NVDA

"Motley Fool Money" Looks at Nvidia

In this podcast, Motley Fool analyst Tim Beyers and host Deidre Woollard discuss:

  • What is fueling Nvidia's growth.
  • How far the demand for GPUs can stretch.
  • What concerns Tim has about Nvidia's capital allocation.

To catch full episodes of all The Motley Fool's free podcasts, check out our podcast center. To get started investing, check out our quick-start guide to investing in stocks. A full transcript follows the video.

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This video was recorded on Feb. 22, 2024.

Deidre Woollard: Now boarding the Nvidia rocket ship. This is Motley Fool Money. Welcome to Motley Fool Money. I'm Deidre Woollard, here with Motley Fool Analyst Tim Beyers. Tim, how's your Thursday going?

Tim Beyers: Fully caffeinated, ready to go and, apparently, I need to be because there are caffeinated stocks.

Deidre Woollard: There's a caffeinated stock that seems to be supercharging the whole market. We got to talk Nvidia today. We're going to talk about it for the whole show. It felt like the market was just poised, ready, waiting for this moment, and I almost don't want to talk about the numbers because it's all these giant numbers, but we got to do it. Revenue, up 22% quarter-over-quarter but up over 265% from a year ago. For the full year, it came in with nearly 61 billion in revenue. We know AI is the story here, but everyone seems to want to know, is this sustainable? What do we make of this?

Tim Beyers: Oh, no. These growth rates are not sustainable. No one should kid themselves and think that these growth rates are sustainable. It will slow down. Law of large numbers never fails. As my friend Karl Teal likes to say, gravity is responsible for most falling deaths. Law of large numbers is always responsible for a slowdown in growth rates. That is inevitable, so it won't keep going like this, but it probably will keep going at very high rates of growth for longer than we expect, and that is what gets people excited about a company like Nvidia, Deidre.

Deidre Woollard: Excited and also a little bit worried. Let's talk a little bit about what's driving this growth, on theearnings call a lot of talk about this transition from general to accelerated computing. You still got a supply problem here. You've got just massive demand, and the orders just seemed to be keep coming in.

Tim Beyers: That's true, and you can see it in the financials. I would direct folks, if you really want to see how this plays out, maybe if you like learning a little bit more about financials, and you want a quick one, whenever you see this divergence, take note of it, and here's where you'll find it. On the balance sheet, take a look at the line that shows Accounts Receivable Net under Current Assets, and then right underneath it, the line that says Inventories. What you're going to see, Deidre, is that the accounts receivable number goes from about 3.8 billion in January 2023 to almost 10 billion in 2024, and inventories are up from 5.2 billion to about 5.3 billion.

Basically what that means, that massive divergence, is exactly that's a financial way of seeing what you just said, which is this: Nvidia cannot keep enough product around to meet the demand that it has not yet turned into cash. That's what a receivable is. A receivable is an order that hasn't yet turned into cash, or it's turned into cash, but we can't yet recognize it because it's a sale over multiple periods or something like that.

When there's a receivable, there's a mismatch between the amount of orders, the amount of demand, and what's actually come in in terms of real revenue, and so there's way more demand for Nvidia than they can possibly fulfill at this point. That's another thing that gets folks excited and why they think growth can persist for a longer-than-expected period of time, and I'll tell you, the revenue number was extraordinary, but even bigger than that is the data center business, which is where all of this AI is going. That was up 409% year-over-year.

Unless I have my numbers wrong, Deidre, let's call it 61 billion of revenue during the quarter, I believe the data center business overall was responsible for pretty close to a $47 billion of that. That is a huge proportion here. No, I'm sorry. During the fourth quarter, data center was 18.4 billion. That 47 billion, that's for the full year, so I got that wrong, but still you're talking about close to a third of the business is data center. That's up 27% from the previous quarter, and again, it's 5X year-over-year, just outrageous.

