Justin Johnson

World Labs’ newly launched Atlas introduces a genuinely new foundation-model primitive — novel-view prediction — unifying 3D reconstruction and generation in one architecture for the first time. Justin Johnson’s formulation: “LLMs are built on predicting the next token… video models are built on predicting the next frame. Atlas is truly a prediction of new perspectives.” Ben Mildenhall adds that each input image carries a 3D camera pose, versus the “slot machine” effect of text-prompted video models.

The capture-economics claim is a 50–100× cost reduction: dense reconstruction that needed 100–300 photos per room now works from three iPhone shots. The Matrix bullet-time effect that took a ring of hundreds of cameras on a green screen now needs three tripods and no expensive calibration — and old footage with 95% of its photos deleted still reconstructs, which Ben says “completely turns the idea of what kind of data can be reconstructed upside down.”

The team insists scaling has barely started — the released model was bounded by a release deadline, not architecture, scale, or data limits. “We’re at the very beginning,” with compute the main bottleneck; every scale-up “got significantly better,” and Fei-Fei Li concedes the surprise that “the first cycle” of pretraining a new paradigm worked at all — she’d expected several iterations.

The robotics thesis rests on data, not silicon: “the biggest problem in robotics right now is actually data. Someday it will be chips, but now it’s data.” The acquired robotics team, formerly Synapse, can use Atlas to improve its painful real-to-sim pipeline, and Johnson proposes data-driven neural simulators as a future foundation; Casado then asks whether the simulator itself could become a player — consistent with the universality thesis of world models.

The known gap — dynamics — is already latent in the model, they claim, not an architectural limitation as it was in Marble. Counterintuitive training insight: even for static output, “the best way to achieve that is to actually show the model dynamics” and let it filter; this checkpoint was merely post-trained toward statics, with waves and moving cars already visible in outputs.

The commercial wedge beyond entertainment is design iteration, where translating feedback into a 3D model “is 95% of the job.” Architecture, construction, even conference booths — with editability as the bar to clear: control must come “without degrading the quality of the model, otherwise it will just become entertainment.”

The closing claim is AI-completeness: “predicting the next viewpoint is equivalent to predicting the next token.” Ben Mildenhall’s evolutionary kicker — “Nature gave animals eyes. But nature did not give trees eyes… when you move, you see a new vantage point” — and Martin Casado’s verdict: “probably the most important model launch this year.”

🔗 Original source & video: Why World Models Could Change Robotics, 3D, and Creativity

World Labs is betting that spatial intelligence will complement language as the next foundation-model frontier, with compute finally large enough to attack it. Justin Johnson estimates roughly 1,000x more performance per card since AlexNet and training across hundreds to tens of thousands of GPUs, producing “a millionfold more” compute per model. Visual, spatial, and world data require far more processing, making world models a plausible next scaling frontier.

Marble is a deliberately two-sided wedge: a useful 3D product now and the first public step toward general world models. It accepts text, one or multiple images, generates editable 3D worlds, and supports precise camera placement and export; emerging use cases include gaming, VFX, film, interior design, and potentially robotic simulation. World Labs intentionally tried not to make it a pure “science project,” while Fei-Fei Li calls Marble “the first glimpse” of a larger spatial-intelligence stack.

The current technical choice—Gaussian splats—turns generation into navigable geometry rather than a sequence of video frames. Splats render in real time on mobile and VR, although targeting 30–60 fps at high resolution on a four-year-old iPhone caps density and fidelity. Other systems already use frame-based generation, and future systems could attach mass or springs to particles or use token-based representations; the data structure is not treated as permanent.

Physics is the largest unresolved capability gap and the boundary between a creative tool and trusted engineering software. Pattern fitting might predict plausible orbits without deriving force vectors or “F equals MA”; Fei-Fei says there is “no indication” latent modeling yields causal law, while Justin hopes emergent physics appears at scale. Plausibility is enough for a film backdrop, but not for a building that must stand.

The commercial expansion path is horizontal, but its ordering remains intentionally unsettled. Creative industries are Marble’s beachhead; interior-design beta users already reconstruct and edit rooms, while robotics could use generated worlds as the “important middle ground” between scarce real-world data and uncontrollable internet video. Fei-Fei says whether to move directly into embodied use cases “is to be decided.”

World Labs’ discussion does not call for throwing out transformers; it points toward multimodality and a richer learning loop. Attention remains, and Justin notes that transformers natively model sets—the 1D order comes from positional embeddings—so spatial tokens need not require architectural demolition. The deeper missing ingredient may be hypothesis, action, falsification, and online updating, not a change of modality alone.

The talent and research bottleneck extends beyond capital: academia is under-resourced and too tempted to imitate frontier-lab scaling. Fei-Fei defends open benchmarks such as BEHAVIOR; Justin wants academia pursuing “wacky ideas,” including distributed primitives beyond matrix multiplication as clusters replace single GPUs. His hardware warning is concrete: Hopper-to-Blackwell performance per watt is “about the same,” leaving room for 10–20-year architectural bets.

🔗 Original source & video: After LLMs: Spatial Intelligence and World Models — Fei-Fei Li & Justin Johnson, World Labs