Yet Another Value Podcast - How investors can improve at expert calls and AI with AlphaSense's Ryan Fennerty

Episode Date: February 15, 2026

Host Andrew Walker speaks with Ryan Fennerty of AlphaSense about how investors can improve their use of expert calls and AI tools. Ryan shares practical ways to run better expert interviews, avoid bias, and extract deeper insight from operators. The conversation examines how AI is reshaping research workflows, accelerating earnings analysis, strengthening conviction, and enabling faster synthesis across expert transcripts and internal data. They also address portfolio monitoring, differentiated views, and the evolving skill set required for investors in an AI-driven landscape._____________________________________________________________[00:00:00] Introduction and sponsor message[00:05:37] Framing expert calls around hypotheses[00:07:32] Transcripts versus live expert calls[00:12:36] Echo chambers and bias risks[00:16:37] Managing investor bias in calls[00:20:53] Expert bias and triangulation[00:23:26] Improving expert screening process[00:26:08] Real-time versus long-term insights[00:29:20] Note-taking and AI synthesis[00:31:51] AI’s biggest investing advantage[00:36:31] Differentiated views in AI era[00:41:17] Does AI commoditize research edge?[00:45:18] AI expanding opportunity funnel[00:49:32] Evolving skill sets for investors[00:51:30] AI in portfolio monitoring[00:54:17] Bias across AI data sources[00:56:31] AI transforming expert networks[01:00:17] Corporate use of expert insights[01:02:36] AI, fraud detection, and limits[01:05:47] Future of fundamental investingLinks:Yet Another Value Blog - https://www.yetanothervalueblog.com See our legal disclaimer here: https://www.yetanothervalueblog.com/p/legal-and-disclaimerProduction and editing by The Podcast Consultant - https://thepodcastconsultant.com/

All right, hello and welcome to the Another Value Podcast. I'm your host, Andrew Walker. Today, I have a really interesting podcast for you. I say that all the time. But look, I think this is going to be a specialized one. I think if you are a small fund, well, I should tell you what it is. It is Ryan Fenerty from Alpha Sense. AlphaSense is obviously a longtime sponsor of the podcast.

So I know what you're thinking, oh my God, this is an infomercial. I don't think it's an infomercial. We talk, you know, AlphaSense is the provider of AI tools to financial firms and expert calls to financial firms. I'm an heavy expert call user and you're going to hear it. I'm going to grill Ryan on how can I be a better user of expert calls? How can I be a better user of AI tools as an investor? If you are an investor and you probably are because you're listening to this podcast or you're one of my handful of friends who listens to this podcast even though they don't care about investing, if you are an investor and you use expert calls or use AI tools or you use both, then you are going

to get a lot out of this podcast, my opinion. And if you are an investor who doesn't use AI tools or who doesn't use expert calls, I'm going to ask you, what the heck are you doing? Get with the times. These are the two most important new tools and investors school kit that have developed over the past 10 to 15 years. So I know what you're going to say. It's an informational. It's not an informational. You're going to learn a lot about how to improve as an expert call user, as how to improve for AI and how to improve an investor. So we're going to get there in one second, but first a word from our sponsor, Alpha Sense. Today's podcast is sponsored by AlphaSense. Like AlphaSense has been a longtime sponsor of this podcast. You're about to listen to a podcast with

One of the people from AlphaSense, who's going to talk about how you can improve with expert calls, with AI, all that type of stuff. If you've been following this podcast for a long time, you know, I believe, over the past 10 years, the two most powerful tools that have come along and changed for investors are expert call networks, which have enabled funds and investors of all sizes to get access to expert calls and AI, which has enabled all sorts of tools for funds and small investors. And AI and expert calls are a match meeting heaven. They're increasingly blending together. And Alpha Sense is rolling out, and Alpha Sense AI let expert call tools that lets you pair experts with a knowledge-based AI interviewer to conduct high-quality conversations on your behalf.

So, you know, if previously you were limited by, hey, I can only do two expert calls a day. Maybe I can't do like full surveys and all this or stuff. You have the Alpha Sense expert AI call, go and do 100 calls. You know, if you've got the budget, you could have them interview every single McDonald's manager who's willing to sign up for an expert call. And you can get some really interesting insights going with that. So I just think it's a matchmate in heaven. Alpha Sense continues to push the edge, push the envelopes, evolve it. I think it's great.

You should check out Alpha Sense. You should check out the AI-led expert calls. I think it's a really interesting tool. You can learn more at Alpha-Sense.com slash YAVP. And now on to the podcast. All right, hello, and welcome to the yet another value podcast. I'm your host, Andrew Walker. And with me today, I'm happy to have on from Alpha Sense.

Ryan Fennarty. Ryan, how's it going? It's awesome. Good to see you. Thank you so much for coming on. We're going to hop into the podcast in one second, but quick disclaimer for everyone. Nothing on this podcast is investing advice. I don't think we're talking any individual securities.

We're talking about generally how to improve as investor and use some interesting tools. But keep that in mind, there's a full disclaimer at the end of the podcast and in the show notes. Ryan, look, the reason I wanted to have you on is you work at AlphSense. You are overseeing the AI tools at AlphaSense and the expert calls at AlphaSense. And I think, you know, I've talked about this before. I think these are two of the areas where, especially for smaller fund managers, the landscape has evolved a lot over the past for AI tools over the past 10 days for expert calls over the past 10 years. But wanted to do an update and talk about all of those for my listeners. So if that makes sense, we'll kind of hop into it.

Yeah, that's great. And then just one piece of context just for your viewers and your audience. So I initially at TIGIS led the expert calls business and helped scale that. And then we were required by AlphaSense. Now I lead financial services sales for AlphaSense. So bring both the lens of how we were building at TIGUS and how that's evolving through AlphaSense, especially as AI becomes a huge part of where the industry is headed. And then in addition, Alphacens is much more of an AI-4 platform to support investors.

And so can speak to some of how we're seeing AI impacting use cases in the market. your journey inside Alpha Sense is like my journey outside Alpha Sense because I knew AlphaCense from Stream and the Batigas. It was all about the expert calls for me. And then you've got these burgeoning AI tools. And I think we'll talk about in the podcast, like the expert calls are awesome. And that's what I think about first when I think Alphacin's. But the AI tools are like kind of reshaping how expert calls and learning from expert calls are done.

And I'm still trying to wrap my head around it. Anyway, so let's start with expert calls. If I can frame, I do a lot of expert calls. I probably do, I was trying to put a number on it. I'm going to say 25 a year, but it could be upwards of 50 a year. And of those, I'd say 10% of them are awesome, 50% are good, 40% are okay, and 10% are bad. So I wanted to frame this conversation around improving expert calls, you know, getting that 10% that are awesome to 30% of awesome, getting all the bad ones out of there. So that's my overall framing, my overall thought process for the expert calls.

