Motley Fool Money - Big Tech Goes Nuclear

Episode Date: October 17, 2024

…again. Amazon is the latest hyperscaler to team up with an energy company to power its AI ambitions. (00:21) Asit Sharma and Mary Long discuss the collaborations laying the groundwork for the coming “Intelligence Age.” Then (18:38), Sanmeet Deo and Ricky Mulvey debrief Tesla’s “We, Robot” event and take a look at the humanoid landscape. Vote for Motley Fool Money in the 2024 Signal Awards for Best Money and Finance Podcast: https://vote.signalaward.com/PublicVoting#/2024/shows/general/money-finance Companies discussed: AMZN, D, MSFT, GOOG, GOOGL, CEG, EQT, SMR, TSLA Host: Mary Long Guests: Asit Sharma, Sanmeet Deo, Ricky Mulvey Engineers: Rick Engdahl, Tim Sparks Learn more about your ad choices. Visit megaphone.fm/adchoices

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We've got the power. You're listening to Motley Full Money. I'm Mary Long, joined today by Asset Sharma. Asit, thank you so, so much for being here on this lovely Thursday afternoon. Thank you, Mary. But, you know, as much as I like you, I have a thing against people planting earworms in my ear. Like, just before I start a conversation. And now, of course, I'm hearing a certain song from back in the day with your intro.

But nonetheless, let's proceed with our conversation. I have to be honest. I almost, I debated singing it and then thought, too much. much, too much. I've already had that song stuck in my head all morning as I thought about that intro line. So there we go. Torturing myself and you and others. So hence the intro line, Amazon just became the latest big tech company to ink some kind of nuclear power deal. The biggest part of this particular deal is that Amazon's teaming up with Dominion Energy to explore the development of small modular nuclear reactors in Virginia. If you're thinking that

you've heard a similar story before. It's likely because you have. Amazon is not the first hyper-scaler to go this route of teaming up with an energy utility company. Microsoft and Constellation Energy earlier last month announced that they were going to restart Three Mile Island, the site of the most serious nuclear meltdown and radiation lake in U.S. history. Alphabet announced earlier this week that it's teaming up with the privately held Chiros Energy to build seven small nuclear reactors. Nuclear and big tech, but we'll just stick on nuclear. Nuclear is the common denominator in these stories.

But that power source is certainly not without its controversies, Asset, and there are a lot of other types of energy. Why nuclear? Well, Mary, you and I were chatting before the show, and you threw out like wind and solar as alternatives. And that's interesting because those come up in the conversation a lot. Why not just things like wind and solar, also non-carbon forms of energy? like nuclear. The reason is we're just not going to get there fast enough. That is, when I say we, I'm sort of talking on the level of a society, but if you look at it from the hyperscalers vantage point, what they need to power data centers to keep that cost from getting out of hand,

just can't be developed quick enough if they go simply a wind route or a solar route or a combination of those. So a natural alternative is nuclear energy. I'll note that all three of the big hyperscalers have made commitments to have non-carbon transitions in their energy sources. Amazon may be the fastest to get there, but each of them is telling shareholders, okay, look, we're not going to build out data centers for AI and have to build new coal power plants to do that. We're going to go other routes.

So this is why the focus is now on nuclear. As you mentioned, that's not without its drawbacks. I'm glad you mentioned timeline because the narrative around a lot of these deals is often exactly as you described, like big tech needs more power for AI and they need it right now. This Constellation Energy Microsoft deal, they want to restart the three-mile island reactor by 28. That's really not that far away. And nuclear power projects do have a reputation for running very long and being very over budget. So I got a follow-up question that's coming. But the first question for you is, how realistic are the timelines of these partnerships?

