And talk about the edge market and why AI is moving to the edge.
Great question. So I think I will open with this and perhaps even close with the same statement. I think the edge market is going to be the next AI gold rush.
And if you really think about the edge market, it's 40,000, 50,000 customers globally. So it's diffused in its footprint. But if you aggregate that, it's a much bigger market than the cloud.
So think automotive, think robotics, think industrial automation, think medical, think the government sector. We use compute in very, very many different facets and everything that we touch in our lives today. And none of them really have significantly adopted AI yet.
And over the next 10 years, the next 20 years, that is the next big gold rush. And a lot of focus and a lot of attention that duly is focused on the cloud today is probably going to shift the edge. And this is the exciting opportunity, I think, that's ahead of all of us.
And so, I think I've been positioning this as the next big thing from a compute perspective and wherever you're going to be focusing. And the reason for it is very simple. Everybody's data needs are going through the roof.
The compute needs are going through the roof. And three key dynamics are forcing people to think about the edge a lot more. Number one, locality.
Not every application where you collect the data has the affordable opportunity to transmit the data back on a 5G or on some network back to the cloud and compute and come back. Proof point, automotive. You're scanning the road, you want to detect a pedestrian, you have milliseconds to make a decision, not seconds.
Robotics is a similar thing. So localized compute is key. Second one is security and privacy.
And as you particularly get into generative AI and LLM elements, access to data and really protecting both the security and the safety of it, and the privacy of it is going to become very, very key and you're going to see a lot of architectures move to localized edge compute, where the data stays with you. The third one is TCO. The cloud is pretty expensive.
And also there's monetization of the data. When you have the data, why not monetize the data for your own benefit? Why give it away to somebody else, right?
So the TCO element is going to kick in. So it's not that the cloud is going to go away. The cloud will continue to be a large entity.
But there will be a better balance between what needs to be at the edge and what has to go to the cloud. And I believe in the next 20 years, you're going to have a hybrid mode for almost every architecture or for every compute aspect that I think we're going to look through. And it should be very exciting times ahead.
Yeah. And I remember when we spoke last time, everyone's looking at the edge of all of the chip designers. And there are two ways that people are approaching it.
One, the big companies that have an existing, you know, very capital-intensive architecture are looking to scale down the architecture to make it work at the edge. And then there are companies like SEMA that are building up from scratch with the edge in mind. Yeah.
And what's the role for each of those? Or in your view, is there no role for the scaling down that's...
Yeah. More than the scaling down, what people are not paying enough attention to is the software. So scaling down the silicon, there's a lot of smarts, a lot of capability.
But AI software is an entirely new paradigm. So you cannot just add AI to an existing infrastructure. It's kind of open heart surgery.
So people need to think about the AI software architecture first before they really look at scaling down. This is where the industry's large gap is. There are very many amazing public companies that have classically served the edge.
But very few of them have an industry leading AI software and an AI roadmap. So that's one large opportunity and gap. I would submit to you that if people don't have an AI roadmap in 10 years, they may not be around as a company.
So there is going to be a mad scramble and a rush to really kind of AI enable everybody's roadmap going forward because it's here to stay. And this is not a fad anymore. And if you don't have an AI story, it's probably going to be yesterday's story, right?
So that's the big shift people are moving to. What we are building is something purpose built. And we really targeted this market.
And I would say we are one of the first few companies to really dedicate our focus to this. We are not the very first, but we are one of the very first few. And we have come a long way in five and a half years, and we are Gen 1 in production for a year and a half.
We are engaged with a wide range of applications and customers and market leaders. And our Gen 2s are on the corner. And so we are pretty excited about what we are going to bring.
The learning I have gone through is, while AI architectures are hard, what's been the Achilles heel remains the Achilles heel for the industry, which is AI software. And this is also perhaps the reason why Nvidia has had a great run and very little competition to date. And so AI software is probably, whoever does a great job in AI software is going to be the lead.
That much is clear.
And when you say AI software, you're talking about CUDA or?
Yes, RDA Coolant, right, a palette in our case. Correct.
Yeah. Yeah. And maybe just explain that a little bit more for listeners that aren't familiar with CUDA or with palettes.
Our palette, right? And so I think customers design their application and they come back and say, hey, this is what I want to go accomplish. And they have an expectation on the performance they want, the power that they want, the accuracy that they want, or the cost that they want to spend for a particular application, right?
So they walk in and say, here's my application description, here's what I want to get out of it. What translates the application from a customer description? Into a chip executable that runs on the chip, and then delivers the promise of the performance, the power, the cost, is really the software programming environment, and in Nvidia's case, it's called CUDA, and in our case, it's called PALT.
So it's really a translation between the customer's description of the application to what eventually runs on the chip.
Yeah, and a translation to what? I mean, to...