Let me start with this kind of maybe not easy question, but this question. If someone's listening right now and they wanted one takeaway, they wanted to say, hey, Ryan taught me one way I could improve as a investor using expert calls. What would just one takeaway that someone could have to improve expert calls be? Yeah. And so obviously at the risk of generalizing, knowing that there are many different people use expert calls for different discrete uses in their investment process, probably the number one thing, I would say, to shift to having more satisfying expert calls is approaching more of the

expert calls through the frame of, I am testing a hypothesis or a thesis, and I want a thought partner who's credible to think through that and the second order implications. I think that's where you find the best expert calls have a goal and something they're trying to validate or invalidate, and they have enough structure to allow for that to happen. But they're going to be a goal. But then they also have enough flexibility for you to probe and go deeper. And so I think anyone who's ever, you know, use an expert transcript library and seen some of the expert calls were like, that was a great expert call. They kind of follow that arc. They're flying at the right altitude versus I think, you know, some people come in really trying to say, like, I just want data that corroborates like X, Y, Z thing.

I'm trying to test. And then they come out frustrated that the expert was evasive or like, you know, gave ranges that didn't make sense. So I'd say the number one thing is to frame expert calls are really well utilized for humans who can help you think through a hypothesis you have and really help test your thinking on that. One thing, so just on having a hypothesis, you know, I am a generalist in most sectors versus, you know, a industry specialist. How should journalists be thinking about using expert calls versus industry specialist? Because for me, I might go in and my thesis might be, hey, We're recording February 9th or 10th.

Software stocks are getting destroyed. I want to talk to someone about this company about how AI is impacting software. Whereas an industry expert might say, I already know how it's impacting software. I want to talk to industry people about, like, in real time, how they're spending changing? Like, how do generalists versus specialists defer

when they're using expert calls? I think that's a really fair question. Here's what I've just say. I think to zoom out because, like, one of the things is how is this all changing, given how dynamic the space is. I would say, in general, a lot of the work that used to happen

around just getting up to speed, getting smart, like first order questioning to get triangulated on things, has moved to the expert libraries where you can go see what others have done. Now, that's not always true, but like a lot of the work

that used to go to expert calls to do that has moved to, let's go look and see what's on these libraries to see who else is talking about that stuff. And then where a lot more of the effort has gone is to shifting into much deeper questions around an investment thesis, or drivers of a company, I think that's where we're seeing a lot more

of the behavior on expert calls. That's sort of like, I'll go talk to 10 people just to triangulate, like how industry structure works, big picture trends. A lot of that has really, a lot more of what we're being utilized is the latter stuff that I talked about. So, again, every person, interviewer comes to the thing with bias.

I am an Alphacin user, Tegas user, all these things. My bias, even though I do a decent bit of Alphicense calls, I read a lot more calls than I use, than I kind of do live person calls, right? When you, your best users who are making, in your opinion, the best use of expert calls to further their knowledge and all this sort of stuff, what is their blend of kind of expert calls that they are driving and they are doing versus transcript usage?

Yeah. So I think, you know, I have a couple friends who've like used, or early users of TECIS and talked about how they shifted their behavior. and how they're using it now. And I'd say one of the biggest things that's happened, if you think about where Tegas came in, the industry model for doing expert calls, we disrupted the price at what it used to be done for an expert call.

So it used to be, you know, 1,500 to 2000 was the average price for a call, right? Yeah. Oh, no, I'm nodding long because I was in consulting in private equity before and like, you know, expert calls it was, hey, we're gonna spend $2,000 on the expert, it's a private call, no one can see it, we're dedying something that's going to be between eight to 40 calls.

And this is the biggest part of the due diligence of the process. And please continue. But I'm not because I so agree with what you're saying. So I named this over like the arc that's happened over literally the last three to four years. So that was the state of the industry. And then models like Teague came in, which basically monetized in a different way through access to an expert transcript library where everyone's expert calls over time were put there to be searched and read.

And what that allowed is basically to do expert calls at cost. So there's no margin. And so you could, it opened up the market. I used to be in a banker, covered airlines, like Spirit Airlines, expanded the market of people who could actually take advantage of a low-cost airline, right? And I think that was one of the giant things that Tegas introduced. A lot of our business was built on mid-market funds that previously couldn't do the volume of calls that could do with us.

So I'd just say that was like the first change, which was I had to be incredibly selective about where I did my expert calls. and I do a lot of them through our own network of people we refer to to being able to take a lot more of those triangulation calls. Then what happened is these expert libraries started to form out in the market. There's multiple ones. Tegas has one. There's other ones out there. And this is where a lot of the get up to speed, let's go just to understand like ultimately

market structure, go to market model, pricing, operating leverage. Like a lot of that kind of just cursory work got done through the expert transcripts. But then what you find is people are using those then as a stopping off point for the second or third or fourth call that they would have done becomes the first call because now they can triangulate on a name. They can see the drivers. Yes. People have asked. And I think like the biggest thing we're seeing in the industry, whether you're public or private, is, you know, investing has always been about access to information and then an investment process that gets you to superior investment outcomes. And the access to information for all that insight that was trapped in expert calls has become a lot more available in the market.

right? So the bar for what people spend expert call time on has gone up. And that's true for private and public markets. What I'll hear a lot is the stuff that we used to spend two weeks just getting up to speed on, we do now in a day using expert libraries. Sometimes if you're niche stuff, you still have to go through the cycle because there's not enough out there. Just it's a blank spot. But then now we're picking two to three drivers that we really think we need to understand for the investment thesis. Not all of them are best suited through expert calls, but some of those questions are. And so then we want to go get three to 10 credible experts really dig into that and validate

that. So that's, I would just say that's, it spans your direct question because ultimately the market, like the cost of doing this work and how it's being done has changed. And that is true for both public investors and private investors. And I would say the biggest adoption shift we've seen is now a lot of private market investors in the last 18 months mimicking what public investors were already doing 18 months before. So let me ask you, most people are using expert calls, expert libraries especially. I worry, a running theme of the next few questions is going to be bias, confirmation bias, basically.

But I also worry about Echo Chamber, right? And I'll give you an example. You've got growthy tech companies are the things that have the most expert calls in general on TECIS, whether it's, you know, we're in the SaaSpocalypse right now, SaaSpocalypse, Buzzy IPO coming up, a few of the kind of cultish tech stocks. And I think everybody can figure out the ones I'm talking about or put them in their mind. I worry about you get Echo Chamber where you have one fund, five funds, whatever it is, driving expert call libraries and they're coming with bias and we'll talk about their bias.

But, you know, if they drive 10 calls on this company in August of 2025, that seats. And everybody who's looking at the company reads those 10 calls. Like everybody's thinking about it and coming at the company the same way. So my question to you is, do you worry at all about that bias once the library gets published? I understand there's information outside of that. But if everybody's using it, you get biased because everybody reads the same thing. And our funds coming to you and saying, hey, how do we think about that bias when we're reading that? Do you hear any concerns about that?

Yeah, candidly we haven't heard that as a concern. I think the other thing to name is, like, ultimately, expert insight as a category of insight is absolutely pro to bias. It's a different set of biases that you as an investor have to, like, interrogate and like apply your lens to. So obviously the whole reason why people have been use expert calls is we all know management guidance has a bias. Cell side research has a bias, just inherently because of the market structure and how that works. Financial data is backward looking. And so expert insight is ultimately what gives it utility is that it is the operator ideally. It's the operator's view to triangulate what's actually true about how this company operates and the drivers of

risks that sit in it. And, you know, like, I think when investors do their expert calls and then those become, like, the top, you know, five funds are the ones doing the line of questioning around the transcripts you're reading. Like, absolutely, that could be investors driving in bias, but I actually think the bigger bias to interrogate and be, to be clear-headed about is, like, the bias that can't appear in experience. That doesn't mean that they don't have massive value.

