Yann LeCun

Yann LeCun is a French-American computer scientist, born July 8, 1960 in Soisy-sous-Montmorency, France. He is the Silver Professor of Computer Science, Neural Science, Data Science, and Electrical Engineering at New York University, the executive chairman and co-founder of AMI, and a co-recipient of the 2018 ACM Turing Award (with Geoffrey Hinton and Yoshua Bengio) for foundational work on deep learning. He stepped down as chief AI scientist at Meta in November 2025 and, the same month, co-founded AMI as a research lab pursuing world-model architectures as an alternative to large language models.

At a glance

• Education: Diplôme d'Ingénieur, ESIEE Paris (1983); PhD in computer science, Université Pierre et Marie Curie / Sorbonne (1987); postdoctoral fellow under Geoffrey Hinton at the University of Toronto (1987 to 1988).
• Current roles: Silver Professor at NYU since 2003; executive chairman and co-founder of AMI since November 2025; co-director of the CIFAR Learning in Machines and Brains program since 2014.
• Key contributions: convolutional neural networks (1989 onward), LeNet handwritten-digit recognition systems at Bell Labs, the 1998 paper "Gradient-Based Learning Applied to Document Recognition", twelve-year tenure leading Meta AI / FAIR, and public advocacy for the Joint Embedding Predictive Architecture (JEPA) family.
• Awards: 2018 ACM Turing Award; 2024 VinFuture Prize; 2025 Queen Elizabeth Prize for Engineering; 2023 Legion of Honour (Knight); 2021 National Academy of Sciences member.
• X / Twitter: @ylecun
• LinkedIn: Yann LeCun
• Personal site: yann.lecun.com
• NYU Courant page: cims.nyu.edu/people/profiles/LECUN_Yann

Origins

LeCun grew up in the Paris suburbs and studied electrical engineering at ESIEE Paris, receiving his Diplôme d'Ingénieur in 1983. He moved to the Université Pierre et Marie Curie (now part of Sorbonne University) for doctoral work, completing his PhD in computer science in 1987. The thesis proposed an early form of backpropagation for neural networks, work that anticipated several of the methods that would later dominate deep learning.

After his doctorate, LeCun spent a year at the University of Toronto as a postdoctoral fellow under Geoffrey Hinton, then a senior figure in the small community of researchers continuing to work on connectionist models through the AI winter. The Toronto period set the personal and intellectual lineage that would carry through the rest of LeCun's career.

In 1988 he joined the Adaptive Systems Research Department at AT&T Bell Laboratories in Holmdel, New Jersey, headed by Lawrence Jackel. The Bell Labs years are where the convolutional neural network and the LeNet handwritten-digit recognition systems took shape, beginning with experiments on USPS zip-code data and progressing through several iterations of the LeNet architecture. By the late 1990s the systems were reading on the order of ten percent of all checks deposited in the United States.

Career

LeCun stayed at Bell Labs from 1988 through 1996, working with collaborators including Léon Bottou, Patrick Haffner, and Yoshua Bengio. The 1998 publication of "Gradient-Based Learning Applied to Document Recognition", co-authored with Bottou, Bengio, and Haffner, is the canonical paper on LeNet-5 and remains one of the most-cited papers in computer vision.

After the AT&T breakup, LeCun moved to the NEC Research Institute in Princeton, New Jersey, in 1996 as a fellow. The NEC period continued his computer-vision and image-compression research, including work on the DjVu image-compression format developed with Bottou and Haffner.

In 2003 he joined New York University as the Jacob T. Schwartz Chair Professor at the Courant Institute, with affiliations across the Center for Data Science, the Center for Neural Science, and the Electrical and Computer Engineering Department. He was later named a Silver Professor and helped launch the NYU Center for Data Science in 2012, serving as founding director until early 2014. Since 2014 he has co-directed CIFAR's Learning in Machines and Brains program with Bengio. The NYU Deep Learning course he co-taught with Alfredo Canziani is freely available on YouTube and remains a primary reference for graduate-level deep learning.

