General world models could overtake specialized vision-language-action models around 2027. @1x_tech founder @BerntBornich made this specific prediction in his interview with @ti_morse Key highlights: 1. Data diversity is the constraint. More recordings of the same tasks add less than exposure to new environments, objects, and ways tasks fail. In Bernt’s words, robotics is “diversity bound.” 2. Internet video could do most of the training. Bernt expects it to provide roughly 99% of 1X’s training data. First-person human video, simulation, teleoperation, and robot experience would supply other kinds of examples. 3. The model should eventually collect its own training data. Bernt wants deployed robots to attempt unfamiliar tasks, record successes and failures, and improve through retraining without a human operating each attempt. 4. That gives the 50,000 target a second purpose. 1X aims to manufacture and ship 50,000 NEOs in 2027. A fleet that size could expose the model to far more variation, provided the robots meet the safety and quality bar for deployment. Interview: