Motley Fool Money - A $13 Billion Bet on Robots?

Starting point is 00:00:01 We're going to talk about a $13 billion bet in robotics. Motley Fool Hidden Gems Investing starts now. Welcome to Motley Fool Hidden Gems Investing. My name is John Kwas. I'm your host today. I'm joined by Foolish contributors Matt Frankel and Rachel Warren. We're going to get to a couple of questions from our mailbag today, talking about the bullish scenarios for buying a stock and also the bearish things that you need to consider. But first, I wanted to talk about this story.

Starting point is 00:00:34 I think it's pretty big. Let's talk about the robot called the micro duck. Now, micro duck, if you ever imagined a Furby having a child with the Pixar lamp, that's what I think this robot duck looks like. It is a cyclops duck that walks around your house. It quacks, it sings. And what is so interesting about this is that it is made in collaboration with Hugging Face. Now, Hugging Face is a company that Invidia is.

Starting point is 00:01:06 reportedly looking to acquire for about $13 billion. So perhaps let's start there. Rachel, we're going to go to you here. What is Hugging Face? And why would the $5 trillion company known as Nvidia want to buy it for $13 billion? Hugging Face is essentially one of those central town squares of the AI world.

Starting point is 00:01:26 It's a cloud repository where you have millions of developers and researchers that collaborate. And they use this space to share, test, and host these open source, open way, AI models, data sets, software libraries. And if you're not familiar with the term open source, maybe you've heard it, you're not sure what it means. It essentially just means that the underlying code is completely public. Anyone can modify it for free instead of being locked behind, you know, proprietary wall.

Starting point is 00:01:53 Now, you've got the closed source tech giants that are keeping their AI lock behind proprietary APIs. But Hugging Face, it's sort of one of those democratizing platforms in this space. It's where you're seeing a lot of the foundational innovations or the latest open source large language models live. Now, why would Nvidia want this? I mean, some of this I've already explained. This obviously is a compelling element to add to Nvidia's wheelhouse. And, you know, $13 billion is essentially a drop in the bucket for Nvidia.

Starting point is 00:02:22 But it comes at a time where the likes of OpenEI and Alphabet are, you know, aggressively developing their own customy eye chips to break their dependency on Nvidia. by buying Hugging Face, Nvidia would gain control over a really vital distribution layer where the entire global developer community meets. They would also take over the team behind an open source software library known as llama.cp. That would allow AI models to run locally on consumer devices.

Starting point is 00:02:50 So there's a lot of interesting things that play here. One final thing I'll note, the estimates vary, but I've seen analysts saying that Invidia would be paying anywhere from 86 to 129 times current revenue for Hugging Face should this deal go through. So they are willing to pay a premium to lock in what they say into view as a very captive customer base. Certainly $13 billion is a lot of money to you and I, but to Invida, it can afford to pay 100 times its revenue and not really notice it at this point.

Starting point is 00:03:20 But it would seem like Hugging Face is a way that Nvidia is trying to keep the AI model companies like Open AI, for example, trying to keep them from becoming maybe too powerful, because if they become too powerful, then they start going into Custom Silicon and then perhaps become a stronger competitor. What do you think about that read on it? Is that what they're trying to do here? Just kind of keep the competition there from the open source to keep the frontier more gated models from becoming too powerful. Yeah, I think that's right. I think that that's one element of this for sure. I think there's a lot of reasons why this would be a smart acquisition for Nvidia. And because this is such a go-to

Starting point is 00:04:00 platform and space where you have all of this open source work from developers and researchers around the world, it's a data gold mine essentially. But I think it would also give them an edge in the open source space that they currently lack. It's interesting because obviously Hugging Face is not a household name. I think it might be easy for investors to overlook this news that we're talking about today. But I do think it's actually something that should this deal go through would be really, really important for Invidia long term. And also, you know, locking in that ecosystem into their broader network, I think Invidia would also have an entirely new captive customer base that can default to their hardware, their computer ecosystem.

