@andrewdfeldman / appears on all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in, tbpn, all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in, all-in
what we're talking about now are data centers, that are in the next several years going to use more power than the previous 50 years on Earth took.
The demand is way outstripping our ability to build data centers and to fill them with hardware. All right? And so, you know, we have a $25 billion backlog.
we made two bets. Um, the first was dedicated silicon would be the answer, and the second was it couldn't look like a GPU. And our view as computer architects is if you want to be 20 times better than somebody Right, your architecture can't look like them
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A moat that turns out to be optional drains the premium out of the most expensive name in AI: frontier models now train with zero CUDA, and the pricing power that justifies the valuation erodes as buyers gain real alternatives.
Yeah. And I think trying to share what that means, what an exponential demand means, and that we're still so early and yet the demand for AI compute is overwhelming. I think sharing that was interesting and I think helpful in educating the financial community. The other thing is that there are lots of ways to do this. That the GPU isn't the only way. You've got a TPU, you've got Trainium, you've got us.
No reach data yet.
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