I don't think they're that realistic. Number one, I think part of this is that we are doing the best we can. We as a society, again, to try all types of non-carbon energy sources at the same time that we are placing unprecedented demand. If you think that the demand is not going to grow at this linear rate, I would just suggest. just look at crypto mining from a few years ago. That was the first blush that we got that maybe computation could be something that places a stress on our power grids. And now we have generative AI. There will probably be something else down the road. So while society is trying to solve these problems, the big giants are putting money in today. They're putting out these

timelines, number one, to try to do what they do in their own work, which is to set something ambitious to move with like agile precision to innovate, to iterate, all this kind of venture capitalist quasi big tech corporate speak we hear about just moving fast, meta famously move fast and break things, right? We don't want to break nuclear. That might not be great. It's important that we don't break that. But they want to be aggressive with the timelines. The history of nuclear power, though, is one of overextended budgets, missed deadlines, especially if you're talking about large-scale nuclear reactors, which is not really the case here. But what we will talk about very soon here is also something that's unproven and I think may not meet the state of deadlines.

Yeah. So to get to that follow-up question that I promised, even at their most generous, are these projects, these promised timelines, are they even moving at the pace that big tech really wants them to? You know, we say we want the power now. Okay, well, now isn't actually now, and it's probably not four years down the road. So how do you square what we need now with the realistic timeline? One way that we square it is to keep developing technologies that have nothing to do with the energy source, but are focused on reducing the power demands within the data center. So innovations in chips, innovations in the way we cool servers, innovations in the way we build up server racks, those are all ways that we can sort of at the margins

help the problem along. But yesterday wouldn't be soon enough when companies like Amazon and Microsoft, Oracle, you name it, look ahead to what these demands will look like in four to five years. I think there's going to be a price reckoning. So someone will have to pay it because we're going to stress the grid as it exists. So with that higher consumption, higher demand will come higher prices, who picks it up? I hope most of it is picked up by big businesses, enterprise businesses that are doing a lot of AI computation, but part of me already understands that we, the consumer, are going to pick that up in one way or another. So it's really to everyone's interest in our society to try to figure out how we can innovate, not just with the power

source, but every part that's involved with training and inference of these AI workloads. I want to focus on a detail of difference between these three different types of partnerships that we've mentioned. Microsoft's deal with Constellation Energy focuses on reviving a currently closed but already massive plant. Amazon and Google, on the other hand, they want to develop this new generation of small modular reactors. What's the difference between those two paths?

The small modular reactor path might be more viable in the future. There are many differences between S. SMRs, we'll just call them that. And these are described in various terms with various acronyms, but let's stick with this one. Many differences between this and your conventional large-scale nuclear power plant. For one, the power output is smaller. So it's a smaller setup. It's what it purports to be, a small reactor.

It is modular. And by modular, this means that it's almost like something you could produce in a factory. In fact, it can be produced. its parts in a factory setting. That's not the case. If you look at any kind of big nuclear reactor, you might have seen driving around, those ones that tower above us and emit those ominous clouds of steam. Those are built on site, maybe they're highly complex.

With these small modular reactors, you can assemble them, and you can assemble them as your energy needs scale up so that you can get started quicker. And this is one of the things Amazon is gunning for and other companies are gunning for. Let's get that first bit in. Let's get it started. So we prove the concept. And then we can add on more energy. And they typically are thought to be a little bit safer than large-scale plants.

They have a different technology of cooling, which has to do with just the intrinsic way the water cools in the system. The other thing that I think most people will gravitate towards to is that they can be put adjacent to a data center. They don't have to be plunked in the middle of nowhere. So they're not something that we typically associate with these large-scale reactors out in the open, a source of concern for different communities. So on so many fronts, they make sense. Again, these two aren't without their own drawbacks. You read about this at all, and you're going to come across some metrics that might be hard for the average person to visualize. Megawatt, kilowatt, this type of stuff.

