Meta's Chief AI Scientist Yann LeCun Plans to Depart for New Startup
Yann LeCun, Meta's chief artificial intelligence scientist and a recipient of the 2018 Turing Award for advancements in deep neural networks, is reportedly planning to leave the company in the coming months to establish his own startup. He is in early discussions to raise funding for the venture, which is expected to focus on continuing his research into world models.
Key details:
- Context at Meta: The move follows CEO Mark Zuckerberg's reorganization of Meta's AI efforts under the new Superintelligence Labs, including the hiring of Alexandr Wang, former CEO of Scale AI, to lead the initiative. LeCun, previously reporting to chief product officer Chris Cox, now reports to Wang.
- AI Investments: Meta has ramped up spending on AI infrastructure, pledging significant investments (reported as $600 billion in the U.S.), amid competition from rivals like OpenAI, Google, and Anthropic.
- LeCun's Background and Views: A pioneer in deep learning, LeCun joined Meta (then Facebook) in 2013 to lead its Fundamental AI Research (FAIR) lab and contributed to developments like PyTorch. He has expressed skepticism about relying solely on large language models (LLMs) for achieving human-level AI, noting they lack human-like reasoning and planning capabilities.
- Company Response: Neither LeCun nor Meta has commented on the reports.
The departure occurs during a period of leadership changes and layoffs at Meta's AI division, as the company shifts focus toward advanced AI models.
Significance of Yann LeCun's Planned Departure from Meta
Yann LeCun's intention to leave his role as Meta's chief AI scientist represents a notable development in the evolving AI industry, as it underscores ongoing talent mobility and potential ideological shifts within major tech firms. As a Turing Award-winning pioneer in deep learning who has led Meta's Fundamental AI Research lab since 2013, LeCun's move to found a startup focused on world models could accelerate independent research outside corporate structures, amid Meta's recent reorganization under new leadership and substantial AI infrastructure investments. This departure occurs during a period of broader changes at Meta, including layoffs in its AI division and heightened competition from entities like OpenAI and Google, potentially signaling divergences in AI approaches—such as LeCun's public skepticism toward over-reliance on large language models for achieving advanced intelligence. Overall, it highlights the dynamic nature of AI leadership, where individual expertise drives new ventures that may influence future technological directions.
Yann LeCun's Timeline of Accomplishments at Meta (2013–Present)
Yann LeCun joined Meta (formerly Facebook) in December 2013 as the founding director of its Fundamental AI Research (FAIR) lab and has served as vice president and chief AI scientist, leading efforts in deep learning, computer vision, natural language processing, and open-source AI tools. Under his leadership, FAIR has grown into a global research organization, emphasizing open science and contributing to breakthroughs that have influenced AI applications worldwide. Below is a chronological overview of key milestones and accomplishments during his tenure, based on reported projects, releases, and recognitions as of November 11, 2025.
- December 2013: Appointed founding director of FAIR in New York City, with a focus on advancing AI through open research; also named chief AI scientist, a role emphasizing long-term paradigms like deep learning.
- 2014: Led development of memory networks, enabling neural systems to store and retrieve information for tasks such as question-answering, including precursors like StackRNN and bAbI datasets (later integrated into ParlAI).
- 2015: Oversaw advancements in generative adversarial networks (GANs), including DCGANs and LAPGANs for image and video generation; introduced Faster R-CNN for real-time object detection; open-sourced Torch framework to support AI research.
- 2016: Released fastText library for efficient text classification and word representations, supporting multilingual processing; launched Torchnet for modular deep learning workflows.
- 2017: Open-sourced PyTorch (initial release in January), a flexible deep learning framework that became widely adopted in research and production; developed Mask R-CNN for instance segmentation (won ICCV Best Paper Award); advanced Wasserstein GAN for stable high-resolution generation; created CNN-based neural machine translation system, improving speed and accuracy.
- 2018: Pioneered unsupervised machine translation techniques; FAIR researchers under his direction earned Best Paper awards at conferences like ACL, EMNLP, CVPR, and ECCV; received the ACM Turing Award (shared with Geoffrey Hinton and Yoshua Bengio) for deep learning breakthroughs, recognized during his Meta tenure.
- 2019: Introduced Panoptic Feature Pyramid Networks for unified image segmentation.
- 2020: Launched No Language Left Behind initiative, enabling translation across 100 languages without relying on English as an intermediary.
- 2023: Released Segment Anything Model for image segmentation; open-sourced Llama and Llama 2 large language models; developed Voicebox and Audiobox for generative speech and audio; introduced Seamless suite for expressive AI translation; created Ego-Exo4D dataset for video perception; marked FAIR's 10-year anniversary with reflections on open science impacts; FAIR won best paper awards at ACL, ICRA, ICML, and ICCV.
- 2023–2025: Continued emphasis on long-term AI research, including world models and critiques of large language model limitations; reaffirmed FAIR's focus on next-generation AI paradigms in public statements.