Deidre Woollard: You've said something that I wanted to go back to: longer than expected. Now longer than expected doesn't mean infinity. It doesn't mean that this goes on and on, and I think that's one of the things that anyone, who is an investor, is wondering, what should we be looking for as a sign that maybe this is shifting? If you listen to Jensen Huang talk about it, it seems like there's other businesses that are coming up behind this which should keep growing Nvidia, but it's not going to be all GPUs all the time for forever.

Tim Beyers: No, not forever. I'm not going to call this a warning sign, but let's say where you would start to see things start to normalize is in the inventory line. Like I told you, Deidre, the inventory has been, you go year-over-year and looking on that balance sheet, is flat. In other words, it's not like it's the opposite of everything must go in clearance sales, like we can't keep things on the shelves, you better get in here and buy now. When you start to see something that's a little more normal, like revenues up, like say, 30%, and inventory is, let's say, up 25% or also 30%, that feels a little bit more normal, and then that would be, like, things are starting to normalize here.

We should expect growth rates to, maybe, settle. Where you would get more worried is if you saw that inventory line growing way faster than revenue, or even worse, revenue and worse than gross profits. If gross profit is say, growing at 60%, and revenue is growing at 55%, and inventory grew at 125% over the same period, you say, wait a minute, maybe they don't have the math right here. They are still anticipating high demand, and that demand is slowing. That's where you might see a little bit of room for concern, and the stock maybe hasn't cratered yet, but more likely, if you start to see something normalize, then you should be, like, this is probably the growth story.

It's still going to be growing for a very long period of time. This is a very resilient company. No way is it going away. I think we're going to need GPUs for just short of forever but will we need them at this rate? No, we will not, and where it'll start to show up maybe little bit of cracks in the armor would be like if the inventory starts really growing quickly, and the revenue and the gross profit growth rates are not really following. Those start to get disconnected, and then we'll say, maybe we need to recheck things a little bit.

Deidre Woollard: I wanted to make sure that I understand this. The inventory is, at this point, mostly GPUs, and GPU is what's driving the business. Is that correct?

Tim Beyers: Yeah, absolutely. There's no question. Let's be clear here. They don't do all of their own manufacturing, and they don't make their own chips. Taiwan Semiconductor makes those chips, but they will do things like source components. They'll have third parties presumably that are doing assembly and then shipment, so there's a part of the inventory process that they'll take control of. They own part of the supply chain because they're a hardware company, but they don't own all of it. They don't build all of their own chips. They don't have the fabrication facilities, but they are still a hardware company that is responsible, fundamentally, for assembling things like systems that go out the door, so they are taking control of supplies and making sure that equipment and orders are fulfilled. They are responsible for that.

Deidre Woollard: That makes a lot of sense. I want to talk a little bit about Jensen Huang as a CEO, and I know you have maybe some concerns about him as a capital allocator. Tell us a little bit about that.

Tim Beyers: There's an infuriating part of this. It's a good report, and I'll give you the hot take here. There's a part of this report, as great as it is, that is just horrible, and I'm not really trying to be facetious here. I'm going to say it's just poor judgment on Jensen Huang's part, which I do not like to say because I think he's a great CEO, but I think on this one, big swing in a miss here, as far as I'm concerned, Deidre, and here's what I want to explain.

Nvidia is buying back quite a lot of stock. Nvidia also issues a lot of stock to employees. Now according to the cash flow statement, in the trailing 12 months, the fiscal year ended in January of this year, their stock-based compensation expense was about 3.5 billion, and they also, over the year, bought back about $9.5 billion in stock. That 9.5 billion, some of that went to common shareholders. A lot of it went to offsetting the dilution that would be created by Nvidia giving a lot of stock to employees.

That's not great. Now, to be fair, Nvidia did retire some shares over the fiscal year. I think it's about 100 million, if I have it right, in shares, and you could argue that that 9.5 billion went to buying shares back at a lower overall price, but a good portion of it, probably a third of it at least, was to buy back shares that were issued to employees, so essentially took money from shareholders, gave it to employees, and then you're pretending that you're giving money back to shareholders, but you didn't. You just moved things around, moved the deck chairs around a little bit. I think that's a bad use of capital.

