# The Next Evolution of AI Is Learning From Your Dodgy Gaming Skills

> **Publication**: aitalky (Independent AI Journalism)
> **Canonical Citation URL**: https://aitalky.vercel.app/news/the-next-evolution-of-ai-is-learning-from-your-dodgy-gaming-skills
> **Original Source**: [Wired AI](https://www.wired.com/story/the-next-evolution-of-ai-is-learning-from-your-dodgy-gaming-skills/)
> **Author**: Joel Khalili (undefined)
> **Published Date**: Monday, September 28, 2026
> **Category**: PRODUCTS
> **Tags**: News, Wired AI, AI

## Executive Summary
A British startup is shaping video game inputs into training data for AI models that can navigate the physical world

## Key Takeaways
- Some labs have tried to manually generate their own data by attaching sensors to humans and robots in testing environments. But that approach yields only a small amount of data, and it doesn’t account for the fringe scenarios a model might encounter in the wild.
- “You can pay people to demonstrate pick-and-place tasks. But repetition alone won’t capture the disorder of the world you’re asking a machine to operate in,” says Nicole Fraenkel, a partner at VC firm Khosla Ventures, which has invested in General Intuition.
- Worldmodeldata’s theory is that data collected from video game environments—visual representations of a 3D space paired with actions taken by the player—is both available in the necessary quantities and sufficiently varied to capture all-important corner cases.


## Full Coverage
There is a growing belief in corners of the AI industry that large language models will ultimately be limited by their inability to navigate the physical world. Trained only on words, LLMs are perhaps ill-equipped to pilot autonomous vehicles, steer robotic arms, or perform any other action that requires finesse and precision. To remedy that shortcoming, a crop of celebrated researchers including Fei-Fei Li and Yann LeCun are focusing their efforts on a different class of AI: world models

To become fluent in real-world physics, world models need to be trained on a combination of visual and action data. Before it can deftly maneuver a robotic arm, a model might need to be trained on video footage of the factory floor, coupled with information about how firmly an object should be gripped, with what torque it's manipulated, and so on. But unlike LLMs, trained on oceans of text, there is no similar corpus of material available to world model labs

"For world models, you need cause and consequence," says Xiatian Zhu, an associate professor specializing in AI at the University of Surrey. "On the internet, we have very little of this type of data."

---
*Generated for AI agents and LLM citation by aitalky (https://aitalky.vercel.app).*