A (4:42)

No, I think that that's a fair read asit and one of the things that we don't get from the survey is how much exposure already exists. We talk to people who Say they already have exposure, but in terms of a total portfolio, that exposure could be smaller than what somebody may want to allocate. So the intention to add may just be actually building out what would then be a full size position to exposure to AI. However that's defined in 2026. But I also think, and this is maybe the irrational hope of mine, that anybody who answers this survey and says that they're planning on adding or maintaining their AI exposure is doing so with the awareness that I'm going to hold these companies for the extreme long term. So, yeah, maybe this is a bubble, maybe there are risks and we do have a crash. But that's okay because the companies I'm invested in have very real appreciable cash flow. And I believe that a decade from now, even if there is a short pullback in share price of a company going to be bigger, better, more important businesses in the future. Maybe I'm giving too much credit here, but I'm sure that investor. Yeah, that's, that's where I hope we're going. Donato, I want to pass the mic to you because obviously you're the head of AI here at the Motley fool. And one of the things that I really liked about the report was that the optimism that ASIT just mentioned and we talked about it wasn't totally blind in this report when they asked about risks. The top two risks from respondents were things like data quality security as well as a sense of overvaluation in the sector. You are somebody who already spends all of your days living inside the world of AI.

B (6:04)

Obviously.

A (6:04)

Obviously, when you see this investor confidence shown in the report, is that matched by what you're seeing in terms of like real world adoption? Or is Wall street still early to the party?

C (6:14)

So the short answer is that I think it matches and we are currently in a healthier place compared to just six or nine months ago. Because at the beginning of 2025, as many others have started worrying about, is it a bubble? But the main indicator I monitor is pretty simple. So are people's expectations connected to how the technology actually works? Because when the expectations disconnect from the fundamentals, that's when you get a bubble, right? So early last year I saw this starting to go sideways because people were getting more and more excited about agents, which are LLMs that are able to perform more complex actions, go beyond just answering questions such as booking your flight or creating an act or managing your calendar. So many people started calling 2025 the year of Agents. But I like Andree Kapati's framing better, which is that this is a decade of agents because they are just getting started and this is an emerging technology. But at the time, the expectations were running way ahead of reality and people were imagining these autonomous entities that could do everything and run your business alone. So yes, agents work and they are at the new disruptive technology that for now proved effective in very narrow scopes and controlled environments. Last year I observed this gap between expectations and reality. But then two things happened. So first, the sentiment cooled down a bit. The hype around agents got more measured. I think, in fact, we are starting to get a little bit past peak hype because people are getting more realistic now about what agents can do, but who knows what's going to happen tomorrow, right? And the second thing is that the most important part is that the agents actually improved dramatically. The technology really caught up with some of these expectations. Not all of them, but I'd say enough that this gap narrowed. So, yeah, I say that we're in a healthier place. I like this direction. The markets have reached new highs last year, but over the past couple of months we've been very flat. And I think that's okay to give people and companies more time to pay, to experiment. And when we look at actual adoption data in companies, it confers in this direction. So the paid AI adoption across US businesses increased a lot from 2025, 2023. It was just around 5% to 44% in September 2025. And if we look at revenue growth in AI companies, Anthropic reported 10x on their revenue two years in a row. Cursor, the AI coding tool, is a similar situation. Went for 4 million last year to hitting a billion analyzed revenue this year. So I said it is not hype. There is real, commercial, commercial structuring these tools and real adoption companies. People are finding real value in these tools. So when you ask Emily if the investor confidence is matched by adoption, I say yes. And the data shows that companies are finding real value.

A (9:15)

It's almost ironic that we talk about AI as a bubble today when I think the skepticism around AI is probably the highest it's ever been. Unlike bubbles in the past, I think we as investors have a new level of awareness of the things like the hype cycle. And it's when, to your point, when things get separate, right? When, when hype separates from reality, that's when it creates a bubble. But to your point, there is reality backing up a lot of this technology. And I love the fact that the survey shows that investors are still largely leaning in to the AI and adoption, but there's still that awareness, that cautious amount of optimism. Up next, we're going to be getting practical about 2026, including where the next wave of opportunities may show up. Stick with us.

