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Friday, October 9, 2026

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@ChrisCamillo

Chris Camillo on X

I don’t have differentiated alpha on the memory bubble debate, but one important perspective: Sell side reports projecting the memory supply/demand imbalance to peak in the next few quarters are number crunchers modeling today’s known AI use cases. The problem is AI’s biggest use cases may not exist yet. I’d rather bet on AI creating unforeseen demand than on a spreadsheet correctly predicting its known memory needs two years from now. $SNDK $MU
@convequity

Convequity on X

I do feel like people have cognitive dissonance here: Jeff Dean isn't only about the model application layer — he is the one who masterminded TPU, GOOG's incredible global distributed systems, and making efficient hardware + software actually work. The story isn't simply that models don't matter anymore for GOOG, that GOOG is all in on just AI infra as a more neutral player at the model layer, avoiding heavy investment that could get quickly depreciated by Chinese open source models because infra is the better business. It is simply that GOOG's current model effort is turning south at an accelerated speed. And they know this area is strategically important — otherwise Pichai wouldn't have fought so hard to keep Jeff and others from leaving. You can also excuse GOOG's strategic failure — not foreseeing this year's huge demand, not reserving enough TPU in-house for both model serving and training, and instead selling the capacity to Anthropic — as a smart move to profit from infra and shield away from model competition. It is just that GOOG's internal divisions don't collaborate with each other: the infra team sold out TPU to Anthropic this year in a negotiation done last year, while the model serving and training team never saw the demand coming and never worked that closely with the infra team. By the sheer fact that there are still people sugar-coating this story, I feel less confident about GOOG — although I am still quite bullish on its TPU and infra business. BTW, Anthropic is looking for building its ASIC with $AVGO directly, and also building and running its infra as well. Already they have pivoted away from directly renting GPU from GCP to hosting its TPU and building its kernel to juice up perf by 50% to 2X, and they will continue this optimization route just happening slower than OpenAI does.

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