Motley Fool Money - Investors Shoot for the Moon

Episode Date: October 19, 2026

Description: The quantum computing space needs to make progress on error correction before quantum computers are useful, and in the past week, both Alphabet and Quantinuum have made announcements regarding substantial progress in this area. Jon, Matt, and Tyler break down this news before turning to the subject of moonshot investing. The team then closes out the episode with a question from the mailbag regarding the risk to big tech companies from open-source AI models. Jon Quast, Matt Frankel, and Tyler Crowe discuss:-Quantinuum’s record-breaking error correction-Alphabet’s own quantum computing magic-Novel power solutions-The potential for electric vertical takeoff and landing (eVTOL)-The risk of open source AI for big tech-Why today’s valuation doesn’t predict tomorrow’s profits Companies discussed: Quantinuum (QNT), Alphabet (GOOG)(GOOGL), Microsoft (MSFT), Amazon (AMZN), Nvidia (NVDA), Meta Platforms (META) Host: Jon QuastGuests: Matt Frankel, Tyler CroweEngineer: Bart Shannon Disclosure: Advertisements are sponsored content and provided for informational purposes only. The Motley Fool and its affiliates (collectively, “TMF”) do not endorse, recommend, or verify the accuracy or completeness of the statements made within advertisements. TMF is not involved in the offer, sale, or solicitation of any securities advertised herein and makes no representations regarding the suitability, or risks associated with any investment opportunity presented. Investors should conduct their own due diligence and consult with legal, tax, and financial advisors before making any investment decisions. TMF assumes no responsibility for any losses or damages arising from this advertisement. We’re committed to transparency: All personal opinions in advertisements from Fools are their own. The product advertised in this episode was loaned to TMF and was returned after a test period or the product advertised in this episode was purchased by TMF. Advertiser has paid for the sponsorship of this episode. Learn more about your ad choices. Visit megaphone.fm/adchoices Learn more about your ad choices. Visit megaphone.fm/adchoices

Investors are shooting for the moon. Motley Fool Hidden Gems Investing starts now. Welcome to Motley Fool Hidden Gems Investing. I'm your host today, John Quast, and I'm joined by Foolish contributors, Tyler Crowe, and Matt Frankel. We've got several things on the docket today, including some moonshots and a question from our mailbag.

But first up, we want to talk about quantum computing. On the Hidden Gems team, we are tracking several investing trends that are going to be very important for the future, including this one of quantum computing. We are considering the top companies. We're developing scoring systems around them, even portfolios. The first bit of news that we have comes from Alphabet,

releasing a press release this past week, and then we also have something from Quantinium. And we're going to break this down into two. Tyler, I want to start with you. I'm going to throw you some questions here on Quantinium, and what it is saying is a record-breaking error correction. But a couple of questions here for our listeners. basically, can you talk about the differences between logical and physical qubits, and then why

does quantum computing even need error correction in the first place? Well, first off, John, thanks for really tossing me some softballs here to start with the show. Let's start with quantum computing, because, you know, it's not exactly the simplest topic, and I am going to grossly oversimplify this. I know there's going to be a lot of people who know a lot more about the subject, some technical experts probably listening to that they're going to shout at me. but I'm going to try to get through this so most everyone can understand. Cubits are kind of basically like the bits and bytes that we know on our classical

computing computers. It's the way that we store the information. Physical ones are the method in which we store in physical space. All forms of quantum computing do it in that we have various ways of actually doing quantum computing, but all the methods basically manipulate known quantum properties like superposition and entanglement. And I'm very glad that we do this on video now because I get to show you the deep research that I've been doing this for years.

It's the book I've been reading my children, quantum entanglement for babies. I'm sure that a lot of people are really going to know a lot more after they read up on that. But, you know, moving from physical to logical to your question, physical cubits, again, manipulating the quantum properties of known atoms, things like that. The logical is it's translating these manipulations. using the information across many physical cubits, sometimes hundreds, thousands, sometimes even millions, to detect when any single physical cubit contains errors.

It's kind of a way of self-correcting for a very, very complex and honestly a delicate sort of system. These systems have to be held at extremely low temperatures or in strong electromagnetic fields or anything like that. And so any single physical cubit, can sometimes act up. And so the idea being that we use logical qubits to kind of make sure to self-correct

for anything that any single physical cubit is doing wrong. Now, this brings us kind of to the continuum theory where what they have announced is that they have the lowest error rate or in the business. It's called logical fidelity. Basically, it's saying that they are detecting about five errors over one million operations.

