Generative video models learn to render pixels; world models learn what happens next and why — the actual substrate of understanding. The difference is architectural. Generative models optimize for visual plausibility; world models optimize for predictive accuracy in representation space.
Prediction in representation space — not pixel space — is what makes Darwin fast, deterministic, and deployable at the edge. There is no decoder generating hallucinated frames, no language model sampling stochastically at the perception layer. Just a model that watches video the way a systems engineer needs: deterministic, timestamped, confidence-scored, queryable in plain English.
The full 'World models, not video models' essay is coming during our August launch.
Explore the technology
How JEPA works
Joint-embedding predictive architecture — semantic understanding without a language model in the perception loop.
Determinism
Same input, same output, every time — why repeatability is the defining property for production video systems.
Benchmarks
What we measure, against which baselines, and honest failure modes.
Real-time streaming
WebRTC ingestion, per-GPU stream backends, and what "real-time" actually costs.