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Sam Altman

CEO, OpenAI

Quotes

It does feel possible. If the models get so smart and so efficient that they can do everything we need and build every piece of software we want, and if the bounds of our attention are such that they just cannot absorb more than what it turns out a fairly limited amount of compute can do, then we can get into oversupply. Also, if we don't drive the cost curve down because we hit some sort of scaling wall, we could also get into oversupply.

2026-07-28-podcast-invest-like-the-best-sam-altman-how-to-make-an-abundant-future-invest· 2026-07-28#ai-roi-reckoning#compute-utilization-overhang-as-latent-supply

Jalapeno is a great example of a very efficient chip. So by saying we're going to make a chip that is really good at a specific workflow and gives it some generality... I think Jalapeno and its successors are going to be a huge competitive advantage for us.

2026-07-28-podcast-invest-like-the-best-sam-altman-how-to-make-an-abundant-future-invest· 2026-07-28#inference-asic-wave-to-tsm-demand-broadening#cuda-moat-erosion-at-inference

So much of our future compute plans will be used to sell inference to customers that even if we can enjoy a modest margin on trillions of dollars of revenue, we can go afford to train some giant models.

2026-07-28-podcast-invest-like-the-best-sam-altman-how-to-make-an-abundant-future-invest· 2026-07-28#compute-as-financialized-commodity#token-price-inflation-favors-asset-heavy-compute

Building one of these is like order of 10,000 construction workers going full time for a year and a half. The energy that flows through one of these things could power a small city.

2026-07-28-podcast-invest-like-the-best-sam-altman-how-to-make-an-abundant-future-invest· 2026-07-28#ai-capex-to-power-and-materials-cascade#phantom-data-center-load
Notes

Sam Altman

One-line summary: CEO of OpenAI; articulates the compute-demand-is-uncapped thesis, the two-path AI-compute-oversupply framing, OpenAI's custom inference silicon (Jalapeno), and the inference-flywheel economics (modest margin on trillions of dollars of inference revenue funds frontier training).

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