World models will be how general purpose robots finally work. They are essentially learned simulators of the world. They will replace traditional robotics simulators which are not flexible enough to enable training of general robotics policies. Robot control policies will be trained and evaluated in learned simulators, and the policies themselves will also be world models in order to predict the consequences of their own actions and thus enable planning. Simulated data will scale much better than expensive real-world robot data, and will allow robot policies to reach LLM-level dataset sizes, and subsequently, LLM-level performance.

It is inevitable that learned simulators will replace hand-coded simulators, as it is a straightforward application of the Bitter Lesson: http://www.incompleteideas.net/IncIdeas/BitterLesson.html

By enabling general purpose robotics, world models will be one of the most useful inventions of all time. For examples of what I'm talking about in current research, check:

Dreamer 4: https://danijar.com/project/dreamer4/

DreamDojo: https://arxiv.org/abs/2602.06949

Tesla's world model: https://www.youtube.com/watch?v=LFh9GAzHg1c

Waymo's world model: https://waymo.com/blog/2026/02/the-waymo-world-model-a-new-f...

The world model is useful for planning. It can "anticipate" consequences of actions. This can be used for a kind of tree search to decide on optimal actions in robotics

They can be base models for a bunch of things. Turning text-conditioned video generation models into robotics VLAs is a fun exercise.

This one is probably too small to be useful for that, and not diverse enough? But I could be wrong.

I believe the idea is to offer simulation of ideas to test out new tasks AND something like dreaming.

It's a step towards something else?

Digital twin?

Right now there is (AFAIK) no world model product booking any meaningful revenue. So there's a decent chance WMs turn out to have no long-term utility at all.

However, there are a few promising markets, assuming WMs continue to get better and cheaper:

1. Robotics training / evaluation: modern end-to-end (sensors-to-control) robot policies require simulators that are almost indistinguishable from reality. If your sim is distinguishable from reality, the evaluation metrics you get from sim don't mean anything and the policies you train in sim don't work. World models will likely be the highest-fidelity robotics simulators, since WMs are data-driven and get arbitrarily more-realistic given more data/compute. This is why so many robotics companies have WM projects [1] [2] [3] [4].

2. Video frontends for agents: in the same way that today's frontier labs are building realtime voice interfaces [5] which behave like a phone call, realtime video interfaces will behave like a video call. Early forms of this don't feel compelling IMO [6] [7], but once the models can instantly blend between rendering the agent itself, drawing diagrams/visualizations, rendering video, etc. I can see it surpassing pure voice mode.

3. Entertainment: zero-shot world generation (i.e. holodeck, genie 3; paste in an image/video/text prompt and get a world) will be a fun toy but I'm not convinced it has any long-term value. I'm more optimistic about proper narrative experiences where each scene/level is a small, carefully-crafted world (behaving like a normal film scene if you don't touch the controls, and an uncharted/TLoU-style narrative game if you do), such that the sequence of scenes builds up a larger story.

[1] https://wayve.ai/thinking/gaia-3/

[2] https://xcancel.com/Tesla/status/1982255564974641628 / https://xcancel.com/ProfKuang/status/1996642397204394179

[3] https://waymo.com/blog/2026/02/the-waymo-world-model-a-new-f...

[4] https://www.1x.tech/discover/world-model-self-learning

[5] https://thinkingmachines.ai/blog/interaction-models/

[6] https://runwayml.com/news/introducing-runway-characters

[7] https://blog.character.ai/character-ais-real-time-video-brea...

Put them in a robot so that it can navigate the physical world like humans. Self-driving cars.

Games. Build campaigns in hours instead of months. Make it possible for users to create their own campaigns, move the action to different game worlds - 'gimme Mario Kart in the ${favourite_game} world', etc.

I tend to think of these NV Labs models as architectural demos and ‘free razor blades’ — they’re more intended to inform internal R&D, get customers something that lets them do what they want quickly, and enhance the state of the art.

In this case, what looks interesting is the one minute coherence and the massive speedup - they claim 36x over open models with similar capabilities. You can tell they aren’t aiming for state of the art visuals — looks very SD 1.5 in terms of the output quality.

When I installed very high quality (CRI 98, R9 94, virtually flicker free) light bulbs to one of my apartment rooms I had headaches and felt occasional confusion for about a week while being in that room, so I had to slowly increase the amount of time the light bulbs were turned on for. To my understanding my brain was very used to the way objects and lighting looked in that particular room so it needed to rewire some knowledge given that I've spent many thousands of hours in that room with previous light bulbs.

I'm curious if a younger me would have adapted much faster.

It is plausible, the model would just need to be trained on a lot of stereoscopic data.

Relax its only been 3 years, it's going to get a lot better not worse from here on.

what subreddits do _you_ subscribe to?

I've been doing some content with people at https://industrialallusions.com

Judging by the examples it wouldn't be useful for that, the environments show little physical consistency.