The development workflow is changing, and Haskell as a language doesn't fit that new workflow anymore. The traditional strong type system basically forced you to spend a long time thinking before writing code. The upside is that this makes the code more logical and robust. But it's the exact opposite of the AI loop. It's not that the type system is wrong—it's that the toolchain that comes with a strong type system has itself become a bottleneck.

With AI coding emerging, a single person can now churn out 100,000 or 200,000 lines. And realistically, from my experience, once you go past 40,000 lines, it's hard to memorize everything. So what do you do? Human coding shifts toward writing tests and gates, and once you feel comfortable that things are safe, you add more features.

AI coding takes this to an even more extreme level. Learning Haskell is great for learning domain modeling—I learned domain modeling through Haskell myself. But now that AI has become genuinely useful, it seems like a fundamental shift in workflow is happening.

Realistically, for commercial competitiveness, the domain of code black boxes is getting larger. The number of lines a single person has to manage is increasing, but their cognitive limits haven't changed. Even the amount of background knowledge required keeps growing.

In that sense, I agree with the author's point. It's not that Python is a better language than Haskell, so people switched—it's that Python has almost no build-up process and it serves as the standard interface for AI models.

The value of a language as a product doesn't come from the compiler's excellence. It comes from its users.

Avi Press's article ultimately reflects the reality that if you can't stay ahead throughout the entire lifecycle, you'll fall behind your competitors.

Maybe I'm just echoing my own thoughts, but it's reassuring to see that a well-known programmer thinks similarly to me.