Yann LeCun Calls LLMs 'Token Generators' While Llama Hits a Billion Downloads

At Nvidia’s GTC 2025, Meta’s chief AI scientist, Yann LeCun, dismissed large language models (LLMs) as mere "token generators," suggesting that their limitations—stemming from their reliance on discrete tokenized spaces—render them ineffective in the long run. LeCun didn’t just criticize LLMs; he predicted their obsolescence within the next five years before as well saying that nobody in their right mind would use them anymore.

LeCun’s skepticism is well-documented. He argues that modern AI systems fail in four key areas: they lack awareness of the physical world, have limited memory and no continuous recall, are incapable of reasoning, and struggle with complex planning. For him, these are fundamental weaknesses that cannot be solved by simply increasing scale. Reinforcement learning, according to LeCun, has been a "huge failure," and generative AI is incapable of establishing real truth or understanding the world.

He contrasts AI’s learning process with human cognition. While LLMs ingest vast datasets, a four-month-old infant, simply by observing its environment, processes 450 times more information than the largest LLM. LeCun sees this as a clear indication that current AI models fall drastically short of real intelligence.

However, his remarks come just as Meta reported a major milestone for its Llama models. Meta announced that Llama, its open-source series of large language models, has now been downloaded a billion times.