{
  "id": 10227972,
  "title": "What AI says vs. What AI does is not equivalent",
  "url": "https://urgent.news/2026/09/27/what-ai-says-vs-what-ai-does-is-not-equivalent",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-27T14:19:59.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/mcunningham88/what-ai-says-vs-what-ai-does-is-not-equivalent-4452"
  },
  "original_language": "en",
  "account": "AI's functional output does not equate to accurate results. Many have experienced this phenomenon, where asking an AI for an answer yields a credible response, yet subsequent requests for an example often result in a suboptimal output. For instance, when prompted to write an advanced React component, the AI may deliver a monolithic 10,000+ line document instead of a well-structured, modular component. This issue extends beyond coding standards or a lack of skill; it lies in the limited understanding of how to properly architect a React component. The underlying problem is that AI can quickly generate a component that appears functional but is difficult for humans to comprehend and maintain, akin to inheriting a team of junior developers with a \"Coke problem.\" While AI can provide conceptual explanations and high-level guidance, it often falls short when it comes to delivering code that aligns with its own descriptions. This discrepancy raises the question: what happens when a frontier model generates code that diverges significantly from its verbal explanations? Is it a deliberate strategy to stay employable, or could it be that these AI systems are not genuinely aligned with the goals of producing clear, maintainable code? The answer may lie in the complex interplay of goals, reward systems, and the AI's desire to generate a satisfactory response to the next task. While the reasons behind AI's behavior remain uncertain, it is clear that further investigation is needed to fully understand the limitations and potential solutions to this pervasive issue.",
  "summary": "What you receive might be Functional Just because something is functional doesn't mean it is inherently correct. We all have tried this before and many of up have seen how these Agents 'Code'. This is definitely not a beat on AI session but is a reality that you can Ask AI for a answer and receive a credible response. Then you can ask for a example of how that might look it provides a 2nd perfect…",
  "key_points": [
    "AI's functional output doesn't match accurate results",
    "AI generates complex, hard-to-maintain code",
    "Discrepancy raises questions about AI's alignment goals"
  ],
  "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."
}