為何人工智慧正驅使開發者轉向型別語言

Hacker News·3 個月前

本文探討了人工智慧（AI）的興起，特別是像GitHub Copilot和大型語言模型（LLMs）等工具，如何影響開發者對使用型別程式語言的偏好。文章強調了AI程式碼生成所帶來的優勢，以及它如何提升開發者體驗，同時也觸及了AI在GitHub生態系統和產業中的廣泛應用。

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Why AI is pushing developers toward typed languages

AI is settling the “typed vs. untyped” debate by turning type systems into the safety net for code you didn’t write yourself.

It’s a tale as old as time: tabs vs. spaces, dark mode vs. light mode, typed languages vs. untyped languages. It all depends!

But as developers use AI tools, not only are they choosing the more popular (thus more trained into the model) libraries and languages, they are also using tools that reduce risk. When code comes not just from developers, but also from their AI tools, reliability becomes a much bigger part of the equation.

Typed vs. untyped

Dynamic languages like Python and JavaScript make it easy to move quickly when building, and developers who argue for those languages push for the speed and flexibility they provide. But that agility lacks the safety net you get with typed languages.

Untyped code is not gone, and can still be great. I love, personally, that I can just write code and not define every aspect of something on my average side project. But, when you don’t control every line of code, subtle errors can pass, unchecked. That’s when the types-driven safety net concept becomes a lot more appealing, and even necessary. AI just increases the volume of “code you didn’t personally write,” which raises the stakes.

Type systems fill a unique role of surfacing ambiguous logic and mismatches of expected inputs and outputs. They ensure that code from any source can conform to project standards. They’ve basically become a shared contract between developers, frameworks, and AI tools that are generating more and more scaffolding and boilerplate for developers.

With AI tools and agents producing larger volumes of code and features than ever, it only makes sense that reliability is more critical. And… that is where typed languages win the debate. Not because untyped languages are “bad,” but because types catch the exact class of surprises that AI-generated code can sometimes introduce.

Is type safety that big of a deal?

Yes!

Next question.

But actually though, a 2025 academic study found that a whopping 94% of LLM-generated compilation errors were type-check failures. Imagine how much your projects would improve if 94% of your failures went away! Your life would be better. Your skin would clear. You’d get taller. Or at least you’d have fewer “why does this return a string now?” debugging sessions.

What Octoverse 2025 says about the rise of typed languages

Octoverse 2025 confirmed it: TypeScript is now the most used language on GitHub, overtaking both Python and JavaScript as of August 2025. TypeScript grew by over 1 million contributors in 2025 (+66% YoY, Aug ‘25 vs. Aug ‘24) with an estimated 2.6 million developers total. This was driven, in part, by frameworks that scaffold projects in TypeScript by default (like Astro, Next.js, and Angular). But the report also found correlative evidence that TypeScript’s rise got a boost from AI-assisted development.

That means AI is influencing not only how fast code is written, but which languages and tools developers use. And typed ecosystems are benefiting too, because they help AI slot new code into existing projects without breaking assumptions.

It’s not just TypeScript. Other typed languages are growing fast, too!

Luau, Roblox’s scripting language, saw >194% YoY growth as a gradually typed language. Typst, often compared to LaTeX, but with functional design and strong typing, saw >108% YoY growth. Even older languages like Java, C++, and C# saw more growth than ever in this year’s report.

That means gradual typing, optional typing, and strong typing are all seeing momentum—and each offers different levels of guardrails depending on what you’re building and how much you want AI to automate.

Where do we go from here?

Type systems don’t replace dynamic languages. But, they have become a common safety feature for developers working with and alongside AI coding tools for a reason. As we see AI-assisted development and agent development increase in popularity, we can expect type systems to become even more central to how we build and ship reliable software.

Static types help ensure that code is more trustworthy and more maintainable. They give developers a shared, predictable structure. That reduction in surprises means you can be in the flow (pun intended!) more.

Looking to stay one step ahead? Read the latest Octoverse report and try Copilot CLI.

Written by

Cassidy Williams

@cassidoo

Cassidy is senior director for developer advocacy here at GitHub. She enjoys building software, advising startups, and teaching developers how to build better. She has a weekly newsletter at cassidoo.co/newsletter where you can get her updates, practice coding problems, and a joke in your inbox!

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