Another day, another programming language feels the heat from AI. A prominent Haskell-based software platform is shifting new development to Python, with its founder arguing that Haskell's tooling and ecosystem have been slow to adapt to AI-assisted development. “Haskell is in real danger,” warned Scarf founder Avi Press, in a post entitled “After 7 years in production, Scarf has reluctantly moved away from Haskell.” “AI is here to stay. The people and ecosystems that use it well are going to move much faster than the people and ecosystems that do not.” Press’ post set off a firestorm of controversy within the Haskell community, which is small but vocal, and likely felt the sting of one of its most prominent users defecting to the comparatively toy-like Python to better serve the needs of agents. “Trying to change the language to work better for some metric of ‘better’ with AI is foolish for many many reasons,” wrote one user on Reddit. “It's foolish to try to change to LLMs in a kneejerk fashion because no one knows what's on the horizon.” Putting the fun into functional programming Haskell, a functional programming language that debuted in 1990, may not appeal to fast-moving startups. It ranks No. 46 in the latest TIOBE index of programming language popularity, with a rating of less than half a percent. Favoring recursion and immutable data over conventional imperative loops and mutable state, the language's mathematical underpinnings can intimidate even experienced developers. Nevertheless, it has attracted a highly dedicated following, particularly within academia. Press has been one of Haskell’s most vocal proponents, and has even served on the language’s foundation board. “Learning it made me a much better programmer,” he admitted. Press used Haskell to build Scarf, which provides usage analytics for open source software. In a 2023 talk entitled “Why Haskell is a Terrible Choice for Startups (and why we picked it anyway),” Press admitted it was difficult to find Haskell programmers, yet the language’s rigorous type safety comes with other benefits. Refactoring is a cinch, which is valuable as business priorities change. Also, documents can be auto-generated from data types. Plus, once you get the hang of it, programming in Haskell is downright fun, Press argued. Because AI But going forward, Scarf’s new features will be added in Python instead. “At Scarf, we started doing all new API work in Python,” he wrote. “New API routes go into Python, existing Haskell code keeps running, and over time the new server becomes the main path and our Haskell footprint will shrink.” Press said Python plays better with AI, which is the direction he sees development heading. A huge part of the problem is Haskell's sluggish compilation times. “If an LLM can produce a working implementation in a few minutes, but your compile step takes dramatically longer, then your language and build system have become a bottleneck in the development loop,” he wrote. Long compilation times, once a minor annoyance, become prohibitive when running multiple coding agents at once. Caching can help, but incurs its own overhead to manage. “I want to spin up multiple worktrees, fork off different lines of work, let agents try things, review the results, and keep the useful ones. In that world, cold start time matters a lot,” Press wrote. Rigor for runtime Using Python with AI practices immediately improved the Scarf production team’s workflows, allowing them to fix bugs with minimal oversight, Press noted. In some cases, AI can fix a bug “before I get off the call with a customer,” he noted. “Resisting this kind of productivity is not an option anymore,” Press wrote. Yet, Press doesn’t feel that the Haskell ecosystem is addressing the shift to agentic-led development. Many of the language’s maintainers focus more on restricting the use of AI, or restricting its use on Haskell entirely, rather than looking for ways Haskell can better serve AI, or vice versa, he argued. Agents have different bottlenecks than human code jockeys, he noted. “They are cheap at generating code and expensive when blocked. They benefit from fast feedback, clear examples, low setup friction, and errors that help them repair the code quickly,” he wrote. Press listed ways Haskell could be made easier for agents to use: Better documentation with actual copy-and-paste industry-targeted examples (rather than “beautiful types”), more informative error messages, and – most importantly – faster build times. Optimize and chill Press’ missive set off a firestorm of discussion across Hacker News, X, Reddit and Haskell’s own message boards. Many questioned the move from Haskell to Python, given Python’s own arguably inferior typing. They also questioned whether it would have been a simpler move to optimize the Haskell compiler for faster builds than switching languages altogether. Other Haskell fans voiced more philosophical concerns. It was as if they were taking the language’s tongue-in-cheek motto, “Avoid success at all costs," seriously. Longtime UK-based Haskell programmer Chris Done questioned the motivation of making Haskell more AI-friendly, given that production use is only one way that people use Haskell. “I don’t subscribe to this growth mindset anymore,” he wrote. “I’ve come to accept Haskell on its own terms. If it dies out and becomes irrelevant like Elm or PureScript, I’ll be fine and still enjoy it.” “It leaves a bitter taste in my mouth,” wrote another contributor on the Haskell forum about Press’ post. “I don’t feel like you take our concerns about the harm of AI seriously at all.” Another accused Press of having a financial stake in AI companies (to which Press replied, “Nope”). Haskell Foundation Executive Director José Manuel Calderón Trilla urged folks to chill in an X message. Haskell is a community that prides itself on doing things “the right way,” he noted, but that shouldn’t “let that blind you into thinking that your way is the only ‘right’ way, and then attacking someone for violating your idea of what the party line is.” ®
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