SemiAnalysis: Challenges in AI Infrastructure Financing and Supply Chain

On October 25, Dylan Patel, founder of SemiAnalysis, and Bernstein analyst Stacy Rasgon pointed out that AI infrastructure investments are facing a dual challenge of rising financing costs and supply chain bottlenecks. The yield on the U.S. 10-year Treasury bond has surpassed 5%, and the yields on unsecured bonds from some emerging cloud service providers are nearing 15%. Non-hyperscale cloud providers are taking on nearly half of the construction demand but lack sufficient balance sheet support, making project financing more difficult without long-term procurement commitments. Capacities for substrates, HBM, optical interconnect components, and clean rooms are facing short-term bottlenecks, with the number of wafers required to produce equivalent capacity HBM being 3 to 4 times that of DDR5, and new clean rooms will not be operational until 2027. Analysts warn of a potential turning point in 2028, as NVIDIA supports high prices through performance improvements, but Google TPU, Amazon Trainium, and OpenAI's self-developed chips provide alternative options for large customers, enhancing their bargaining power. Even if NVIDIA's revenue grows, market share and profit margins may not necessarily increase in tandem. In terms of memory, current HBM gross margins are as high as 88% to 90%, but market valuations are not high, raising concerns that the release of new capacities after 2028 could be concentrated. If the pace of expansion exceeds demand growth, the current high prices and profits could face a reversal. Companies are likely to prefer purchasing tokens through cloud services, and future AI infrastructure demand will heavily rely on the usage of inference services.

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