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When one of the founders of modern AI walks away from one of the world’s most powerful tech companies to start something new, the industry should pay attention.
Yann LeCun’s departure from Meta after more than a decade shaping its AI research is not just another leadership change. It highlights a deep intellectual rift about the future of artificial intelligence: whether we should continue scaling large language models (LLMs) or pursue systems that understand the world, not merely echo it.
LeCun is a French-American computer scientist widely acknowledged as one of the “Godfathers of AI.” Alongside Geoffrey Hinton and Yoshua Bengio, he received the 2018 Association for Computing Machinery’s A.M. Turing Award for foundational work in deep learning.
He joined Meta (then Facebook) in 2013 to build its AI research organization, eventually known as FAIR (Facebook/META Artificial Intelligence Research), a lab that tried to advance foundational tools such as PyTorch and contributed to early versions of Llama.
Over the years, LeCun became a global figure in AI research, frequently arguing that current generative models, powerful as they are, do not constitute true intelligence.
LeCun’s decision to depart, confirmed in late 2025, was shaped by both strategic and philosophical differences with Meta’s evolving AI focus.
In 2025, Meta reorganised its AI efforts under Meta Superintelligence Labs, a division emphasising rapid product development and aggressive scaling of generative systems. This reorganisation consolidated research, product, infrastructure, and LLM initiatives under leadership distinct from LeCun’s traditional domain.
Within this new structure, LeCun reported not to a pure research leader, but to a product and commercialisation-oriented chain of command, a sign of shifting priorities.
But more important than that, there’s a deep philosophical divergence: LeCun has been increasingly vocal that LLMs, the backbone of generative AI, including Meta’s Llama models, are limited. They predict text patterns, but they do not reason or understand the physical world in a meaningful way. Contemporary LLMs excel at surface-level mimicry but lack robust causal reasoning, planning, and grounding in sensory experience.
As he has said and written, LeCun believes LLMs “are useful, but they are not a path to human-level intelligence.”
This tension was compounded by strategic reorganisations inside Meta, including workforce changes, budget reallocations, and a cultural shift toward short-term product cycles at the expense of long-term exploratory research.
LeCun’s new venture is centred on alternative AI architectures that prioritise grounded understanding over language mimicry.
While details remain scarce, some elements have emerged:
In LeCun’s own framing, this is not a minor variation on today’s AI: It’s a fundamentally different learning paradigm that could unlock genuine machine reasoning.
Although Meta founders and other insiders have not released official fundraising figures, multiple reports indicate that LeCun is in early talks with investors and that the venture is attracting attention precisely because of his reputation and vision.
LeCun’s break with Meta points to a larger debate unfolding across the AI industry.
This would have profound implications across industries, from robotics and autonomous systems to scientific research, climate modeling, and strategic decision-making.
Meta’s AI strategy increasingly looks short-term, shallow, and opportunistic, shaped less by a coherent research vision than by Mark Zuckerberg’s highly personalistic leadership style. Just as the metaverse pivot burned tens of billions of dollars chasing a narrative before the technology or market was ready, Meta’s current AI push prioritises speed, positioning, and headlines over deep, patient inquiry.
In contrast, organisations like OpenAI, Google DeepMind, and Anthropic, whatever their flaws, remain anchored in long-horizon research agendas that treat foundational understanding as a prerequisite for durable advantage. Meta’s approach reflects a familiar pattern: abrupt strategic swings driven by executive conviction rather than epistemic rigor, where ambition substitutes for insight and scale is mistaken for progress. Yann LeCun’s departure is less an anomaly than a predictable consequence of that model.
But LeCun’s departure is also a reminder that the AI field is not monolithic. Different visions of intelligence, whether generative language, embodied reasoning, or something in between, are competing for dominance.
Corporations chasing short-term gains will always have a place in the ecosystem. But visionary research, the kind that might enable true understanding, may increasingly find its home in independent ventures, academic partnerships, and hybrid collaborations.
LeCun’s decision to leave Meta and pursue his own vision is more than a career move. It is a signal that the current generative AI paradigm, brilliant though it is, will not be the final word in artificial intelligence.
For leaders in business and technology, the question is no longer whether AI will transform industries; it’s how it will evolve next. LeCun’s new line of research is not unique: Other companies are following the same idea. And this idea might not just shape the future of AI research—it could define it.
ABOUT THE AUTHOR
Enrique Dans has been teaching Innovation at IE Business School since 1990, hacking education as Senior Advisor for Digital Transformation at IE University, and now hacking it even more at Turing Dream. He writes every day on Medium.