AI Investing Writer Rob May: The Bottleneck in AI Infrastructure Is Migrating from Chips to Power, Cooling and Networks
AI investing writer Rob May presents a constraint-migration framework: once one bottleneck is relieved, the economics shift to the next thing that can't keep up. He cites roughly 120 GW of scheduled data center capacity through August 2028 and 24-to-48-month utility interconnection queues as evidence that the binding constraint has shifted from silicon to power, thermal management, networking and edge inference, and offers a three-question test for evaluating AI infrastructure companies. The author also acknowledges that the second-order trade is quickly becoming consensus, and his third-order judgment involves his own commercial interest.
重要度重大証拠E3 検査可能執筆詳細
Everyone knows AI needs compute — and that itself is the investment problem. When consensus is already fully priced in, you need perfection just to break even. AI investing writer Rob May proposes a framework in the latest issue of Investing in AI: the most durable, highest-margin AI opportunities won't be in foundation models or GPUs, but in the physical and digital infrastructure required to keep those GPUs running.
The idea comes from Howard Marks's second-order thinking. First-order thinking is simple and reactive: AI is growing, so buy the chipmaker. Second-order thinking asks the next question: if millions of AI chips get deployed, what breaks? What has to adapt? What becomes indispensable?
History keeps validating this pattern. In the 1920s, the first-order trade was Ford, GM and the tire makers; but the bigger economic story was everything mass car ownership made possible — the interstate highway system, the suburban real estate boom, motels, drive-through fast food. Nobody looking at a Model T was underwriting McDonald's, but the car was a big reason McDonald's became McDonald's. The mobile internet ran the same play: in 2007 the first-order trade was Apple and the cell tower companies, but always-on, GPS-enabled connectivity is what made Uber (2009) and Instagram (2010) possible. The bottleneck quietly shifted from the handset to cloud storage and mobile data networks.