A $13 Billion Bet on Robots?

In this episode of Motley Fool Hidden Gems Investing, Motley Fool contributors Jon Quast, Matt Frankel, and Rachel Warren discuss:

• Nvidia's potential acquisition of Hugging Face.
• Hugging Face's new robot: Microduck.
• What is an investment thesis?
• Things that break an investment thesis.
• Things that make an investment thesis.

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A full transcript is below.

This podcast was recorded on Aug. 31, 2026.

Jon Quast: 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 Jon Quast. 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. I think It's pretty big. Let's talk about the robot called the Microduck. Now, Microduck, if you ever imagined a Furbee 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. What is so interesting about this is that it is made in collaboration with Hugging Face. Now, Hugging Face is a company that Nvidia is reportedly looking to acquire for about 13 billion. 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?

Rachel Warren: Hugging Face is essentially one of those central town squares of the AI world. 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 OpenAI models, data sets, software libraries. 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 a proprietary wall. Now, you've got the closed-source tech giants that are keeping their AI lock behind proprietary APIs, but Hugging Face, it's 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? Some of this I've already explained. This obviously is a compelling element to add to Nvidia's wheelhouse, and $13 billion is essentially a drop in the bucket for Nvidia. But it comes at a time where the likes of Open AI and Alphabet are aggressively developing their own customer I 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.CPP. That would allow AI models to run locally on consumer devices. There's a lot of interesting things at play here. One final thing all know, the estimates vary, but I've seen analysts saying that Nvidia would be paying anywhere from 86-129 times current revenue for Hugging Face should this deal go through. They are willing to pay a premium to lock in what they seem to view as a very captive customer base.

Jon Quast: Certainly, $13 billion is a lot of money to you and I, but to NVIDIA, it can afford to pay 100 times its revenue and not really notice it at this point. But it would seem like Hugging Face is a way that Nvidia is trying to keep the AI model companies like OpenAI, 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 keep the competition there from the open source to keep the frontier more-gated models from becoming too powerful?

Rachel Warren: Yeah, I think that's right. I think 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. Because this is such a go-to 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 important for Nvidia long term. Also locking in that ecosystem into their broader network. I think Nvidia would also have an entirely new captive customer base that can default their hardware, their software ecosystem. There's a lot of winds that could come for Nvidia out of this.

Jon Quast: 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 Microduck is different about from other consumer robots that I've seen is it allows more personalization. It's users, the customers, the owners of these robots are going to be able to train it, to teach things. These things are going to kind of take on a little bit of personality over time and so that really 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.

Matt Frankel: When I was younger, we used to camp out at Best Buy for the latest Playstation. Now I feel like Jon's going to be camping out for the Microduck with his kids. Times have changed for sure. It's an impressive device. I feel like Nvidia is making a bet making this acquisition. Nvidia 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. I'm not sure I'm going to get in line to spend $399 for it, but it's a need advancement, you can train behaviors and simulation. You can 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 clear. This is a data play more than anything, as thousands and thousands of these working in different households, it's going to really create an excellent dataset for locomotion and manipulation that Nvidia will eventually own if this acquisition goes through. I see this really as a data play. Not that Nvidia wants to own the Microduck, but they want to own the data behind it.

Jon Quast: Rachel, walk us through. What is Microduck. What do we get out of the box?

Rachel Warren: Microduck, I would say it's a milestone for AI hardware. It was actually developed by a start-up called Pollen Robotics, and Hugging Face acquired Pollen Robotics last year. Microduck is this very tiny, 25 centimeter 800 gram 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. Look up a photo of this. It's interesting. But essentially, its purpose is to give everyday developers, as well as students, an affordable way to test real-world AI software. 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 machine. Because Microduck's code and simulation environments are completely open source, 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 and students, as I mentioned. Hugging Face has said that Microduck has already blown past $2.6 million in preorders when it had its first 24 hours of launch. That's over 6,500 units in a single day. A lot of excitement here.

Jon Quast: Now it's time for my way too early over the top take here. 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 adopted. Now I'm looking at this, saying, Are they really pushing into the robotics? Some of the companies that we follow strongly, Tesla comes to mind. A large part of the investment thesis is the growth and domination of its future optimist robots. 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 that a threat at all to the thesis of these more robotic companies?