It just means you need to be very careful about evaluating what is the same thing. the bias that this individual might have as I'm taking this, and how do I think about where to apply what this person is saying? And then secondly, I think there's no getting around and why it's really exciting with the nature of the industry is changing to make this much more possible. The end count matters. Like still at the end of the day,

like the way to avoid bias with operators is to go get multiple operator views. You don't need 30, but relying on one operator view to really invalidate or like prove or disprove a thesis is obviously dangerous, right? That's a leap of faith. You front ran, your bias answer, it front ran a lot of my questions, both on the expert call side and when we talk AI, but I'm going to ask them or modify them anyway because I'm very interested in them. Again, I'm going to put it into my personal shoes, right?

I get on an expert call. I talk to an expert. A lot of times I have a view, you know, as you said, I generally don't do expert calls, the first expert call where I've just got no information on the company anymore, right? Like I've read a little bit. I've got enough to be dangerous. I generally have some bias. My question for you is, how much do you think experts when they're on the call, they naturally can tell, oh, this guy is interested in them as long.

So they're kind of responding to me, my prodding and being more positive on that. And how do I, or how are you hearing other funds, you know, I know when I've gone on a call and I'm bullish and the expert has been bullish, I've been like, this expert knows what he's talking about. And a lot of times if I go on a call and the expert's bearish and he can't point to like really specific examples, I'm like, this guy's a clown. We'll talk about some expert bias in a second, but, you know, how do funds think about them individually with their bias when they're coming into these interviews and how it might

influence both the interview and their takeaways? Yeah. Okay. So there are a couple, like, in, we did a piece, I think it's available online recently, and like what are some of the things that some of the top investors use expert calls a lot do repeatedly, things that they've learned to try to, to like spot and counteract some of these biases that can come on a call. And so there's like three things that jumped out from that.

One is a lot of them do like a double click as soon as they get on to confirm where this person sat in the organization and their purview so that they understand the perspective they actually had. So screening for some of that, but that's incredibly important hygiene to say this is the lens from which this person is coming from what they saw and what they couldn't see. So they've already got that piece, right? Then the second one is at the end of the day, an expert, someone who's providing expert consultation is a human.

And we know humans are subject to the line of questioning to give you very different answers really when you're trying to try and go to the same thing. And so one of the things that a lot of investors will have, they'll say it's my burner question, which is it's a way to gut check this person's positivity or negativity at some point in the conversation. So an example that was given would be,

I'll go through a lot of the question. They'll give me a lot of things about how they're really bullish about the business model. And then they'll throw a question about like, talk to me about the culture. How is that shifted? And you can see that a question just like that can take someone who's saying, hey, all these things are great. And they'll go, well, actually, there's a really deep problem there. Actually, like, we should speak to that. The culture's gotten a lot worse recently. And what does that mean is it helps you immediately go, oh, well, that's interesting. Tell me more. So while they might have been very positive on market structure business model, starts giving you a hint that like there might

be misalignment, right? Internally. And that's, that I think is really unique to expert calls and why they are very interesting as a place to find differentiated insight in the market, because the more you can treat that as structure, but then a human, that if you ask open-ended questions and probe in the right way, you can unlock really unique insight. That is unique to that source of insight in the investment process. The last thing, please continue, no. And then the last thing I'll say, one of the questions is like, the open-ended questions at the end can be pretty revealing.

And it's really interesting. This is like a real parallel with how to interview really well. Like when you think about like interview processes for a candidate to hire, they're absolutely prone to bias. Most of the information is absolutely garbage that you're getting. Really, it's just track record and the verified through references multiple. And one of the questions like that these investors asked that is also very popular in the way I've interviewed in the past is to say, let's say we're both wrong on what we just discussed.

We've both agreed to it. What do you think we might have missed? What could go wrong? those questions at the end are very revealing and sort of going a layer deeper into things that this expert might have in thinking about risks in the business and drivers. I think one of the biggest thing experts are very good at is helping you understand second order and third order risks in a business that aren't obvious from the outside. You know, one of the questions I asked earlier was generalists versus specialists.

And what I have personally found is like, look, if the risk is in a 10-K or something, yes, I can see it. But where I've gotten maybe not the most use, but a lot of uses when I hop on call with an industry specialist and, you know, start talking to them and I mentioned the risk. And then they'll just come and there'll be some risks that they live and breathe that I've literally never thought of and they'll be able to talk to me. And, you know, it talks to me about how this specific company is impacted by it. Let me stick on the bias question for a second. Sure. We talked about the investor bias. That's what I was talking about. Let's go to the expert

bias. Because for me, most of the expert you can talk to are one of two things. You know, they are, you're looking into Coke and they're a Pepsi employee because current Coke employees can't talk about Coke, but maybe a current Pepsi employee can. And that's obviously just hypothetical. Or current Coke employees can't talk about Coke. Former Coke employees can talk about Coke. And what's the reason most people are farmers? Most of you are former Coke employees because there was a round of layoffs or they wanted to be the CEO. They got passed over for the CEO's spot. They left. So a lot of the experts I find have a negative bias towards the company. How do you think investors can deal and address and kind of calibrate for that negative bias?

Yeah, that's a really fair question. I think the number one thing is just to know that that is a bias. So what you're going to be when you're asking questions around where there's risk, need to understand that they might be overstating what's likely or possible. It's just reality. And then the other thing I name is talking to multiple formers helps you put people on a spectrum, right? So if you have three out of three people saying broadly similar things about the same risk, it's probably credible information, right?

If you have three out of three all like speaking negatively about something, but there's varied levels of tonality in that, then you can make a different assessment. I think that's how a lot of people have, like, approach that same thing of invariably some of these people are going to speak poorly about management or the culture because they left or decision making because they're disgruntled. But I think it's just, you know, I'm going to go back to interviewing. some of the art of like running really good reference calls, which are very similar to do an expert call,

is trying, is being able to triangulate where someone is being fact-based in their assessment about it versus applying a heavy color, you know, heavily colorizing it. And I found that, you know, when I go conduct references,

I have to do seven to eight references to really triangulate to truth. Every time I do that, I get one or two that had I taken that at face value, would have really colored the picture very deeply. Okay, so let me, again, and I, coming with this with my own biases, but let me go back. When you do an expert call, the first thing you're going to do is, you know, you reach out to your expert recruiter, and you say, hey, I'm looking to do an expert call on Coke, find me Coke formers, Pepsi farmers, whatever, who can talk to me about the industry. And a lot of times if you're, you know, not starting from step one, you're starting from step two, three, four, you're saying, hey, I really want to think about how sugar taxes are going to impact Coke or how, you know, ongoing sugar litigation impacts people's view of course. GLP1's impact co-consumption, right?

So you'll have that. You get experts back. The first and most critical tech is kind of picking the right expert. And I find this can be hard, right? Because you'll put generally some questions and experts don't want to answer all your questions, right? They don't want to give the horse away for free because if they put all their answers in the written question, what's the point of having discussion? So how can people, again, I'm just bringing it to myself, my voice. How can people improve at this screening process for expert calls?

How can they get better at choosing experts? How can they ask better questions? And how can they make sure it sucks when you waste time and you talk to a bad expert, right? You've generally kind of got to pay them anyway. It's a waste of time. It's a waste of money. So how can you get better at making sure you get the right experts? Yeah, I think the first thing I'd say is like we get thousands of projects every day from investors.

