Why Do AI Labs Run Many Chip Platforms?

Walter Goodwin says labs deploying AI at scale try to run as many separate chip platforms as they can, because compute is existential and they need a diversity of supply. A joke in the market says first-party chip efforts exist mainly to reduce the price people pay Nvidia, and there may be some truth in the joke.

In-house chips are architecturally quite similar to what labs can already buy. The bet does not enable a fundamental capability other chips cannot enable. It is gamesmanship: build some silicon internally, or buy from AMD, to get a better price from Nvidia, while also adding aggregate capacity and control.

Chips that give new capabilities are a different category. Everybody who wants to deploy AI at the frontier needs a way to run models orders of magnitude faster. Frontier labs exist for a window of premium capability and premium intelligence. Otherwise people would use Kimi models all the time. Pressure from open source makes the fastest deployment as important as the best model, so a lab can do the most reasoning in the shortest amount of time.