A note before you start This post is more technical than the other pieces in our physical AI series. It is written for readers who want the details beneath our position. If you prefer the plain-language route, start with Two ways to predict the world and The real bottleneck in physical AI, then return here. … Read more
The real bottleneck in physical AI A robot can succeed many times in simulation and still fail the first time it reaches into the real world. This gap between simulated competence and real-world performance is the defining obstacle in physical AI today. Aicadium, a Singapore-based industrial AI company, has tested these systems first-hand. The pattern is … Read more
Two ways to predict the world A Major League Baseball batter faces a fastball travelling at over 150 kilometres an hour. There is no time to watch the ball arrive and then react. Instead, the batter predicts where the ball will cross the plate. Then they start the swing before the ball gets there. That single act of prediction sits … Read more
Who should care about world models, and why Most chief executives can safely ignore the week’s AI research. This is one of the exceptions, and it is worth ten minutes of your attention. A world model is a system that learns how a physical environment behaves, and predicts what happens next, depending on the actions taken. … Read more
There was a time when a number could not get into a decision without first surviving a few questions. An analyst put a figure in front of you, and before it went any further, you asked where it came from, why they believed it, and what they would say if someone challenged it. This was … Read more
In July 2025, Deloitte, one of the world’s largest consulting firms, handed the Australian government an independent assurance report it was paid roughly A$440,000 (about US$290,000) for. It looked impeccable: 237 pages, properly cited, written in the house style of a firm that has audited the world for a century. By every signal an organisation … Read more