And I think if you talk to a team that services and executes on those projects, they'd say the best outcomes are when the investor takes a hot, second to be really specific about, hey, here's what, here's who we want to talk to and why and the questions we're trying to answer. That then really helps inform the teams that do this day and day out, to be like, okay, well, let me give you some perspective of like people that other people have had really good experiences with that we've already worked with. And then we're going to go fresh source people that we think along to your criteria. You'd be surprised at how often people are like, we want to talk to people with this title. And that's the amount of context. If you do that,

then you're not leaning on the teams that do this all day to help you go find people who are more likely to fly at the right altitude. Where this is really common is, you know, someone will be wanting someone who can comment on, you know, operating leverage, inventory supply chain things, and they're looking for someone who's just too disconnected from that level of business and the titles that they're seeking. Seniority is not the same, right?

And so ultimately, I mean, on your discrete question there, like the answer is we do enough screening questions to see, is this someone credible who can speak specifically to what's being asked? or are they too high level and unwilling to go there? And then ultimately, it's a joint decision on we'll recommend to you, we think this person's credible, we've worked with them before, or if they're freshly sourced, are we getting signals that this person is kind of faking it?

And we wouldn't recommend you take them or they've passed our screen. This might apply to some stuff we've already talked about, but I do want to hit again. There's two types of calls you can do, and obviously they're broader, but the two types of calls in my mind are, I want real-time information, right? And we're not looking for an NPI, we're not looking for quarters, But you are, again, recording February 9th, there's the SaaSpocalypse. You might want to talk to people who are the CIO for a company. And you might want to say, hey, how much are you reevaluating your software budget, your SaaS budget, your Percy budget, right now, right?

That's a real-time temperature check versus the longer term. You know, you want to talk to the CIO and say, hey, how are you thinking about Zoom versus Microsoft teams in the long term? Or that's a very specific example. But, you know, you might want to look at the overall industry. landscape. You might want to say, hey, you run duolingo. How are you guys thinking about the five-year evaluation progression? Like, where else can you expand the duolingo? You were in learning, now you're in chess. Can you apply it to fourth grade math? Can you apply it to learning how to play basketball? I don't know. But that's a longer-term thing versus more in the moment thing. Where do you think

expert calls like really excel? Do you think they excel both? Do you think people see one is better than the other? How do you think people can use these the best? I think they can do both. And I think what's increasingly possible opens up a lot of opportunities that were harder to get to. So I'll speak to both. So generally, as you laid it out, there are deeper questions around understanding business models, drivers, et cetera. And those, I think generally for a fundamental investor, have been more satisfying than large end counts when done properly because those conversations lend themselves

that way. What you're describing on the forum are sort of real-time market impact, what's all what's happening here? Like, that is absolutely something, but that is absolutely a place where people go for real-time insight to get perspective on the market. That's very important. They'll always be there. I don't know if you said it directly, but you're alluding to another form, obviously, is, you know, in surveys and channel checks. So increasingly treating these conversations as places to collect signal on trend, specific data points.

And that, I think, is where more and more people, unless they have really sophisticated internal setups to do that, have found experts. frustrating or unreliable. And what I will say is what has changed, well, one, first, they were just incredibly cost prohibitive. So the cost to operationalize those for an expert network didn't look that different from an individual expert call. You're not going to spend $100,000 for a single survey, whereas you could spend it on $2,000 for an expert call. But AI is actually one of the biggest places where, like, we're early. But I expect this to have a big impact on, you know, your question there of like, where expert call is made most powerful? I actually think on the things like survey like and channel check like insights,

AI makes the entire cost model and the operating model behind that, like vastly different from what we've ever experienced in the industry. So you could get, you know, with AI interviewers, they're not a human that has to arrange time. You could have them, you know, go talk to 10 CIOs and do it on their clock. So it's their availability to get really quick insight on a question like that in real time. And that was just stuff that was really hard to operationalize even six months ago. It's so funny because the way I've structured this interview, structured my notes is

expert calls front half, AI, second half. And like four, this is like the fifth point where we've hit the end state and I'm like, oh, I should talk about how AI is going to evolve this thing and stuff. But even just like doing this interview, you can see how AI is creeping into a lot of these things. But I'll say, let me just note taking, okay? So I just did an expert call last. I think you and I did a pre-screening call on Wednesday, and I was literally coming from an expert call, right? I do an expert call, I read an expert call, whatever it is. One of the tough things I personally find is keeping track or note-taking on these expert calls, right?

Like, I'll highlight it in the Teegis or Alphacense app. I'll write down notes, but it can be hard. You know, you read four expert interviews over six months on company XYZ, and it can be hard to remember these things, and it's hard to remember anything you read about a company, but especially an expert call, it can kind of blend into it. AI, when we get there, will probably help a little bit. But how do you find the best people, especially in real time when they're doing the interviews?

How are they taking notes? What are they focused on so that they remember and kind of ingrain whatever learnings they're having of these expert calls? Yeah, I think a best practice is obviously to book enough time right after to go synthesize and take stock. But I think that skill set and that discipline, I wish it was obsolete with us. already. But look, all roadmaps are leading in this direction where you do an expert insight, you do an expert call through Tegas, it's table stakes that that should be able to be something

that's recorded, instantly transcribes them to you, which we do today. But more importantly, there's an AI summary and synthesis that mirrors the way you want to organize your note taking on that. Like, I think the fact that we're not there is, I mean, within months, I think, like, most people are going to be moving in that direction. But to answer your question, like, Traditionally, I think the funds that have done this really well and systematically have a discipline around. As soon as we're done, we take the notes, it goes into internal drive that we can all extract from. And then the other thing I'll say that is a really big part of, you know, we'll get to the next conversation is traditionally people thought of like there's experts, there's all these services for proprietary research and doing investment research. Then there's all these like tools, the traditional, you know, data feeds and other providers.

and then there's AI tools, and then there's our internal content. And increasingly, what's happening is you're doing expert calls as a firm all the time. You have investment memos. And then there's external data providers and plugging all that in and using AI to extract those insights, that's ultimately where things are going. And then as we get to the AI conversation, I'll talk through some use cases that I'm seeing that are really interesting on how insights are coming out of that. But ultimately, I think the world of having to be a really expert note taker on the back

of your call has a very short half-life. And one of the things AI should be able to do for you is not make that a huge part of your routine. It's you being able to have that and immediately send you a summary of exactly the insights and the structure you want. That's the technology can do that. Right.

Well, we keep coming into AI. So let's start transitioning to AI. And I will say in my head, the AI discussion has like almost two parts to it. There's just using AI tools in general. And then because we started with expert calls, there's how AI tools are shaping and evolving expert calls, is obviously a subpiece of that, but I think it kind of fits into this.

So let me start with the same question I did for expert calls. If I'm a listener and whether it's using AI on expert calls or AI in general, if I'm a listener and I'm going to walk away from this conversation with one thing about how I can use AI to be a better investor, what would you kind of, how would you kind of answer that? Yeah, look, I'll tell you where we're seeing all the action for public markets focus investors, right? And that is like one use case where you can immediately start getting leverage and making your life instantly better is around earnings, right?

