How AI Will Transform These Stocks in 2026

Speaker A: Welcome back to Opto Sessions. In this special episode, you'll hear highlights from some of the best conversations we've had this year on one of the biggest forces reshaping markets right now, artificial intelligence. We'll explore how AI is actually showing up in products, cost bases and competitive dynamics and what that could mean for the stocks on your radar. To kick things off, we're starting with the near term, how AI could impact these businesses over the next year and what retail investors might want to watch for. You'll hear from companies operating in lending, customer service, voice, AI and automation. Let's get into it.

Speaker B: If I'm a borrower, um, if I work 9 to 5, I can't use a user interface. Um, I want to do a, ah, voice call. I can just use voice. If I have questions that I want to use chat, I can use chat. Um, soon you'll be able to speak to one of our avatars in the middle of the night. You're nervous about your mortgage application. You want to have a FaceTime call with an expert mortgage advisor, you can do that. So, so that's one of the benefits of having a gen AI tool that's able to access end to end, ah, platform information about the borrower, the property and pricing options.

Speaker C: Areas that uh, I'd love to see FIN expand into. Like, could FIN possibly read the source code of a product to infer how it works? And in that regard could it never have to, like, the business would never have to tell it, it would just know because it could read, it could follow your blog and read your announcements and all that sort of stuff. Like there are areas where I think we can still go further ourselves again with existing technology. Our goal with Fin is that the human support team should be answering things for the first time and the last time. Like one human rights, the canonical response. Fin ingests that and then that query never gets handed over again. That's where we want to get to.

Speaker A: We certainly expect to be a big player in home loans. I mean home, home, uh, loans. I think in some sense, uh, when people think about credit, uh, why do they care about credit? It's because one day they hope to be a homeowner. They hope to be able to buy a house when they need it and probably they hope to be able to get the loan quick enough that they can actually close on the home that they're trying uh, to buy instead of uh, running out the clock or someone else grabbing the house. Um, so uh, I guess, uh, in that sense I think it is inevitable uh, that home is going to be a central piece of what we care about here at upstart.

Speaker D: As I said, we're native core voice AI. That's what we do, that's what we know, that's what we're going to continue to deliver. We do believe this next major horizon and again, possibly as big as the Internet, you know, boom, uh, maybe bigger is generative AI and what it's enabling in terms of expansion of use cases. And again, any human anywhere, generally through natural conversations can quickly adopt technology and get things done. Whereas if you needed access to other technologies or keyboard or all that, that's like one incremental um, hurdle. So natural conversations is maybe the biggest inflection of where um, human technology interaction is going. And we're just at the early, early stages of that. Within that our vision ultimately is the voice enable the world with conversational intelligence. So that doesn't mean, that's not saying unique to any one industry, ultimately the vision do all these things. And that voice commerce pillar I talked about, we're trying to enable transactions, commerce and you know, um, discovery through all the ways consumers want to discover. So I mentioned the example of coffee on the way to work. But you can imagine also you're stuck in traffic and you need to book an upcoming vacation and you want to have reservations or you need to reorder your contact lenses or there's so many transactions that ultimately can happen through voice. And so we're trying to build the infrastructure for that to scale and build and generate that flywheel. So we want to keep generating and we know that voice AI's moments now we're seeing that massive inflection. So we want to stay, you know, fully on the accelerator. We see just massive opportunity continue to grow at very healthy levels. And I think consistently for us to grow north of 50%, you know, for the foreseeable future is, is. I don't even. We don't consider it a high bar internally because of the opportunity, that competitive differentiation, the technological differentiation and then also the massive tailwinds that are going into this space. And then we'll be thoughtful about if there are acquisition opportunities that make sense for us that can help catalyze us to even greater levels of growth like we've been seeing the last few quarters. Certainly we'll take advantage of that as long as the return on capital makes sense relative to the risk adjusted cost of capital. And that's sort of the framework we use. Um, but yeah, I think ultimately because the tailwinds, this is the way the world is moving because we think we have differentiation. Continuing excellent growth rates is something we certainly expect. Where does the real money get made long term? Um, from your point of view, is it the companies building AI or the companies actually using it?

Speaker E: You know, I, I think I'm, I'm an optimist on this front. I actually think kind of all layers of the stack end up, end up benefiting in some way. Um, you know clearly right now pole position is, is Nvidia. So you want to be, you know, you want to be Nvidia over the past five years, uh, because you've had, you've had control of, of the underlying margin structure of, of AI effectively and you, you've had the raw material that everything needs. So um, you want to be Nvidia starting out, uh, I think being the hyperscalers that deploy the chips and have kind of um, a relative stronghold on that set of chip, uh, supply chain, uh, I think ends up being very helpful for the hyperscalers because there's only three or five companies that really can scale out that set of chips. Um, uh, infrastructure. So you want to be them and then for the application providers, actually the model providers are obviously, you know, doing well. They're burning money because they're in many cases just in scale up mode. But, but I think there's unquestionably you'd want to be OpenAI and anthropic et cetera of the past couple years and then I think now it's the application layer because if you just, you kind of just see that flow through, you have to get the chips, you know, created, they have to get into the data centers, the models have to use them. So that kind of represents the first kind of act of the infrastructure build out. Now at the application layer we're finally at the point where enterprises can utilize AI across their business processes. So I think you're going to see that now value flow through at the software layer and you know, whether that flows into Salesforce and ServiceNow and Workday or Box or goes to startups that are brand new like a cursor or something like that, you're going to see I think value creation happen really kind of across the ecosystem. But now I think is the point when you're going to see a lot of that value get generated at the application layer.

