While everyone talks about generating videos with AI, the most ambitious companies in the sector are already looking one step further: world models. They are artificial intelligence systems that don't limit themselves to predicting the next pixel or the next word, but learn to simulate how the physical world works: how objects move, how light behaves, and what consequences each action has. This guide explains what they are, how they work, and why they could be the next big bet of AI.

The concept jumped to the center of the conversation when Runway's CEO, Cristóbal Valenzuela, stated that AI-generated video is just a "prelude" and that his company's real goal is world models, as reported by TechCrunch. Runway, valued at around USD 5.3 billion, stopped presenting itself as a tool for filmmakers and began to compete on equal terms with labs like Google DeepMind and OpenAI on the next frontier of AI.

What a world model is

A world model is an AI model that learns the rules of the environment it represents. The difference from a traditional video generator is profound:

• A video model predicts which images come next so that the clip looks coherent.

• A world model understands the underlying physics and logic: it knows that a ball falls, that water wets, and that if you push an object, it moves.

Put another way: generative video produces something that looks real, while a world model builds a simulation that behaves like the real world. That ability to simulate consequences is what opens up so many new applications.

How they work

These models are trained with enormous amounts of video and physical-world data to learn patterns of cause and effect. Instead of memorizing sequences, they internalize rules that they can then apply to situations they have never seen. Runway, for example, presented its first family of general-purpose world models, designed for everything from robotics training to explorable virtual worlds and interactive avatars.

The computing power required is enormous, and that's why these companies partner with latest-generation AI hardware manufacturers to accelerate training, as detailed in the announcement of the partnership between Runway and NVIDIA. It's the same race for infrastructure that defines the whole industry today: if you're interested in how that board is moving, this analysis of Google's investment in AI infrastructure explains it in detail.

Why they are the next big bet

The reason is strategic. Generative video is already a competitive market, while world models open doors to entire industries that today depend on expensive and slow simulations:

Robotics

A robot can be trained millions of times inside a realistic simulation before touching the physical world, drastically reducing costs and risks.

Design and entertainment

They allow creating interactive virtual worlds for video games, film, and immersive experiences, where each element responds to physics in real time.

Education and business simulation

Imagine training a team in a simulated business scenario that reacts to their decisions, or teaching scientific concepts in environments where the student experiences the consequences. For LATAM, where access to labs and equipment is unequal, this can democratize practical learning.

What it means for professionals in LATAM

World models are still in an early stage, but they mark where the demand for talent is heading. Understanding how these systems work, knowing how to integrate them into products, and designing agents that operate within them will be increasingly valued skills. You don't need to be an elite researcher: it's enough to build solid foundations in AI and stay up to date with the frontier.

Recommended Coderhouse courses

To not be left out of this new wave, the best thing is to build knowledge from the base to the applied. These three options cover different levels:

• Introduction to Artificial Intelligence Course: ideal for understanding the fundamentals behind video models, world models, and the rest of the ecosystem.

• AI Engineering Course: for those who want to build and deploy solutions based on AI models in a technical way.

• AI Agents Course: perfect for learning to design autonomous agents, one of the direct applications of the models that simulate environments.

Take the first step today: the sooner you understand this technology, the better positioned you'll be when world models move from research to the market.

Frequently asked questions

What's the difference between a world model and a video generator like Sora or Runway?

A video generator produces clips that look realistic by predicting images. A world model goes further: it understands the physical rules of the environment and can simulate how objects react to different actions. The video is the visible result; the world model is the engine that understands why what happens happens.

What are world models used for in practice?

Their main applications are the training of robots in simulations, the creation of interactive virtual worlds for games and entertainment, and simulation for education and research. They are useful in any scenario where testing something in the real world would be expensive, slow, or dangerous.

Will world models replace language models like ChatGPT?

No. They solve different problems. Language models work with text and reasoning, while world models simulate physical environments. What's most likely is that in the future they will be combined: agents that reason with language and act within simulated worlds.

Do I need advanced knowledge to work with this technology?

To research at the frontier, deep training is required. But to use, integrate, or build products on these models, a solid base in artificial intelligence and practice with current tools is enough. Starting with the fundamentals and advancing toward AI engineering is the most realistic route.

Why are so many companies investing in world models now?

Because generative video is already a mature and competitive market, while world models open up entire industries (robotics, simulation, education) that move much more money. Whoever leads this frontier will have an enormous strategic advantage, and that's why investors are betting big.