{
  "id": 9693672,
  "title": "I Think AI Is Making Coding Easier and Learning Harder",
  "url": "https://urgent.news/2026/09/25/i-think-ai-is-making-coding-easier-and-learning-harder",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-25T03:57:07.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/jaideepparashar/i-think-ai-is-making-coding-easier-and-learning-harder-5hjf"
  },
  "original_language": "en",
  "account": "AI is making coding faster, but it's also making learning harder. Writing code is now easier with AI, which can explain unfamiliar libraries, generate functions, suggest fixes, write SQL, and even debug errors. It can also explain the code it generates, making developers productive. However, this raises a new concern: developers might become productive before they become competent. The learning process of coding used to involve writing code, encountering errors, searching for explanations, and trying alternative solutions. This process was frustrating but crucial for building a mental model and understanding how systems behaved. AI changes this equation, as developers can now get immediate explanations for errors. While this is useful, it can also skip valuable learning steps. The problem lies not in AI itself, but in how we use it. For instance, asking AI to write functions we don't understand when learning Python may not foster learning. Productivity can sometimes mask the learning gap. For example, a developer who solves a problem quickly using AI might not recognize the pattern when the same issue arises later, unlike a developer who spent time struggling and learning. As productivity increasingly rewards completion, AI's efficiency in completing tasks could lead to a lack of understanding. Although the author doesn't argue against using AI, they advocate for preserving the struggle that comes with learning. The author suggests a rule: try first, then ask AI. This approach allows developers to build a mental model of the problem, making AI's answers more useful. For instance, instead of asking AI to solve a problem, ask it to review your solution and find weaknesses, or generate questions to investigate. This way, AI becomes a thinking partner rather than an answer machine. This shift in perspective highlights a crucial distinction in AI-assisted development: knowing when not to ask AI. As AI becomes better at answering questions, the skill of asking good questions becomes more important. Knowing when to answer questions yourself first can make developers more proficient at reasoning, an essential skill in AI-assisted development.",
  "summary": "I can write code faster today than I could a few years ago. AI can explain an unfamiliar library. It can generate a function. It can suggest a fix. It can write SQL. It can help debug an error. It can even explain the code it just generated. From a productivity perspective, this is incredible. But I've started thinking about a different question: What happens to the learning process when the…",
  "key_points": [
    "AI accelerates coding but complicates learning",
    "Developers may become productive before competent",
    "Try first, then ask AI to preserve learning process"
  ],
  "editors_take": "The increasing reliance on AI to accelerate coding raises concerns that developers may become productive without first becoming competent, potentially undermining the learning process and deeper understanding of systems.",
  "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."
}