And that's about 10 times better than the record that Quantinium had set only a month ago. So I think the big news and why actually the stock is moving quite a day because of it is because this is like a major leap forward and it's happening over a relatively short amount of time. Tyler, I'm all for you reading to your kids about quantum computing. I just hope that Schrodinger's cat isn't in the book. That might damage some little. No, no Schrodinger's cat. Although, uh, I think, couple other ones that might be a little challenging for a three-year-old. Matt, let's turn to you and talk about some Google news here.

So in the press release it had last week, it said it's working on something called Magic State Cultivation, reducing errors by 40X. It sounds weird to talk about Magic States when we're talking about an investing podcast. Let me just say this. Magic States basically, you need them to do powerful operations in quantum computing. They're single use. So once you use up one of these things, they are gone. And that's kind of the problem because you need millions of them.

And up to this point, we're using a method called distillation, basically taking a bunch of fuzzy magic states and distilling them into one clear usable state. And Google developing a different strategy here, cultivation, kind of growing these from scratch and pruning out bad ones and using the good ones, potentially a more efficient way to make them. The problem is, since they are single use,

the quantum computer is actually using computers, compute power to make them. And most of the power is going just to make these magic states leaving little for the actual operation it's trying to perform. So doing it more efficiently sounds good. But what else do we need to know from this Google release? Well, I mean, first of all, I will say to Tyler, he did a really good job explaining. I have a physics degree and I'm not sure I could do that much better of a job explaining quantum computing to the average person. So the quantum technology and terminology is going to take some getting used to for sure. I love to use words like magic and fuzzy in that lead in right there.

But in plain English, building an error corrected quantum computer is only one part of producing superior performance to traditional computers. To unlock the real potential, you need these things called magic states. And like you said, the problem is these are single use and making clean ones is not an easy process. Google itself has estimated that as much of 95% of a quantum computer's capacity is going to end up simply making these. So the new process called cultivation is a working solution to this problem. It's the first working solution that's not just in theory. And Google says that it produces magic states at 99.99% fidelity and with about 40 times fewer errors than without cultivation. So here's why it matters.

So if magic states are going to eat up most of your hardwomen, whereas capacity, then making them cheaper and easier shrinks the number of cubits that are needed to produce useful work, which useful work is really what we're going for. We're not just looking for error-tolerant. We're not just looking for a computer that produces magic states cheaply. We're looking for a quantum computer that outperforms what currently exists for the computers that the three of us are using right now. So most error correction headlines, like quantumums, are about storing information in an error-free way. This is about the step that involves the computer doing actual useful calculations

and will get it over to where it's a superior level of performance. And I feel like I need to point out here that both companies are saying that this is more testing that it's doing. This isn't actually scaled up into something that could be used on a grand scale. So that still has to be tested out. However, it does seem like the space is moving in the right direction and moving there quite quickly. And so I just want to pose this question to each of you. You have on one end of the spectrum here, you have Google, which is a large business in many areas,

and yet it has this quantum branch. And then you have a pure play such as quantinium. Which one would you prefer to invest in? The pure play with potentially a lot of upside or the diversified business that is more of a sure thing. Matt, you go first. Yeah, so in full disclosure, I own IBM for the exact reason that you're asking this question, because I like kind of a call option on quantum instead of a peer play. So with quantum, we've moved from what I would consider a physics problem to an engineering problem,

manufacturing problem, really. It's a better problem to have. We're still years away, even with what Tyler was talking about with quantum, we're still several orders of magnitude away from where we need to be on error tolerance. we need these systems to be a whole lot bigger and more capable. So with plays like quantum, you're essentially paying for 20-30 sales today, whereas with a company like Alphabet,

you're paying for a fantastic business that just happens to have a really strong call option in quantum computing if it works out. You know, most of the time, I would mostly say I would want to go with the, you know, a large business has got, as you said, a call option. I think the thing that makes, quantinium a little bit more unique than, say, like some of the most pure places, this is actually

one that was carved out of Honeywell's kind of dissolution most recently. And so this is something that Honeywell was working on for about 10 years within its Honeywell ecosystem. So it's not like some fly-by-night still trying to raise capital. This is something that's been working on for decades. So there's part of me that says, at least with this particular one, Quantinium, I trust it a little bit more because it's been baking in the oven so much longer than some of the other recent startups or IPOs that we've seen in the space. It looks like Tyler may be favoring the moonshot option here a little bit more than Matt. And incidentally, that's exactly what we're talking about.

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Get your first year of PC Express Pass for just $2.50 a month. Learn more at nofrills.ca. Welcome back to Motley Fool Hidden Gems Investing. In this segment, we're going to talk about some moonshots because a group called Deal Room has done some number crunching. So since the beginning of 2024, venture capital firms has spent more than 150 billion dollars on what's called deep tech. This is unproven technology with unproven business models at this point. And yet these capital funds are willing to pour more and more money into

these things. And in fact, the money that has gone into it since 2024 has exceeded everything that was invested in the 2010's decade. And what they're saying is different this time is that AI is allowing these technology companies to more simulate what they can do, experiment faster, perhaps even develop that path to monetization that ramps up faster than what would otherwise be possible. And so this is an interesting thing here. We did talk about quantum computing in the previous segment, which definitely plays into this, but there are a lot of developing deep tech spaces out there.

