I still love Haskell, had dedicated several years to it while at the university. Was impossible to land a job with Haskell. Did some Scala but it’s in demise. F# and OCaml are extremely niche. Ever fewer opportunities and engineering management is convinced it’s impossible to hire functional programmers.
In this new AI-driven world, is there still place for such a luxury as functional programming?
I mean few people still code by hand, few read the generated code, models aren’t trained on functional languages, it’s inefficient token wise to use functional languages - while a lot become self-proclaimed software engineers overnight by just prompting LLMs.
Hiring for Haskell is very easy, unless you need to hire more than 100 people on the spot, or don't offer remote. I've been doing Haskell hiring for 10 years, with more than 50 interviews, the applicants are always great, each time we had to pick the best of multiple candidates.
Cannot confirm functional programming being inefficient token-wise, or worse at being generated than other paradigms. Claude is great at Haskell.
The difficult parts about Haskell, such as understanding type checker error messages, are gone thanks to LLMs.
Type-safe, side-effect free code degrades correctness a lot less under heavy LLM action in my experience.
I’m really happy to hear that. I wish Haskell were more widely adopted and more generally accepted as a solid enterprise choice without the bus factor. I can confirm that the applicants are usually great in every respect. I tried to introduce Haskell in multiple enterprises but failed at engineering managers’ reluctance to admit Haskell as an option - every time because of the bus factor argument. Scala used to be easy to advocate for as an alternative. To me, with LLMs there’s really no need to care about the choice of the runtime language unless there are SLOs to meet or library choices due to domain peculiarities.
As for token-wise inefficiency, this is based on my sporadic observations and discussions with friends and colleagues from the past three years. I can’t give you a fresh benchmark with latest models in a reproducible way. But I’m happily willing to accept your assertion at face value. This makes me curious to see for myself how the latest models perform, will perhaps set up a quick evals just out of curiosity.
From my past experience, the latest I’ve seen were Opus-4.6 and GPT-5.5 struggle a lot with standard GHC Haskell, no extensions, nothing fancy. Not that they produced impeccable Python or Rust. But it appeared to take several turns for obvious expressions, while at Java and TypeScript they were much better - fewer turns, time, and cost for the same verified results. I had assumed that the training set wasn’t large and diverse enough for Haskell and Scala, and this was the go-to explanation with everyone I talked about it. Generated F# code was good enough but not OCaml. With C++ there’s still quite some struggle, expectedly.
To me it resorted to the question, if we’re mostly generating code through LLMs now, which ones and what will it cost in total terms per task/capability completed. I’m wondering now if DeepSeek/MiMo or GPT-6 Luna can benefit from the terser, more (forgive the pun) load-bearing expressions.
For what it’s worth I recently decided to learn Haskell for fun and to keep my brain alive since work turned into a prompt factory.
Fable has made an excellent tutor. Maybe it’s just because the exercises I have it generating for me are relatively simple, but I have yet to catch it suggesting non functional or non compiling Haskell code.
jmaker · · focus · HN ↗
In this new AI-driven world, is there still place for such a luxury as functional programming?
I mean few people still code by hand, few read the generated code, models aren’t trained on functional languages, it’s inefficient token wise to use functional languages - while a lot become self-proclaimed software engineers overnight by just prompting LLMs.
nh2 · · focus · HN ↗
Cannot confirm functional programming being inefficient token-wise, or worse at being generated than other paradigms. Claude is great at Haskell.
The difficult parts about Haskell, such as understanding type checker error messages, are gone thanks to LLMs.
Type-safe, side-effect free code degrades correctness a lot less under heavy LLM action in my experience.
jmaker · · focus · HN ↗
As for token-wise inefficiency, this is based on my sporadic observations and discussions with friends and colleagues from the past three years. I can’t give you a fresh benchmark with latest models in a reproducible way. But I’m happily willing to accept your assertion at face value. This makes me curious to see for myself how the latest models perform, will perhaps set up a quick evals just out of curiosity.
From my past experience, the latest I’ve seen were Opus-4.6 and GPT-5.5 struggle a lot with standard GHC Haskell, no extensions, nothing fancy. Not that they produced impeccable Python or Rust. But it appeared to take several turns for obvious expressions, while at Java and TypeScript they were much better - fewer turns, time, and cost for the same verified results. I had assumed that the training set wasn’t large and diverse enough for Haskell and Scala, and this was the go-to explanation with everyone I talked about it. Generated F# code was good enough but not OCaml. With C++ there’s still quite some struggle, expectedly.
To me it resorted to the question, if we’re mostly generating code through LLMs now, which ones and what will it cost in total terms per task/capability completed. I’m wondering now if DeepSeek/MiMo or GPT-6 Luna can benefit from the terser, more (forgive the pun) load-bearing expressions.
eunoia · · focus · HN ↗
Fable has made an excellent tutor. Maybe it’s just because the exercises I have it generating for me are relatively simple, but I have yet to catch it suggesting non functional or non compiling Haskell code.