V-JEPA and I-JEPA: Meta's Experimental Results on Video and Image World Models

V-JEPA (Video JEPA) and I-JEPA (Image JEPA) are Meta's experimental validations of the JEPA architecture on real data. I-JEPA, published in 2023, showed that predicting image representations in masked patches (rather than raw pixels, as in MAE) produces representations that transfer better to downstream tasks with fewer labeled examples. V-JEPA extended this to video, showing that predicting future frame representations in abstract space — rather than predicting actual pixel values — produces video encoders with better semantic content and better physical understanding. These results matter because they're empirical validation of LeCun's theoretical argument: learning in abstract representation space is more efficient and produces better abstractions than generative prediction of raw data. The practical implication for AI development: if V-JEPA's representations continue to improve with scale (as early evidence suggests), it could lead to AI systems with genuinely better physical understanding than LLMs trained only on text. The limitation: V-JEPA has so far been evaluated on recognition and transfer tasks, not on reasoning or planning, which is what LeCun's world model hypothesis ultimately requires. The next critical experiment is whether JEPA-trained representations support better planning in downstream tasks.