Becky Quick (2:40)

Renee, thank you for being with us today. It's really a pleasure to have you here.

Rene Haas (2:43)

Good afternoon.

Becky Quick (2:44)

I I know what you all do is a little different than what so many others do because you all are in the licensing business, but I saw a statistic that blew me away something like four chips for every human on the Planet was sold by ARM in the last year. Is that accurate? Am I getting that statistic correct?

Rene Haas (3:03)

Yeah, something like that. There's some crazy statistics that says enough ARM chips have been shipped to cover every person who's ever lived on the planet and then some. What we do is a cpu. It's a compute platform that's the heart of just about every device you can think of, whether it's smartphones, data centers, automobiles, washing machines. Our business model is licensing. So we license that CPU intellectual property to someone who makes a chip. That chip goes into the device. A lot of devices end up shipping.

Becky Quick (3:33)

So how should we think about you in this really competitive chip space at this point? Who's your partner? Who are you competing against? How does that work?

Rene Haas (3:40)

It's ironic that we're in Switzerland. ARM is, you're neutral, referred to as the Switzerland of the semiconductor industry. It would be hard, Becky, to find a company that doesn't use army. Nvidia, Grace Blackwell, that's all ARM. Apple's iPhone, that's ARM. Samsung's Galaxy phone, that's ARM. Pixel's Google phone, that's ARM. Microsoft shipping surface tablets based on ARM. So we supply our CPU IP to everybody in the industry. And the reason that is important is that a cpu, which is the digital brain of every modern device, everything needs a cpu. So as a result, ARM is pretty, pretty much everywhere.

Becky Quick (4:18)

And what do you think when you sit back and look at these wars that do you care how this competitive landscape plays out? Do you just figure it doesn't matter, you're going to be there no matter what?

Rene Haas (4:28)

To some extent. I think right now it's an amazing time in our industry with everything going on. Obviously with artificial intelligence, it's hard enough to talk about it. We see artificial intelligence workloads running everywhere, whether it's inside the data center or in your smartwatch. And that's a huge opportunity for ARM because as more complex AI gets, energy efficiency is everything, which we're really good at. And that's an area we're quite focused on.

Becky Quick (4:52)

So what is your latest advancement in that exact area in energy?

Rene Haas (4:58)

Yeah, so we do the low powered processor. That's the heart of the Grace Blackwell platform. And Jensen then also talked about Vera Rubin, I think at CES a couple of weeks ago. That's all arm that uses six times the number of ARM CPUs in the platform that the previous platform did. But we're also in small devices like wearables that consume less than a watt of power. And I think over time it's going to be very critical to be able to run those AI workloads in small devices such that we're not consuming so much energy in the cloud.

Becky Quick (5:27)

The rush that's on right now for any of the data centers that are trying to make sure they have enough electricity, that they have enough copper, that they have all of these different ingredients. And does that impact you at all? Because it does impact your customers, sure, yeah.

Rene Haas (5:40)

Anybody who's in the semiconductor supply chain is impacted by this. Whether it's not enough memory, not enough wafers, not enough turbines, to some extent, we're all impacted by it. But what's happening is because the amount of compute that's required is just so large. That's why you're still seeing pretty large numbers being reported by all the chip guys, just because the volumes are are so darn big.

Becky Quick (6:03)

Andy Jassy told us this morning that the whole idea of the shortage for energy still exists for data centers, but it's better than it was 18 months ago. Would you agree with that?

Rene Haas (6:12)

I would agree that it's better than it was 18 months ago. But one of the problems that we have, particularly in the US is not so much energy, because in the US we actually have a fair bit of energy, but it's actually getting that energy to the places where the data centers need to be, transmission lines that require contracts and easements and all the things that our industry never had to worry about. Some small village having to get an easement to get a new transformer cut across county lines. So, yes, some things have gotten better, but still the scaling problem exists.

Andrew Ross Sorkin (6:43)

You know, last year we had this deep seek moment actually here in Davos where everybody had a freak out and said, oh my goodness, the Chinese not only are able to create a large language model, but they're able to do it with less processing power than it seemed everybody else and then there were questions about whether they were stealing stuff and this and that. But is there any concern that you have that there could be such a great technological breakthrough? I mean, this would be great in one way, but maybe terrible in another. If you're in the chip processing business.

Rene Haas (7:11)

From an ARM standpoint, I'd be hard pressed to think of a terrible outcome. That being said, though, I think there will be some innovation around these models, language models, for example, which are very good at taking text and providing words. Are they experts at science? Are they experts at drug discovery? Are they experts at certain things? The truth is not known yet, and I think there could be some innovation in that. Area that could be a new chip architecture, a new memory architecture, less energy. I think what's going to happen over time with these AI models is they'll become very domain specific, specific to certain areas and problems which will be good for the industry, but but may change who we think today's winners and losers are.

Becky Quick (7:50)

What do you worry about then? If that's not a big issue at.

Rene Haas (7:53)

The end of the day, one of the things that we think about more broadly is where does AI go, right? Does AI start to replace workers? Does AI start to invent? At what point does AI create? What is the next product? Could you create an AI that says, create me a brand new company with brand new workers, et cetera, et cetera? I don't know if I worry about it so much, Becky, but it is an area to sort of explore in your mind.

Andrew Ross Sorkin (8:14)

It's predestined that that happens. Meaning we all talk about it as if it's just a scaling law, that if you keep adding more energy and more chips to this, that we will ultimately get there. Is there any, by the way, there are other people, Ilya and others in the large language model world who say actually we need a major breakthrough to get there. That you're actually just throwing processing power at the problem is not going to get you there.

Rene Haas (8:36)

It's not an if question, it's a when question. This will happen, right? To the extent that we have been able to solve technology problems to the level that we have, if you look at the level of innovation in the last 10 years compared to the previous 50 or the previous hundred, it will get there. The question is when, not an if.

Joe Kernan (8:52)

In my mind, we don't know after that, we have no idea. That's why it's called a singularity. We have no idea what comes after. I really don't think we do.

Rene Haas (8:59)

I don't know how we could answer that question right when machines start to think and create an event to the level that humans do. And you get into questions of consequences.

Andrew Ross Sorkin (9:06)

But the when part does matter because for a long time we talked about self driving cars, for example, example. And we're still, we're there, but we're.

Joe Kernan (9:12)

Not there kind of there.

Andrew Ross Sorkin (9:14)

We're pretty much there sort of and sort of not.

Joe Kernan (9:17)

I don't want to get there.

Andrew Ross Sorkin (9:18)

And so the question is, I like the driving is, you know, is 2030 your year? Is 2040 your 2050 is your year? And if. And there's. That's a, that's a big cone of risk in between there's a big cone.

Rene Haas (9:29)

Of risk, but self driving cars as a constant is going to happen. There will be a generation that will look at standing parking garages and say what was that generation thinking? That they actually had cars that sat idle for 23 hours of a day.

Becky Quick (9:42)

Renee, thank you for joining us today. Rene Haas.

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