Alexandra Lietzke shares insights from Idan Gazit of GitHub Next on how AI is transforming programming language trends, developer workflows, and the future of software development.

How AI Is Shaping Language Trends in Software Development: Insights from GitHub Next

Author: Alexandra Lietzke

In this interview, Idan Gazit, the leader behind GitHub Next—the R&D group responsible for innovations like Copilot—discusses how artificial intelligence is not just speeding up coding but fundamentally changing the decision-making process before a developer writes their first line.

The AI Feedback Loop

AI’s influence extends deeper than code completion. Gazit argues that AI is prompting developers to rethink their choice of language and frameworks.
TypeScript has surged as the most-used language on GitHub in 2025, surpassing Python and JavaScript, as documented in the latest Octoverse report.
The reason isn’t simply popularity; it’s that typed languages like TypeScript offer the structure AI tools can leverage for safer, more reliable code generation.

If an AI tool is going to generate code for me, I want a fast way to know whether that code is correct. Explicit types give me that safety net.

—Idan Gazit

Language Choice and AI Compatibility

Typed languages are rising because they help AI tools reduce hallucination and provide developers with more reliable output and validation.
Developers are now considering how well AI tools support a language when starting a project, shifting language adoption from personal or cultural preference to AI-compatibility.
AI tools, trained on massive datasets, naturally excel at generating code in mainstream languages (TypeScript, Python, Java).

“Duct Tape” Languages and the Drudgery Layer

AI relieves much of the pain associated with scripting languages like Bash, which experienced a 206% year-over-year growth in AI-generated projects.
Developers can now lean on AI to quickly produce boilerplate or less enjoyable code, choosing the best tool for the job without being restricted by manual effort.

Enterprise Adoption and Developer Roles

AI adoption is accelerating across enterprises, with tangible benefits for onboarding junior developers and shifting senior developer roles from writing complex code to guiding architecture and code validation.
Key changes include:
- Greater focus on architectural skills rather than just code volume
- Increased leverage of tooling in shaping what AI can automate
- The rise of typed languages to enable more automation

The Rise of WebAssembly (WASM) and Future Interoperability

WebAssembly is gradually erasing runtime fragmentation, enabling code written in different languages to run anywhere.
Combined with AI-generated code and WASM targeting, developers will focus less on syntax and more on ecosystem, tooling, and interoperability.

Key Takeaways for Developers

The primary lesson isn’t to switch to a specific language, but to optimize for languages and tools that offer the most leverage through both ecosystem maturity and effective AI support.
Developer success will be measured by how well they can combine their skills with the new opportunities created by AI and interoperability platforms like WASM.