In August 2025, TypeScript became the most used language on GitHub. This was the largest shift in GitHub’s language rankings in the last ten years and it occurred during the period of most accelerated adoption of coding AI agents.
Coding AI agents had previously been predicted to lower the importance of language selection. It was assumed that organizations would become stack agnostic and select tech stacks based solely on the needs of the business problem, leaving behind the considerations of the available developer hiring pool. Instead, a mere two years after the widespread adoption of AI coding tools, the market appears more restricted, with a rapid narrowing of available coding languages, and most of the focus is on a single family of languages.
GitHub’s October 2025 Octoverse report counted TypeScript contributors. Its 2.64 million monthly contributors marked a 66% increase year-over-year. During 2025, TypeScript saw over a million developers write their first TypeScript code in GitHub.
This growth is in addition to an already highly dominant position. In 2025 Stack Overflow sent a developer survey that collected more than 49,000 responses. Of these responses, 66% self-reported using JavaScript. For nearly every year since 2011 JavaScript has dominated this position. In conclusion the JavaScript family is both the most used and the fastest growing on GitHub.
It is worth briefly addressing an issue with GitHub’s counting method; counting activity on GitHub is counting activity on their own site which presents a conflict of interest as they may want it to look good. Also, fashion trends impact the kind of information that is included in public repositories. However, the indicators still align with survey data, which is reasonable considering the scope of this particular trend.
Models write best in the code they have seen most
How it works is quite simple. Models learn from the code that is published, and most of the code that is published is written in JavaScript and TypeScript. In fact, much of the code is centered around React.
This creates a substantial gap in the output that developers can see in their agents within a day of changing their stack. Request a coding agent to generate a typed React component and the output will usually compile, conform to the standards of the codebase, and require minimal edits. In contrast, when you ask the same agent to generate code for a Svelte, Solid, or a less popular backend framework, the output tends to be much thinner. Also, you will see more invented APIs, and the scaffolding will need more corrections before it is executable.
As a result, this is now changing the way that teams are selecting their stacks. It is no longer simply a matter of deciding which framework is the most efficient or easiest to work with, but rather which framework is most compatible with the team’s tools because the productivity gap in usable agent output during an extended build cycle is compounded. When that team produces output, it gets published, scraped, and included in the subsequent training runs, widening the productivity gap.
None of this points to a technical verdict. Solid and Svelte are good frameworks, and several more modern frameworks outperform React on raw speed. The market rewarded the choice the models already knew.
The models are built in Python, but the products are shipped in JavaScript
A valid counter to the above points is that AI development happens in Python. Model training, evaluation, and most research tooling run in Python, and that hasn’t changed.