I've been a power user of LLMs for software development for a while now, and I've found two things to be true:

- The benefits of more "extreme" type systems are more accessible and valuable than ever. I have a fairly involved project built on Lean that I hope to open source this month, and it's been a joy to work in even for uses outside of mathematics.

- Readability, build time, infra complexity, and everything that affects your speed after finishing your implementation--these things now matter more than ever.

It's sort of a dual ergonomics problem, in some sense. And given that, the author's lament makes complete sense to me, especially:

"An AI-enabled Haskell ecosystem would ask different questions. How do we make Haskell easier for agents to use well? How do we get more high-quality Haskell examples into model training data? How can we scale reviews? How do we make library docs full of copy-pastable, realistic examples, not just beautiful types? How do we make project bootstrap fast? How do we make error messages more agent-friendly? How do we reduce cold build times? How do we make common industrial patterns obvious to a model that is trying to help?"