The next AI moat will not be a better model The Translated Bureau is the translation team under 36Kr, focusing on sectors including technology, business, workplace and life, with a core focus on introducing new technologies, new perspectives and new global trends from overseas. Editor's Note: Stop the blind race for larger models! The core bottleneck holding back the real-world deployment of physical AI is not insufficient model intelligence, but the outdated engineering verification workflow. Without addressing the inefficiency of integration and testing, even the most cutting-edge AI will remain nothing more than a laboratory toy. This article comes from a translated piece. Over the next decade, billions of machines will become autonomous or intelligent. From passenger cars, trucks, tractors and mining haulage vehicles, to defense systems, warehouse robots and humanoid robots — the entire physical economy will be reshaped around software that can perceive, make decisions and take actions. The industry's current widespread assumption about how to achieve this goal roughly goes as follows: models will keep improving, world models will mature, foundational large models for the robotics field will emerge, and autonomy will eventually come naturally. Intelligence is seen as the entire focus of this race; as long as we scale up intelligence, machine autonomy will be achieved effortlessly. For the past ten years, I have been dedicated to helping Applied Intuition build the software infrastructure behind many of the world's most ambitious physical AI projects, covering software-defined vehicles, autonomous trucks, construction machinery, mining systems, defense platforms and robotics. This perspective has allowed me to observe up close where the industry is accelerating and where it keeps hitting roadblocks. I am as bullish on the development of intelligence as anyone else, but this widespread assumption has a fundamental logical flaw. Deployed physical AI is the product of two