So the advice and the way you'd kind of phrase it to me, I was like, the number one thing you need to do is pick a place where you find yourself spending a huge amount of time doing hand-to-hand comment on synthesis and taking multiple data sources and forming a view under time pressure. that is ultimately where AI is strongest. And so earning season is where we're seeing that in public markets quite a bit. I'll give you some examples. There are things that habitually people would have to do on the back of like, I've got a name in my portfolio. Like, there's a real investor conversation. I have read it.

They just published earnings. Management guidance was very positive. Now I've got to go basically update the thesis on whether or not, you know, we want to stay in the stock and what's, happening around us. The things that you used to have to do very hand-to-hand you can do now within hours. And so one of the prompts that this individual has set up is, okay, here's management guidance. I want you to compare what the CEO is saying to the actual cash flow statements of the last five comps that I tell you that have already reported. And what that's allowing people to do very quickly

is say, okay, this individual is speaking positively, but the cash flow statements show that there's a lot a negativity on everyone else. So what does that tell you? Two things. One, Reddit's an outlier and things are going really positively and why or two, management's overconfident, right? And we're already setting up for a question mark there. These are the types of things that are happening right around, you know, what AI is really

good at is synthesizing insight for multiple sources and drawing connections that are very hard for human to do quickly. And that's probably the number one place, I would say, public markets investors, there's multiple things that people are doing right now all the time. So that's super interesting. but if I could just push back on you slightly. True. This is another value podcast.

My average podcast is a guest comes on and we talk about one stock for an hour. It's a deeply research, generally concentrated investor. When you say earnings and things that need to be done quickly, you know, I just know in my mind, my first thought process was he is talking to pod shops who are trading quarters and whisper numbers and all this sort of stuff, right? So let me just reframe the question. If I was ignoring immediate term stuff. How would someone who's, you know, five stocks, concentrated long-term investor,

how are they using AI to evolve their process? Yeah. So I think there's another area is when you are going to, you know, take a position at a company, I think there's ultimately a heavy, heavy amount of work and what are the fundamental drivers of this business? And can I get a differentiated view versus consensus? Yes, yes. I love that you said differentiated view there.

Yeah, and I think ultimately some of the really interesting use cases there are, you know, like consensus is formed across multiple layers, right? What is like, what are cells, if it's a, you know, widely covered name, what are the key debates on the cell side and what they're saying about it? What are all the people saying, what are all the experts saying on this on the key drivers that matter? And then what is our internal view on those? And you can triangulate, you can compare those perspectives. One thing that AI, I think you had asked me a question coming into this, is like, what is AI actually really good at uniquely that surpasses the ability of the average investor, right, versus where it's merely coming up to the ability to do what an analyst that you, you know,

junior analysts you bring into the fund can do. And one thing I will say, it is the ability to go synthesize and compare perspectives across tons of different sources in a grid-like format. And so one of these things that I think we had to be. I've seen investors using more of the fundamental verse is that you can look at so many different components and compare what is management guidance saying on this, what is the sell side saying on this, what are the expert calls we're doing, how do they compare to what's being said? What are the expert calls in the transcript library? And I think that's allowing people to say, hey, these are the real

debates on this name that are really fundamental to the value creation story. And that's where we're going to do a lot more work. And I think that's the kind of stuff that like you just wouldn't know to do that level. I'm talking about like an 80 by 80 grid comparison of inputs across multiple data sources. It's just not feasible that a human analyst would do that. But that reveals really insightful places to go and dig deeper for investment. We'll probably come back to this, but like one thing that just jumps out to me is there are some names on Tegas where there's 80 expert calls a year, right? There's no, I mean maybe, but if you're saying, hey, I'm going to follow 30 companies, there's no effen chance you're going to read 80 expert calls on 30 different

companies. AI can do it in half a second and summarize it for you, right? So I want to ask two questions on that. The first question, you know, I know I'm not alone in this. There are lots of tools that will automatically build financial models for you and extrapolate them, you know, Comcast reports Q3 earnings, they'll automatically put it in, update the model and everything. I kind of, I build all my models by hand, especially as I get like close to making an investment

because there's something about just going and doing it that like makes me learn and makes me think and all that sort of stuff, whereas if I just had to present it to me. With AI tools, like, I kind of worry about that, right? If I just have AI summarize 80 expert calls versus now 80s a lot, going and reading, like there is something about getting the summary that maybe I don't quite understand it or internalize a lot. So when you talk to firms, especially portfolio manager level people, how were they talking about that tradeoff, right, of I could never read 80 expert interviews, especially across 30

names versus, hey, if I just get 80 summarized for me, I don't understand. internalized. I don't think it through as much. I'm kind of losing that edge, that inside, I'm just outsourcing it all to AI. How are you hearing people talk about that tradeoff? Yeah, look, I think it's a fair tradeoff. And it's a very, it's very understandable emotional reaction. I mean, I've had it myself. I went through the experience of building company models. And I know that, like, what you're describing, that like, it clicking the drivers and the sensitivities by actually, actually building the drivers myself and running the sensitivities and the scenarios through it. Here's what I'm.

I'd say, though, I think ultimately, like, I believe in my bones that by 2030, they're going to be really high-performing portfolio managers to this next generation coming up who like never, who absolutely never had to go through that. Like, you know, they've never built a super detailed M&A model. And yet they're pretty good at leveraging this stuff to get to insights and triangulate on what really matters and get good investment outcomes. And so the debates we're having in the industry or more about exactly what you said, which is like, until I can fully trust this stuff, it's still prone to like errors in judgment, data that just like I don't trust or believe in. And I think so a huge part of, you know, like to name our philosophy for how we've built is that everything in Alpha Sense is fully traceable down to the source.

And that's really important because like when I go through workflow, even for my own research for like go-to-market, I need to see instantly where that insight is coming from. Otherwise, it just interrupt my workflow. I don't want to get two hours in and then suddenly have it all be on a shaky foundation. So I think that's really important. And then the second thing I'll say is like, look,

AI is very prone to if you prompted a certain way, it'll pound the table. And I had that experience where I say like, you know, like build my go-to-market plan for Alpha Sense and the lens of a CRO reporting board, like the conviction. it will give me in certain things. And I go, like, that makes no sense.

Like, my judgment suggests that, like, well, that might be true. There's a verbatim series of calls we had with customers saying X was true. I know enough that, like, the tam of, of, like, that segment doesn't make any sense for that recommendation. So I think that's ultimately, I'd say, for the investor, like, the value that comes from judgment and understanding market structure and business models, I think, like, goes higher. But a lot of the, like, you know, a lot of us in the, you know, For those who stayed in the industry, I left the industry, but for those who stayed, a lot of your

sense of self as an investor is your technical prowess and your analytical skills, I think those over time are getting commoditized, and what's much more important is your pattern recognition, judgment, ability to push on these things. Look, everything you just said, especially towards the end, just like matches my worldview. So let me ask this. You mentioned, if I'm quoting, having to differentiate a view when you're making an investment, right? that's kind of what you're looking for when you're making, especially a concentrated long-term investment.

