# Why AI is far less useful than they say | Ilyaz Nasrullah Source: https://www.youtube.com/watch?v=b_HQaVmziPQ Recap page: https://rapidrecap.app/video/b_HQaVmziPQ Generated: 2026-10-17T11:07:41.681+00:00 --- ## The Gist Artificial intelligence tools and large language models are vastly overhyped and fail to deliver on productivity and intelligence promises because they lack true understanding and rely on flawed, automated processes. ## Quick Overview Artificial intelligence models are fundamentally limited by a lack of real understanding, meaning they operate as powerful automated tools rather than revolutionary cognitive entities. Host Dr. Joeri Schasfoort and computer scientist Ilyaz Nasrullah dissect the seven major myths of artificial intelligence, exposing why adoption does not automatically equal productivity and why hype cycles risk distorting economic reality. **Key Points:** - Ilyaz Nasrullah published a book in Dutch titled This Is AI, focusing on the seven false promises of artificial intelligence. - A Harvard study by Fiona Chen tracking seven hundred companies and seven hundred thousand software workers between January 2021 and March 2026 found that coding agents increased individual task flags by twenty-three percent, but produced a fifty percent increase in review time and a thirty-five percent increase in review comments. - A Jellyfish data set analyzed in the research revealed zero organizational productivity gains despite widespread individual software tool adoption. - Google introduced the Facts Suite benchmark in December 2025 to evaluate the factual accuracy and grounding scores of automated models. - The 1990 chess match where IBM Deep Blue defeated Garry Kasparov demonstrated computational brute force rather than true human cognitive intelligence. ![Screenshot at 17:15: The discussion focuses on the software sector crash and the public illusion surrounding automated coding tools.](https://ss.rapidrecap.app/screens/b_HQaVmziPQ/00-17-15.jpg) **Context:** Dr. Joeri Schasfoort hosts Money and Macro Talks to explore macroeconomic realities behind major technological trends. In this episode, he sits down with computer scientist, columnist, and AI advisor Ilyaz Nasrullah to examine the economic and technical boundaries of large language models. ## Detailed Analysis The conversation deconstructs the grand promises made by tech executives like Sam Altman and Dario Amodei, comparing their utopian narratives to economic reality. While individual developers experience short-term efficiency boosts when using coding agents, organizational metrics reveal that code quality drops and review overhead surges. Nasrullah argues that artificial intelligence systems use brute force computation rather than true comprehension, creating a dangerous illusion of intelligence that mirrors past market bubbles. ### The Seven Myths of Artificial Intelligence The core premise of Nasrullah's book centers on dismantling seven specific myths surrounding artificial intelligence capabilities. - The myth of paradise claims artificial intelligence will solve all human problems, from climate change to cancer, without justification. - Additional myths target productivity increases, intelligence, humaneness, objectivity, self-learning, and autonomy. - Tech leaders present dystopian or utopian visions of the future to justify massive capital investments. ### Software Development Productivity Realities Empirical studies on software engineering reveal a stark contrast between individual task speed and overall organizational output. - Fiona Chen's Harvard study on seven hundred companies showed individual software developers using coding agents increased flagged completed tasks by twenty-three percent. - Review times increased by fifty percent and review comments increased by thirty-five percent, indicating a significant drop in code quality. - Jellyfish data spanning seventy thousand workers from 2021 to 2026 proved that individual productivity gains translate to zero organizational productivity gains. ### Computational Power Versus Human Understanding Machine computation relies on brute force calculations and lacks the inherent contextual comprehension possessed by humans. - IBM Deep Blue defeated chess champion Garry Kasparov in the 1990s by calculating hundreds of thousands of moves ahead, unlike human strategic reasoning. - Google introduced the Facts Suite benchmark in December 2025 to measure factual accuracy and grounding scores in response to persistent model hallucinations. - Language models operate like a culinary soup created from human texts, where blending outputs repeatedly dilutes quality rather than creating new insights. ### Market Bubbles and Economic Impact Historical tech bubbles mirror current capital deployment into large language models and token-maximizing corporate strategies. - The 2017 cryptocurrency and blockchain boom demonstrated how massive hype can temporarily distort economic indicators before reality sets in. - Major technology companies invest trillions of dollars into data centers and hardware despite operating at substantial financial losses. - Token maxing and overzealous automation campaigns lead to burned budgets and wasted engineering hours across multiple industries. ![Screenshot at 68:20: The economic discussion compares the current large language model boom to past technology bubbles.](https://ss.rapidrecap.app/screens/b_HQaVmziPQ/00-68-20.jpg)