Starting point is 00:04:41 There's a lot of wins that could come for Nvidia out of this. Okay, so let's turn to Microduck here because I think this is the interesting part of this news. when you look at what Hugging Face has done so far is regarding open source, it's more in the large language models or the LLMs. Now this is more in the robotic area. And what Microduc is different about from other consumer robots that I've seen is it allows more personalization. Its users, the customers, the owners of these robots are going to be able to train it,

Starting point is 00:05:11 to teach it things. And these things are going to kind of take on a little bit of personality over time. And so that really kind of creates this open source robotic operating system, if you will, from Hugging Face. And so I just want to get your thoughts here. My kids think that this robot looks pretty cool. When I was younger, we used to camp out at Best Buy for the latest PlayStation. Now I feel like John's going to be camping out for the micro duck with his kids. Times have changed for sure.

Starting point is 00:05:34 It's an impressive device. I feel like Nvidia is kind of making a bet, making this acquisition. And Vida has such financial flexibility. They can make bets with $13 billion. This is like, just to name. a company that I follow very closely. This is like if PayPal spent like $70 million on an acquisition. It's not a rounding error, but it's not a big chunk of money. So I'm not sure I'm going to get in line to spend $399 for it, but it's a neat advancement. You can train behaviors in simulation. You can

Starting point is 00:06:02 then deploy them to the physical robot. It's a cool teaching platform for physical AI. It's not really a household appliance or anything particularly useful at this point. This is not the optimist robot, to be very, very clear. This is a data play more than anything. thousands and thousands of these working in different households, it's going to really create an excellent data set for locomotion and manipulation that video will eventually own if this acquisition goes through. So I see this really as a data play. Not that Invidia wants to own the micro duck,

Starting point is 00:06:31 but they want to own the data behind it. All right, Rachel, walk us through. What is micro duck? What do we get out of the box? Microduck, I would say it's a milestone for AI hardware. So it was actually developed by a startup called Pollen Robotics and Hugging Face Acquired. at Pollen Robotics last year. So microduck is this very tiny, you know, 25 centimeter, 800 gram

Starting point is 00:06:51 bipedal duck. It's a $399 playground, essentially, for physical AI. It has over a dozen motors. There's a camera, miniature LiDAR. It has an articulated beak that acts as a gripper. You know, look up a photo of this. It's kind of interesting. But essentially, its purpose is to give everyday developers, as well as students, an affordable way to test real world AI software. So it really bridges that gap between digital code and reality. Users can train a digital twin in a simulation environment. They can then deploy custom behaviors like roller skating or playing soccer directly onto that machine. And because microducks code and simulation environments are completely open source,

Starting point is 00:07:33 basically the consumer can become a creator. This is incredible for professionals that are using this, but also just for individuals that are experimenting here in students, as I mentioned. So HuggingFace has said that MicroDuck has already blown past $2.6 million in pre-orders when it had its first 24 hours of launch. That's over 6,500 units in a single day. So a lot of excitement here. All right. Now it's time for my way too early over-the-top take here.

Starting point is 00:08:02 I want to get your opinions because if I look at Nvidia, definitely there's an incentive to keep the open source large language models growing and adopt. adopted. Now I'm looking at this saying, are they really pushing into the robotics? And some of the companies that we follow strongly, Tesla comes to mind. A large part of the investment thesis is the growth and kind of domination of its future optimist robots. And then you also have companies such as figure looking perhaps for an IPO in the future. This is also a robotics company. These would be more locked ecosystems that they're trying to create. Is Nvidia trying to keep that open source? And is it? Is that a threat at all to the theses of these more robotic companies? Yeah, I mean, I think that the future of robotics is definitely going to feature an open source faction. And I don't think this has to be a winner-takes-all environment. I think that there will probably be a healthy mixture of open-source and closed sources. We look into the robotics landscape over the next decade and beyond. The reason I think the open-source piece is so important is solving physical AI.

