David Rosenthal: prediction

Transcript context

…All right. Just to list the bull cases, one, Jensen is right about accelerated computing. The majority of workloads right now are not accelerated. They’re bound to CPUs. They could be accelerated and that shifts from some crazy low number, like 5% or 10% of workloads being accelerated today to 50%-plus in the future, and there’s way more computers happening in parallel and that mostly accrues to Nvidia. I have one nuance I want to add to that. On the surface, I think a lot of people look at that and they’re like, yeah, come on. But I think there actually is a lot of merit to that argument in the generative AI world and everything we’ve talked about in this episode. I don’t think Jensen and Nvidia are saying that traditional compute is going away or again gets smaller. I think what he’s saying is that AI compute will be added onto everything and the amount of compute required for doing that will dwarf what’s happening in general-purpose compute. It’s not that people are going to stop running SharePoint servers or that whatever products you use are going to stop using their whatever interfaces that they use. It’s that generative AI will be added to all of those things and the use cases will pop up, which will also use traditional general-purpose CPU based computing. But the amount of workloads that go into making those things magical is just going to be so much bigger. Also, just a general statement on software development. Writing parallelizable code is really hard unless you have a framework to do it for you. Even writing code with multiple threads, like if anybody remembers a CS college and class where they had a race condition or they needed to write a semaphore. These are the hardest things to debug. I would argue that a lot of things that could happen in an accelerated way aren’t just because it’s harder to develop for. If we live in some future where Nvidia has reinvented the notion of a computer to shift away from von Neumann architecture into this stream processor architecture that they’ve developed, and they have the full stack to make it just as easy to write applications and move existing applications, especially once all the hardware’s been bought and paid for and sitting in data centers, there are probably a lot of workloads that actually do make sense to accelerate if it’s easy enough to do so.…

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