AMI + JEPA: a unified theory of world models, from robotics to finance
There was substantial publishing activity this week as three interlocking papers on hierarchical planning, unstable-mode preservation, and multi-embodiment scaling, backed by GitHub activity and a wave of high-engagement social posts, all appeared. Together, they show the team believes it can extend, stack, and scale the JEPA framework to solve problems neither pure diffusion nor pure autoregression can. The launch of RoboJEPA was billed as the first scaling law for multiembodiment world models. That gives that thesis its most ambitious public test yet. Meanwhile, the quiet confirmation of a VP of Finance and the arrival of a founding self-supervised learning scientist signal that AMI is simultaneously hardening its organizational infrastructure.
JEPA goes hierarchical and robotic: AMI's architecture is being stress-tested at scale
researchThe most consequential cluster of work this week centers on pushing JEPA beyond its original single-timescale, single-embodiment form. H-JEPA's lead author is Wancong (Kevin) Zhang, a PhD student at NYU Courant, where Yann LeCun advises him. His research focuses on world models, planning, and representation learning, aiming to build agents that can learn from observation and interact with the world in an open-ended way. He became a Research Intern at AMI in May. The other four co-authors are Basile Terver, AMI Co-Founder Michael G. Rabbat, LeCun, and Randall Balestriero. That's notable for two reasons.
First, all five authors identify themselves as researchers at Advanced Machine Intelligence. Until now, most research published by AMI team members had legacy ties to previous employers, such as Meta. Even if this is research rather than a product, it's pretty clearly tied to AMI. And second, this is the latest of several papers that identify Balestriero as an AMI researcher. The Brown professor hasn't publicly announced he's been hired, but his long-standing work with LeCun points in that direction.
As for the JEPA paper, it introduces a hierarchy of action-conditioned JEPAs where each level predicts farther ahead in its own learned latent space and passes subgoals down to the next level. The claimed result — lifting Visual AntMaze success from 18% to 73% with three levels — is the kind of jump that, if it holds up, reframes long-horizon planning as an architecture problem rather than a data problem. The paper and code dropped on 5 October, and Terver's GitHub activity on the `kevinghst/H-JEPA` repository — specifically the merging and deletion of `droid_v2` branches — suggests the team was integrating real-robot (DROID dataset) experiments up to the last moment before release.
On the same day, Artem Zholus launched RoboJEPA with a social post that earned 564 likes and 104 retweets — by far the week's highest-engagement item. The project, which scaled JEPA predictors to 8B parameters across multiple robot embodiments and claimed to establish scaling laws that correlate compute with planning performance, now has its own GitHub organization (`robojepa/robojepa.github.io`, created 7 October).
A third paper, 'Preserving Unstable Modes Through Inverse Dynamics in JEPA World Models', provides theoretical grounding for why naive next-step JEPA prediction fails for robotic control and how inverse dynamics can fix it. Together, these three works read as a deliberate package: theory (unstable modes), architecture (H-JEPA), and scale (RoboJEPA) — each paper answering a question the others raise.
JEPA's surface area is expanding — finance and autonomous driving are now on the map
researchTwo papers this week extend the JEPA family into domains that have nothing to do with laboratory robotics. Randall Balestriero's NeurIPS-accepted 'Towards Financial World Modeling' applies JEPA-style reasoning to financial time series — noisy, non-stationary data with no ground-truth state — and the post announcing it (185 likes) frames LeJEPA as a practical investment tool. This is a deliberate provocation: applying a world-model framework designed for physical prediction to market dynamics claims the framework's generality, not just a quirky application. Separately, 'AD-E2E-JEPA' benchmarks existing JEPA world models (LeWM, DINO-WM, JEPA-WM) for end-to-end autonomous driving, identifies gaps, and proposes a SIGReg-regularised improvement. That paper lands alongside Balestriero's repeated public endorsements of SIGReg as the key stabilizer for end-to-end JEPA training.
The pattern here is strategic domain expansion through the same architectural lens. AMI is not building separate models for robotics, finance, and driving — it is arguing that one framework, properly extended, covers all of them. Whether that argument is borne out empirically is a question the coming months will answer, but the positioning is clear and deliberate.
Last refreshed Oct 12, 2026
Yann LeCun and Arthur Mensch back Zurich robotics startup in $30m pre-seed
What is AMI Labs?
Advanced Machine Intelligence is one of the most closely watched AI startups in the world. Founded in 2025 by Yann LeCun and Alexandre LeBrun, it's building AI systems that reason about the physical world, not just language.
About the company →What are World Models?
World Models are AI systems that learn an internal representation of how the world works — enabling machines to plan, predict, and reason about physical reality the way humans do.
The science →The Team
AMI Labs has assembled researchers, engineers, and operators across Paris, New York, Montreal, and Singapore. We track every key hire and leadership change as the team grows.
Meet the team →How a world model sees the future
Given what it observes, the model reconstructs the present and rolls out what happens next — a prediction of physics, not a retrieval of text. Below: a reacher arm, its reconstruction, and the model's forecast, side by side.
Global hiring accelerates
AMI Labs begins scaling its team across Paris, New York, Montreal, and Singapore — recruiting world-class researchers, engineers, and operators to build World Models.
$1.03B seed round at $3.5B valuation
AMI Labs closes a landmark $1.03 billion seed round — one of the largest in AI history — backed by Jeff Bezos, Bezos Expeditions, NVIDIA, Samsung, Eric Schmidt, and 60+ investors.
Yann LeCun joins as Executive Chairman
Turing Award laureate and former Meta Chief AI Scientist Yann LeCun co-founds AMI Labs, bringing his vision for World Models and next-generation machine intelligence.
AMI Labs brand launched
PengyouCo rebrands as Advanced Machine Intelligence (AMI Labs), unveiling its mission to build AI systems that go beyond language — grounded in the physical world.
Corporate structure formalised
Articles of association updated with early share restructuring: 1,000,000 shares divided into ordinary shares and SP preference shares carrying 10× voting rights.
Every Monday, The French Tech Journal sends a digest of the latest AMI Labs news — funding, research, hiring, and more. Independent coverage. No spin.
Stay updated
Weekly digest of new news, jobs & updates from AMI Labs.