So often to underscore and illuminate the type of, to illustrate how much energy AI takes up, we use comparisons, right? So a single chat GPT prompt consumes the same amount of energy that it takes to power a light bulb for an hour. A Google search by comparison is the equivalent of powering a light bulb for two minutes. I've also heard this described in terms of water. A 100-word email generated by something like chat GPT4 requires 519 milliliters of water, which is a little bit more than a water bottle. Okay, that helps me visualize what this energy consumption looks like, but I think for a lot of people we've become so used to asking the computer a question that we kind of take for granted how that happens. and you'd be forgiven for not thinking about how the process that actually powers that. So what does that chain look like?

How does this innocent little asking of a question typing something into Google or chat GPT suck up so much energy and water? Yeah. So, Mary, when URI types that question into chat, GPT, we're sending that question over to a computer on a server. and that is starting a series of calculations. So those calculations are interacting with stuff that's stored in memory. What is that thing that stored in memory?

It's a picture of the world. So we think in terms of large language models, those are representations of different objects, different concepts, different words, statistical representations, right? We all understand that ChatTBTBT is sort of predicting what should come next in a sequence. it has to constantly interact with that model when we send the question over. So think about a lot of computation that's involved to pick the different parts of that model to form the response. That's the inference part.

But then on the chip level, too, there's so much going on. If you picture a GPU, what you're thinking about here is a chip that's performing computations, but it also has to access memory in generative AI. to answer the question. So it could be going off of the chip to access memory. There are some chips, some GPUs, that have memory that's built in three-dimensional space

around the chip. So picture data zinging around the chip, then going up in a stack to access a bit of memory and come back down, and then having to go to a whole cluster of other GPUs. Some GPUs now are linked together in the hundreds and in the thousands. Elon Musk has been,

a version of this. So what I'm trying to communicate here, help listeners visualize, is that a simple question involves a lot of mathematical operations and a lot of memory because we're relying on the computer to access its vision of the world that we've built by training it on billions and billions of parameters. It's way different than what we used to do with computers, which is just to type in a request and go to something that's already indexed, that's static, and that's Google search, for example. Just consult this index and pull me a result. That takes so much less computation.

Looking back on the third quarter, utilities was the best performing sector of that period. Energy, meanwhile, was the worst performing sector of that period. And yet here we've got these stories where big tech companies are pairing up with utility company, energy companies, to move forward, our progression towards AI and this so-called intelligence age, if you're an investor looking for a picks and shovels play in the AI game, how do you play this? Especially considering that, okay, just over the past quarter, these two sectors that are kind of close have also had pretty different results.

I think energy companies are interesting in so many ways because they are getting more and more requests to help solve this. this puzzle. EQT is a company that is basically a natural gas company, the whole pipeline of natural gas, but it has a role to play in generative AI as well, as an alternate source for energy. So suddenly, you know, that becomes an AI play. Looking at regional utility companies is so interesting. There are certain parts of the United States where so many data centers are being built out. You can see this going on in many southern states, especially Virginia, Northern Virginia is like an amazing global hub for generative AI.

And it is flush with data centers. If you ever driven around Northern Virginia, for those of you who haven't, then in the Pacific Northwest, we have projects that are going to come online there. So I like looking at these different hotspots and seeing which are the utility companies and energy companies that are playing in this space. Because inevitably, as you sort of alluded to, Mary, they're already in all sorts of talks and partnerships with the big cloud companies. And so they have this new attractive revenue.

new source over the years. So that's one way to play it. And then keep an eye on the small companies too. I think you referred to New Scale Energy, which is one of the companies that's publicly traded that plays in the space of small modular reactors. I think it's symbol is actually that SMR. Keep an eye on those. But as you've mentioned, like so much of this hasn't been realistic in terms of time frame. So you have to be a patient investor. If you're going to pick up some of these small companies, don't expect the moon tomorrow, be ready for some volatility, and be ready for some ups and downs as they receive contracts, constructions delayed, they get more contracts. It's going to be a while before these companies, very few of them, which have discernible revenue