In December 2013, Mark Zuckerberg recruited LeCun to launch Facebook AI Research (FAIR), the company's first research-focused organization. LeCun served as director of FAIR from 2013 to 2018, then as Vice President and Chief AI Scientist at the company through its 2021 rebrand from Facebook to Meta. Over twelve years at the company he oversaw the publication of the PyTorch deep-learning framework, the Llama family of open-weights models, and successive JEPA-family research artifacts including I-JEPA, V-JEPA, and V-JEPA 2.

The June 2025 restructuring of Meta's AI organization placed Alexandr Wang as Chief AI Officer above the FAIR organization, with LeCun reporting to Wang. LeCun confirmed his departure on November 19, 2025, after twelve years at the company. Within days he announced the founding of Advanced Machine Intelligence Labs in Paris, with Alexandre LeBrun as chief executive and a founding research team drawn substantially from former FAIR colleagues. AMI's $1.03 billion seed round at a $3.5 billion pre-money valuation was announced on March 9, 2026, with Bezos Expeditions, Cathay Innovation, Greycroft, Hiro Capital, and HV Capital as co-leads.

Affiliations

• Bell Labs: Researcher, 1988 to 1996
• NYU CDS: Silver Professor and Founding Director, 2013 to present
• Meta AI: Vice President and Chief AI Scientist, 2013 to 2025-11
• AMI: Executive Chairman, 2025-11 to present

Notable contributions

LeCun's contributions span foundational research, applied systems, public-facing commentary, and institution-building.

• Convolutional neural networks (1989 onward). The architecture, developed at Bell Labs, applies the convolution operation to images so that filters are reused across spatial locations. The approach became the dominant model for computer vision for the better part of two decades and remains a core component of contemporary multimodal systems.
• LeNet-5 and the 1998 document-recognition paper. "Gradient-Based Learning Applied to Document Recognition", co-authored with Léon Bottou, Yoshua Bengio, and Patrick Haffner, packaged the convolutional approach into a complete system for handwritten-character recognition and demonstrated commercial-scale results on bank-check processing.
• 2018 ACM Turing Award, shared with Geoffrey Hinton and Yoshua Bengio "for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing." The three are commonly referred to as the godfathers of deep learning. LeCun's accompanying Turing lecture, "The Deep Learning Revolution: The Sequel", was delivered at FCRC 2019 in Phoenix.
• JEPA family of architectures. LeCun proposed the Joint Embedding Predictive Architecture in a 2022 position paper, "A Path Towards Autonomous Machine Intelligence", arguing that artificial general intelligence requires systems that learn world models from observation rather than from language tokens alone. The I-JEPA, V-JEPA, V-JEPA 2, and VL-JEPA models from his Meta group implement the approach across image, video, and vision-language modalities.
• AMI launch (November 2025). Co-founder and executive chairman of AMI, the Paris-based research company built around the JEPA thesis.
• Public commentary. LeCun's X account, blog, and frequent media interviews are the primary vehicle for an outspoken position against pure-LLM scaling as a path to general intelligence. He has argued repeatedly that current language models lack the planning, reasoning, and physical-world grounding needed for AGI, and that world-model architectures are the missing ingredient.

Investments and boards

LeCun's executive chairman role at AMI is the principal board position. The CIFAR Learning in Machines and Brains co-directorship and the founding directorship of NYU CDS are academic-leadership posts rather than corporate-board roles. No public investor activity on record in AI, semiconductors, datacenters, software, or energy as of May 2026.

Network

LeCun's strongest professional relationships sit in the academic deep-learning lineage. Geoffrey Hinton, his postdoctoral advisor at the University of Toronto, has remained a close colleague through the four decades since. Hinton, Yoshua Bengio, and LeCun shared the 2018 Turing Award and have appeared together at AI conferences, in joint authored statements on AI policy, and through CIFAR's Learning in Machines and Brains program, which LeCun and Bengio have co-directed since 2014.

Léon Bottou, Patrick Haffner, and Bengio were principal Bell Labs collaborators on the LeNet-5 work and remain longtime co-authors. At Meta, LeCun built FAIR and worked closely with Joelle Pineau, who led FAIR through 2025 before departing for Cohere ahead of LeCun's own departure. Mark Zuckerberg personally recruited LeCun in 2013 and was the principal counterparty across the twelve-year Meta tenure.