Starting point is 00:09:06 It's too complex for a single company to do alone. And I don't think you need to have a single company do it alone to have a really interesting space. full of wonderful opportunities, both for customers and for investors. And proprietary ecosystems, a lot of these closed source systems, there's also some bottlenecks that that can create in terms of general purpose learning, data aggregation. And having that open source foundation, obviously it democratizes the AI stack, but it also pulls real world data from thousands of diverse environments. It could really accelerate how quickly the machines learn when it comes to navigating what is, you know, in fact, a very messy and unpredictable real world. So I do think,

Starting point is 00:09:43 if Nvidia is able to successfully acquire this company and push forward that open source robotics framework. I think we could potentially see some kind of shift in the landscape, but also this means that a lot of the smaller startups, academic research labs, independent hardware builders, could potentially gain access to a lot of these state-of-the-art models. And that could really drive the industry forward as a whole. And then there's a lot of downstream benefits for Nvidia. So I think this can all maybe seem a bit confusing to non-creators, but I personally think this is a fascinating acquisition to watch if you have Nvidia in your portfolio on your stock watch list. Keep an eye on what happens with this. I agree with Rachel for the most part,

Starting point is 00:10:20 even though I feel like I'm the only one of the three of us who's not going to go by the microduck. What she's describing is known as cross-embodiment learning. That's the official term for this. It's already happening at scale at Hugging Face. Vidia is not paying $13 billion for a model library or for the physical hardware. They're paying to own the place where the world's robotic data is stored. The open source is a lot to do with it. I want to point out some important context about the maturity of what's going on here. I mean, the own limitation of crossbody is the majority of things are like pick in place. Like John mentioned the beak that can pick things up and move it to another place. This is not complex multi-step manipulation yet. Right now,

Starting point is 00:10:55 it's a million demonstrations of a duck picking a block up off the table. There's still a lot of research advancement that would need to take place before this gets to the point where Biddy is hoping it to get to. But it's definitely a really interesting step in the right direction. And I look forward to seeing what both of you do with your microdux. Yeah, we'll see about that in the future. After the break, we're going to dip into our mailbag and talk about the work that goes into buying a stock. You're listening to Motley Fool Hidden Gems Investing. Two and five Canadians will hear the words, you have cancer. That's why every step and dollar raised matters. On September 19th, join thousands in Toronto for the Princess Margaret Cancer

Starting point is 00:11:38 Foundation walk. Challenge yourself, friends, and family to walk 21 kilometers in support of life-saving research. Together, we can carry the fire and help create a world free from the fear of cancer. Register today at pmcf walk.ca.ca. Welcome back to Motley Fool Hidden Gems Investing. We're going to double dip into the mailback today, and up first, we got a question, and it really revolves around this concept of an investment thesis. Before we read the question, I don't want to take for granted that everyone knows what an investment thesis is. So Matt, give us the quick rundown on this term. Yeah, a lot of people, you know, don't know what the term means or they have the wrong definition of what it means. So it's definitely worth clarifying. So in simple terms, an investment

Starting point is 00:12:24 thesis is your reasoning behind why a particular business or stock might be worth more at a later date than it is today. That's the simple one sentence description of it. It needs to be specific enough that you would know pretty quickly if you were wrong. So an investment thesis should answer three questions. One, what needs to happen for me to be right? Number two, when do I expect that to happen? And number three, what would prove me wrong? So that's kind of my general overview of what an investment thesis is. But there's a whole lot more to it than that. All right, well, let's get into this question here from Ravi. I'm not going to read it word for word, but I am going to just kind of run down. Robbie says that they have a stock portfolio of roughly 40 to 50 stocks, and they have an

Starting point is 00:13:10 investment thesis before buying. And they have five key questions that they ask themselves here. The first one is why buy this business? Why now? Reasons for future growth. What are the major risks slash bear case scenarios? What disconfirming evidence should I watch for? Now, Ravi, we're going to get to the first three things there. That's more of like what for the stock that I'm and then the second two seem to be more like what not do I like about the stock or what is it going against this stock. So let's focus on those bearish ones first here. What are the major risks, bear case scenarios, and what disconfirming evidence should I watch for? The reason that I like this is that is something that's often overlooked. A Charlie Munger quote comes to mind. He said,