Rachel Warren: Yeah, I think that the future of robotics is definitely going to feature an open source faction. 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 source as 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, 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. Proprietary ecosystems, a lot of these closed-source systems. There's also some bottlenecks 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, in fact, a very messy and unpredictable real world. I do think if Nvidia is able to successfully acquire this company and push forward that open source robotics framework, I think we could potentially see some shift in the landscape, but also, this means that a lot of the smaller start-ups, 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. Then there's a lot of downstream benefits for Nvidia. I think this can all maybe seem a bit confusing to noncreators, 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.

Matt Frankel: I agree with Rachel, for the most part, even though I feel like I'm the only one of the three of us who's not going to go buy 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. Nvidia 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 has a lot to do. I want to point out some important context about the maturity of what's going on here. The only limitation of crossbody is the majority of things are like pick and place, like Jon mentioned the beak that can pick things up and move it to another place. This is not complex multistep manipulation yet. Right now, it's a million demonstrations of a duck picking a block up off a table. There's still a lot of research advancement that would need to take place before this gets to the point where Nvidia 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 Microducks.

Jon Quast: Yeah, we'll see you 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.

Welcome back to Motley Fool Hidden Gems Investing. We're going to double-dip into the mailbag 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. Matt, give us the quick rundown on this term.

Matt Frankel: A lot of people don't know what the term means, or they have the wrong definition of what it means. It's definitely worth clarifying. In simple terms, an investment 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. An investment thesis should answer three questions. One, what needs to happen for me to be right? No. 2, when do I expect that to happen, and No. 3, what would prove me wrong? That's my general overview of what an investment thesis is, but there's a whole lot more to it than that.

Jon Quast: Well, let's get into this question here from Robbie. I'm not going to read it word for word, but I am going to just run down. Robbie says that they have a stock portfolio of roughly 40-50 stocks, and they have an investment thesis before buying. 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/bar case scenarios? What disconfirming evidence should I watch for?

Now, Robbie, we're going to get to the first three things there. That's more of like what for the stock that I'm buying and then the second two seem to be more like, what not do I like about this stock or what is it going against this stock? 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, 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. In other words, you need to understand the bear argument better than the bears do before you can have a bull thought on a stock. I really like that, and I feel like that goes right along with the spirit of Charlie Munger.

Matt, you're up first here. What do you think about Robbie's process here, and what do you have to say about how we incorporate that bear thinking into our thesis?

Matt Frankel: 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. That's similar idea here. First, just to give some credit where it's due. That five question process is better than how some investment professionals, I know, structure their investment thesis. Pat on the back right there. I'd reframe that Question 5 about disconfirming evidence just a little bit. You're not looking for just bad news. You're not looking for a bad quarterly earnings report, for example. You want to identify specific observable things that would make you sell. Fo

r example, if Google Cloud Alphabet 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. As far as a sustainable cadence is concerned, use your Question 3 reasons for future growth when you're doing this. For each reason for future growth, identify a specific trackable metric, like your specific reasons for selling. If part of my thesis is that a company's operating 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 tripwire, as I would call it would be if operating expenses grew faster than revenue for more than two consecutive quarters. That allows for a natural quarterly cadence. You don't have to obsess about your stocks every day every week or anything like that. It's a very specific observable point to track how your thesis is playing out.

Rachel Warren: I think that those are all really good points. Charlie Munger felt so strongly about this, and I love that quote you referenced, Jon, because I think he knew that our brains as humans, as investors, were naturally itching to be right. That can make us vulnerable to confirmation bias. When you fall in love with a company's story, you can 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. It forces you to think like an inspector looking for damage.

This is something that I incorporate into my own investment approach. 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. 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. That really helps me take an honest assessment of the risks that a particular business presents. There's a lot of ways to practice this without being a professional analyst without getting overwhelmed. I personally like to take a look at the risk factor section of a company's annual 10-K filing. It's not particularly exciting, and there's ways to use AI to help filter through some of those and summarize that information. But it's really informative and be a seemingly dry part of a company's financial reports, but that's what tells you what keeps management up at night.

