The math behind a $5 trillion annual AI infrastructure market

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 11:30

   with accelerated computing to create opportunities for them. Is that right? And so one, general purpose computing is shifting to accelerated computing and AI. Two, the first use case of AI is actually already everywhere. It's in search, recommender engines, isn't that right, in shopping. The basic hyperscale computing infrastructure used to be CPUs doing recommenders is now going to GPUs. doing AI.

2. 12:01

   So you just take classical computing, it's going to accelerated computing and AI. You take hyperscale computing, it's going from CPUs to accelerated computing and AI. And then now that's the second point. Just feeding the Metas, the Googles, the ByteDances, the Amazons, and take their classical traditional way of doing hyperscaling and moving it to AI, that's hundreds of billions of dollars. And because that may be 4 billion people on the planet today, if you take TikTok, Meta into account, Google into account, who are already demanding workloads that are driven by accelerating computing.

3. 12:41

   That's exactly right. And so, without even thinking about AI creating new opportunities, it's about AI shifting how you used to do something to the new way of doing something. And then now, let's talk about the future. So far, I've only spoken kind of largely about just mundane stuff. Just mundane stuff. The old way is now wrong. You're going to go—you're no longer going to use fuel light lanterns.

4. 13:08

   You're going to go to electricity. That's all. Right. Okay? And you're no longer, you know, prop planes. You're going to go to jets. That's all. And so, you know, so far, you know, that's all I've talked about. And then now that the incredible thing is, when you go to AI, when you go to accelerated computing, then what happens? What are the new applications that emerge as a result? And that's all the AI stuff that we're talking about.

5. 13:32

   And that opportunity, what does that look like? Well, the simple way of thinking about that is where motors replaced labor and physical activity. We now have AI, these AI supercomputers, these AI factories that I talk about. They're going to generate tokens to augment human intelligence, right? And human intelligence represents, what, 55%, 65% of the world's GDP. Let's call it $50 trillion. And that $50 trillion is going to get augmented by something.

6. 14:03

   And so let's come back to a single person. Suppose I were to hire a $100,000 employee, and I augmented that $100,000 employee with a $10,000 AI. And that $10,000 AI, as a result, made the $100,000 employee twice more productive, three times more productive would I do it. Heartbeat. I'm doing it across every single person in our company right now, right? They all have co-agents. That's right. Co-workers.

7. 14:28

   That's right. Every single software engineer, every single chip designer in our company already has AIs working with them. 100% coverage. As a result, the number of chips we're building is better. The number is growing. The pace at which we're doing it is right. And so we're growing faster as a company. As a result, we're hiring more people. Our productivity is greater. Our top line is greater. Our profitability is greater.

8. 14:52

   What's not to love about that? Now, apply the NVIDIA story to the world's GDP. And so what's likely to happen is that that $50 trillion is augmented by, let's pick a number, $10 trillion. dollars that 10 trillion dollars needs to run on a machine now the reason that ai is different than it in the past in a way software was written a priori and then it runs on a cpu and it doesn't it runs it a person would operate it in the future of course ai is generating tokens but a machine has to generate the tokens and it's thinking so that software is running all the time whereas in the past the software was written once now the software is in fact writing all the time it's thinking in order for the AI to think it needs a factory and so let's say that that ten trillion dollars of token generated fifty percent gross

9. 15:47

   margins and five trillion of it needs a factory needs an AI infrastructure right so if you told me that on an annual basis the CapEx of the world was about five trillion dollars I would say the math seems to make sense. And that's kind of the future, right? Going from general purpose computing, accelerated computing, replacing all the hyperscales with AI, and then now augmenting human intelligence for the world's GDP.

10. 16:16

    And today that market is about, our estimate is about $400 billion annually. So the TAM is a 4 to 5x increase over where it is today.