I really dislike that. I don't have a problem with Nvidia wanting to reward its employees for doing incredible work. In fact, I think they should do that. Here's what could have been great, really awesome. Just take that $9.5 billion. If you are insistent upon offsetting dilution, if you're going to insist on it, just match it to the stock-based compensation expense and then use the rest of that money, $5 billion, $6 billion, you know what you could do? You could do two things that are amazing, one for employees and one for shareholders.

The first thing, pay a bunch of bonuses in cash to the people that have really done the work to get you where you are, and even better in that, if you've got that capital, I know we're going to talk about software, but you're trying to build out a software advantage, go find the best software engineers you can find that are going to help you and pay them a ridiculous amount of money and steal them from your competitors. How's that for an idea, instead of putting this into buying back stock? I just dislike that. Now here's the thing you could do for shareholders. Right now Nvidia pays a very meager dividend on an ongoing basis. It's tiny. It's almost insignificant, really. In fact, I would say it is insignificant, but you know what, you have an option. Nvidia, you can have a special dividend that ties you down to nothing.

It ties you to nothing, and you could put $2-$3 billion into a special one-time dividend because we're amazing right now, we're generating way more cash than we need, and we could take $2-$3 billion and just reward common shareholders and not pretend that buying back stock when you're also really offsetting dilution is rewarding shareholders because it isn't. It really isn't. If you gave me a one-time dividend as a shareholder, phenomenal. Love it. You don't even have to repeat it. You can just do it one time. I think you can tell, that really bothered me about this report because Nvidia has a lot of cash.

It has more than enough capital to do the work that it needs to do. I have no problem with them putting additional capital to work in other ways to benefit shareholders. I'm probably hammering them more than I should because they probably bought back stock at attractive prices, given where the stock is today. I think there were way better things to do. There were so many better things to do and you still could have bought back some stock.

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Deidre Woollard: Welcome back to Motley Fool Money. Now let's get back into my conversation with Tim Beyers about Nvidia's earnings. Let's switch gears a little bit and talk about some of the future for Nvidia because, as you talked about with the inventory, the GPUs, eventually everybody has, if not enough, GPUs. The demand isn't going to be what it is right now. They're going into some other areas that I want to make sure that I understand as an investor. One of them is they talk a lot about Ethernet networking for AI, and they also talked a lot about software, and I want to make sure I understand the software component here because Jensen Huang was saying the Nvidia AI Enterprise becomes this operating system for AI. Now, if that's the potential, that sounds really huge, but is that what this is?

Tim Beyers: I think operating system is a little bit of a misnomer here. I know why he's saying it. He's talking about orchestrating all of the things that go into making AI functional in an environment, like providing all the software tooling, providing management, providing the hardware, providing some of the backbone technologies. You mentioned Ethernet here. It's easy to forget that Nvidia doesn't just make chips. They also have systems that are not fully functional primary compute systems. They are still what Nvidia calls, they use this term, accelerated computing. Accelerated computing means, using the car analogy here, your engine is your general purpose compute, that's the CPU. Every car needs an engine, but if you're going to turbocharge it, you have other components.

Maybe you have nitrous oxide in the back, and that's to accelerate, to give more speed, more power, things like that. Nvidia makes those turbocharged components, not the engine. That's not what they do. They make the "accelerated", I'm using air quotes here, listener, the "accelerated" parts of the compute that exist inside a large-scale environment like this. That's what they do. Jensen's argument is that those accelerated components are for the general purpose task of AI, are way more important. They are the things that you must have in order to do AI well, and they do other versions of large-scale cloud compute where you're putting a lot of compute to bear on maybe like a very big workflow across a lot of regions around the world, and you're doing that in a Cloud environment, so accelerated computing really matters, and accelerated computing equals GPUs, but it doesn't just equal GPUs.

Again, let's get back to Ethernet, what this means. In order for an accelerated environment to really function, it's not just that you compute a lot of data. You also have to transmit it, you have to network it, you have to bring data into a large-scale environment, so how you network systems, GPUs, all this together, really does matter. A few years ago, Nvidia invested in a technology called Mellanox. They invested in essentially a backbone technology that was for connecting lots of GPU compute together in a network environment. He's talking about now. The classic technology that Nvidia has used for a long time is called InfiniBand, which is very precise.

