In fact if I have to ship in a different language, I'll often do a Haskell version first to steer towards isolating IO, enforcing referential transparency, and generally picking up other functional patterns that are a good idea no matter what. Then I'll port the Haskell version to whatever the solution demands.
As lower-hanging fruit, to get some style steering, but not the strictness/correctness assist in implementation, there's also doing code sketches in hs - writing without testing - and then asking for whatever code as inspired by / in the spirit of / based on the reference sketch.
Token efficiency is specifically the place where strict languages get a little trickier. With dynamic languages where the model has a ton of training data, like Python, the agents are really good at writing code that just works. You still have challenges there, but they're just different than the challenges you hit with a language like Haskell/Lean/Agda etc. With Lean, for example, the amount of things the agent decides it needs to prove for a relatively trivial operation can lead to an absurd amount of code. And the way it decides to prove them can be borderline labyrinthian. It sort of plays perfectly towards current agent weaknesses, where agents can run off and reinvent a lot of machinery and build unnecessarily complex abstractions.
And those iterations have a cost. Even if the final artifact is more terse, the path to getting there can involve a huge amount of backtracking and rewriting and off-by-one syntactic corrections.
Ultimately, a lot of this comes down to developer experience. Getting useful error messages for the agent, having fast compile times so that the agent can iterate quickly, providing useful utilities for debugging, etc. There was a popular blog post specifically about Haskell that generally makes this point--the Haskell language is, in a vacuum, ideal in a lot of ways for AI, but the Haskell developer experience and ecosystem made it hard for this team to build with it via agents: https://avi.press/posts/2026-07-10-after-7-years-in-producti...
Note: I am not advocating against using these kinds of language. Obviously, I still work in Lean and a little in Agda and some related languages/projects, so I think this direction is right. Probably biased in that I love them with or without LLMs, but I do think they're also more ideal for AI development. There are still just challenges in getting agents productive in them, and most of the challenges are not about the type systems but the actual ergonomics and dev experience.
So, I could write what I want in haskell, and it's faster for me than to have an LLM do it. The haskell would be high-level enough and precise enough to describe what I want the program to do with less iterations than I'd have with an LLM writing code, finding its own bugs, fixing them, then having me ask it what it did, tell it what to do differently, etc.
Direct control is very powerful. LLMs take that away from us, in exchange for a great speedup. But perhaps we can use LLMs to give it back to us by making our actual programming languages more powerful.
But I think this is actually in the design of many languages already, especially in languages like Lean, where you can prove that the presentable API is correct.
Here's one rough example that is related to this direction:
A common convention in Lean is to write essentially two versions of a function, with a theorem proving their equality or correspondence.
So you can write a more demonstrative version that is more easily understood, which you can treat sort of as a spec that all other implementations must satisfy. And then you can set the agent loose to do whatever nastiness it wants to do to improve performance.
Here's a fun write up from someone doing just this with zip file compression: https://kim-em.github.io/blog/2026-7-24-why-lean-is-faster-t...
> With dynamic languages where the model has a ton of training data, like Python, the agents are really good at writing code that just works.
I'm confused how the former supports the latter here.
Surely, you mean that LLMs are not the right kind of "AI". Languages like Haskell expose how fundamentally porous LLMs are with information.
I wouldn't even say it's the dynamic nature of Python that leads to good results, but the consistency. Javascript is the obvious counterexample. There's far more javascript out there to train on than any other programming language, yet LLMs are terrible at writing it too.
Admittedly I'm not much better, Agda has some crazy scoping rules at times and its operators are a mess, so much worse than Haskell, but at least I don't have to re-learn from scratch after every compaction.
I mean, free functions and no general nice trait/struct/enum based architecture, random `Mutex`/`RwLock` everywhere, all your big futures inside a `tokio::select!` etc.
Yes it's great when you do your first Rust project. After a year it's a nightmare if you don't keep a clear control how the code is generated and organized.
But I just don’t think terseness matters much; most of the cost is spent on reasoning tokens, not the sheer volume of code output.
The benefits of strict are still the same: once the code grows bigger you have alll kinds of guarantees built into the software, that JS/Py/Bash/C needs lots of test achieve the same.
Since writing code is cheap, it like to go more on the tests and the strong typing, simply because it is so cheap. Why write Ruby/Py/JS when you can write Haskell/Kotlin/Rust? There's simply no reason not to be doing TDD these days.
A priori, it does seem strange. But the relative Haskell vs Python tokens in the internet go a long way to explaining it, as another commenter has said.
Nobody prepared it for adoption nearly as much as Rust evangelists did for their language. Years of hard work they invested into maintaining bindings as hobby projects paid off.
My former company was generous enough to publish most of these[0] as open source projects. But what's really telling is that oftentimes vendoring a commonly used OSS library was unavoidable.
[0] Here's a potentially incomplete list: https://chatgpt.com/s/t_6af76fd8f2a081918fa37f8c30bdb3ba
I used prolog for a project and GPT 6.1 Sol repeatedly made dumb mistakes as if it doesn’t understand the symbolic resolution capabilities.
For vibe coded Haskell id not be surprised to see it just not use strict types, hucking strings around
Looks like the claim is true that Sol sucks at the long tail of languages (vs. battle opponents). https://micjm.com/blog/gpt-6.1-sol-long-tail-languages/
The ideal language for the engineer driving the agent has maximum comprehensibility, since review and understanding are the real bottlenecks now. I'm also pretty sure this doesn't describe haskell. It incompletely describes rust I think.
It does describe ocaml. And also gleam and maybe scala. And moonbit, developed by someone who is a proven expert in exactly this domain and explicitly targeting this problem space.
"higher level projections" - wtf is this even about?
That's doubtful. It's not strict in any way that matters, especially when LLMs are writing it. You still have to rely on tests for everything. The languages that are strict are still largely ignored.
It is seeing adoption because of a well executed advertising campaign combined with "C++ without the dependency hell" being a real value proposition.