yet, to be like free cash flow propositions, but it is fun to keep an eye on them. And, you know, you did a great job explaining earlier how we get from me typing a query into chat GPT to the process behind that. We didn't quite touch on the fundamentals of nuclear physics, but a guy named Warren Buffett tells me that I should only invest in companies that I fully understand. Do I need to have a PhD in nuclear physics to be investing in this space at all? Yeah, that's so interesting. I think that Warren Buffett said that he only invest in companies he understands and has very humbly owned up to missing some great companies because he didn't understand them. And so I think a good way that we can all have a

fruitful twist on what Warren Buffett says is to only invest in the companies that you're curious about. Because you can learn as you go along. If you're curious about a company or a technology, it's okay to invest in it if you don't understand it. As long as you're willing to put in the work over time, if it becomes material to your portfolio, to make sure you understand it. Because the last thing you want to do is to wake up with a great winner in your portfolio and not really know how it makes money, because then you won't know what to do with it. Do I sell it? Is it going to go further from here? I don't know what to do now because I didn't, you know, put in the work. So if you're buying companies, you won't want to think about later,

that could be counterproductive. I happen to have the Oxford Dictionary of Physics on my bookshelf. Not that I consulted, but I was thinking of this when you and I were planning the podcast, like, you know, I had to dust that puppy off. And maybe it'll help me understand a little bit more about this industry. So maybe that kind of approach is a little bit better for most of us. Not all those can be Warren Buffett because, you know, he can totally avoid things he doesn't understand and still make so much money with all that capital and all that acumen. Awesome. I already thought you were a Renaissance man.

You write. You invest. You have all these various hobbies. And now I'm learning that you also have a physics textbook in your library. Let's correct that. Now you also know that I have dusty books on my bookshelves. So appearance only gets you so far, unfortunately. That's part of it. Part of it. I like to think that if you have enough books, you can learn through osmosis sometimes. Totally. Awesome. Charma, thanks so much for joining us and for the insight into what can be a pretty complex topic.

Thanks a lot, Mary. This was a ton of fun. Today is the last day to vote for Motleyful Money as Signals Best Money and Finance podcast. if you enjoy the show and haven't yet had a chance to cast your vote for us, you've got till the end of today to do so. We so appreciate you listening always, and we really appreciate your vote. There will be a link to vote in today's show notes. Reminder, case you forgot, you will have to share your email to prove that you are a human, not a robot.

Speaking of robots, Elon Musk likes to talk a lot about human rights, but Tesla is not the only company making progress in this space. Up next, Ricky Mulvey talks with full analyst San Mate Deo about the present and future possibilities of these not quite human robots. Sam, meet this conversation was about to be very different if we recorded it just a few days ago. Last week, Tesla demoed its optimist robots at the Wii robot event in addition to the robotaxies and the new Tesla van. Here's the layer is that these robots were remote controlled by humans. According to a Bloomberg report, Elon Musk told the engineers,

you need to get these robots ready for prime time at the event. The engineer said we can only do that if there's teleoperation. And alas, we have this controversy where Tesla CEO Elon Musk didn't necessarily reveal that those robots were operated in help by humans during that event. But before we get to that controversy, what was your reaction first to the demonstration of the optimist robots? And then how did that change when you learned they were teleoperated?

Honestly, I was like, are you kidding me? You know, apparently parts of it were remote controlled, but, you know, it was kind of disappointing. You're rolling out these humanoid robots. They're supposed to mimic humans and you got someone in the back just kind of controlling them. So what wasn't so exciting? It was a little bit disappointing. But, I mean, it also limits the fear of robots taken over if the human is controlling them. There's also, there's a video of the event where one of the optimist robots is making drinks

and this person taking the video keeps asking like, are you operated by a human? Are you operated by a human? And while it's pouring drinks, the optimist robot or the person behind the optimist robot, the third person becomes very difficult in these sentences when we're referring to a teleoperated robot. Anyway, the robot says, I'm assisted by a human right now. So it was kind of revealed during the event, but Elon Musk, who I, he's a showman. He's an innovator and he's also a showman, was talking about all the solutions that these robots

would provide, but not necessarily where they currently work. when you look at the current state of where humanoids are, not just with the Tesla Optimus, but also with agility robotics, which is working with Amazon, Boston Dynamics, what can these humanoids do right now and what can't they do? Yeah, you know, we're getting to see a new technology kind of grow before our eyes.