Speaker A: Now we'll zoom out to a five year horizon and look at how these companies and their wider industries could evolve as AI becomes more deeply embedded in their business models and the broader economy.

Speaker C: I think it will Be that the market finds a solution that is into the 90s or high 90s for resolution rate, um, and that the Battleground is not 3D AI, avatars and virtual faces and all that. But it's basically who can deliver the better, faster, cheaper solutions, including proactive support. I think that's the one area. Once you solve all the reactive stuff, um, everyone's minds are going to turn to this idea of like, how can we get ahead of problems for our users, uh, versus how can we detect a struggling user, somebody who's misconfigured, somebody who's about to have a problem. Like every time you're releasing a product and you have like a red blinking light or you pop an error message, you should really assume that that's the beginning of a support conversation. So the question should really turn immediately into how do I make sure, rather than showing the error, why don't I just show the solution? Why don't I just do the solution? So I think proactive support will become the future battleground of this space.

Speaker A: In five years time, I think we'll have successfully brought, uh, AI lending to the parts of the consumer credit market that actually are big and matter to people. Uh, so that's really, I'd say home and auto and some flavor of revolving credit are the three big categories. And I think when that happens, I think there's going to be a seismic move in uh, how people perceive the relevance of AI and lending today. It's this weird niche thing that's just like maybe this cute little company upstart does, maybe they'll go away, who knows, or maybe they'll stay in their little corner. But um, I just really think that the perception around it is going to completely change when uh, you bring it to these, these much larger, uh, markets. And uh, I think when that happens, I think everybody is going to care about this. And the end result of that is I think you're actually going to asymptotically approach, uh, the real levels of access to credit that should exist in this country, which as I said earlier is something like 80% of Americans should have access to bank quality credit. They should have it on demand with virtually no work required of them. And the other 20% should have a clear and defined path of how to get from where they are to where they want to be.

Speaker D: By 2030, what percentage of loan decisions will be made by AI? Uh, I think we're the wider market

Speaker A: here, 100% of consumer.

Speaker D: And how quickly do you think, um, these agents and improving the Workflows the AI is going to permeate through the biggest companies, enterprises. Are we going to see big changes over the next year that are going to influence how businesses operate?

Speaker E: Yeah, I think it's going to. First of all I think we have to be um, somewhat realistic that it's going to be a multi year journey. The change management in a large enterprise is non trivial Getting um, people to use a new set of tools in a new behavior. If you think about let's say there's sort of a two by two of behavior change and tool change that occurs when you're adopting something. Um, for a lot of the past 10 to 20 years of software we didn't really have behavior change, we just had tool change. So I upgraded from a chat system to Slack, I upgraded from Word to Google Docs, I upgraded from on premises files to Box. So that was a uh, tool change. What we have here is behavior change which is the way I actually interact with software is totally different. Um, I'm no longer uh, doing all of the work myself within software. I'm actually uh, interacting with a effectively AI labor on the other end which is a total behavior change of, of, of you know, how do I actually leverage software. So you have tool and behavior change that represents obviously a much more transformational thing that is going to occur because of, of the behavior shift. But it does mean that it's, it's, it can take longer and it can be harder to go and actually execute that which means there's more change management, there's more uh, that you have to re engineer your workflows to get the benefit of agents. Um uh, you have to retool some of the ways that you're operating. That does matter a lot.

Speaker D: Yeah, it feels like um, Box is extremely well positioned ah because it has access to um, all this data that other platforms don't have access to because you control that data side. There's a huge increase in value to enterprises from the advances in AI that they can utilize this data in a much more effective way. And um, nobody else or very few other people have direct access to it.

Speaker E: Yeah. So fortunately we've spent now about two decades building and earning the trust of enterprises to manage their most important unstructured data. And if you think about all of the things that go into managing data in an enterprise you need security, compliance, governance, you need to be able to have a lot of scalability. We have to have open APIs, we have to integrate with all of our customers data environments. Um, so that's what We've spent now nearly two decades doing that is the price of entry into being able to do AI on data. And so we're in a position now where we benefit from all of that work that we've done and now we can actually get more of these use cases on your information as a result of all the work that we put in over the years to be able to work with this information. So I think it is a big moment for being able to use your data and thus I think a big moment for us.

