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

**Ben Gilbert** (0:00)
Still got Swedish House Mafia Greyhound in my head from the pump up.

**David Rosenthal** (0:04)
Nice. Nice.

**Ben Gilbert** (0:06)
It is funny how all GPU companies, I was watching a bunch of Nvidia keynotes and AMD keynotes to get ready for this. I'm Ben Gilbert, and I am the co-founder and managing director of Seattle based Pioneer Square Labs and our venture fund PSL Ventures.

**David Rosenthal** (0:52)
And I'm David Rosenthal, and I am an angel investor based in San Francisco.

**Ben Gilbert** (0:57)
And we are your hosts. When I was a kid, David, I used to stare into backyard bonfires and wonder if that fire flickering was doing so in a random way, or if I knew about every input in the world, all the air, exactly the physical construction of the wood, all the variables in the environment, if it was actually predictable. And I don't think I knew the term at the time, but modelable. If I could know what the flame could look like if I knew all those inputs. And we now know, of course, it is indeed predictable, but the data and compute required to actually know that is extremely difficult. But that is what Nvidia is doing today.

**David Rosenthal** (1:41)
Ben, I love that intro. That's great. I was thinking, like, where is Ben going with this?

**Ben Gilbert** (1:45)
And this was occurring to me as I was watching Jensen sharing the Omniverse vision for Nvidia and realizing Nvidia has really built all the building blocks, the hardware, the software for developers to use that hardware, all the user-facing software now and services to simulate everything in our physical world with their unbelievably efficient and powerful GPU architecture. And these building blocks, listeners, aren't just for gamers anymore. They are making it possible to recreate the real world in a digital twin, to do things like predict airflow over a wing, or simulate cell interaction to quickly discover new drugs without ever once touching a Petri dish, or even model and predict how climate change will play out precisely. And there is so much to unpack here, especially in how Nvidia went from making commodity graphics cards to now owning the whole stack in industries from gaming, to enterprise data centers, to scientific computing, and now even basically off-the-shelf self-driving car architecture for manufacturers. And at the scale that they're operating at, these improvements that they're making are literally unfathomable to the human mind. And just to illustrate, if you are training one single speech recognition machine learning model these days, one, just one model, the number of math operations like ads or multiplies to accomplish it is actually greater than the number of grains of sand on the earth.

**David Rosenthal** (3:16)
I know exactly what part of the research you got that from because I read the same thing and I was like, you got to be freaking kidding me.

**Ben Gilbert** (3:22)
Isn't that nuts? I mean, there's just nothing better in all of the research that you and I both did, I don't think, to better illustrate just the unbelievable scale of data and compute required to accomplish the stuff that they're accomplishing and how unfathomably small all of this is, the fact that that happens on one graphics card.

**David Rosenthal** (3:42)
Yeah. So great.

**Ben Gilbert** (3:45)
This is a great time to tell you about one of our very favorite companies, Crusoe.

**David Rosenthal** (3:50)
So Crusoe, as listeners know by now, is a clean compute cloud provider specifically built for AI workloads. Nvidia is one of their major partners and literally Crusoe's data centers are nothing but racks and racks of A100s and H100s. And because Crusoe's cloud is purpose-built for AI and run on wasted, stranded or clean energy, they can provide significantly better performance per dollar than traditional cloud providers.

**Ben Gilbert** (4:15)
Yes. We talked about that on our ACQ2 episode with Crusoe CEO, Chase Lockmiller.

**David Rosenthal** (4:21)
The other element that makes Crusoe special is the environmental angle. Crusoe, of course, locates their data centers at stranded energy sites. So, think oil flares, wind farms that can't use all the energy they generate, etc. And uses that power that would otherwise be wasted to run your AI workloads instead.

**Ben Gilbert** (4:39)
Yep. Obviously, it's a huge benefit for the environment and for customers on costs, since Crusoe doesn't rely on the energy grid. Energy is the second largest cost of running AI after, of course, the price you pay Nvidia for the chips. And these lower energy costs get passed on to customers.

**David Rosenthal** (4:55)
It's super cool that they can put their data centers out there in these remote locations where quote-unquote, energy happens, as opposed to the other hyperscalers, such as AWS and Google and Azure, who need to build their data centers close to major traffic hubs where the internet happens because they are doing everything in their clouds.