Starting point is 00:13:55 I'm not entitled to have an opinion on this subject unless I can state the arguments against my position better than the people do who are supporting it. So other words, you need to understand the bear argument better than the bears do before you can have a bullish thought on a stock. So I really like that. And I feel like that goes right along with the spirit of Charlie Munger. And so, Matt, you're up first here. What do you think about Ravi's process here? And what do you have to say about how we incorporate that bear thinking into our thesis? Yeah, I love that Munger quote. And I've said before that if you haven't tried to talk yourself out of an investment, you shouldn't buy a stock. So that's kind of similar idea here. So first, just to give some credit where it's due, that five question

Starting point is 00:14:35 process is better than how some investment professionals I know structure their investment thesis. So pat on the back right there, I'd reframe that question five about disconfirming evidence just a little bit. You're not looking for just bad news. You're not looking for a bad quarterly earnage report, for example. You want to identify specific observable things that would make you sell. For example, if Google Cloud, Alphabet's a stock that I have in my portfolio, if their cloud revenue were to unexpectedly decelerate, specific and measurable. Or if it started to lose market share to AWS and Azure, specific and measurable, it could prompt me to sell the stock as cloud growth is a very big part of my investment thesis. So as far as a sustainable cadence is concerned,

Starting point is 00:15:16 like use your question three, reasons for future growth when you're doing this. So for each reason for future growth, identify a specific trackable metric, kind of like your specific reasons for selling. If part of my thesis is that a company's operating with, leverage will improve as it scales, meaning that it's going to get more efficient over time, I might track operating expense growth versus revenue growth. And my trip wire, as I would call it, would be if operating expenses grew faster than revenue for more than two consecutive quarters. So that allows for a natural quarterly cadence. You don't have to obsess about your stocks every day, every week, anything like that. And it's a very specific observable point to track how your

Starting point is 00:15:55 thesis is playing out. Yeah, I think that those are all really good points. I mean, Charlie Munger falls so strongly about this, and I love that quote you referenced, John, because I think he knew that our brains as humans, as investors, we're naturally itching to be right. And that can make us vulnerable to confirmation bias. You know, when you fall in love with the company's story, you can sort of subconsciously filter out bad news and maybe actually only focus on the positive metrics that make you feel good about holding the stock. It's a natural inclination. But I think Munger's rule, there's some psychological inversion that comes out of that because it stops you from acting like a cheerleader for your own portfolio.

Starting point is 00:16:30 you to think like an inspector looking for damage. And this is something that I incorporate into my own investment approach. You know, when I am approaching an investment thesis before buying a stock, or even when reviewing holdings in my portfolio, for me personally, I don't consider that thesis complete until I can either physically or at least in my mind write a convincing one-page short report, if you will, on my own position. So in my mind, I'm thinking, if I can't state a short seller's argument with total clarity, maybe I don't fully understand the risks I'm taking or the business I'm buying. And that kind of really helps me take an honest assessment of the risks that a particular business presents. And, you know, there's a lot of ways to practice this without being

Starting point is 00:17:09 a professional analyst, you know, without getting overwhelmed. I personally like to take a look at the risk factor section of a company's annual 10K filing. It's not particularly exciting. And, you know, there's ways to use AI to help kind of filter through some of those and summarize that information. But it's really informative and maybe a seemingly dry part of a company's financial reports, but that's what tells you what keeps management up at night. And then I also like to go and seek any bearish research I can find from independent analysts. It doesn't necessarily change my decision to buy a stock in the end, but it helps me have a much more holistic picture of the business I'm buying and one that I hope to hold for a long time. So I think that treating your

Starting point is 00:17:48 thesis is a living document and really taking an honest look at both the bear and the bullish sides. That can help you succeed in the long run, but it can also help you catch maybe a failing business before the rest of the market does. Yeah. Ravi, we love this question. We love your frame of mind. And also, I just want to point out 40 to 50 stocks in the portfolio, that does a great job of mitigating a lot of risk in and of itself right there, just that diversification. So good for you on that. And we're going to answer the rest of your question in the next segment

