Advanced Machine Intelligence Labs, the new startup led by deep learning pioneer Yann LeCun, is reportedly seeking to raise €500 million (about $586 million) at a €3 billion valuation (about $3.5 billion) before launching a product, signaling strong investor conviction that a major shift in artificial intelligence development is underway. LeCun, serving as executive chairman with Alex LeBrun as chief executive, is positioning the company around “world models,” a concept that trains systems to understand physics, maintain memory, and plan complex actions rather than simply predicting the next word. He argues that large-scale language models represent “an off-ramp, a distraction, a dead end,” contending that “large-scale language models are not the path to human-level intelligence” and that true intelligence will require a completely different approach focused on internal simulation of the physical world.
The article frames LeCun’s move as a direct strategic threat to OpenAI at a moment of heightened vulnerability. OpenAI’s model lead has reportedly shrunk from six months in 2024 to potentially zero as of November 2025, while competitors and open-source projects erode its advantage. Anthropic’s Claude has captured 32% enterprise artificial intelligence market share in 2024 compared to OpenAI’s 25%, with Claude generating approximately 40% of OpenAI’s revenue despite having just 18.9 million monthly users, roughly 5% of ChatGPT’s user base. At the same time, OpenAI’s cost structure is pressured by compute expenses estimated at 55-60% of its $9 billion operating expenses, driven by the “NVIDIA tax,” where manufacturing H100 GPUs costs NVIDIA approximately $3,000-$5,000 per unit while hyperscalers such as Microsoft pay $20,000-$35,000+ per unit in volume. Google’s over $70 billion in free cash flow across the last four quarters of 2024 and $98.5 billion in cash at the end of Q3 2025 are cited as giving it a 4-6x cost efficiency advantage with vertically integrated infrastructure.
LeCun’s exit from Meta after 12 years underscores an ideological rift with the Silicon Valley establishment as Meta doubles down on scaling its LLaMA large language models, including acquiring Scale artificial intelligence for $14 billion and centralizing research around product goals. He instead promotes open-source development and objective-driven artificial intelligence focused on self-supervised learning, internal simulation, and curiosity-driven motivation, building on ideas from his 2022 paper “A Path Towards Autonomous Machine Intelligence” and prototypes such as I-JEPA and V-JEPA. The startup plans to base its headquarters in Paris, which LeCun frames as a necessary move “outside the Valley” to pursue this new research direction, leveraging Europe’s regulatory and research environment to challenge American artificial intelligence hegemony.
The world model vision is presented as the future of artificial general intelligence, with systems learning directly from multimodal physical-world data like video, sensors, and spatial information, running thousands of internal simulations before acting and implicitly learning physical laws. The article describes potential applications across robotics, autonomous driving, drones, logistics, and industrial automation, and contrasts this with OpenAI’s text-heavy ecosystem at a time when ChatGPT user engagement is cooling and technical help queries have declined from 18% to 10% between July 2024 and July 2025. Parallel to this technical critique, the piece emphasizes the rapid rise of open-source artificial intelligence, recounting how researchers instruction-tuned LLaMA to create Vicuna within 4 months of ChatGPT’s November 2022 release, and quoting a June 2025 analysis that the question is no longer whether open source will compete with proprietary models but how quickly it will become the dominant paradigm.
Financial expectations around OpenAI’s future underscore the uncertainty created by these shifts. The article notes that FutureSearch forecasts a wide $10B-$90B range for OpenAI’s 2027 revenue, reflecting deep doubts about the durability of its advantage amid talent churn and intensifying competition from both proprietary and open-source providers. By 2025, roughly 64% of global venture capital deal value had pivoted to artificial intelligence, yet most funding flowed into a narrow monoculture of large language model labs, GPU infrastructure suppliers, and application layers dependent on a small set of APIs, a pattern LeCun warns “will soon hit a wall.” The €500 million raise at a €3 billion valuation is described as a $3.5 billion bet against this orthodoxy, with investors effectively wagering that world models and objective-driven artificial intelligence will supersede the current text-prediction paradigm and potentially render much of OpenAI’s infrastructure obsolete.
Throughout the article, OpenAI is portrayed as facing an existential question rather than routine competition, with the rise of open-source models, Meta’s open Llama strategy, Anthropic’s efficiency, and Google’s cash-rich infrastructure all eroding its early lead. The author suggests that the dominance OpenAI once enjoyed is giving way to a more fragmented landscape where multiple architectures and platforms coexist, each aligned with different use cases and philosophies. In that evolving context, the emergence of the Yann LeCun world model paradigm is cast as a turning point that prioritizes causal understanding, physical interaction, and collaboration over statistical mimicry and closed control, leaving OpenAI’s fate contingent on whether it can adapt or become a cautionary tale of disruption in the artificial intelligence era.