If everyone is using AlphaSense and AI to summarize the same AlphaSense expert library, like, this is why I don't read cell side reports, right? Because if you read all the cell sides and then you make your conclusions based on that, then you've kind of just got like the market view or you've got that cell siders view. If everyone's using AI to summarize and everything, how are people thinking about, hey, that's the table stakes, right? I need that. I need that basis. How are people thinking about, hey, how do I get a different? Or where, where is my special sauce where I'm going to kind of have a differentiated viewpoint, then everyone else is using the same AI to summarize the same expert calls. Yeah, I think with a lot of these, like, technology innovations, it just shifts the baseline.

So, you know, I think like the, like, you know, you can think of like doing financial analysis before the PC and Excel, like, right? Like that no longer was it like having these really sophisticated ways of doing that. Like that became the baseline if you weren't doing financial and outright. Like, and so I think where we're getting to is like, It's always been about access to information and then your ability to have an investment process that yields results that others can't get to. And I think what AI is doing, you know, we've always talked about like markets are efficient,

everyone has X and extremely, but we know that's not true. It's like why we were all trained to like sweat the notes and go deep into the 10ks and the 10 ques and like really synthesize all these disparate things and get to something that was differentiated. Even before we talk about getting an edge through like alternative data sets, I think what's happened with AI is just the technology is so powerful that any gains from that are getting harder to come by. And so really the alpha comes from, I think, some of the same things we've always talked about. It's like the ability to then have these systems work for you so that you can make decisions much faster with conviction.

I'll give you an example. In private markets, it's like I've seen this really come to play in the last 12 months. And like, you know, this is parallels that we talked about for like a large. long-term concentrated public market investor, there are very big parallels to, you know, a PE fund that makes a couple concentrated bets here. Yep. Yes. And like when I've asked them, I'm like, hey, how's this impacting you?

Are you looking at more names, more opportunities? Yes. Are you making more investments for year? No, that's not our strategy. We're still going to only make three to five. But we are much, much more convicted on those three to five as a result to what's possible. So the due diligence we used to do that would get us to point from investment process from point A to point C, like the time to get through point A to B in our process has compressed to a day from weeks.

Therefore, the amount of time and energy we spent are really diligent B and C, which is usually the key debates in the Investment Committee around where the value creation comes from, where what are the drivers of the business on our different view on that? That's where all the real work is going. Do you think they should be going? So you said three to five and they say, hey, we're more convicted. I think you suggested at the beginning, hey, because they can go from point A to point B faster, should they, instead of doing three to five, should it be eight to ten?

Should it be the other way? Like, if they're getting more convicted and they're able to go deeper into B to C, which is probably where they're addressing the real niche cases and their real differentiation, instead of three to five, should it be, hey, we're more convicted, so we should be more concentrated. We should be doing one to three instead of three to five. Do you think that should be the right answer? Yeah.

I don't know, because I do think there are some funds who've said, yeah, it actually has an increased amount of things we'll do in a year, right? And there are others who are saying, that's just not. our operating philosophy and will only be, you know, three to five that we usually do. And yeah, sure, maybe some, it's been like, we're going to go, we have even higher conviction now. So we're going to bet the fund on one or two ideas. I haven't seen that as much. I think just the general principle, though, is I think everyone recognizes, like, valuations are elevated.

It's more competitive. There's more to put to work. And so when we go, like, we have to be much more convicted to go bid for these good assets. Like, that's the scarcity. And therefore, so much more of the work is making sure that we have a credible story for how we're going to have value creation and a real exit. And that bar has just shifted dramatically over the last two years. Not because we chose it to, but we can feel it around us, like how quickly people are moving

on opportunities with conviction, we have to stay in line. I think that's ultimately what's happening. No, I just asked because exactly what you're saying. I have some friends who used to do, let's say, five investments for a year. And now, like, hey, because of the AI tools and I can get up to use faster, I do 10. And then I have friends who say I did five, but now I do five with a lot more conviction. But I haven't had anyone be like, because I have more conviction, I do three instead of five. You know, so it's just, I haven't heard that yet.

But I don't want to understate, too, though, that like the, but I didn't mean to say that while the end result in the funnel might result in like the same three to five, the amount of things I get looked at before they even get to that has extent. I think that's, you know, ultimately you think of like, how many assets can you look at that might get there if that universe is expanded sometimes materially. Like some have said, I look at twice as many things now because, you know, you get a SIM. You can analyze that SIM instantly with AI with all of internal stuff and get a green, yellow, red in a way that, like, took weeks of analyst capacity. And so I think that's been a huge difference. So earlier you were talking about, hey, 50 years ago, you know, financial analysis, it was literally like Excel Sprintest, spreadsheets. It was because before you put it into the computer, there was literally a physical piece of paper, a spreadsheet that you would build up. Right. So eventually that goes online,

that gets commoditized. Now there's all sorts of stuff that will automatically build up a lot. So I would posit to you that 60 years ago, you could make money with quants in your head, right? If you were a really good literal financial analyst, right? You could make money by modeling, you know, think about Ben Graham, just calculating networking capital. Totally. I would posit that maybe 10 to 15 years ago, you could make a lot of money probably better on the qualitative side, right? The financial analysis got commoditized. The qualitative was all the money made. And I would just say, look at the past 15 years.

If you thought it through Google, Facebook, Amazon, whichever one, these are the best business ever. The world is trending that way. The internet returns to capital scale, all this sort of stuff. If you could figure that out, that was not a spreadsheet number, that was qualitative that got you there. AI is kind of, I'm not seeing replacing the qualitative, but AI actually. really raise the bar quality. What do you think the next skills are that kind of generate alpha

if, you know, financially analysis has already come down. A lot of that quality comes down. There has to be some skills that get elevated, whereas, you know, six years ago, if you were great qualitative and you were terrible financial, you couldn't make it work, but then when the financial gets commoditized, qualitative makes, if that's coming down, what's the next skill set, do you think? Yeah, I'll give you my thesis and, you know, there's a lot to be proven out here. I think, so I'll talk private markets first and I'll talk public markets because I think there's some parallels, but they're going to be different. I think on the private market side, I think what's been happening is like the returns from being really good at, you know, financial structure in dealmaking have been going to, I think that's like widely discussed in the industry. And so really it's about like what AI will probably help is facilitating the ability to act really quickly on a much bigger opportunity.

set and win more deals when they fit in your, I think, like, firms that focus on portfolio value creation post, right? I think there's a lot of opportunities. We can go there here. This is more of a lens on like AI in the investment process. But I think one of the other things, I was at a mid-market PE conference last year. And like all the buzz in the room was like the thing people could do taking AI to portfolio companies to drive value creation stories.

So I think that is a likely place where I think some of the big gains, come from, you know, using AI, like, really effectively to drive more places where you can look and get higher conviction on the deals in the way we discussed. But I think a lot of it will also translate to portfolio value creation. And I think that because I actually think AI has a real fundamental set of use cases where it's changing operating models and cost structures that make sense in that world. On the private front, this is part on the public side, which is where I'm focused. But on the private front, I actually think it's going to be, you know,

If AI is a tool that everyone can use, so you have the old Warren Buffett, you know, if you're at a parade and you stand on your tip toes, you get a better view, but then everyone stands on their parade so no one's net better off. Actually, everyone's a little bit worse better off. I actually think it's going to be financial analysis commoditized, AI, and a lot of quality to get commoditized. I think the people who are best at human resources and people are actually going to be the people who, I think that's going to be a skill set that gets elevated on the private side.

