# Ep.192: Expert Argues Large Language Models Will Never Achieve True Intelligence

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Podcast: [The Power AI Podcast](https://stenobird.com/podcast/the-power-ai-podcast-7037522)
Published: 2025-12-18T09:37:31+00:00
Episode link: https://unboxedai.blogspot.com/p/podcasts.html
Audio file: https://dts.podtrac.com/redirect.mp3/api.spreaker.com/download/episode/68916866/expert_argues_large_language_models_will_never_achieve_true_intelligence.mp3
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Duration seconds: 794

## Resource

Todays deep dive presents a significant argument challenging the notion that Large Language Models (LLMs) are close to achieving Artificial General Intelligence (AGI) by focusing on the language-intelligence fallacy. Benjamin Riley, a prominent voice in the field, asserts that LLMs are simply sophisticated emulators of communication and lack genuine thought or reasoning capabilities, a view supported by neuroscience indicating that language processing is separate from core cognitive functions. This critique suggests that scaling LLMs will not solve their inherent architectural limitations, leading Riley to liken them to "dead-metaphor machines" that are perpetually confined to their training data. Furthermore, other leading AI figures like Yann LeCun express skepticism about current methods, advocating instead for "world models" that learn from diverse physical data. Research confirms these limitations, concluding that these probabilistic systems cannot generate truly novel outputs, reaching an inescapable ceiling on creativity that restricts them to remixing existing knowledge.

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