Joey Brookhart - AlphaRepo - Software Buyside Realities

The Cloud Returns Podcast covers all types of software investing, whether seed, venture capital, growth equity, private equity, debt, and even the public markets. Excited to have Joey Brookhart on the podcast today, and as is custom, we'll just let him describe himself and give him his background and introduce himself. Perfect. Thanks so much, Matt. I come from a background of long-short investing. So I was an analyst for many years and then, you know, spun out or started my own fund, you know, about 3 years ago in 2022. And a big focus throughout my career has been on software companies. So both infrastructure and application software with kind of a long-short lens. So everything from kind of shorter term quarterly to more thematic and longer term theses., but also do a bit of my fund and previously did a lot of like growth consumer investing, public markets. So think smaller companies that are growing stores, restaurants at a pretty high clip or have some thematic background to them. So I've been an analyst for, you know, 5 years prior to that and then a PM for, you know, close to 3 now. And from there, you know, we started a company within my fund last year called AlphaRepo. And the big thing with AlphaRepo is I was looking to really track my Excel models, so understand my accuracy, how my numbers were moving over time and comparing that to consensus, alt data sources, and kind of rolling that up to like a dashboard or CIO, you know, PM type view to really understand, you know, our differences, our divergences across our coverage and understanding where IRRs, you know, risk reward was at any time. And was really looking around and couldn't find you know, much that solved the problem. I was asking my friends at different shops how they did stuff, and it was either very manual, they had some very old system for it, but there wasn't anything that really sat in Excel and stayed out of the PM and analyst's way in a way, you know, I wanted the tool to. I needed to be really custom to, you know, very specific things like within software, like net new ARR, net new CRPO bookings, things like that. And I wanted to be able to track all those metrics. So we built that within my fund and then realized, hey, there's other people, you know, could definitely use this. This solves a need in the market. So then I've been working, you know, on AlphaRepo since early 2024 and the go-to-market there and some of our product work and just running that business. So we've grown that since then and really have made a big push to get into, you know, a lot of long-short shops and your bigger long-onlys. So that's kind of a rough background on me, but, you know, kind of turned from, you know, investing, you know, public markets investing to solving a problem that I had and now running that, that business and then selling to other public market investors. Awesome. Perfect background for this. And also I think for the audience to know, it's remarkably hard to get public equity investors to speak freely onto a podcast, just lots of compliance. Firm reasons. And so to kind of like get a window and, and someone who's transparent on their process is a real opportunity for us. And we also know we have a lot of buy-side investors who listen. You guys should come on once in a while and, and share, but I'm glad we got Joey. So with that, when you're investing in software companies, what are your particular metrics? Not just what everyone does, but like what do you orient to most? Yeah, I think a lot of people's research processes start in the same place. They want to understand, you know, read the filings. So 10-Ks, you know, the annual reports, S-1s, you know, maybe IPO up until for a lot of these newer SaaS companies, understand the story, the narrative over time. So going through their conference calls, you know, quarterly transcripts, any, any things that's been published by management or as a primary piece of research or, you know, primary document. What I then focus on is really what drives, you know, you kind of want to distill in and synthesize to really what drives that company. How does the COO, CFO, CEO think about that business and how are they kind of driving it forward? I would say overall for software, the big ones, you know, I'm looking at is net new ARR and net new ACV, you know, those two hand in hand depending on what's disclosed publicly and really, you know, things around sales efficiency and kind of that sales motion. So why is this company why do they have some advantage in how they land, you know, new logos, new ARR, or maybe, you know, they have really, really great economics and how they expand, you know, usage or product kind of upsell, that, that just makes for a good SaaS model. Because at the end of the day, with any, you know, public market company, what we're really looking for is free cash flow per share growth. And, you know, those two things driving top line effectively, and then how profitably you can drive top line, which for every software company is your sales motion is really, you know, and how you service that customer throughout that customer lifetime is really kind of the North Star. It's very depends on the company because, you know, when you get into the specifics, you know, an enterprise application software company, you know, you're really going to focus on just net new ACV essentially for, you know, those top, you know, customers that, that, you know, maybe 20% of customers make up 80% of revenue. And I want to spend a lot of my time on that. Versus, you know, a consumption software company or infrastructure software company that bills on consumption. You know, it's more of, hey, let's, let's go look at average daily consumption revenue and bookings and billings really doesn't matter as much. The real tough thing is within public markets, you know, there's not really— not many companies