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Yann LeCun closed $1.03B for AMI Labs on March 10, 2026; the seed round was…

2026-05-09 · 05:01 UTC ·@aakashgupta ·1 min read

Brief

LeWorldModel (LeWM) claims a JEPA trained end-to-end from raw pixels with 15M parameters on a single GPU in a few hours, using two losses (next-embedding prediction and a Gaussian latent regularizer), no EMAs or pretrained encoders, one hyperparameter, and up to 48x faster planning while remaining competitive on 2D/3D benchmarks. Yann LeCun closed $1.03B for AMI Labs on March 10, 2026 (seed at a $3.5B pre-money valuation with Bezos, Nvidia, Samsung, Toyota); the LeWM paper was released three days later by authors at Mila, NYU, Samsung SAIL, and Brown.

Why it matters

Yann LeCun closed $1.03B for AMI Labs on March 10, 2026; the seed round was reported as the largest in European history at a $3.5B pre-money valuation with investors including Jeff Bezos, Nvidia, Samsung, and Toyota.

Key details

  • LeWorldModel (LeWM) is presented as the first JEPA trained end-to-end from raw pixels: 15M parameters, trains on a single GPU in a few hours, uses two loss terms (predict next embedding and enforce a Gaussian latent), removes EMAs and pretrained encoders, reduces six hyperparameters to one, and reportedly plans up to 48x faster while staying competitive on 2D and 3D benchmarks.
  • The paper appeared three days after the March 10 funding; authors are affiliated with Mila, NYU, Samsung SAIL, and Brown (none at Meta). The post ties this timing to Yann LeCun’s November 2025 departure from Meta and claims the work makes world modeling "laptop-cheap," resetting cost assumptions for his world-model-first bet.
Source evidence

Yann LeCun closed $1.03B for AMI Labs on March 10. Three days later, this paper dropped from his NYU collaborators.

15M parameters. Single GPU. A few hours of training.

LeWorldModel is the first JEPA that trains end-to-end from raw pixels. Two loss terms: predict the next embedding, keep the latent space Gaussian. Previous JEPAs needed exponential moving averages or pretrained encoders to avoid representation collapse. LeWM doesn't.

Six hyperparameters down to one.

The numbers are the story. Foundation-model-based world models require hundreds of millions of parameters and serious compute to plan a control task. LeWM plans up to 48x faster while staying competitive on 2D and 3D benchmarks. The whole thing fits on a laptop GPU.

Look at the trajectory. Yann announced his Meta departure in November 2025 after 12 years and called founding FAIR his "proudest non-technical accomplishment." On March 10, 2026, AMI Labs closed the largest seed round in European history at a $3.5B pre-money valuation. Bezos, Nvidia, Samsung, and Toyota all wrote checks.

Three days later: a paper showing that JEPA-from-pixels is no longer fragile and no longer compute-heavy. The engineering scaffolding that made it look like an academic curiosity is gone.

The authors sit at Mila, NYU, Samsung SAIL, and Brown. None at Meta.

Yann's bet was that the path to machine intelligence runs through world models, not language models. He left a public company to build it. Each JEPA paper from his network resets the assumed cost structure for that bet. This one makes world modeling laptop-cheap.

Meta still has the GPUs. The architecture left.