Andrew, just two weeks ago, you had a big event.
Yeah. The biggest IPO of the year so far. The largest ever semiconductor IPO. If my math serves, you've been in the industry a long time. I've been watching it for a very long time. We don't see a lot of chip related startups, much less chip startups that make it to IPO.
Well, first, chips are really hard. And so, most of us have either aged out or died. They're not expensive to make to us here. Yeah. This one here was, is what we built. It's the largest chip ever made. Thank you. They're clapping for big chips. Yeah. That's it. That's a switch. It only costs half a billion dollars and ten years of my life to get it to work. And what we got for that was the fastest AI processor ever built. We solved the problem that had been open in the computer industry for 75 years, which is how to build a big chip. And we solved it and we delivered a product, and we were so proud. And we announced this in August of 2019. And absolutely nobody cared. Nobody cared. And it took the world a little while to catch up. And starting in 2025, the AI models got smart enough such that people began to use AI. And once people began to use AI, speed mattered. And in the way we use AI, as with inference. And what we do is the fastest inference in the world, not by a little bit, but by more than 15x. And so, that's how we ended up in this sort of extraordinary place, getting public two weeks ago.
And in the run up to that, you scored some pretty significant validating customer wins, including AWS. And I find that relationship fascinating because it really exemplifies the way you handle inference and you disintegrate the process. Right? Where the AWS Tranium handles one part, and then you take the part of inference where you decode. So talk a little bit about that and more to the point. Talk about how that is a blueprint or can be a blueprint for work with other hyperscalers.
We had a pretty good 90 day run. We did a deal north of $20 billion with OpenAI, in a committed take or pay deal. And then, 45 days later, we signed a big deal with AWS. The idea in most cases, as a computer architect, is to try and look at a problem and think about what the right machine is for it. Should you design the machine? Can we use somebody else's machine? And what we saw in 2015 and 16 was the rise of a new workload. And we thought, well, this workload is going to eat a lot of compute. This new AI is going to eat an extraordinary amount of compute. And we made two contrarian bets at the time. The first was, we will build dedicated silicon for it. And number two, we will not build something that looks like a GPU. We will start with a clean sheet of paper and build something entirely different. And both of those, at the time, people thought we were mad and it turned out we were right. Now, if you fast forward ten years, there is so much demand right now on inference because the AI, starting in about 2025, became so smart that it can do important things. And we're using it more and more and more. And so again, we looked at the work. What is the essence of the inference problem. And it's comprised of two parts. It's comprised of a part called processing the prompt. And don't be fooled for a minute. We just invent complicated names for no reason and I just, it's just we are so full of it. It's amazing. We're going to call that prefill for no reason at all. And all that is, is it's processing the prompt. And then there's a second part which is generating the answer. So you process the prompt and you generate the answer. We call the first part prefill, and the second part decode, and it turns out to have some very different compute characteristics. And so we thought to ourselves that there are machines that are better than us at this profile. It is a parallelizable problem. It has fundamentally different characteristics than the decode, which is a strictly sequential problem. And so we took this observation and we went to talk to AWS and said, we can use your training chip to do the prefill, and we'll use our big chip to do the decode. And what we will get is this extraordinary solution. And turned out to be really well received. And we're now engaged in that process of using other people's chip for part of the problem and our chip for another part of the problem, sort of with all members of the community, other hyperscalers that aren't, that aren't Nvidia. So everybody but them.
I want to come back to that in a minute. We talked about disintegration but in chip making isn't it inevitable that generalization and kind of getting away from a disintegrated approach wins out. So isn't it the case that isn't there almost inevitability there. And what happens if that's the case.
No. I think the battle between the specialist and the generalist is a really interesting sort of battle. And whether it's in the savanna in Africa, whether it's with small companies going up against big companies, the thing that determines whether the specialist beats the generalist or the generalist beats a specialist is the shape of the resource landscape. Right. If the vein of resources at the specialist is targeted at is very large, the specialist crushes it and they win. If the resource landscape is made of lots of little different pockets of resources, the generalist wins. So where did x86 win? In this landscape that was filled with lots of different use cases. Where did the GPU win? It won on discrete graphics work, one workload. Where did the x86 machine win? Everywhere. Why didn't it also win in the cell phone? Because ARM built a thing that was 100% focused on running off of battery and very low power. And so here are two examples where the specialist absolutely clocked the generalist. In other cases, the resource landscape wasn't enough. Members of the industry, myself included, tried to build a specialist. There wasn't enough for us to eat. So we ate a little bit and we starved as the generalists sort of gathered up all sorts of resources. So what we saw in 2015 was that this rise of AI would create so much demand for compute that it was best served with specialists. And that was sort of one of the winning observations.
The other big customer, when you mentioned OpenAI, uniquely structured, we're seeing that OpenAI and other LLMs are having to, you know, become increasingly creative about the ways that they finance. And they pay for these, these, you know, these compute agreements, like with yours because of this great demand that you reference and that is all around us, right? Hundreds of billions of dollars being spent. Do you have concerns about their ability to generate the revenue and raise the financing that they need in order to meet their obligations.
I think maybe another way to ask that question is, do I think there's an AI bubble? Can the leaders keep going? He says. I think if you start, the growth is sustainable. And will that user demand materialize in a quick. You know, we've both been around this. This is not our first rodeo. No. But one of the few advantages of not being young is it's not your first rodeo. I think the following. I think historically, bubbles were characterized by a notion of if you build it, they will come. And I saw some people I recognized in the audience who were with me in the late 90s when we were building out data networking equipment, and people were putting huge amounts of fiber in the ground, on the assumption that it would come. And economists like to go back for reasons that are unclear to me, to the railroads and, you know, yeah, a lot of good analogies from the 1870s. And, you know, there they were also, if you build it, they'll come. What is unusual about AI right now is the builders are so far behind the demand, it's absurd. We have a backlog of more than $25 billion of demand that there are none of us. Not us, not AMD, not Nvidia that can keep up with the demand that your employees are driving. And that's sort of, in a lot of ways, the opposite of a bubble we are chasing, right? Our customers and their customers are moving at the speed of software, and we're moving at the speed of real estate data centers. Right? And so we are behind.
