Well, actually... 'World models' are the new darling of AI funding, and Fei-Fei Li would like your attention
Researchers and investors are pouring billions into AI systems that claim to develop genuine physical understanding of our world, not merely statistical pattern matching. World Labs, founded by Stanford computer scientist Fei-Fei Li, who created the ImageNet dataset, represents one of two major well-funded efforts in this direction. The term 'world model' here denotes an AI architecture that builds internal representations of objects, spaces, and physical dynamics.
This teaches you to distinguish between generative models that predict likely next tokens versus structured models that maintain consistent beliefs about physical reality. The latter could eventually enable reliable simulation, robotics planning, and scientific reasoning. Your workflow thinking should shift: ask whether your task requires mere text generation or actual situated understanding.
Fei-Fei Li at World Labs, alongside another unnamed well-funded competitor in the world models space. Li's credibility stems from her creation of ImageNet, the dataset that catalyzed modern computer vision. Billions of dollars in capital have flowed to these efforts collectively.
Step 1: Open a free AI tool like ChatGPT or Google Gemini and ask it to describe what happens if you tip a glass of water on a tilted table; note any physical inconsistencies in its explanation. Step 2: Try the same query with a specific spatial setup, like 'a 30-degree slope with a wet tablecloth,' and observe whether the model maintains coherent physical reasoning across the scenario. Step 3: Compare two runs of the same prompt to check for consistency in its 'understanding,' which reveals whether you are getting genuine simulation or plausible-sounding variation.