overflow’s survey results, and research estimates measure different things. Taken together, they point to strong Python activity in AI-related work and a notable rise for TypeScript on GitHub, alongside continued human review and debugging of AI-generated code.
What the language rankings actually show
There is no single ranking that captures programming language use everywhere. A platform can count contributions or code pushes; a survey can ask developers what they have worked with. Those measures can produce different leaders without contradicting one another.
| Evidence | What it found | What it measures |
|---|---|---|
| GitHub’s 2024 Octoverse report | Python overtook JavaScript in overall contribution activity, while JavaScript remained first for code pushes alone. | Activity on GitHub, not all software development. |
| GitHub’s 2025 language analysis | TypeScript became GitHub’s most-used language in August 2025, overtaking Python and JavaScript. | GitHub’s language ranking and contributor activity under its methodology. |
| Stack Overflow’s 2025 Developer Survey | Python adoption among respondents rose by seven percentage points from 2024 to 2025. | Survey respondents’ reported development work, not GitHub activity. |
The measures answer different questions. GitHub’s 2024 report also recorded a 92% increase in Jupyter Notebook use, in a period when it connected Python’s growth with machine learning, data science, scientific computing, and wider participation from STEM communities. In its 2025 analysis, GitHub counted more than one million additional TypeScript contributors over the prior year. It reported roughly 850,000 more Python contributors, a 48.78% year-over-year increase, and roughly 427,000 more JavaScript contributors, a 24.79% increase. These are platform-specific figures, not a census of developers worldwide.
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Familiar languages can be easier for models to generate
Models learn from examples. When a language has abundant public code and documentation, a model may be more likely to encounter examples of its syntax, libraries, and common patterns. GitHub’s 2025 discussion argues that models are strong in popular languages including TypeScript, Python, Java, and Go. That is an interpretation of language trends and model behavior, not a controlled benchmark proving how well models perform across languages.
GitHub Next head Idan Gazit described the training-data argument this way: “If the model has seen a trillion examples of TypeScript and only thousands of Haskell, it’s just going to be better at TypeScript,” he said. “That changes the incentive before you even start coding.” The statement captures a plausible incentive, but it is not an independently measured comparison of model performance.
Types can make generated code easier to inspect
Explicit types and interfaces can help developers spot when generated code does not match expected inputs, outputs, or contracts. GitHub has suggested that this advantage may contribute to a feedback loop around typed languages such as TypeScript. However, the fact that TypeScript rose to the top of GitHub’s ranking in August 2025 does not establish that AI caused the change; other project and ecosystem factors may also matter.
Existing ecosystems still count
AI assistance does not remove the need for libraries, deployment options, team expertise, or maintainable project conventions. A language that fits the application and its existing ecosystem can remain the practical choice even if a model appears more familiar with another one. AI can change the cost of writing or learning code, but it does not decide what a project must integrate with or who will maintain it.
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Which languages developers are using for AI
Python stands out in AI-focused activity, but “used for AI” and “most used overall” are different claims. GitHub reported that nearly half of new AI projects on its platform were primarily built in Python as of August 2025. Separately, Stack Overflow’s 2025 survey found a seven percentage point increase in Python adoption among respondents compared with 2024. The first figure concerns AI projects on GitHub; the second concerns survey-reported development work across respondents.
TypeScript’s rise in GitHub’s overall ranking is another trend, not evidence that it has displaced Python in AI project work. Language choice varies with the type of software being built, the available libraries, and the team’s workflow. These sources do not establish a universal best language for AI-assisted development.
AI assistance has not made review optional
In Stack Overflow’s 2025 survey, favorable sentiment toward AI tools was 60%, down from more than 70% in both 2023 and 2024. Among respondents who answered the survey’s frustration question, 66% said they encountered AI solutions that were almost right but not quite, and 45% said debugging AI-generated code took more time. These are reported experiences, not a controlled measure of how much time AI saves or costs.
The findings make a practical point: generated code still needs to be checked against requirements, tested, and maintained. A language’s type system, tooling, and test ecosystem can help with that work, but none makes review unnecessary.
AI coding is not the same as prompt-only development
Stack Overflow’s 2025 survey found that 72% of respondents said vibe coding was not part of their professional development work; another 5% emphatically said it was not part of their workflow. These responses caution against treating AI-assisted programming as a wholesale replacement for conventional software development or language expertise.
In a separate survey of 2,000 respondents across the United States, Brazil, Germany, and India, GitHub reported that more than 97% had used AI coding tools at some point. Respondents described benefits that included faster adoption of programming languages, but the survey asked about ever having used the tools. It does not establish how frequently they were used or whether they caused respondents to choose a particular language.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What estimates of AI-written code can—and cannot—tell us
A 2025 working paper, Who is using AI to code? Global diffusion and impact of generative AI, analyzed 80 million GitHub commits from 2018 through 2024 and used a classifier to estimate AI-written Python functions. For December 2024, it estimated the following shares of Python functions by contributors in selected countries:
| Contributor location | Estimated AI-written share of Python functions |
|---|---|
| United States | 30.1% |
| Germany | 24.3% |
| France | 23.2% |
| India | 21.6% |
| Russia | 15.4% |
| China | 11.7% |
These are classifier-based estimates for Python functions in the paper’s GitHub sample, not measured shares of all code, all languages, or all development in those countries. The paper’s fixed-effects model also estimated that moving to 30% AI use was associated with 2.4% more quarterly commits. That is a model-specific association, not proof that AI caused a general productivity increase.
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- Check the measure: distinguish contribution activity, code pushes, project language, and survey-reported use.
- Check the time frame: GitHub’s 2024 snapshot and August 2025 ranking are different observations; the Stack Overflow result compares respondents’ reported adoption in 2024 and 2025.
- Check the work type: AI-focused projects, data science, backend systems, and web development can favor different ecosystems.
- Separate correlation from cause: platform rankings and self-reports show what happened or what people reported, not what AI alone caused.
The evidence supports a measured conclusion: AI is becoming part of how developers write and learn code, while language trends continue to reflect ecosystem and project needs. Python is prominent in GitHub’s AI-project activity, and TypeScript led GitHub’s overall language ranking in August 2025. Neither observation establishes a universal industry ranking or a single AI-driven explanation.
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