It ends up being fundamentally a microcode or an executable that is then downloaded onto the chip, so that the chip can be programmed to perform as the application requested.
no subject
Great question. So I think I will open with this and perhaps even close with the same statement. I think the edge market is going to be the next AI gold rush.
And if you really think about the edge market, it's 40,000, 50,000 customers globally. So it's diffused in its footprint. But if you aggregate that, it's a much bigger market than the cloud.
So think automotive, think robotics, think industrial automation, think medical, think the government sector. We use compute in very, very many different facets and everything that we touch in our lives today. And none of them really have significantly adopted AI yet.
And over the next 10 years, the next 20 years, that is the next big gold rush. And a lot of focus and a lot of attention that duly is focused on the cloud today is probably going to shift the edge. And this is the exciting opportunity, I think, that's ahead of all of us.
And so, I think I've been positioning this as the next big thing from a compute perspective and wherever you're going to be focusing. And the reason for it is very simple. Everybody's data needs are going through the roof.
The compute needs are going through the roof. And three key dynamics are forcing people to think about the edge a lot more. Number one, locality.
Not every application where you collect the data has the affordable opportunity to transmit the data back on a 5G or on some network back to the cloud and compute and come back. Proof point, automotive. You're scanning the road, you want to detect a pedestrian, you have milliseconds to make a decision, not seconds.
Robotics is a similar thing. So localized compute is key. Second one is security and privacy.
And as you particularly get into generative AI and LLM elements, access to data and really protecting both the security and the safety of it, and the privacy of it is going to become very, very key and you're going to see a lot of architectures move to localized edge compute, where the data stays with you. The third one is TCO. The cloud is pretty expensive.
And also there's monetization of the data. When you have the data, why not monetize the data for your own benefit? Why give it away to somebody else, right?
So the TCO element is going to kick in. So it's not that the cloud is going to go away. The cloud will continue to be a large entity.
But there will be a better balance between what needs to be at the edge and what has to go to the cloud. And I believe in the next 20 years, you're going to have a hybrid mode for almost every architecture or for every compute aspect that I think we're going to look through. And it should be very exciting times ahead.
Yeah. And I remember when we spoke last time, everyone's looking at the edge of all of the chip designers. And there are two ways that people are approaching it.
One, the big companies that have an existing, you know, very capital-intensive architecture are looking to scale down the architecture to make it work at the edge. And then there are companies like SEMA that are building up from scratch with the edge in mind. Yeah.
And what's the role for each of those? Or in your view, is there no role for the scaling down that's...
Yeah. More than the scaling down, what people are not paying enough attention to is the software. So scaling down the silicon, there's a lot of smarts, a lot of capability.
But AI software is an entirely new paradigm. So you cannot just add AI to an existing infrastructure. It's kind of open heart surgery.
So people need to think about the AI software architecture first before they really look at scaling down. This is where the industry's large gap is. There are very many amazing public companies that have classically served the edge.
But very few of them have an industry leading AI software and an AI roadmap. So that's one large opportunity and gap. I would submit to you that if people don't have an AI roadmap in 10 years, they may not be around as a company.
So there is going to be a mad scramble and a rush to really kind of AI enable everybody's roadmap going forward because it's here to stay. And this is not a fad anymore. And if you don't have an AI story, it's probably going to be yesterday's story, right?
So that's the big shift people are moving to. What we are building is something purpose built. And we really targeted this market.
And I would say we are one of the first few companies to really dedicate our focus to this. We are not the very first, but we are one of the very first few. And we have come a long way in five and a half years, and we are Gen 1 in production for a year and a half.
We are engaged with a wide range of applications and customers and market leaders. And our Gen 2s are on the corner. And so we are pretty excited about what we are going to bring.
The learning I have gone through is, while AI architectures are hard, what's been the Achilles heel remains the Achilles heel for the industry, which is AI software. And this is also perhaps the reason why Nvidia has had a great run and very little competition to date. And so AI software is probably, whoever does a great job in AI software is going to be the lead.
That much is clear.
And when you say AI software, you're talking about CUDA or?
Yes, RDA Coolant, right, a palette in our case. Correct.
Yeah. Yeah. And maybe just explain that a little bit more for listeners that aren't familiar with CUDA or with palettes.
Our palette, right? And so I think customers design their application and they come back and say, hey, this is what I want to go accomplish. And they have an expectation on the performance they want, the power that they want, the accuracy that they want, or the cost that they want to spend for a particular application, right?
So they walk in and say, here's my application description, here's what I want to get out of it. What translates the application from a customer description? Into a chip executable that runs on the chip, and then delivers the promise of the performance, the power, the cost, is really the software programming environment, and in Nvidia's case, it's called CUDA, and in our case, it's called PALT.
So it's really a translation between the customer's description of the application to what eventually runs on the chip.
Yeah, and a translation to what? I mean, to...
It ends up being fundamentally a microcode or an executable that is then downloaded onto the chip, so that the chip can be programmed to perform as the application requested.