You have things such as data centers in space. You have things like novel power solutions, health breakthroughs, all sorts of things. And I just wanted to pose this to our guest today, Matt and Tyler, if this is where venture capital is pouring money, do you think that this is a good idea for the regular Joe investor as well? And what sort of areas are you looking for a breakthrough and that gets you excited to imagine a future? Oh, man, there's a lot to unpack there. So, you know, first of all, to put some context where those numbers, you were talking about with $150 billion since the start of 2024.

Trillion dollar moonshots were not a thing in that decade ending 2019. So a lot of it, it's not necessarily that VC firms want to make more of these big deals, more moonshot investments. A lot of them are just bigger. To, you know, make a meaningful investment in free IPO SpaceX in 2024 was expensive. So that's a lot of where the big number comes from. But I mean, you ask if it's still good for, you know, everyday investors. And the short answer is yes.

But one thing I would caution before getting into any of these, say, moonshot industries is that pre-revenue and pre-customer are two totally different things. There are a lot of companies I like in these industries that look like moonshots, but they have signed contracts. They have government funding to develop their technology, like some of the quantum computing companies we mentioned. make sure there's enough runway, meaning money in the bank, to outlast the next milestone without dilution. But, I mean, the best setup, honestly, for everyday investors is usually

a real revenue business that has a moonshot attached, like we talked about with alphabet and quantum. If I were to name one of the moonshot industries that you mentioned that I'm particularly interested, it's probably novel power. Electricity is literally the biggest bottleneck for pretty much everything else that you mentioned, as well as just AI data centers in general, quantum computing, and a few other moonshot technologies as well. You know, you can't do any of those unless you have enough power. Earlier this year, even Microsoft said it has about $80 billion in unfulfilled Azure orders simply because it can't get enough power yet. So that's the moonshot industry that I'm most likely to be making bets. Tyler, what do you think about novel energy?

Because you're kind of our energy guy, and that is one of the things that people are talking talking about a lot, specifically nuclear fusion as opposed to fission. What do you think of this whole trend? I'm a little, I don't, dubious is probably too strong of a word here. It's one of the things that I've seen, you know, there's a lot of discussion about fusion, small modular nuclear reactors, a lot of these novel concepts with power. I think one of the challenges that we're actually finding is that renewable energy is still moving so rapidly down the cost curve. that it's going to be, you know, maybe 10 years from now when these moonshot novel power generating techniques

are commercially viable, we're even going to see greater efficiencies that solar. I'm wondering that if they can even compete economically by the time that actually runs around. I think it's funny, too, that we're talking about all of these moonshots today because all the funding is going. All these moonshots existed in the 2010s.

think the VCs are really throwing all their money at them now because they kind of squeezed all the juice out of the software ideas that they hit in 2010 and those were easy to fund. They were easy to scale. You know, got a lot of easy hits. But now with, you know, software on the way in a lot with AI coming and frankly, to move the needle, as Matt said, they're much bigger entities now. They have to take these big swings on things like moonshots. And that is for investors a challenging thing because these are, they're, they're,

They may be pouring these into these ideas, but these are massive $100 billion VC's venture funds that are, you know, they're not going to necessarily break the bank if any one of these moonshots actually make or, you know, doesn't make it because they've spread themselves out so thin. So if people are excited by these, and I'm certainly, there's lots of people are, they are exciting topics. You just have to like, from an allocation standpoint, tap the brakes a little bit. Don't get too excited because, you know, these are companies with hundreds of bets across many different companies. Trying to, like, pick that single winner is extremely challenging. Is there a moonshot space that you like better than novel power? You know, I'm a little bit more intrigued by this electric vertical takeoff and landing,

airborne mobility, autonomous driving space. You know, maybe I watched too many episodes of the Jetsons growing up or something like that. But I think a lot of the current offerings in this space are probably like the Netscape navigators and ask Jeeves of their respective industries. I'm really excited by the concepts and the topics and the things. I just, I don't think anyone's developed a viable economic model for it yet. And so I really want to keep watching this industry, but I'm not really sure I'm ready to like throw a lot of capital at any of them as an investor.