The AMI founding team includes Alexandre LeBrun (CEO), Michael Rabbat (Vice President for World Models, longtime Meta colleague), Saining Xie (Chief Strategy Officer, NYU and Meta lineage), Pascale Fung, and Laurent Solly.

Position in the field

LeCun is a senior figure in the academic deep-learning community, holds the Silver Professor title at NYU, and is a Turing laureate among three. The combination of Bell Labs origin, twelve years leading a frontier industrial research organization, and a continuing academic appointment is structurally unusual relative to peer figures.

The launch of AMI positions him as a working executive at a pre-product Insurgent lab pursuing a deliberate architectural alternative to the LLM paradigm. Public coverage has consistently characterized AMI as a contrarian bet against the LLM-frontier strategy of OpenAI, Anthropic, and Google DeepMind, with LeCun's research credentials and the JEPA thesis as the principal validating data points for the $4.5 billion post-money seed valuation.

The figures most often paired with LeCun in coverage are his Turing co-recipients. Hinton has shifted into a public-AI-risk posture since departing Google in 2023; Bengio has focused on AI safety policy through the Mila institute and the international AI safety summit process. Among current operating chief executives, David Silver at Ineffable Intelligence is the closest comparator on a different-architecture-thesis axis. LeCun's public commentary on AGI timelines and on the limits of LLM scaling has placed him as a leading skeptical voice from a senior researcher with industrial credibility.

Outlook

Open questions over the next 6 to 18 months:

• AMI's first public artifacts. Whether AMI will produce papers, models, or demonstrations during 2026, and whether they validate the JEPA thesis at frontier scale.
• JEPA scaling beyond toy domains. Public JEPA-family results to date are research artifacts. The empirical question is whether the architecture scales to capability levels comparable to the leading LLM frontier models.
• Compute deployment. AMI's $1.03 billion seed implies substantial compute investment. The vendor relationships, cluster scale, and timeline have not been disclosed publicly.
• LLM-skeptic public position. LeCun's commentary trajectory is itself a watchable signal. If LLM-family models continue to deliver capability gains through 2026 and 2027, the skeptical position becomes harder to defend; if scaling stalls, his position gains ground.
• FAIR after LeCun. Meta's AI research organization under Alexandr Wang's authority and the post-LeCun direction are visible from the outside through publication output and personnel moves.
• AGI-timelines commentary. LeCun's public statements on AGI timelines remain a contrarian counterpoint to the operator commentary from frontier-lab chief executives. The gap between his framing and the consensus operator framing is the signal to track.

Sources

• Yann LeCun. Wikipedia biographical entry; covers career, awards, publications, and recent moves.
• Yann LeCun. ACM Turing Award page with the 2018 citation and biographical detail.
• Gradient-Based Learning Applied to Document Recognition. The 1998 LeNet-5 paper, co-authored with Bottou, Bengio, and Haffner.
• Yann LeCun NYU Courant page. NYU faculty profile.
• Meta chief AI scientist Yann LeCun is leaving the company. CNBC on the November 2025 Meta departure.
• Yann LeCun's AMI Labs raises $1.03B to build world models. TechCrunch on the March 2026 seed-round announcement.
• Yann LeCun's new venture is a contrarian bet against large language models. MIT Technology Review on the AMI thesis.
• The rise of Yann LeCun, the 65-year-old NYU professor who is planning to leave Mark Zuckerberg's highly paid team at Meta to launch his own AI startup. Fortune profile of the career and departure context.
• Yann LeCun Criticizes Alexandr Wang as 'Inexperienced' in Leading Meta's Superintelligence Labs. Coverage of LeCun's post-departure commentary.
• Turing Award honours CIFAR's pioneers of AI. CIFAR's record of the Hinton-Bengio-LeCun lineage and the Learning in Machines and Brains program.
• Photo: Wikipedia entry on Yann LeCun, CC-BY-SA 2.0 Jérémy Barande (École polytechnique, June 2024).