My thoughts on the future of Go in the AI era
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My thoughts on the future of Go in the AI era<br>One slightly controversial opinion I have is that AI might actually make boring languages more valuable, not less.
Alex Pliutau<br>Jul 15, 2026
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To be fully honest, nowadays I don’t write as much Go code as I wanted.<br>One reason is that we don’t use Go at my current company. We mostly use TypeScript, about which I still have mixed feelings, but to be fair, it works pretty well for us nevertheless. So I won’t blame TypeScript much here.<br>I still maintain a few Go projects, so I write at least some amount of Go from time to time.<br>But oh boy, I miss writing more of it.<br>Especially I miss the developer experience. Nothing even comes close for me, and interestingly, I think that becomes even more important in the AI era. And in this article I’d like to rant a bit about that.
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Things are clearly changing now with coding agents, and the way we write code. I don’t mean all of us, by the way, I still have huge respect for people who manually craft software without relying heavily on AI. I think there will always be space for that level of engineering craftsmanship.<br>So, things are changing, for better or worse, I am still not completely sure, but you can already see some trends forming. Rust and TypeScript are becoming dominant choices in AI-generated code, while some other languages… let’s just say you hear less and less about them.<br>So the real question is: what programming languages will still matter in 5 years from now?<br>I think we will see fewer dynamic Backend languages in new projects. Ruby on Rails, PHP, maybe even Python for some Backend workloads. Not because those languages are bad, obviously. Many of them are fantastic for humans.<br>You see, without AI, languages competed a lot on human ergonomics. Now they also compete on machine ergonomics.<br>Take Ruby on Rails for example. It is incredibly human-friendly, but I would argue not necessarily machine-friendly, performance-wise and typing-wise. Humans can navigate conventions, implicit magic, and DSLs very naturally because they understand context. Models struggle more with that. Explicit systems are simply easier for them to reason about.
And then there are new so-called AI-native languages.<br>Zero<br>Honestly, reading that I am still not sure what are the strengths here
Vera<br>they even route the docs differently for humans or agents. Is it different?
And honestly, I still don’t fully understand the pitch. Both of them feel like AI generated slop languages made with a prompt like “Hey, make me a programming language, do not make mistakes”.<br>Models today are trained mostly on existing languages and existing ecosystems. So how exactly are they suddenly supposed to generate perfect code in a completely new language with no mature tooling, no massive production codebase, no battle testing, and very little real-world data?<br>What about security, compilation? Feels like a blackbox.<br>So right now, ecosystems still matter a lot.<br>And that is where Go becomes really interesting.<br>Because in my opinion, and many people will disagree, Go is actually an extremely good language for LLMs. Not just for humans, but for models too. And honestly, I am really rooting for it to win this competition.
So let’s review a few things that I think make Go uniquely good for the agentic era.<br>First, the standard library is absolutely massive and incredibly well-designed. You can build real production systems with surprisingly few dependencies. HTTP servers, JSON handling, crypto, testing, concurrency, file systems...