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But, but the challenge with that is, if you're building on top of something like TensorFlow, or PyTorch, or, you know, you're trying to get onto novel kinds of hardware, like a TPU or something like that. Well, you actually get exposed to all this accidental complexity, because it all leaks.
if you're, you know, one of the massive companies like Google, where you have thousands and thousands of researchers, what you'll do is you'll have this massive hardware pool and then you'll have the researchers that are all like effectively putting in their slot so they can use the machines when they come up and then they run their batch job for perhaps hours, perhaps days, perhaps months, right? And they get allocation for it.
But the thing they forget is that nobody had the convolution kernels, the algorithms to implement resonance that didn't exist on a GPU back. So the reason Alex net happened is a combination of three things. Actually, it's a combination of data, the amount of compute that was available, and then the bet that Jensen and his team made on programmability to allow some researchers to go invent some new algorithms and then do it on their platform. And then fast forward a few years, it turns out, yeah,
And so one of the things you're pointing out is crypto, right? Well, they didn't design a crypto accelerator. Crypto wandered up and said, I need tremendous amounts of compute. And they were there and ready to serve it. And because they had programmability, they're able to scale into the opportunity. You know, they talk about luck, right? Well, how do you, how do you get lucky? Well, part of it is being ready to take advantage of the luck that presents itself. And I think that is really what, wh
And so a lot of people were building the call of duty accelerator and there's a bunch of competition and just make games go faster, just make games go faster, just make games go faster. And Jensen and team, but I think it was the Geforce three on saying, okay, well, hard coding for graphics is not enough. Let's make it so you could do more general compute on this hardware. And so it's not going to be like a CPU. It's a different thing. It's a different category they created, but let's do this. A
Nvidia is one of our most important partners.
to deploy ML onto an Apple platform, you have to use their point solution called core ML and core ML is not compatible with all the models. And so there's all this friction just to get onto an Apple device. ... And one of the challenges here, the fundamental, the incentive structure problem is that hardware makers like Apple, like many other hardware makers always want to build a solution for their hardware. And nobody's trying to build something that scales across everything.