High Signal Podcasts Evidence ledger
Method
Browse
← Back to evidence

Evidence receipt / belief

Published · transcript-backed

David Rosenthal: belief

20 Apr 2022 Acquired Nvidia Part II: The Machine Learning Company (2006-2022)

“I think people have just lost trust and interest. There were so many years, they were so early with CUDA and early takeout.”

— David Rosenthal

Source trail

Everything needed to verify it.

Speaker
David Rosenthal
Attribution
Verified speaker
Claim type
belief
Recorded
20 Apr 2022
Publisher
Acquired

Transcript context

…No, he's trying to tell us all that this is the future. People are still skeptical. Everyone's not rushing to buy the stock. We're watching this freaking magic happen using their hardware, using their software on top of it. Even semiconductor analysts who are like students of listening to Jensen talk and following the space very closely think he sounds like a crazy person when he's up there espousing that the future is neural networks, and we're going to go all in. We're not pivoting the business, but from the amount of attention that he's giving in earnings calls to this versus the gaming. I mean, everyone's just like, are you off your rocker? I think people have just lost trust and interest. There were so many years, they were so early with CUDA and early takeout. They didn't even know that AlexNet was going to happen. Jensen felt like the GPU platform could enable things that the CPU paradigm could not, and he really had this faith that something would happen. He didn't know this was going to happen. For years, he was just saying that we're building it, they will come. To be more specific, it was that, well, look, the GPU has accelerated the graphics workload. We've taken the graphics workload off of the CPU. The CPU is great. It's your primary workhorse for all sorts of flexible stuff. But we know graphics need to happen in its own separate environment, have all these fancy fans on it, and get super cooled. It needs these matrix transforms. The math that needs to be done is matrix multiplication. There was starting to be this belief that like, oh, well, because the apocryphal professor told me that he was able to use this program that matrix transforms to work for him, baybe this matrix math is really useful for other stuff. Sure, it was for scientific computing. Then, honestly, it fell so hard into NVIDIA's lap that the thing that made deep learning work was massively parallelized matrix math. NVIDIA is just staring down their GPUs like, I think we have exactly what you are looking for.…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.

Search evidence