Speaker A: We've heard how AI might shape these companies over the next year and where the big opportunities could lie over the longer term. Um, but with so many businesses suddenly branding themselves as AI powered, it's getting harder to separate genuine innovation from clever marketing. In this final section, you'll hear from Leah from Better as she explains how to distinguish AI hype from real durable AI value inside an operating business. And from Des Traynor, uh, co founder of Intercom, as he sets out four core criteria he uses to judge whether an AI company is actually worth backing.

Speaker B: Okay, yeah, so I'll tell you, while I was at fhfa, this became very clear. Uh, AI washing is, um, something that the SEC is really has been hyper aware of. Basically any company can go out there and make claims. Oh, we have all this AI, it's really driving our efficiency and it's really cool. And show off some chatbot that they've slapped on top and there they are, they're an AI company, they should trade at a higher multiple, um, and they can get a lot of attention. It's remarkable how many companies would come in and demo their AI solution to us at fhfa. Uh, and I would see it and I would be like, wow, so actually that looks like the same chatbots that have been around for the last 10 years. Can you explain to me exactly, like, what is special about this right now? And a lot of times they just couldn't get very deep into it. So what I'm going to say to, to all of the investors out there, you need to do the homework yourself. And it's not hard in the financial services space. So it's one thing for me to talk to you here and say, Betsy is amazing, but borrowers can talk to her 24 hours a day. The good thing is you can just go to our website, dial the phone number, call Betsy yourself, see if she'll take your application. I want you to go to our website, better.com, start to fill out an app and you'll see Betsy Pop up, push her. Push our AI hard. Ask her hard questions, drill her. Why do you need my phone number? Why do you need my Social Security number? I already gave you my phone number. Why do you need it again? Push her. Then I want you to go to some other company websites. I'm going to give you an example. So Rocket is the second biggest, uh, mortgage lender, independent mortgage lender in our country. They go on their earnings calls, they talk all about AI, they master AI, they are putting a lot of investment there and they go out and talk about it. I want your investors to also go to the Rocket webpage, create an application, try their Gen AI tool, do a side by side comparison. Say, uh, hey, I already gave you my phone number. Why do you need this? Ask them about your application, see how far you can push it yourself. You will see how far ahead Betsy is and how much benefit uh, can come to borrowers versus a company like Rocket. And I'm just mentioning two companies that are out in the world talking about their AI. You could do this with any company, any financial services company that has some kind of consumer facing aspect. That's how you really need to do your homework. Do not only listen to an earnings call. Um, I'll say on our recent earnings call, Vishal insisted, our CEO insisted on uh, webcasting and he included a demo. We want to start challenging our competitors to also do live demos. Really show what you have and how that's going to benefit borrowers and the industry. Um, that's why I want to, that's what I want to see if I can get my competitors out there, uh, feeling like it's a little bit uncomfortable. That's when I know I've done my job and I think they're starting to feel it. So that's my advice to get through the hype versus what is real. Just do it yourself. Push, push.

Speaker C: I think I often refer to um, four criteria that I need to see if I'm investing in a company or if I'm taking another company seriously. Um, from any professional perspective it's really like uh, in order, it's, this has to be real. Uh, how would you say AI is rife with what I call shelfware? Like something somebody bought something. You see this a lot with say something like agent force or whatever it was, bought it sitting on a shelf, no one's touched it for a year. Like uh, it's really important the product is used by customers. It's really. So that, that's criteria one. It sounds ridiculous to say that but you watch, you know what I mean? Um, number two is it should solve a real business problem. And again, that sounds ridiculous, but there are a lot of cool tools out there. And I really just mean cool with like a capital C, if you know what I mean. Like, it's fun to play with this thing. It's great that you can generate fun email image newsletter headers or something like that, but no one on earth is going to pay for it. And, um, it doesn't matter how cool it gets, right? But it is cool. Uh, so I think AI is awash with cool tools along with shelfware. The third is that there needs to be deeply differentiated AI, so it can't just be a thin wrapper that you just shut. It can't be something that I could build in a weekend by just handing the whole problem over to, uh, OpenAI or Anthropic. So there has to be something that's meaty there. And that's why I referenced that 27 system sub architecture of Fin. There has to be a real gnarly problem at the core. It can't be really simple. And then the last one is there just has to be some evidence that, uh, the margin can turn positive. Now, people often ding me on this one and they don't like this answer, but really what I mean by that is going back to the earlier idea that, hey, it turns out executing code can cost money. Um, it's really important that you're not spending a phenomenal amount of tokens to do something that is not of proportionate value at all. So if you're like, um, if you're burning the entire haystack to find the single needle, and people don't even really care that much about the needle, that's not an efficient business. And it actually doesn't really matter how cheap things get. Like it does in some sense matter. But like things can get, uh, it can get messy if you're gonna, if you're gonna be burning a phenomenal amount of expensive tokens, uh, to do something that's only incrementally barely useful, even if it is a real problem or whatever. Like, I think the dynamics of that might not work out over time. So I'd rather see an actual path to like, efficiency here. And by efficiency I just mean don't like, um, you know, find a better way to solve the problem quicker, faster, cheaper.