Starting point is 00:18:19 as we answer another question from the mailbag. You're listening to Motley Fool Hidden Jems investing. Two and five Canadians will hear the word. you have cancer. That's why every step and dollar raised matters. On September 19th, join thousands in Toronto for the Princess Margaret Cancer Foundation Walk. Challenge yourself, friends, and family to walk 21 kilometers in support of life-saving research. Together, we can carry the fire and help create a world free from the fear of cancer. Register today at pmcfwalk.ca.ca. Welcome back to Motley Fool Hidden Gems Investing. A quick note. We want to make

Starting point is 00:19:03 you a part of the conversation, just like we're doing right now. So if you have a stock or an investment questioning for anyone on this show, send it into podcast at fool.com. Try to keep it as short as you can. Try to keep it foolish. And remember, we don't give out personal advice. But if you can meet all those, then send them in to podcast at fool.com, podcast at fool.com. And so here we go again. Now, I'm not going to read this question word for word either, but essentially a writer by the name of Nauin writing in and saying that they bought a stock and they were able to sell for a profit after it was acquired. So taken private. Now, they list out some valuation metrics that they liked when they bought the stock in particular. It was a low forward price to earnings multiple.

Starting point is 00:19:48 It also had no debt. This particular company, positive cash flow, all these things, but they're aware of the risks as well. It was a small cap company. It had its IPO through a special purpose acquisition company. Matt and I can tell you a lot about that from 2021. So they kept it in a small position at 1% of the portfolio. And now this listener, Nguyen, is asking, basically, what did I do here? Did I have a risky gamble and win? Did I get lucky? Or should I have bought more because I really thought this through? So a really interesting question here. And Matt, I'm going to let you speak to this first. Did they do the right thing here? So did they get lucky with a gamble or were they correct on an investment thesis?

Starting point is 00:20:28 My short answer is both. I'd say lucky in the sense that the investment return didn't come from the investment thesis. It came from a go-private transaction. It's like when a company in my portfolio gets acquired, I didn't see it coming, but I'll gladly take it. Sometimes it's better to be lucky than correct. I think keeping position sizes small with all these small kind of speculative companies is the right move. The question of should I have bought more isn't the right way to look at it. So let's say I buy a speculative position because I think it could be a 100x investment. Let's say it ends up being 100. but because I limit my position size, you know, I didn't make that much. Nextdoor tickers

Starting point is 00:21:06 NXDR as an example of a stock I view this way in my own portfolio. If it ends up being a home run, it's right now like a $2.30 stock. If it was $2.30 in a few years, the small amount that I own will still be enough to produce really financial life-changing returns in my portfolio. If it goes to zero, that small position ensures that my overall portfolio performance won't be decided just by one stock going to zero. So the small position is the right way to go. It's not that your investment thesis is wrong, but it's a speculative investment by nature. That's really how I would think about it. I would have called this an example of a gambling in the stock market. I mean, you say you bought a business with zero debt, positive cash flows, sticky customer base at a low valuation. I think that sounds

Starting point is 00:21:52 like a compelling value-oriented investment. And, you know, when a company is sound, but maybe discounted or ignored by the markets because it came out of the back or sits in a more liquid corner of the market, it often becomes a prime acquisition target. I personally think keeping that position under 1% was also wise in terms of risk management. You know, when you're dealing with these types of businesses, there's a lot of tail risks that can be quite high. And that means essentially things can go wrong through no fault of your own. And so I think by keeping that position small, you allowed yourself to participate in the upside without exposing your portfolio to more risk. And, you know, if it was me, obviously, we can't give personal investment advice, but if I was

