Patel argues scaling continues because hyperscalers are building multi-gigawatt data centers and connecting them with billions in fiber purchases.
“Why is why is Amazon building these multi gigawatt data centers? Why is Google? Why is Microsoft building multiple gigawatt data centers plus buying billions and billions of dollars of fiber to connect them together”
Gurley notes shifting narrative suggesting inference scaling is preferable to training CapEx.
“There was a podcast recently where they kind of flipped everything on their head and they said, well, if we're not doing that anymore, it's way better because we can just move on to inference, which is getting cheaper and you won't have to spend all this CapEx.”
Gurley says if scaling laws are hitting limits it has implications but doesn't mean AI is done.
“if the the comment, you know, that you read earlier is true and that these and that they're not getting the benefit, there are implications.”
Gurley says NVIDIA differentiation is greatest at largest cluster size for LLM pre-training.
“One, the NVIDIA differentiation as we've talked about is greatest at the largest cluster size. So if pre training, once again, I only equate this to LLMs, you know, I don't think of FSD problem.”