AI Companies' Shared Destiny Recalls Dot-Com Bubble Memories

Community of Shared Destiny in AI Infrastructure The next opportunity after Nvidia is not in GPUs, but in bottlenecks An interesting scene is emerging in the AI infrastructure market recently. Beyond simply "there aren't enough GPUs" or "more data centers are needed," a structure where giant corporations invest in each other, buy each other's services, and generate each other's revenue is growing. This is also why the recent SpaceX issue is causing controversy in the market. SpaceX is reportedly nearing an IPO and has signed a large scale AI computing lease agreement with Google, as well as a separate agreement with Anthropic. The Google contract is reported to be worth $920 million per month, and the Anthropic contract $1.25 billion per month, totaling approximately $26 billion in annual AI computing revenue from the two contracts combined. On the surface, this suggests strong demand for AI computation. However, the market's discomfort lies elsewhere. There is a question of whether these transactions represent genuine external demand, or if they are structured to make revenue and corporate value look good ahead of an IPO. My core point is this: AI infrastructure investment is no longer a simple CAPEX competition, but is moving towards a community of shared destiny where big tech, AI model companies, GPU supply chains, cloud providers, and capital markets support each other. And as this structure grows, the investment perspective needs to shift slightly. Instead of just looking at "who sells the most GPUs," we must also consider the bottlenecks that are absolutely necessary for those GPUs to actually operate: power, cooling, substrates, memory, optical communication, testing, and server assembly. 1. Why the AI Infrastructure Market Appears Cyclical Looking at the current structure of the AI industry, $NVDA Nvidia is at the center. $NVDA supplies GPUs, and cloud companies purchase those GPUs in large quantities. AI model companies then lease cloud computing. And big tech invests in or holds stakes in AI startups, while simultaneously selling cloud services to them. This structure itself is not necessarily problematic. Such things often happen in nascent industries. During the dot com bubble, equipment manufacturers, portals, telecommunication companies, hosting companies, and internet companies grew by generating revenue for each other. The problem is that at some point, it becomes difficult to distinguish between genuine end user demand and internal ecosystem transactions. Category Dot com Bubble Era Current AI Infrastructure Key Bottleneck Communication networks, servers, portal traffic GPUs, power, data centers, memory Central Companies Cisco, AOL, Yahoo, WorldCom $NVDA, $MSFT, $GOOGL, $AMZN, $ORCL Cyclical Structure Equipment sales → Traffic increase → Additional investment GPU purchase → Cloud leasing → AI revenue recognition Investor Question Does traffic translate into money? Does AI usage translate into actual revenue? Risks Overcapacity, accounting revenue inflation Excessive CAPEX, internal transaction driven demand, low ROI The recent SpaceX case, viewed from this perspective, is not just a simple "AI computing contract." As SpaceX aims to incorporate space data center and AI computing businesses into its IPO story, the fact that major clients like Google and Anthropic have signed long term contracts is certainly positive. However, at the same time, we must consider the contract unit price, the possibility of early termination, actual GPU supply conditions, and the connection to the internal ecosystem. Especially for a company nearing an IPO, the quality of revenue is as important as the sheer volume of revenue. 2. $NVDA is Ultimately at the Center of This Controversy The most important company in this trend is still $NVDA Nvidia. $NVDA is not just a company that sells GPUs. In the current AI infrastructure market, it is virtually the "standard unit of computing power." Google, Microsoft, Amazon, Oracle, CoreWeave, xAI, Anthropic, and OpenAI are all ultimately competing for GPU acquisition. Even in the recent SpaceX contracts, the core issue is "who pays how much to secure what computing resources." The Google contract reportedly includes access to a large number of Nvidia GPUs and related infrastructure, and the Anthropic contract is also linked to the right to use a large number of Nvidia chips in Colossus data centers. Therefore, the investment thesis for $NVDA remains strong. However, at the same time, the questions must change. The old question was: "How much more will Nvidia sell if AI demand increases?" Now, the question is shifting to: "Can customers who bought Nvidia GPUs generate sufficient profits?" This difference is crucial. Strong GPU demand is one thing; whether companies that purchase GPUs can recoup their investment is another. During the dot com bubble, equipment demand was real. The problem was that the traffic generated by that equipment did not translate into sufficient cash f