One of the things I'm intrigued by is if there's 150 billion in funding that's gone in there and probably more in the coming years. Where is that trickling down to real world businesses behind that? I think that's more of the fun Hidden Gems thing that we'll be happy to unpack in the coming weeks and months. But when we come back, we're going to be dipping into the mailbag. You're listening to Motley Fool Hidden Gems investing. Unlock unlimited free grocery delivery with PC Express Pass. Shop online and get the same low, no frills prices delivered. Get your first year of PC Express Pass for just two $0.50 a month.

Learn more at no-frails.ca. Welcome aboard via rail. Please sit and enjoy. Please sit and sip. Play. Post. Taste. View.

And enjoy. Via rail. Love the way. Welcome back to Motley Fool, Hidden Jems Investing. A quick note, we want to make you part of the conversation. So if you have a short, foolish question that does not include personalized investing advice,

We'd love to take it, and you can send that into Podcasts at Fool.com. Podcasts at Fool.com. And this question is from John in New Jersey, basically asking, one, he says, I'm investor in the Mag 7, but I'm also fearful the AI boom might turn into more of a bust. If open source models become the primary source for AI, will companies, such as Google, Microsoft, and Amazon, see a steep decline? their price to earnings ratio, and this is a valuation metric, they don't suggest that they're outrageous,

and since they're making money on AI right now, their earnings should not be overly impacted. So if their PEs are solid, why should they take a big hit? And so I see really two questions here in what John is asking. First of all, to his point, Alphabet, Meta, Microsoft, and Nvidia, they all trade between 23 times and 27 times forward earnings.

And so that is about the market average or perhaps even a little bit cheaper than the market average. So I guess the question here that he's asking, one, and I ask to you, is there legitimacy to John's fear? Could open source models really impact the earnings of some of the biggest players in the AI infrastructure trade? And secondly, the question about valuation, if the valuation looks reasonable right now, then why should anyone be worried about a big decline in some of these so-called cheap names? And Tyler, I'm going to let you go first here. Sure. You know, I really love this question because it's been an, the open source model specific aspect of it is something that's been a bit of an intrusive thought for me while I have been thinking about AI and the AI infrastructure

buildout especially. I think John from New Jersey here, I think he's headed in the right compass direction with his thinking, but maybe a little bit on the warm path with looking at the alphabet, meta, invidia kind of companies. What I'm thinking is that these open source models could be a major headache for a lot of companies, but less concerns with the ones mentioned. I'm thinking more along the ones who are tied more to Anthropic and Open AI and their buildouts because, you know, open source models, they also tend to be the ones that are from the Chinese companies, and I think Mistral AI in Europe is a little bit more on the open source sort of track. If they become the sufficiently good, but at much cheaper prices, going towards these,

they're still going to need compute power. They're still going to probably need cloud computing unless we go to some even more distant future where we go back to on-premises infrastructure. I don't know if that's a thing yet, but that's been, I've thought about it. In that regard, I'm less concerned about these ones because they're going to be the cloud infrastructure providers for a lot of the compute power necessary for these open source models, regardless if it's anthropic, open AI, or some other. So in that regard, good direction, maybe just slightly misfiring on the specific companies. Matt, what do you think about open source? Yeah, I mean, I would agree with what Tyler said,

that Google, Microsoft, and Amazon, they're mostly landlords when it comes to the AI trade. They provide the compute power. The open source models still run in their clouds. So that's a net positive for those companies in particular. But I mean, open source can be a headache, as Tyler said. I mean, Stanford's latest AI index, the gap between the best open source models and the best closed models is about a 3% effectiveness rate.

It's really not a big difference these days. But enterprise customers are for now still paying for closed models. It makes up roughly 90% of enterprise production in AI. Big companies are willing to pay for reliability, security, and support. Where open source is the real threat, and it's not to these three companies, is pricing power. And that's where the bust risk is. It's not necessarily open source, but it's the tech companies that are, you know, like Google, Microsoft, Amazon, that are investing hundreds of billions of dollars, being able to get a reasonable payback on their AI spending.

and if the pricing models are disrupted, then that might not be the case anymore and you might see a slowdown. And to the question here, that is what the risk is as far as the price to earnings ratio. So let's say that you have a company that's valued at $100 million and it's generating $10 million in profits. If the pricing power goes away, the profits go down to say $2 million, it was trading at 10 times profits. Now it's trading at 50 times because the earnings went way down. And so that is where the price to earnings ratio, if it looks reasonable now, that doesn't necessarily tell you what's happening in the future. It does matter how that pricing holds up. And so the open source is a risk out there that we're monitoring.

And that was good commentary from Tyler and Matt just don't put everything on that price to earnings ratio. 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. but don't buy yourself 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. See our full advertising disclosure. Please check out our show notes. Thanks again to our producer Bart Shannon and the rest of the Motley Fool team behind the glass. For Matt, Tyler, and myself, thank you so much for listening to our show today and we will see you again next time.