You know, while humanoid aren't ready for prime time yet, you know, they're developing at a rapid pace. You know, many of them can walk, maintain balance. You know, they walk, albeit at a slow pace. They can perform some basic tasks, lifting, moving objects, and loading trailers, moving packages in logistics environments. You know, with AI, you know, they're able to kind of understand and respond to voice commands and learn from kind of experiences. What they can't do is perform, you know, tasks in an efficient

pace. They say that if humans worked at the pace of these humanoids right now, then we'd be fired. You know, they don't have the human flexibility and fine motor skills for precision tasks, and their understanding is kind of limited. You know, it's funny because when you watch these humanoid and as they try to pick up things, you kind of have more appreciation for for your own hands and how precise they can really be when you're trying to do very basic tasks. Yeah, like the ability to crack an egg, for example. And I think we're going to see a lot of them show up first, maybe not walking your dog or babysitting your children, but factories. You could imagine Amazon being very interested in having humanoids working in their factories, robots that don't

need 401k's, robots that don't need to take much of a break besides battery recharging, robots that don't go on strike. And in fact, they're working on bringing more robots to their factory with the digit robot. Yeah, you know, Amazon's journey with robotics kind of began in 2012 when they acquired Kiva Systems for $775 million and kind of launched Amazon robotics. And, you know, they've been using robots in their fulfillment centers to move, you know, shelves of inventory, pallets, large items, sorting and handling packages. A lot of the robust they have had prior to Digit is, you know, think of like larger Rumbas that are these big kind of rumba vacuums that are holding pallets and boxes.

Digit is a more official humanoid, bipedal humanoid, which is hopefully going to improve efficiency, you know, automate repetitive tasks, and it'll be, you know, well suited for human tasks. on some of the more dangerous tasks that humans might be taking and reduce that chance for employees to hurt themselves. Yeah, I mean, what is your, I know you've looked into this space quite a bit, and there's a range of outcomes between, I think, there's a pretty clear industrial use case, and then we also have a use case of humanoid's is dog walkers, lawnmowers. Heck, they could even be your friend. Where's your bullishness on humanoid's lie? What do you think they're going to be doing?

Well, so, you know, if you take a step back, you know, a lot of projections are saying that the global humanoid robot could reach anywhere from like $38 billion by 2035, which Goldman Sachs says, to other estimates that are over $4 trillion by $2035. So regardless, I think it's going to be a huge market. But where do those, where do those humanoid kind of land? What do they do? I think some of the key areas are major job needs where we're seeing gaps in employment when it comes to manufacturing. agriculture, elderly care. You know, it's said that we're going to face like an 8 million plus job gap in essential manufacturing.

You know, that's something that human employees could easily take on as they start ramping up. Just Morgan Stanley estimated by 2040, United States may even have 8 million working humanoid robots that would have a $357 billion impact on wages. So some of these jobs where it's, you know, the employee safety is of concern of, of repetitive tasks. You know, the digit actually, one of its tasks is literally emptying the tote bags

where products are in and putting them away. Like, that is actually done by human right now and, you know, it's repetitive, is boring, and I'm sure us humans have better things to do than that. Yeah, the robots don't get bored. I think the concern comes from, in my brain, is when you start matching these humanoid robots that are physically very capable, and we'll see as they continue to develop their balance

and their ability to perform these repetitive and creative tasks, and these large language models, which are able to make really good inferences. And it's that merging in between them that Tesla is working on and Boston Dynamics is working on. I think that's where you have sort of the greatest bull case speculation and also the greatest concern of what are these things going to be capable of when we develop a machine that is bigger, faster, stronger, and smaller, and small. smarter than you send me. Well, you know, one of the one of the companies that you haven't mentioned is figure, which is a private company.

