Why did Nvidia suddenly report edge computing separately in the latest quarterly financial report? Nvidia's latest financial report clearly states that the company is switching to a new reporting framework and will disclose in the future according to two market platforms: data center and edge computing. Among them, edge computing includes data processing devices for generic AI and physical AI, such as PCs, game consoles, workstations, AI-RAN base stations, robots and cars. Nvidia also disclosed that its Edge Computing revenue for the latest quarter was $6.4 billion, an increase of 10% compared to the previous quarter and a year-on-year increase of 29%; The data center revenue for the same quarter was 75.2 billion US dollars, a year-on-year increase of 92%. This set of data is critical in itself: edge computing is not the main engine of NVIDIA's revenue, accounting for less than 8% of the total revenue; But it has already been placed in the "second platform" position alongside data centers by Nvidia. Why should a sector with a relatively low proportion be listed separately and kept on par with the main business. My personal understanding is: 1. NVIDIA is actively reshaping its narrative: from "selling data center GPUs" to "AI full stack operating systems" In the past, Nvidia's valuation narrative mainly focused on cloud based AI factories, but the new classification approach is equivalent to Nvidia cutting its business back into two big worlds. This is not an accounting technique issue, but a valuation framework issue. In the past, Nvidia's edge related businesses were scattered across disciplines such as Gaming, Professional Visualization, Automotive, OEM, and more. In merging these into Edge Computing, it essentially tells investors that these are not scattered businesses, but the second growth curve of the same AI era. 2. It wants to prove that the CUDA moat is not only in data centers, but can also extend to the physical world What Nvidia really wants to sell is not a single GPU, but a platform from cloud to edge to robot: CUDA + GPU + networking + Isaac + Omniverse + Drive + Jetson + RTX + AI-RAN。 If this set of things only stays in the cloud, Nvidia's ceiling will be data center capital expenditures. But if we enter the fields of automobiles, robots, factories, edge servers, AI PCs, and AI base stations, Nvidia's logic shifts from being a "data center chip company" to being a general computing platform company in the AI era. Nvidia also highlighted the edge highlights in its financial report, including RTX local agent AI and autonomous driving Cosmos、Isaac GR00T、 In the fields of industrial software, AI-RAN, etc. This indicates that it needs to prove one thing: AI is not only answering questions in the cloud, but also seeing, understanding, moving, operating, and making decisions in the real world. 3. Reduce market concerns about the "cloud vendor capital expenditure cycle" The biggest problem for Nvidia now is not insufficient growth, but market concerns: what will happen to Nvidia's growth rate if Microsoft, Google, Amazon, and Meta slow down their AI capital expenditures one day? So Nvidia needs to tell the market: My next stage is not just about hyperscalers, I also have enterprise AI, industrial AI, robotics, automotive AI PC、AI-RAN。 That's also why it further breaks down the Data Center into hyperscale and ACIE, while listing Edge Computing separately. It is giving investors a new map: The first growth curve: cloud based AI factories. Second Growth Curve: Enterprise and Industrial AI. The third growth curve: physical AI and edge AI. 4. Defining the investment narrative of 'Physical AI' in advance Lao Huang has been emphasizing physical AI for the past two years. The so-called physical AI is not ordinary chatbots, but AI that can interact with the physical world, such as autonomous driving, robots, factory automation, warehousing robots, AI cameras, medical robots, drones, and smart grid inspections. During the earnings conference call, Nvidia management stated that many industrial companies must place their calculations in context where actions need to be taken, and cannot rely solely on the cloud; For example, chip factories cannot run all real-time controls to the cloud and then come back. The management also emphasized that the next wave will be physical AI, and there will be a large number of autonomous and robotic systems entering the physical world in the future. This is the core signal of Nvidia's single column Edge Computing: It aims to transform 'physical AI' from a long-term story to a traceable revenue account