Investors Shoot for the Moon

In this episode of Motley Fool Hidden Gems Investing, Motley Fool contributors 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.

To catch full episodes of all The Motley Fool's free podcasts, check out our podcast center. When you're ready to invest, check out this top 10 list of stocks to buy.

A full transcript is below.

This podcast was recorded on Oct. 19, 2026.

Jon Quast: 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, Jon 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 Quantinuum, 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 Quantinuum, 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? Then why does quantum computing even need error correction in the first place?

Tyler Crowe: Well, first off, Jon, thanks for really tossing me some softballs here to start with the show. Let's start with quantum computing, because 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 it are going to shout at me. But I'm going to try to get through this so most everyone can understand. Qubits are basically like the bits and bytes that we know on our classical 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, and we have various ways of actually doing quantum computing, but all the methods basically manipulate known quantum properties like superposition and entanglement. 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 moving from physical to logical, to your question, physical qubits, again, manipulating the quantum properties of known atoms, things like that.

The logical is translating these manipulations. Using the information across many physical qubits, sometimes hundreds, thousands, sometimes even millions, to detect when any single physical qubit contains errors. It's a way of self-correcting for a very, very complex and honestly a delicate system. These systems have to be held at extremely low temperatures or in strong electromagnetic fields or anything like that. Any single physical qubit can sometimes act up. The idea being that we use logical qubits to make sure to self-correct for anything that any single physical qubit is doing wrong. Now, this brings us to the Quantinuum story, 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 1 million operations, and that's about 10 times better than the record that Quantinuum had set only a month ago. I think the big news and why actually the stock is moving quite a bit today because of it is because this is a major leap forward, and it's happening over a relatively short amount of time.

Jon Quast: Tyler, I'm all for you reading to your kids about quantum computing. I just hope that Schrödinger’s cat isn't in the book. That might damage some littles.

Tyler Crowe: No, Schrödinger's Cat, although a couple other ones that might be a little challenging for a 3-year-old.

Jon Quast: Matt, let's turn to you and talk about some Google news here. 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. Once you use up one of these things, they are gone. That's 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. Google developing a different strategy here, cultivation: growing these from scratch and pruning out bad ones and using the good ones, but potentially a more efficient way to make them. The problem is, since they are single-use, the quantum computer is actually using 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. Doing it more efficiently sounds good, but what else do we need to know from this Google release?

Matt Frankel: 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. The quantum technology and terminology is going to take some getting used to, for sure. I love you use words like magic and fuzzy, and 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. 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.

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. Here's why it matters. If magic states are going to eat up most of your hardware's capacity, then making them cheaper and easier shrinks the number of qubits 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 through the computers that the three of us are using right now. Most error correction headlines, like Quantinuum’s, 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.

Jon Quast: 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. 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. 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. Then you have a pure play such as Quantinuum. 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.

Matt Frankel: Yeah, so in full disclosure, I own IBM for the exact reason that you're asking this question because I like a call option on quantum instead of a pure play. With quantum, we've moved from what I would consider a physics problem to an engineering problem, a 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. With plays like Quantinuum, you're essentially paying for 2030 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.

Tyler Crowe: Most of the time, I would mostly say I would want to go with the large business has got, as you said, a call option. I think the thing that makes Quantinuum 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 dissolution most recently. This is something that Honeywell was working on for about 10 years within its Honeywell ecosystem. It's not like some fly by night still trying to raise capital. This is something that been working on for decades. There's part of me that says, at least with this particular one, Quantinuum, I trust it a little bit more because it's been baking in the oven so much longer than some of the other recent start-ups or IPOs that we've seen in the space.

Jon Quast: It looks like Tyler may be favoring the moonshot option here a little bit more than Matt. Incidentally, that's exactly what we're talking about when we come back. You're listening to Motley Fool Hidden Gems Investing.

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Jon Quast: 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. Since the beginning of 2024, venture capital funds have spent more than $150 billion on what's called Deep Tech. This is unproven technology with unproven business models at this point. Yet these capital funds are willing to pour more and more money into these things. In fact, the money that has gone into it since 2024 has exceeded everything that was invested in the 2010s decade.