D (9:56)

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A (10:51)

Welcome back to Motley Fool Money. Today we're discussing the AI investor outlook report for 2026 and where AI technology may be headed. Donata, as head of AI here at the Fool, I'd love dig in a little bit deeper to where you see AI going. Now, you said in this report that the right mental model is somewhere like three to five years in terms of a timeframe for investors in the sector and that investors shouldn't get too caught up in things like the present day cost of LLMs since the intelligence per dollar ratio for models has been doubling roughly every six months. That goes over my head. So when I, when I hear that coming out of your mouth, I mean, can you provide some more context as to what exactly that means and if AI capabilities keep improving at that same pace where you expect value to accrue in the year ahead?

C (11:34)

Yeah, that's exactly right. So currently there's a lot of focus on the AGI. This is the big question, well, will AI become super intelligent? I think this is not always the right questions question for investors because it's really impossible to predict that. But I say that the more impactful, important question right now is that when does current level intelligence become cheap enough to be everywhere? And I think this is happening right now. So if we take a look at how costs evolved over the years, just two years ago, GPT4, which was the flagship model by OpenAI, available at the time, cost 30 to $60 per million tokens. A million token is around three, four books. And so you need 30, $60 to process this amount of information. But today you have available GPT5 mini, which is a way better model, just cost $2. So the models got around 15 to 30 times cheaper for more intelligence in just two years. That's what we call the intelligence per dollar curve. And watching that curve as one of the most important indicators, if we take a look at also how many tokens the companies are processing, Google reported that they're processing a quadrillion tokens per month. That's a crazy number. So it's a 5x increase year over year. So people are deploying this token at scale. And so how is it possible the cost falling so fast is coming from multiple directions. So first we have algorithmic improvements. There are new reinforcement learning and training techniques like GRPO by Deep SEQ OR RLVR by OpenAI. It can get you better result for less compute. You have more different architectures like mixture of experts that can just turn on a portion of your model instead of paying for the whole model. And we have smarter thinking models that can think adaptively based on the difficulty of the query. So the thing is, we don't even know how to use the intelligence we already have right now. And most companies are really still experimenting to figure out what AI can do and how to deploy it. So I say that the bottlenecker now is not that AI is not smart enough, but really the cause is that what can transform every industry.

A (13:56)

And it's really easy for investors to forget about that cost curve, how quickly it can change. We saw it change over the past two years. You can think about how different it'll be in 2028 and we can circle back and have this conversation about the cost of models then. And I think the fundamentals and the impact that it has on a lot of the companies that are, say, building data centers or using the compute will look fundamentally different. Donato, before we move on though, I do want to also ask how we can apply your technical expertise to an investing framework for our listener. You've helped lead the charge with AI changes here at the Fool. If an investor is looking to evaluate the investments and performance of other companies as it relates to their AI ambitions and capital expenditures, what do you think they should be looking for?

C (14:39)

Yeah, that's a great question. I think right now we're in a phase where companies are just throwing AI at everything to see what sticks. And honestly I think that's, that's pretty healthy because that's how you figure out what works, right? You experiment. You don't have all the answers from the beginning. You have to just take risk, see how the products evolve. Some fail and some succeed. But I think this phase won't last forever. So eventually the experimentation phase ends and you need real results in the company. So when a company announces any initiative or a significant AI spending, I'd want to ask a few questions. So first, is this solving a real problem? It sounds obvious, but you'll be surprised about how often the answer is no. So is AI addressing an actual business problem? And I give you a simple test, can this problem be solved without AI? And sometimes the answer is yes. So simpler is better. And if company is having an AI announcement just to have an AI announcement, I'd be skeptical. So the second is, is this actually in production in front of users or is just a demo or pilot? Because right now companies can still get headlines for a demo. Startups can raise lots of money on a good prototype. But I'd argue that this window is slowly closing because everyone has a demo at this point. But the hardest part is to bring the demo to production in front of real users and scaling the app and making it secure. So that's what I want to see. I'll add another one, which is the data advantage. So are they building on proprietary data or just plugging in generic tools? Because the models themselves are becoming more and more interchangeable. You can use GPT, Gemini, Cloth, Groq, they're all great and they all have different strengths, but we all have access to the same models. So what's not a commodity is your customer data, your years of refinement and a B testing to figure out what your customer want, domain expertise, and so on. So I believe the companies that we get real value from AI are the ones using it on data that their competitors cannot access. Because if I can do the same thing, chatgpt, what would I pay for their product? So the differentiation lies in the data and in the specific company context. So to recap, the first, is AI solving real problems. Second, is it a prototype or is it in production? And third, does it use proprietary data and assets or just generic tools that others can easily reproduce? And I believe that the best AI investments sometimes just look boring. Is the company that may be quite using AI internally to make their people 20% more productive. Those companies compound on the long term. And if you have a long term mindset. I think that's where the real value.

