Ruby 4.0 Is Here. Why Is AI Still Writing Ruby 3.0?

Ruby 4.0 has been released, but AI coding assistants still generate Ruby 3.0-era code, ignoring modern idioms like hash value omission and anonymous block parameters. This creates a feedback loop that slows adoption of new language features, raising concerns about AI's role in preserving outdated practices.

June 17, 2026 Artificial intelligence has become an indispensable tool for Ruby developers. We ask AI assistants to write methods, refactor services, generate RSpec tests, explain stack traces, and even architect new features. For many developers, AI is no longer an experiment—it’s part of the daily workflow. That’s exactly why we should expect more from it. If I’m writing a Ruby 4.0 application, I don’t want my AI assistant producing Ruby that looks like it came from 2021. Valid Ruby Isn’t Enough One of the most common arguments is that AI-generated code is “still correct.” Technically, that’s true. But correctness isn’t the only measure of quality. Imagine asking for Rails 8 code and receiving Rails 6 patterns. The application would probably still work, but you wouldn’t consider that a good answer. The same applies to Ruby. When I ask an AI to generate Ruby code, I expect it to use the idioms of the current language—not simply produce syntax that happens to compile. The Evidence Is Everywhere Take Ruby’s hash value omission syntax. Instead of writing: create session user: user Modern Ruby allows: create session user: Yet most AI assistants still default to the longer version. The same happens with hashes: { user:, project:, account:} Instead, AI frequently generates: { user: user, project: project, account: account} Another example is Ruby’s anonymous block parameter. Modern Ruby lets us write: numbers.map { it 2 } Yet AI almost always produces: numbers.map { |number| number 2 } None of these examples are wrong. They’re simply not modern Ruby. Why Does This Matter? Some developers might dismiss this as a stylistic preference. I don’t think it is. Programming languages evolve because their communities identify better ways to express ideas. Cleaner syntax reduces repetition. Modern idioms improve readability. New language features exist because the Ruby core team believed they made the language better. If AI assistants ignore those improvements, they’re teaching developers habits from older versions of Ruby. That’s not just a cosmetic issue. It’s a quality issue. AI Is Creating a Feedback Loop There’s another consequence that’s easy to overlook. AI models learn from existing code. Developers increasingly learn from AI. That creates a feedback loop. - Ruby introduces a new feature. - AI rarely generates it because it appears infrequently in training data. - Developers continue seeing the old syntax. - Developers keep writing the old syntax. - Future AI models train on even more old syntax. The result is that modern Ruby features spread more slowly than they should. Ironically, the tools designed to accelerate development may also be slowing the adoption of the language itself. “Just Use RuboCop” A common response is to let RuboCop fix everything. And yes, RuboCop can automatically rewrite: { user: user, project: project} into: { user:, project:} That’s useful. But it also misses the point. RuboCop is a formatter. An AI coding assistant is supposed to be a programming assistant. Those are different responsibilities. If every AI-generated snippet requires another tool to modernize it, then the AI isn’t producing the best answer available. It’s producing an acceptable first draft. Developers deserve better than that. This Is Bigger Than Hash Syntax This isn’t really about user: versus user: user. It’s about whether AI keeps pace with the languages it claims to support. Ruby continues to evolve. Developers upgrade. Frameworks evolve. Style guides change. AI coding assistants should evolve alongside them. If they don’t, they risk becoming one of the strongest forces preserving outdated coding practices. Raising the Standard AI has dramatically improved developer productivity. There’s no question about that. But as these tools become trusted programming partners, our expectations should increase as well. Producing code that merely works is no longer enough. The best AI coding assistants should understand the current language, embrace modern idioms, and generate code that reflects today’s Ruby—not yesterday’s. Ruby 4.0 is already here. It’s time for AI to catch up.