Is It Worth Learning New Programming Languages in the Age of AI?

I've been doing the whole software development song and dance for around 10 years now. That means I'm young enough to not remember when you had to deploy things onto servers sitting in a closet, but old enough to remember wrestling with syntax problems that led you to a Stack Overflow post where some guy called you an idiot and told you to learn Rust.

At the beginning of my career, I started the way most developers do: slow mastery of programming, data structures, architecture, and general software design fundamentals, expressed through an even slower mastery of a chosen language that was usually dictated by whatever my employer was working with. At Amazon, that was mostly Java. Lots of AWS services were (are? not sure, it has been a while) written in Java. Around the edges, I picked up Python because of its popularity, and some JavaScript/TypeScript to work on frontend projects. That trio became the centerpiece of my developer toolbox. Oh, and also some C from college (plus one interesting firmware project update), but C is for real programmers, not fake Pythonistas like myself.

As I moved through projects and companies over the years, I occasionally found myself out of my depth in unfamiliar programming languages. Retrofitting a PHP script. Writing a new endpoint inside a C# application. Modifying some protos for a Go service. Whenever that happened, I would stare at the code thinking, "damn, I should really learn how to do this," before stumbling through an implementation that I knew was not idiomatic, clean, testable, or maintainable. I simply just did not know the syntax and conventions of the language.

Did you know mocking is all kinds of weird in Go? Because I did not until I had to write some. Or that PHP has its own special collection of quirks, like 1 == "1"? And for those of us without deep JavaScript experience, maybe you have run into the dreaded question of what the hell "this" actually means.

Recently, I found myself in the middle of a large code migration, trying to rip out 15 years of a custom implementation and replace it with a SaaS product. You know, the standard "millions of lines of code across more than a decade, spread across dozens of services, slowly refactored piece by piece using AI agents" type of project. Things were cruising along fine until I hit the fringes. The edges of the enterprise where apps are written in nonstandard languages, running versions that are 10 years old.

Did I trust myself to update Go version 1.0 from 2012 when I am not even sure what a "panic" is? Probably not. I know what I want the code to do, but I do not necessarily know how to make it do that without a nontrivial amount of fumbling.

This time, though, I was not alone. AI-assisted tools know how to do this. They know the conventions. They know how to properly type objects. They know how to write tests, configure build files, generate docstrings, and handle exception patterns. All I had to do was drop a prompt the AI-assisted developer tool du jour, and out came idiomatic, working, crisp code (at least, in theory...)

It begs the question: is it still valuable to be a programming polyglot in 2026?

As more modern software development moves away from manual coding and toward AI-assisted tooling, language trivia knowledge seems to be becoming less valuable. Even the most AI-skeptical programmers are adopting AI-assisted tools. Even if you do not believe we will reach "apps on demand," or that software development will become obsolete in the next three years, you probably still use these tools because they make development undeniably easier and faster.

So where does that leave us? Is learning the syntax, conventions, and configurations of every language (and framework, while we are at it) no longer a productive use of time, if it ever was? Or is this AI-driven laziness that will slowly erode our capabilities to deliver quality software? Time will tell for either case, but for now, new engineers can still master the fundamentals: sharpening their understanding of core data structures and patterns, writing clean and maintainable code, improving testing practices, and mastering system design. Focus on doing that well in your primary language, and AI can help you port those skills to others.