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. 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. I just wanted to pose this to our guests today, Matt and Tyler: if this is where venture capital is pouring money, do you think this is a good idea for the regular Joe investor as well? What areas are you looking for a breakthrough in that gets you excited to imagine a future?

Matt Frankel: Oh, man, there's a lot to unpack there. First of all, to put some context toward 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. A lot of it is not necessarily that VC firms want to make more of these big deals, more moonshot investments. A lot of them are just bigger to make a meaningful investment in pre-IPO. SpaceX in 2024 was expensive. That's a lot of where the big number comes from. But, I mean, you asked if it's still good for everyday investors. 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 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. That's the moonshot industry that I'm most likely to be making bets in.

Jon Quast: Tyler, what do you think about novel energy? Because you're our energy guy, and that is one of the things that people are talking about a lot, specifically nuclear fusion, as opposed to fission. What do you think of this whole trend?

Tyler Crowe: Dubious is probably too strong of a word here. It's one of the things that I've 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 maybe 10 years from now when these moonshot, power-generating techniques are commercially viable, we're even going to see greater efficiencies to solar. I'm wondering 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. I think the VCs are really throwing all their money at them now because they 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 got a lot of easy hits.

But now, with software on the wane, a lot with AI coming, and frankly, to move the needle, as Matt said, there are much bigger entities now they have to take these big swings on things like moonshots. That is for investors, a challenging thing because they may be pouring into these ideas, but these are massive $100 billion VCs venture funds that are they're not going to necessarily break the bank if any one of these moonshots actually make or doesn't make it because they've spread themselves out so thin. If people are excited by these, and I'm certainly as 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 these are companies with hundreds of bets across many different companies. Trying to, like, pick that single winner is extremely challenging.

Jon Quast: Is there a moonshot space that you like better than Novel Power?

Tyler Crowe: I'm a little bit more intrigued by this electric vertical takeoff and landing, airborne mobility, autonomous driving space. 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 things. I just I don't think anyone's developed a viable economic model for it yet. 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.

Jon Quast: 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.

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Jon Quast: Welcome back to Motley Fool. Hidden Gems Investing. A quick note. We want to make you part of the conversation. 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 podcast@fool.com. This question is from John in New Jersey, basically asking one, he says, I'm investor in the MAG seven, 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. Since they're making money on AI right now, their earnings should not be overly impacted. If their PEs are solid, why should they take a big hit? 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 bond earnings. That is about the market average or perhaps even a little bit cheaper than the market average. I guess the question here that he's asking, one, I asked 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? 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? Tyler, I'm going to let you go first here.

Tyler Crowe: Sure. I really love this question because it’s been the open-source model-specific aspect of it is something that’s been a bit of an intrusive thought for me while I’ve been thinking about AI and the AI infrastructure build-out, 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 wrong path with looking at the Alphabet, Meta, Nvidia companies. What I'm thinking is that these open-source models could be a major headache for a lot of companies. But less concerned with the ones mentioned, I'm thinking more along the ones who are tied more to Anthropic and OpenAI and their build-outs, because 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 track.

If they become 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 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, OpenAI, or some other. In that regard, good direction, maybe just slightly misfiring on the specific companies.

Jon Quast: Matt, what do you think about open source?

Matt Frankel: 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. 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, that 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. That's where the bust risk is. It's not necessarily open source, but it's the tech companies that are like Google, Microsoft, Amazon, that are investing hundreds of billions of dollars, being able to get a reasonable payback on their AI spending. If the pricing models are disrupted, then that might not be the case anymore, and you might see a slowdown.

Jon Quast: To the question here, that is what the risk is as far as the price-to-earnings ratio. 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. 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. The open source is a risk out there that we're monitoring. That was good commentary from Tyler and Matt, 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. Throw 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. 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.

Jon Quast has no position in any of the stocks mentioned. Matt Frankel, CFP® has positions in Amazon and International Business Machines and has the following options: long January 2029 $340 calls on International Business Machines and short January 2029 $370 calls on International Business Machines. Tyler Crowe has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Alphabet, Amazon, Honeywell Technologies, International Business Machines, Meta Platforms, Microsoft, and Nvidia. The Motley Fool has a disclosure policy.