I want to connect together in a network environment a lot of compute systems, and they're going to be super precise. They're going to be really efficient. If I whisper into your ear a particular thing, and I want to get the message across, that's a very direct way to do it, and you're going to get that message because I've been really clear here. I've gotten as close as I need to get, and there's very little chance that you're going to mishear me. I got close, and now I've communicated what I needed to communicate. Very precise, InfiniBand. Ethernet, Deidre, is really messy. When I was first learning networking, the way a friend described it to me, it's like shouting across a crowded room where it's noisy.

That's Ethernet. Ethernet is designed as blunt instrument networking. I'm just going to send a bunch of packets, and I'm just going to keep sending them, and if doesn't go through, I'm going to send it again. I'm just going to flood the pipes with data, lots of packets, and I've done enough of that, that even if there are losses, the message is still going to get through. In other words, I'm going to shout loud enough that you can hear me. Does that make sense? What Nvidia is saying is we need to have a more general purpose. Ethernet is everywhere, and if we want to network, and we want to network AI, we probably should have a good way to use Ethernet. Let's make Ethernet better. In other words, to use the analogy again, let me give you a bullhorn so that if you have to shout across the room, and you shout with a bullhorn, I'm going to hear you, or you're going to hear me. Does that make sense?

Deidre Woollard: That does make sense.

Tim Beyers: I think they are putting a lot of these tools together to orchestrate and make AI environments more fruitful, easier to build for, easier to manage, easier to expand and plug into other environments, easier to network. All of these things matter for expanding Nvidia environments for AI. You can't just have the chips. You got the steak, and you got to trimmings. A steak is good. A steak and trimmings is better. This is what he's saying. We got really good steak with those GPUs, and here's a bunch trimmings. We're going to give you a whole meal.

Deidre Woollard: Does the trimmings become more of the meal over time, or is it still going to be the steak, and this is always going to be the trimmings?

Tim Beyers: I don't know. That's a really good question, and you're probably going to see much more of a buffet, and that's because environments will change because they always do in tech. It's a really good question. I don't know the answer to that, but I think one of the encouraging things about Nvidia is that they've been thinking about this at this broad level of "we don't just make the chips, we need to make something that's bigger, that makes the entire environment better," starting with the CUDA software that they had all those years ago and just gives them such an advantage because there's a lot of developers that understand the CUDA toolkit. In order to make AI real, they use these accompanying Nvidia tools.

Nvidia has known for a very long time that it isn't just about the chips. It's not just about the steak. It is about the trimmings, and they've been doing that for years, and that puts them in such a good position right now, especially because AI really isn't about chips generally. It really is about GPUs and making GPUs available in your environment. You could not be positioned better for that movement than Nvidia is. They're right at the center of it.

Deidre Woollard: Probably will be for some time to come.

Tim Beyers: You would think so. It's hard to value this company because it really is difficult to say how long these growth rates persist, but they're in a very good position. If it's me, and I have some control over a Real Money Portfolio in a Motley Fool service connected with interconnected opportunities called Cloud Disruptors, it's a Real Money Portfolio, has a fairly substantial position in Nvidia, and I can tell you, Deidre, I am not in a hurry to sell.

Nvidia shares here. I promise you that I'm going to watch it closely, but I'm not in a rush to sell a business that is compounding and has good advantages to continue compounding.

Deidre Woollard: Really good point. Thanks for your time today, Tim.

Tim Beyers: Thanks, Deidre.

Deidre Woollard: As always, people on the program may have interests in the stocks they talk about, and The Motley Fool may have formal recommendations for or against, so don't buy or sell stocks based solely on what you hear. I'm Deidre Woollard. Thanks for listening. We'll see you tomorrow.

Deidre Woollard has positions in Nvidia. Tim Beyers has positions in Taiwan Semiconductor Manufacturing. The Motley Fool has positions in and recommends Nvidia and Taiwan Semiconductor Manufacturing. The Motley Fool has a disclosure policy.

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.

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