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…
inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
Why it matters
A layer-by-layer read on where AI capital is over- and under-deployed, with the structural argument that applications must be worth more than the infrastructure they pay for — useful for judging where cost and capability pressure lands.
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