A (17:20)

Is and I hope everybody listening does have that long term mindset. What I love about your response Donato is it's so incredibly measured. You're the head of AI here at the fool and it's easy for people to say, well we're in a bubble. Anybody who operates in the space of AI is probably over enthused over investing, over indexing, overhyping. But the reality is is that the way that you speak about what you look for in an AI investments, hopefully exactly the same thing that our listeners look for. It's something practical and purposeful and to your last point, there maybe something that looks a little boring. So don't be afraid of adding boring to your portfolio and asit not to put you on the spot, but I think you might have some let maybe boring. We'll see stock ideas and proof points I guess ahead for what businesses may perform well in AI in the year ahead. So up next we're going to be passing that mic to Asset to evaluate these investment opportunities as well as some risk management strategies for portfolios. Stick with us.

D (18:15)

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A (19:25)

To Motley Fool Money. As we wrap up today's show centered around our AI investing outlook for 2026 report, I want to pull off it into the conversation to get a better sense of specific opportunities and some risk management strategies. Asa, you made a really specific point in the report that I want to mention because it highlights something unique other than like the same big tech names that everybody already knows. You said, and I quote, for the biggest opportunities, look to smaller semiconductor and data center ecosystem players such as data interconnect specialists, high bandwidth memory providers and cutting edge data storage designers. That is also a mouthful, but I think that's a really fairly unique perspective and I kind of want you to translate that into something specific for me. Are these businesses or stocks that fit that description without those, or are those just like AI vaporware?

B (20:10)

Yeah, such a great question, Emily. And I would argue that for all of these, they're really. The concept is simple. In the first case, I'm describing companies that help sling data around faster within data centers. When I talk about data center interconnect specialists, I'm going to name some names here and these really aren't meant to be. Hey, these are my high conviction buys. Go out and load up the truck. But more types like, types of companies you can start researching, understand they all come with risks. So the first example is Astera Lab symbol A L, A B. This is a company that simply helps different components within a server talk to one another with lower latency much more quickly. And this is the type of boring thing that Donato talks about, maybe on the inference side. So how AI is helping companies. Also for those that play in this ecosystem, they're, they're doing really simple stuff at a high level, at a complex level. So the second thing that you mentioned, which you were referring to our survey companies that are helping businesses like Nvidia, symbol NVDA manage memory within GPUs. That's a persistent bottleneck at that level of computation. And so we talked about high bandwidth memory or HBM providers. An example of this is Micron Technology, symbol mu. This is a business that for a long time played in a very boring space of the memory market. But lo and behold, it has very good technology to help sling data around a GPU faster than existing methods. So they're seeing some love in the marketplace. And thirdly, these cutting edge storage designers, these are businesses that are building specialized memory storage that are used within AI data centers. Your computer, my computer need memories to operate. Actually my brain needs memory to operate. That's why I try to sleep at least seven hours a day. It's not so much different within a data center. So these information workloads that move around, you need storage drives for those. And that's a commodity business. But there are a handful of companies that are sort of at the bleeding edge. At the end of the day, what they're doing is making storage that's faster to access, it's very configurable and it provides a lower total cost of ownership over the life of that component to the operator of the data center. So pure storage symbol pstg Emily, that's a company you and I have both studied. It's a great example of a business of this type. So there's a common thread running through all of these essentially, if investors understand the inherent value of an Nvidia or Advanced Micro Devices chief competitor to Nvidia, to the AI story, I think. And in some ways much of the value is sort of priced in these businesses have had a great run and investors are naturally looking now to suppliers within the spectrum of the value chain that exists between your keyboard where you input a query and your screen where you get the response back from ChatGPT. So what happens in between? It's not all about the GPU makers. Yes, valuations are elevated. They feel sort of dangerous to me right now. So let's make sure we're clear about risk here. Since we commented on these high bandwidth memory providers in the survey, those have been under this acute supply chain shortage. So since we mentioned, they've really run up even more. So I hesitate even to talk about a micron, but there you have it. Be careful out there. Anyway, overall I think there's going to be many investable opportunities outside of the GPU builders or the cloud hyperscalers that make up up the rest of big tech. Think Amazon, amzn, Microsoft, MSFT or Alphabet G O O G over the next few years. And that's why we want to think in holistic terms about a whole industry that's being built up.

