You: “Can you convert this entire codebase to Go?” AI: “I’ll get to work. Starting the first iteration…”
Migrating from one programming language to another used to be a process of blood, sweat, and JIRA tickets. Calluses on your fingers from programming. And when you finally deliver, the client or product owner says: “It looks exactly the same! Have you been working on this for half a year?” That’s why it was and still is something no one likes to get their fingers burned on, and why it doesn’t happen often. But with the rise of LLMs, this process will soon become relatively easy.
An AI rewrites a Python codebase to idiomatic Go. The developer watches, and fine-tunes where needed.
How does it work?
The AI understands the semantics of the code. It doesn’t just read the syntax, but also understands what the code does. It can follow the logic, recognize dependencies, and even understand the context in which the code runs.
The result? The AI can rewrite the code in another language, preserving the original functionality. And that’s not just a matter of syntactic translation. It’s about understanding the underlying principles and applying them in the new language.
Why do you think this is so easy?
Migrating a codebase is a perfect example of a task that can be relatively easily verified. You already have tests that must pass before you implement changes. You run these tests before and after the migration. If the migration doesn’t meet all test cases, the AI goes back to the drawing board for another iteration.
Compare this to how Deep Blue already defeated Kasparov in 1997 in the clearly defined game of chess: strict rules in which AI excelled. After that came AlphaGo: more complex, more freedom, but still well-defined boundaries. Here too, AI eventually defeated humans. Codebase migrations are in the same spectrum: clearly defined, easily verifiable, and therefore ideal to be performed faster and better by AI than by human developers.
“Codebase migrations are a clearly defined, easily verifiable domain and therefore a perfect use case for AI.”
From chess to codebases: AI excels in verifiable domains.
What does this mean for developers?
It means you’re no longer stuck with the language you started in. You can choose the language that best fits your current needs, without fear of migration costs or the time it takes to rewrite everything.
Why is this game-changing?
Because language transition no longer needs to be a strategic obstacle. In the past, choosing the “wrong” language was an expensive mistake. Now you just choose again. You think in abstractions and let AI make the translation to the right language, depending on the use case, scalability, or team preference.
Is this then the end of specialization?
On the contrary. It’s the beginning of higher abstraction in software development. Developers become composers of systems, choosing the tools that fit best, without being stuck to earlier choices. AI takes over the syntactic translation, you maintain control over semantics and architecture.
What used to be a half-year project is now an iterative process of chatting, reviewing, and refactoring. No more expensive rewrite trajectories. No more fear of legacy. Just: “Can you convert this to…”, and your AI does it.
“Migration of codebases becomes a standard process, largely performed by AI.”
Have you already used ai for migrating codebases?
Have you already experimented with AI for migrating code? Maybe you’ve already had small modules converted or are considering a larger migration. Or perhaps you’re still in the exploration phase and wondering if this can really work for your specific codebase. I’m curious about your experiences with AI-driven code transitions. Feel free to share your successes, challenges, or questions.
