The AI Bubble Question, Answered Separately for Each Layer
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Is there an AI bubble? With the massive number of dollars going into AI infrastructure such as OpenAI’s $1.4 trillion plan and Nvidia briefly reaching a $5 trillion market cap, many have asked if speculation and hype have driven the values of AI investments above sustainable values. However, AI isn’t monolithic, and different areas look bubbly to different degrees. - AI application layer: There is underinvestment. The potential is still much greater than most realize. - AI infrastructure for inference: This still needs significant investment. - AI infrastructure for model training: I’m still cautiously optimistic about this sector, but there could also be a bubble. Caveat: I am absolutely not giving investment advice! AI application layer. There are many applications yet to be built over the coming decade using new AI technology. Almost by definition, applications that are built on top of AI infrastructure/technology (such as LLM APIs) have to be more valuable than the infrastructure, since we need them to be able to pay the infrastructure and technology providers. I am seeing many green shoots across many businesses that are applying agentic workflows, and am confident this…
Context
Growing demand for AI can coexist with poor returns for some AI suppliers. In a November 2025 essay, Andrew Ng separates applications, infrastructure that runs models, and infrastructure that trains them because their economics differ.
Ng reports constraints on their teams' access to capacity, the computing used to generate model responses, while arguing that application opportunities remain underfunded. Ng also considers a scenario in which overbuilt infrastructure forces providers to sell capacity cheaply. Application builders could benefit from that abundance while infrastructure investors earn disappointing returns. The distinction explains why useful technology and attractive investments are separate questions; the essay supplies a business argument, not a valuation model.
- inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
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