- Conclusions: 1) Lowering the cost to train will increase the ROI on AI. 2) There is no world where this is positive for training capex or the “power” theme in the near term. 3) The biggest risk to the current “AI infrastructure” winners across tech, industrials, utilities and energy is that a distilled version of r1 can be run locally at the edge on a high end work station (someone referenced a Mac Studio Pro). That means that a similar model will run on a superphone in circa 2 years. If inference moves to the edge because it is “good enough,” we are living in a very different world with very different winners - i.e. the biggest PC and smartphone upgrade cycle we have ever seen. Compute has oscillated between centralization and decentralization for a long time.
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