A (24:07)

I think that's a wonderfully measured approach and I love the fact that you mentioned the risk associated with a lot of these names. Interesting companies across the different value proposition of AI. And the last question I want to pose to you asit before we sign off here is around that risk. Like when you're building your own AI investing framework, do you have any rules, things, position sizing, time horizon, milestones, anything like that?

B (24:28)

Sure, I have some rules. My first personal rule is to stay invested in the AI leaders. I own many of the companies I just mentioned, but especially those bigger names like Nvidia, amd. Avoid concentrating in any single idea asit. You've done that before, earlier in your investing career in tech companies. It didn't work out. Now, speaking of concentrations, another personal rule, when I'M assessing ecosystem players. I try not to shy away from customer concentrations for very specialized suppliers. It sounds counterintuitive, like why would you buy a company that only has a few businesses, even though they're gigantic businesses as customers? Well, there's an example in a business like Arista Network symbol A N E T, which for a long time was highly concentrated in those cloud hyperscalers like Amazon and Microsoft. But it grew well over the years and it's a little more diversified now. You're going to see this time and again in this infrastructure because supply chains are limited and specialists abound. There are fewer players that have enough skill and technology to serve everyone, so their supply is getting snapped up by just a few players. But I position size accordingly because there are so many concentrations. If I enter a new position of a company that I've been interested in, it comes in somewhere at a half percent or percent of my total performance portfolio, even sometimes a little bit less. And then finally, personal rule for this year. Drill down into sectors and industries that are outside of my own core expertise. Look at last year, Emily Construction companies with expertise in like building these complex mechanical, electrical and plumbing systems for data centers. They just had a stellar year. It was outside of my wheelhouse. I really didn't pay attention until it was a bit too late. But I learned the lesson. The breadth of the AI trade and the opportunity both are very wide. But you have to be willing to turn over some new stones to benefit I think in 2026 and beyond.

A (26:27)

I love that it's a bit of curiosity, but also the all importance patience for investors. I know. After our conversation today, it's clear to me that investors still have an appetite for AI. I hope that's clear to everybody. But they're also naming a lot of really key risks that are worth considering when managing investments and portfolios, both for 2026 as well as obviously the many years us of which I hope everybody is staying invested for. As a reminder, anybody who wants to read more can always Access the Motley Fool's 2026 AI Investor Outlook Report@fool.com research AI Investor Outlook Again, don't have to memorize that. That link will be in the show. Notes Donata and Asit. Thank you both so much for joining today. As always, people on the program may have interest in the stocks they talk about, and the Motley fool may have formal recommendations for or against. So don't buy or sell stocks based solely on what you hear. All personal finance content follows the Motley fool editorial standards and is not approved by advertisers. Advertisements are sponsored content and provided for informational purposes only. To see our full advertising disclosure, please check out our show notes. For ASAD Sharma, Donato Riccio and the entire Motley Fool Money team. I'm Emily Flippin. We'll see you tomorrow.