{
  "id": 6192378,
  "title": "AI addicts won’t make better workers",
  "url": "https://urgent.news/2026/09/07/ai-addicts-wont-make-better-workers-6192378",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-07T23:00:00.000Z",
  "source": {
    "name": "Free Malaysia Today",
    "slug": "free-malaysia-today",
    "url": "https://www.freemalaysiatoday.com/category/opinion/2026/09/08/ai-addicts-won-t-make-better-workers"
  },
  "original_language": "en",
  "account": "Federal Reserve chair Kevin Warsh and leading economists maintain that artificial intelligence (AI) will significantly boost labour productivity. However, the broader implementation of these models could actually lower output per worker. The evolution of technology has always been pivotal in driving economic growth. During the early stages, rigid text-based tools were the norm. The emergence of the World Wide Web revolutionised internet communication, giving rise to user-friendly browsers and search engines that were affordable and efficient. Google's success dramatically reshaped the market, but it also led to a decline in user experience and productivity due to the proliferation of clickbait and sponsored results. AI chatbots, such as ChatGPT and Google's Bard, have experienced rapid adoption, but their performance has proven to be unreliable. These chatbots struggle to filter out their own fabricated responses, consuming more time than traditional search methods and ultimately reducing productivity. Despite their potential advantages, large language models (LLMs) struggle with handling rapidly changing information, which can lead to outdated or irrelevant results. Users often blindly trust these chatbots, contrasting with the more discerning approach required when using traditional search engines. The indiscriminate use of trillions of parameters in LLMs amplifies the issue of extrapolation errors, making it more likely for them to produce false or unreliable answers. Traditional statistical models, on the other hand, allow for better control over variables and data sources, mitigating these issues. Moreover, LLMs' reliance on statistical extrapolation hinders their ability to replicate human-like sense-making and contextual understanding, rendering their conversational interfaces as linguistic chimeras. AI companies are actively pursuing consumer addiction, with Google's AI overviews and AI Mode serving as enticing free trials. As these addictive interfaces gain popularity, hyperscalers will undoubtedly seek to monetize them by charging high prices. The comparison to food addiction provides a chilling reminder of the potential consequences of AI addiction on professional productivity and personal well-being. The authors contend that techno-optimists must reconsider their faith in AI's productivity promise, as the current trajectory of AI development may lead to unintended consequences that outweigh its benefits.",
  "summary": "The narrow applications for AI programmes do not justify the massive investments.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 2,
    "also_reported_by": [
      {
        "outlet": "Free Malaysia Today",
        "title": "AI addicts won’t make better workers",
        "url": "https://urgent.news/2026/09/07/ai-addicts-wont-make-better-workers",
        "published": "2026-09-07T23:00:00.000Z"
      }
    ]
  },
  "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."
}