Then I also like to go and seek any bearish research I can find from independent analysts. 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. I think that treating your thesis as a living document and really taking an honest look at both the Bar 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.

Jon Quast: Robbie, 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. Good for you on that, and we're going to answer the rest of your question in the next segment as we answer another question from the mailbag. You're listening to the Motley Fool Hidden Gems Investing.

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Jon Quast: Welcome back to Motley Fool Hidden Gems Investing. A quick note. We want to make you a part of the conversation, just like we're doing right now. If you have a stock or an investment question 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. 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 Nawen 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. 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. They kept it at a small position at 1% of the portfolio, and now this listener, Nawen is asking, basically, what did I do here? Did I have a risky gamble and when? Did I get lucky? Or should I bought more because I really thought this through?

A really interesting question here, and Matt, I'm going to let you speak to this first. Did they do the right thing here?

Matt Frankel: Did they get lucky with a gamble or were they correct on an investment thesis? 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 speculative companies is the right move. The question of “should I have bought more” isn't the right way to look at it.

Let's say I buy a speculative position because I think it could be a 100X investment. Let's say it ends up being 100X, but because I limit my position size, I didn't make that much. Nextdoor, Ticker’s NXDR is 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 cent stock. If it was $230 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. 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.

Rachel Warren: I would have called this an example of, you know, gambling in the stock market. You say you bought a business with zero debt, positive cash flows, sticky customer base at a low valuation. I think that sounds like a compelling value oriented investment. When a company is sound but maybe discounted or ignored by the markets because it came out of a 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. 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. I think by keeping that position small, you allowed yourself to participate in the upside without exposing your portfolio to more risk.

If it was me, obviously, we can't give personal investment advice, but if I was looking in my portfolio, and I had a range of other small caps that represent small allocations for my capital. Instead of maybe wishing you'd bought more this winter, maybe look at them for the same lens. Do they have those same quality balance sheets, the sticky customer traits? 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.

Jon Quast: Let's circle back to what we were talking about in the previous segment regarding the investment thesis from Robbie. He had three things that he said that he was looking at for developing his own investment thesis. Now that we are talking about this, one of the things I would like to point out here from the question with Nawen 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. Just

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, what would your first couple of questions be when you're trying to evaluate whether or not to take a position in a stock?

Rachel Warren: Valuation metrics are very important, but to me, they're just really the starting line. It tells you what a business is worth today. Choose your preferred valuation metric, but not necessarily what it can become tomorrow. 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 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. 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, like operating margins, revenue growth, cash flows. If you only look at valuation multipliers, it can actually be easy to fall into a value trap. These are very important.

Look at those metrics, but it's really important to remember, but 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. 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.

Matt Frankel: It's a good list. I would also caution that not everything Rachel just said applies to every stock. That's really important to know, as well. What I mean by that is, I'll just name the last two stocks I bought in my own portfolio. One was cybersecurity company ZScaler. The other was a real estate investment trust called Realty Income that I added to my position in. With a growth story like ZScaler, the qualitative definitely comes more into play. I want to know what the growth tailwinds in AI cybersecurity are. I want to know what their mode is. I want to know what their management strategy is. With an established, slow and steady compound or like realty income, I'm more about the metrics. It's really about what stock you're investing in, what industry you're looking at, what growth trends you want to capture. It's a great list, but the short answer is, it depends on the stock.

Jon Quast: I'm going to get the final word in here. I want to quote the great author Morgan Housel, because we are talking about putting in the analytical effort beforehand when we buy stock, and we really want to mind our Ps and Qs and really make good decisions. At the same time, there is risk inherent in investing because we don't know the future. 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 effort. 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, 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. To see our fool advertising disclosure, please check out our show notes. Thanks to our producer Kristi Waterworth behind the glass, the rest of The 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.

Jon Quast has no position in any of the stocks mentioned. Matt Frankel, CFP® has positions in Nextdoor, Realty Income, and Zscaler. Rachel Warren has positions in Alphabet. The Motley Fool has positions in and recommends Alphabet, Nvidia, Realty Income, Tesla, and Zscaler. The Motley Fool has a disclosure policy.