{
  "id": 14780210,
  "title": "AI Has a Problem With Functional Programming",
  "url": "https://urgent.news/2026/10/15/ai-has-a-problem-with-functional-programming",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-15T20:09:09.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/kaleman15/ai-has-a-problem-with-functional-programming-kpm"
  },
  "original_language": "en",
  "account": "A recurring issue has emerged in AI-generated code, particularly when it adopts a functional programming style. When instructed to develop straightforward functionalities, AI often produces code that appears modular and well-structured at first glance. For instance, it might define functions like `processRequest`, `getData`, and `transformData`, each with a single responsibility. However, these functions frequently serve as minimal abstractions, merely forwarding calls to other functions. This creates a labyrinth of file openings, where the codebase transforms into a tedious series of steps, diluting the overall understanding of the code.\n\nThis phenomenon isn't a direct consequence of functional programming itself. Functional programming doesn't inherently lead to shallow abstractions. The problem lies in how AI-generated code frequently emphasizes shallow abstractions - functions that primarily call other functions. Abstraction incurs costs, introducing additional names, paths, and interfaces that need to be managed in one's mind. While the objective of writing clean, modular, maintainable, and single-responsibility code is commendable, AI seems to produce this code mechanically. Each function is named, and the model doesn't comprehend the code's implications like a human would.\n\nMoreover, this issue extends beyond just the code's structural quality. Studies have shown that AI-generated code can still contain structural problems, such as code smells. An ICSE 2026 study measured the likelihood of LLM-generated code containing code smells and found that generation strategy, model architecture, model size, and prompt formulation significantly influence these smells. Another study noted an increase in code clones within the same repository, indicating that locally reasonable code might not necessarily result in a cleaner system globally.\n\nThe widespread use of LLMs in software development, including code generation and summarization, presents both opportunities and challenges. While LLMs can expedite development, concerns about debugging and the unnaturalness of generated comments persist. A 2026 paper suggests that code detected as likely AI-generated exhibits substantial intra-repository code cloning. However, the percentage of human-labeled bugs associated with LLM-generated code remains relatively small.\n\nIn conclusion, while AI-generated code can be functional and modular, it often falls prey to shallow abstractions and code smells. The challenge lies in how we instruct AI to generate code. Instead of mechanically satisfying goals like clean, modular, and maintainable code, we need to focus on generating code that humans can understand and maintain over time.",
  "summary": "There is a pattern I keep seeing in AI-generated code. You ask it to implement something relatively simple and you get this: const result = await processRequest ( request ); async function processRequest ( request ) { const data = await getData ( request ); return transformData ( data ); } async function getData ( request ) { return repository . get ( request . id ); } function transformData (…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}