But, you know, I don't smoke, but maybe I'm just smoking something or, you know, just too far out their galaxy brain. What about on the public side? What do you think skill sets get elevated? Yeah, you know, I think this is like the common discussion in every industry, which is like, like, there's a long-term problem with this answer. But I do think, like, you know, one thing that we've talked about is like this shift from the analyst skill set to the architect's skill set. So people who are really adept at using these things to create leverage in the investment process from, like portfolio.

I know like for a concentrated three to five name long term investor, this is probably less resonant, but I do think this will impact public markets. I think you'll see a lot more people using AI in fundamental still fundamentally fundamental investment work to do portfolio monitoring, idea generation, just like look at a lot more, a lot more quickly. I think that is going to change like the stuff you said like how pod shops behave. I actually think that pressure is going to move into more places in the industry. industry. And then long term, I think ultimately, like the real question mark is like, what happens to fundamental investing in the way you described?

Like, ultimately, do we have this like cohort of people who grew up in the world that you and I grew up in and are deep experts in it and understand for years of investing pattern recognition and we lose that with another group? Or does this new group that come in leapfrog that somehow and start looking at, you know, like truisms that we've all lived with that like our understanding. correlated in the data and actually don't matter. And then, like, there's a whole different version. It's not quant investing. It's not fundamental, but it's something in between, right? I'm very worried. I am already a dinosaur.

Let me go back to our bias discussion, right? This is something I think about a lot. Actually, before we go there, just on the public market side, I have to ask for my own curiosity, the portfolio monitoring side, how are you seeing concentrated fundamental investors use AI for portfolio monitoring. Yeah, I think like really big examples would be, you know, I think there's like these, I'll give you like the extreme scenarios and then there's like day-to-day scenarios. So the extreme scenarios was like Liberation Day last year, right?

Like we saw people who had these portfolios and like were instantly like, what is my exposure and what are the recommendations or where should I go dig across, you know, like 10, 15 names, right? And what AI was very good at was like in those kind of fire drill moments, like within hours, right, had kind of indicated all the places, all the different research that was like different from their view and aligned their view and where their exposure was. And then that's where the work was done. I think that is like an extreme example, but we also saw that again around, actually, as you described, more recently around all this like bearishness around SaaS and AI exposure. Like people have been using AI to very quickly get their head around, things like that. From an ongoing portfolio monitoring perspective, ultimately what really matters, though, for this to work well is like, it's only as good as a number of data sets you have access to in the market.

But ultimately, like, I think the market has shifted from things that help me go find answers to questions I'm looking for to things that help me produce like kind of these workflows I'm constantly doing to now custom autonomous things that run reports as if I had an analyst working on it. So people are using portfolio monitoring, say every Friday, I want to report in this format that tells me trends and inflections on these parameters against my portfolio, right? And that's like those are the types of things where portfolio monitoring is just like always on custom way. Just imagine if you had infinite analyst resources, where it would be some like nice to have discretionary things you'd ask for that would make you feel more in command of your portfolio. And that's the kind of stuff that AI does pretty well. Let me go to bias real quick.

So there's three types of bias I could see in AI, right? if I'm crafting prompts for AI, there's bias myself, right? If I'm crafting a prompt on a company I'm bullish on, I can bias myself in the prompts. There's bias in terms of the company side, right? If I have an AI read every investor day and every earnings call a company's ever done, well, management teams are generally pretty darn bullish on themselves, and they've got a lot of bias in the way they present. And analysts aren't exactly going to get on and scream at the company,

because then they'll get cut off and they'll never get to talk to the company again. So I worry about bias for myself when I ask. I worry about if I have AI train on a company's data set, bias on the company side. And then on the expert side, if I have AI read a bunch of expert calls, as we talked about with expert calls, experts, in my opinion, tend to be a little bit more negatively biased. So if I have the AI train on expert calls, I worry about negative bias in the training data on AI. So how are investors thinking about kind of those three biases when they're using an AI tool? Yeah, I think this goes back probably to the last question of like, where is the skill of an investor become differentiated over time? And I actually think like, look, look at, like, look at to evaluate an investment.

What's different now is you can triangulate multiple of these sources with their biases in a way that was really hard to do as comprehensively. And so I think what investors are doing is like rather than trying to oversolve for how to eliminate all bias, I think there's a record. that they all have that and they're doing increasingly sophisticated ways of comparing and contrasting sentiment perspective to see where the debates are and then forming their own independent view like who's wrong and who's right like management obviously have a certain bias and as you said these experts might be really negative but where do we think the the truth lies and how can we get smarter on that if we can't based on what we're looking at here i don't know that look that wasn't a super direct to answer question but like i i i think that's just what i see

happening is like in a prior world, you had fewer sources you could evaluate on the time you had and they had bias. Now you have more sources you can evaluate, all of whom are biased, right? But the triangulation you can do across these different sources and perspectives is infinitely higher than you could have before. And that's ultimately great investors. There's no perfect answer. Let me let me end by asking AI and expert calls, right? AI really shifts the use case for especially expert call libraries, but also expert calls. And the two, which we've kind of hidden that, I'll just summarize, like, Number one, if I wanted to do 100 expert calls on a company, I just wanted to dive really deep. I can't.

I'm limited by my own time. I could have an AI agent, like, serve as the questioner for 100 things and do that. And, like, I theoretically could have that happen, right? That's number one. Number two, I can't read 80 expert call transcripts on 80 different companies. AI can. So there's two ways that fundamentally, now you can use more expert call library transcripts, and maybe you can get more expert calls if you want to.

How are you seeing AI and expert calls kind of evolve together? And how are you seeing kind of your customers who at the far, far, far, the end of using AI and expert calls? How are they marrying the two? Yeah, I think, so ultimately, expert calls have always, as we kind of, in the first part of our conversation, they're a really unique source of insight because they're humans and they're varied, and they're not going to give you yes or no answers, right?

You can tease out a lot from them. So I think the first thing, you know, when we talked at TIGAS as we're building, like, the business and the expert called, we were saying, like, this is probably one of the most unique data assets in the market. It's like, like you can think about the amount of expert knowledge that sits out there on all the investable markets, research names, companies, and it's off platform. It doesn't, it's not, you can't extract it anywhere. And so I think AI basically, you know, we had these business model innovations that opened up the amount of expert calls that could be done and how much it could be captured and searched.

That was like version 1.0 of like the TIGIS model versus traditional. Then too, what AI is basically doing is playing like further on that trend, which is like now you can have, you know, tons of AI. If the next gate was invest for time to actually conduct those calls, like that's no longer constraint, right? So ultimately, it's just the amount of resources available to go run at all these things. So I think to answer a question a little bit abstractly, I think one thing that's really interesting is the amount of expert insight out there that can be captured, queried, looked at longitudinally, and compared and contrasted over time, that is like a real-time data asset that's building every day.

And that wasn't true in a few years ago. And so I think that's where I think investors, some investors are really recognizing that and recognizing also especially really large ones that they have their own, they do massive amounts of expert calls. Some of them are crossover funds, public and private, and they're comparing insight against those. So now you suddenly have two different data sets. What's happening in private markets that we can see and public?

And that helps shape our conviction on different names. I think that's where AI is an accelerant of a trend that was already happening in the market around expert networks. And I think investors are really seeing this as like one of the more unique data assets that is being built and they want to stake in it, and they also bring their own proprietary stuff that others can't see to it. So I think one of the biggest things we're seeing is a lot of investors initially were just like looking for an expert transcript library and AI tooling to search it.