And they are working also with OpenAI. They're working with BMW. And, you know, their founder is, has worked on some other interesting new age kind of creations or innovations, inventions, I should, I guess,

say. And he's a little more tempered than Elon Musk. He's a lot more rational. And those actually impress me the most. They're, they're doing some, some great stuff. That's one to look at too. So these startups for humanoid robots, we have some of the big companies like Tesla getting involved with it. Startups have raised about 1.6 billion in venture

capital to develop these bots. But those are for the private investors. Those accredited investors for those, the lowly, the rest of us. I'm not an accredited investor. I'm in the lowly. Is this space investable for me yet, or is it too early? Yeah. You know, it's in terms of pure play, like publicly traded humanoid companies. I don't know of many. You know, there are, like you said, you know, the figures, the Jilly robotics, all the private companies. Obviously, if you invest in Tesla, it gives you exposure to optimists.

But then you're getting in like EVs and autonomous driving and battery tech all in there. Hyundai Motors actually owns Boston Dynamics, which are famous for the Atlas in the spot and the robots that kind of do all those fancy, funny tricks jumping and such. One other area where private investors could kind of explore, retail investors could kind of explore is, you know, crowdfunding platforms like Republic or Start Engine microventures. It is much riskier than the publicly traded markets is a whole other game. So you want to really do your research and really look into that.

But in terms of other publicly traded investing vehicles, the thing that I'm going to look into more is, you know, the picks and shovel stuff, the things that make up those humanoid and those robotics that will power them. What are the picks and shovels? What's powering them? Well, with AI, you know, you got the chip names, semiconductor names, you know the standard ones, Nvidia and the likes. I don't know specific companies yet, but I'm looking into like, you know, AI vision technologies, sensor technologies, lots of different things that, you know, I'm going to go digging around one day and probably go into a rabbit hole of a breakdown of these humanoid.

You know, they do those breakdowns on YouTube and such and kind of dig into that. But that would be worth exploring. And for anyone thinking about the crowdfunding stuff, I would, especially if you're a newer, if you're a newer investor, I would be extraordinarily cautious of getting into any investment where you don't have liquidity, where you're not able to take your investment in one day and pull it out the other day where you have things like lockup periods. because liquidity is a lot like oxygen in the investing world. You don't recognize how important it is until you really, really need it.

There's a lot of use cases for humanoid that some of them are scary. Some of them are fairly common, like lifting things in a factory. Are there any sort of less expected use cases that you're going to be watching as this technology develops? Well, a couple ones. I'm actually very, very intrigued by elderly care. You know, I have, you know, parents are, they're getting older. I know friends that are parents getting older. Many times they live at home alone.

Their kids might be, you know, living very far away. So they have a lot of trouble doing basic stuff. So that will be an interesting area where humanoid can kind of play a part. And I always say, too, just household tasks. I think I don't go a day now doing dishes and laundry where I think, isn't there a humanoid or robot that can do this for me? Because it's pretty, you know, low-risk kind of stuff that, you know, once you train them up and get them going,

they should be able to do. We need a Rosie the robot from Jetsons. I'm okay with a robot crushing a couple plates if it means I don't have to do dishes. That's great. This is probably the, this is going to be the best out for anyone who doesn't want to do the dishes. That's a job for the humanoid's now. Samito, appreciate your time and your insight. Thanks for looking into this technology. We're going to keep talking about it on the show. Thanks, Reggie. As always, people on the program may have interest 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 Mary Long. Thanks for listening. We'll see you tomorrow.