Core Viewpoint - The article emphasizes that in the pursuit of general physical intelligence, the model serves as the ceiling while the data infrastructure acts as the floor, highlighting the importance of both elements working in tandem to create a competitive barrier [2]. Group 1: Shift in Talent Demand - There has been a noticeable shift in the automatic driving and AI sectors, with a growing emphasis on recruiting talent for "data infrastructure" [3]. - Leading companies like Tesla and Wayve are focusing on extracting data from large-scale fleets to build automatic scoring systems rather than relying solely on manually written rules [4]. - The consensus is that while model algorithms are becoming rapidly replaceable, the foundational infrastructure for data extraction and defining quality remains a significant competitive advantage once established [6]. Group 2: Evolution of Physical AI - The article outlines three evolutionary stages of "Physical AI" using references from popular anime, illustrating the progression from early simulation to advanced world models [8]. - The first stage involves basic simulation and remote teaching, while the second stage incorporates augmented reality, overlaying virtual elements onto the real world [10][12]. - The third stage envisions a world model where AI can train in accelerated time, significantly enhancing learning efficiency [14]. Group 3: Data Infrastructure and World Models - The construction of a robust data infrastructure is essential for translating the chaotic physical world into a comprehensible format for world models [16]. - The article discusses various layers of data processing, including metrics for physical world perception, data classification, and automated evaluation systems [17][21][23]. - The ultimate goal is to create a closed-loop system where real-world data informs and refines AI training, enabling rapid iteration and improvement [18][20]. Group 4: Future of Physical AI - The transition from a "Bug Driven" approach to a "Data Driven" model is crucial for the advancement of physical AI [24]. - The article argues that while models may evolve quickly, the foundational infrastructure for data collection and processing will remain invaluable [27]. - The future development of AI will likely rely on a symbiotic relationship between world models as generators and data infrastructure as discriminators, ensuring that AI systems are grounded in reality [36][38].