Increasingly, they're also bringing their internal content alongside. And that's where, you know, this is much more relevant. I think for larger well-resourced funds that do a ton of work. But that's a big trend in the market. Like they're able to see things that others can't because of all the things that all the research that they're doing in the market across disparate teams. Softball-ish question. I have two more questions.

Softball-ish question. And then I'm going to end with a true knuckleball question. Softball-ish question. We focused on investors and public, private. Alphacin does a lot of companies. Are companies going into the expert call library and using expert calls to source and think it and change strategy? Or even just seeing the questions investors are asking to change kind of how they're

responding to the IR? Or you could also tell me, dude, the companies are the experts. They don't need to go to an expert library. They can just call up their supply manager and have them. So I'm kind of curious if you're seeing companies kind of adjust and adapt to how both expert call and the AI libraries work.

Yeah, look, companies are like a big part of our business. At Alpha Sense, we almost 40% of our business is built on corp dev, corpstrat, and IR teams. And so they are huge consumers of the same insight that investors look at. Their use cases are nuanced. and slightly different, but I think this went from an industry that was, you know, very focused on fundamental investors to now becoming very much a core part of how, you know,

sophisticated corporate decision-making is made. I was just, that's exactly I was wondering if they're using Europe. Okay. Knuckleball question, super weird, but if I can give you the background, you know, in 2011 to 2014, there was this big Chinese reverse merger fraud in the stock market, right? And it was, you would read the 20th of these companies. And they would say, hey, we have 600 million acres of woodland in China. And people would say, oh, well, an acre of woodland's worth $600,000, it's a buy.

Well, it turns out 600 million acres of woodland doesn't even exist in China. Like, all these things are frauds. I wonder if there is a return to, if the scale and returns to fraud improves in an AI age, because, you know, if you can get, if you're a company and you're running a fraud and you're getting to 10K, and AI is just detecting it. And they don't have that human who's going and saying, dude, their headquarters is like a P.O. Box in Boko Raton. Now, yes, it's the front page of the 10K, but the human person who goes and says, this management team is out of their mind.

Do you think just AI kind of increases the return to fraud or the return to like far left really nasty companies? Because if they just get bucketed into this big quantitative AI pool, it's kind of tougher for them to detect. Does that make sense? Yeah, can you say one more time or reframe just slightly for me? I'm just wondering, like, if I'm thinking about the Chinese reverse merger frauds is really what I'm thinking about, right? If I went to Alphacin, I said, hey, find me undervalued companies on an asset value. If it was just reading the Chinese reverse merger fraud 20th, it would say this is the best value in the stock market, right? Every other pier with woodland acres trades for a dollar per acre.

This is trading for five cents per acre. And it would be telling me buy, right? And there were a lot of these things out there. I just used the woodland. But there were a lot of these things out there. And it took somebody like kind of hauling around going to do being. And plenty of investors felt for these things. But I wonder if in four years, you know, all these things that a human reading it would say,

hey, there's something wrong here. Or a human who like literally flies and says, oh, you know, this $4 million company, their headquarters is in the third floor of them all. This is kind of weird. And AI wouldn't see that. So I'm wondering if like AI increases the returns to fraud because as you get more quantitative money and as you get more quantitative things, that kind of human check goes away. Yeah, so two thoughts on that. Interesting question. I think, you know, first thing that comes

to mind is, like, ironically, AI is being used a lot in the fraud detection industries, right, like to find patterns and things that just indicate that something, like, is amiss. And so I'd say that I don't think there's anything inherent about AI that suggests, um, it becomes an accelerant for the fraud that's possible. Like, because I think equally it can be, when used right, a pretty powerful weapon for detecting fraud and pretty idiosyncratic and straightforward ways in other industries. So I don't think there's anything that, you know, like stops it from looking at,

you know, like to your example there, like go looking at visual imagery, satellite imagery of Great point, yeah. Right. And being like, hey, there is something that is mismatch versus company guidance. Like we can go see from imagery on shipping lanes. that the traffic is not even remotely what company guidance is. I actually think it could be powerful in helping investors parse those pieces

that used to be required by having someone on the ground or going and sending someone to go look. On the other hand, what I will say, and like this goes back to the core of what we're discussing, is like, just like AI ultimately does some things in a superhuman way. And I think ultimately that is synthesis and finding really discrete details and connection points in a way that's very, like humans cannot replicate what

AI is able to do in that domain. On the other hand, it is absolutely not at the level of a PM or a sophisticated investor on anything that remotely looks like our investment recommendation. Right. So think of AI is like, I think what I get really excited and bullish about is I love underdogs. I ultimately think what AI has like enabled here is like it has absolutely collapsed the resource advantage that the biggest funds have had versus your mid-market funds. Like you can basically go do stuff as if you had a crows.

team of, you know, incoming KKR analysts, right? And what they're able to do, like, it can do a lot of the stuff they can do, right? But what it can't do is what very likely you can do, which is look at that report and your spiky sense goes, this doesn't make sense. I got to go dig deeper, right? No, it's funny you say level the point, because I do worry, like, as a small investor, like, you have a lot more nimbleness, but you mentioned it when you were talking about the AI use cases, the big funds with like lots of off-market data. I worry that there will be no more role for a small investor because AI levels the playing field so much that it's larger funds that are sending their analysts to every industry conference out there

and having them put notes from every industry conference and getting all this data analytics that just a small investor can do. I worry like the returns to scale actually accrue up and like there's kind of only a place for larger funds that are generating literally proprietary information by sending people in person to do all of this different stuff, but probably a conversation for another day. Ryan, this has been so much fun. I, as you can tell, I think about the stuff and I think about where it's going all the time,

and you were the perfect person to have on. Any last hits you want to do expert calls, AI? I think we've been pretty comprehensive, but I could probably go for another two hours, to be honest with you. Yeah, no, look, I really enjoyed the discussion. And, like, I think the number one takeaway I just to have for your users is that,

like, I think there's going to absolutely be a role for fundamental investing. I think the thing. Fingers crossed over here. Absolutely. And I think these debates that we've talked about, like, whether being a journalist or a specialist, like those, like, I don't think that, like,

there will be wildly successful specialists and journalists in the AI future. Like, I don't think that changes at all. I think some big funds will, like, have absolute advantages from what we're talking about. But then I think there's going to be a lot of, like, smaller funds that are really nimble with this stuff that I'll compete them.

I don't think that story changes. I just think ultimately, like all major technology changes, the baseline for what's possible, shift, and so people have to figure out very quickly what is table stakes to not fall behind and what's true advantage. And I think we've talked a bit about what that looks like in practice right now. There's a lot of hype, but there's also a lot of real stuff happening. So it's perfect.

Ryan, hey, look, I really appreciate you coming on. Again, these are just things I think about all the time and I appreciate you walking you through and helping me get a little better at using AI and thinking about how to use XPercall. So Ryan Benity, Alps, thanks so much. Great. Thanks, Andrew. A quick disclaimer.

Nothing on this podcast should be considered. investment advice. Guests or the host may have positions in any of the stocks mentioned during this podcast. Please do your own work and consult a financial advisor. Thanks.