How Smart Do We Want AI to Be? World Models May Understand Things Better Than We Do

How Smart Do We Want AI to Be? World Models May Understand Things Better Than We Do Step aside, LLMs. The next big step for AI is learning, reconstructing and simulating the dynamics of the real world. Barbara PazurContributor Barbara is a tech writer specializing in AI and emerging technologies. With a background as a systems librarian in software development, she brings a unique perspective to her reporting. Having lived in the USA and Ireland, Barbara now resides in Croatia. She covers the latest in artificial intelligence and tech innovations. Her work draws on years of experience in tech and other fields, blending technical know-how with a passion for how technology shapes our world. In recent years, AI has learned to write text, generate images, create videos and even produce working computer code. As those capabilities became mainstream, attention shifted to a deeper question within AI research: Can machines learn how the world actually works, and not just how to describe it? For researchers, that question has real-world consequences, from how robots navigate homes to how self-driving cars anticipate what's likely to happen at an intersection. That's where world models come in. In 2025, it expanded further into world foundation models, popularized by Nvidia's Cosmos, which won Best AI at CES 2025. Meta's V-JEPA 2 also came out in 2025 and claims to understand physical rules like gravity. So what exactly are world models, who is building them and why are they becoming one of the most important areas of AI research right now? Let's dive in. Don't miss any of our unbiased tech content and lab-based reviews. Add CNET as a preferred Google source. World models vs. foundation models vs. world foundation models We first need to clarify the terminology. "World models" originally referred to AI systems built to understand and predict what happens inside a specific environment, such as a robotic arm workspace or a video game level. For example, an agent learning how objects move inside an Atari game. Foundation models are large, general-purpose systems trained on massive datasets to handle multiple tasks simultaneously. This includes large language models, such as ChatGPT or Gemini, which learn broad patterns primarily from text, as well as multimodal models trained on images, audio or code. World foundation models combine both ideas by taking the scale of foundation models and training them specifically to simulate physical reality using video and sensory data (think Nvidia's Cosmos or Genie 3). However, the term "world models" is often used as shorthand for these larger world foundation models, rather than the narrower systems the phrase originally described. From book-smart to world-smart Large language models (LLMs) are good at sounding informed. However, that knowledge comes from reading vast amounts of text, not from direct experience of the world. They are trained to predict the next token, meaning the next word or piece of a word, based on patterns in text. So they can describe how gravity works or how traffic flows without ever having a sense...

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