The author argues that world models—systems that represent environments, predict outcomes, and plan actions—are where AI is heading, evidenced by Yann LeCun, Demis Hassabis, and Fei-Fei Li all pivoting toward this approach. They're using it as a new editorial lens to track how AI systems will move from generating text to making consequential decisions.

• Three major AI researchers from different backgrounds are independently converging on world models, suggesting this is where the field's momentum is shifting
• Companies investing billions in AI aren't chasing better text generation—they want systems that can predict consequences, test scenarios, and choose actions in real environments
• The practical applications span software development (agents that understand codebases and predict edit effects), robotics (agents learning in environments with consequences), and business (moving from analyzing past decisions to testing hypothetical futures)

Computer scientist Yann LeCun discusses the nature of intelligence as a learning process in a recent interview. He explores the implications of AI's predictive capabilities and the ethical considerations surrounding its development, while also sharing insights into the current state and future of artificial intelligence.

• LeCun argues current LLMs are fundamentally limited because they lack world models and can't plan or reason like humans/animals do
• He predicts today's autoregressive LLM approach will be largely obsolete within a few years, replaced by systems trained on video/sensory data to build predictive world models
• He downplays near-term AGI/superintelligence fears, framing intelligence as requiring grounded learning from the physical world rather than just scaling text-based models