Starting point is 00:22:31 looking in my portfolio and I had a range of other small caps that represented small allocations for my capital, instead of maybe wishing you'd bought more of this winter, maybe look at them for the same lens. Do they have those same quality balance sheets? It's a sticky customer trades. I think it's important to, if you have that core underlying thesis, you know, revisit it from time to time, but trust your asset allocation process and importantly, keep those positions sized where you can sleep peacefully at night. So let's circle back to what we were talking about in the previous segment regarding investment thesis from Ravi. He had three things that he said that he was looking at for developing his own investment thesis. And now that we are talking about this, one of the things I

Starting point is 00:23:12 would like to point out here from the question with Nguyen, there are some favorable valuation metrics that are cited, a clean balance sheet, and things like that. But nothing really compelling as far as what I was looking for in the business that got. me excited and made me think that it was going to be a good stock to buy over the long term. And so just kind of circling back as we are talking about building out our own reasons to buy a stock or reason that we think that it's going to increase in value over the long term, I want to ask both of you, what is important for you individually in building an investment thesis? Kind of like what would your first couple of questions be when you're trying to evaluate whether or not to take

Starting point is 00:23:50 a position in a stock? Valuation metrics are very important, but to me they're just really the starting line, right? It tells you what a business is worth today, you know, choose your preferred valuation metric, but not necessarily what it can become tomorrow. And I think a truly compelling bullish thesis, at least in my view, it also has to be rooted in more qualitative factors, secular tailwinds, a widening competitive moat, quality management with skin in the game. I want to know that a company is riding a wave that will carry it forward for the next decade. And I often look for those signs that they can defend their market share. That often shows up in high pricing power, customer retention metrics, a lot of obviously specific growth numbers on the balance sheet,

Starting point is 00:24:32 like operating margins, revenue growth, cash flows. You know, if you only look at valuation multipliers, it can actually be easy to fall into a value trap. These are very, very important. Look at those metrics, but it's really important to remember that sometimes a company might look cheap because, in fact, it is undervalued. But in some cases, it might look cheap because you have a business model that has fractured. So for me personally, I also look at businesses that can efficiently reinvest their own cash back into the business at high rates of return, how a management team allocates their capital, whether they're expanding their total addressable market and then profitably growing the business. These are all factors that I bake into a thesis. That's a good list. I would also caution that

Starting point is 00:25:12 not everything Rachel just said applies to every stock. That's really important to know as well. And what I mean by that is I'll just name the last two stocks I've bought my own portfolio. One was cybersecurity company Z-scaler. The other was a real estate investment trust called Realty Income that I added to my positioning. With a growth story like Z-scaler, the qualitative definitely comes more into play. I want to know what the growth tailwinds in AI cybersecurity are.

Starting point is 00:25:34 I want to know what their mode is. I want to know what their management strategy is. With a established slow and steady compounder like realty income, I'm more about the metrics. So it's really about what stock you're investing in, what kind of industry you're looking at, what kind of growth trends you want to capture. It's a great list, but the short answer is it depends on the stock. Well, I'm going to get the final word in here. I want to quote the great author, Morgan Housel,

Starting point is 00:26:00 because we are talking about putting in the analytical effort beforehand when we buy a stock, and we really want to mind our P's and Q's and really make good decisions. At the same time, there is risk inherent in investing because we don't know the future. And so there's always talk about luck and there's always talk about risk. And Morgan Housel writes, luck and risk are siblings. They are both a reality that every outcome in life is guided by forces other than individual efforts. So when we're right, let's not get too high because there was a little bit of luck that went into that. And when we get things wrong, let's not get too down on ourselves because we don't know the future perfectly.

Starting point is 00:26:35 And the important thing is to stay in the game over the long term. As always, people in 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. All personal finance content follows Motley Fool editorial standards and is not approved by advertisers. Advertisements are sponsored content and provided for informational purposes only.

Starting point is 00:26:58 To see our full advertising disclosure, please check out our show notes. Thanks for our producer, Christy Waterworth, behind the glass, the rest of Motley Fool team. For Matt, Rachel and myself, thank you so much for listening to our show today, and we will see you again next time.

Motley Fool Money - A $13 Billion Bet on Robots?

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