disclose ARR or like a real ARR number. So it's kind of backing into like a current bookings, you know, CRPO. Bookings number or net new, you know, billings, you know, current billings number, you know, to really figure this stuff out. And, but it's, you know, it's very company specific, but, you know, North Star is really getting to how fast are we growing net new on the top line and how efficiently is our sales cycle and how we monetize that, that customer lifetime kind of returns of how that flows down to free cash flow ultimately. And if you're willing. Anything that you can share about like the next layer and further down of how you look at the sales efficiency, sales productivity, and your effectively your sales modeling process? Yeah, I mean, it's given the way these companies, you know, the lack of disclosure, it's really difficult. And I think sometimes in management meetings, it's a little easier privately, you would understand those things and get a little more color., but I'm really focused on what's the cost to land like a new logo, a new, like, you know, dollar of new logo ARR, you know, what's the cost for a company to expand, you know, to do that expansion motion. So if you see a company with 120% net dollar retention for that 20%, what's, what's that really cost? Is that, you know, really like high touch upsell where we have to attach a lot of new product or is that like a simple seat or usage based increase? And then two, what are you spending to retain?, you know, that customer. So is it a really competitive space and low switching costs where, hey, we have to do a lot from a customer success and ongoing basis to keep this customer? Or do you have a really sticky platform and, you know, you might spend, you know, 3 cents per dollar of ARR you're retaining in, you know, your kind of best customers? Beautiful. And a bit of a straw man here, like, what are the metrics or focus areas that you find to be overrated? Yeah, I think anything that's really backward looking or doesn't really tie to business, you know, isn't a main driver of that business. So I think, you know, one thing might be net dollar retention is really helpful when you look at some of those cohort charts, people given S-1s or, you know, the IPO prospectus. Really interesting to go in and kind of pixel count those graphs and really understand how those cohorts grow. Like, are the newer cohorts, you know, do they not expand as much? And is it because they land higher?, you know, with a higher dollar amount, or, you know, is there something fundamental changing in that, in that subsector? But it might be even something like, you know, for a specific company, like net customer adds that quarter. So I can think of something in certain companies where, you know, 80% of their revenue comes from their 100K+ customers, and that might only be, you know, 10% of their customer base. You know, you really don't look at net customer adds there because, you know, it's not going to help you drive the model because their business isn't about acquiring a number of new logos. It's, you know, how do we really, you know, grow the number of those, you know, Global 2000, Fortune 500, you know, logos. So it's really dependent on the business. Like I said, for consumption, you know, do I really care about bookings or billings as much as like that days adjusted consumption revenue type metric of how people are like currently using things? I remember in like the kind of optimization, you know, SaaS days a few years ago, You know, that was kind of a big, you know, what's quarter over quarter, you know, average days, you know, days, average days in the quarter consumption look like for some of these companies. And so it's really dependent on, you know, companies, but I think, you know, there's a wide range there between application and infrastructure of what's important and what's less important. And this will transition a bit into the next topic around positioning, but let's use that example of the company with, you know, 10% of their customer base is above $100K. Drives 80% of the revenue, right? And then it comes out that that company had a bit of an underperformance on number of ads, even though the majority of those ads are small businesses that don't add up a lot in revenue. So that's kind of like the setup, but the earnings day reaction, people like might over or under focus on that SMB ad story. Right. And where that all gets is if you look at like the volatility in earnings day reactions for these software stocks, which can be a good thing from many perspectives, it's quite high, right? And that leads me to this question of like, where are we in the sophistication of the buy side in really interpreting the drivers as you've laid out?, right, where you understand certain of these things optically don't matter and the core drivers. And as people digest those earnings, like kind of on a 1 to 10 scale, how good is the buy side at quickly digesting? Yeah, I think it's close to like an 8.5 or 9, actually. There are a lot of different market participants, even within public buy side, with, with very different timeframes and very different mandates. So someone who, you know, and there's varying levels too, I think, of underlying SaaS and business model knowledge of, there's definitely different people. You know, you have, you have software specialists and I think they really understand how a software company operates from the sales and, you know, the sales organization to the product organization, you know, to management and kind of understand, hey, what's, what's the, how does management drive this business? Think about it. And that's how we think about it. Or., you know, and getting deep into, you know, what are they exposed to in terms of subsectors, you know, or different themes that aren't, you know, maybe related to what the business does. They just sell into, you know, high percentage of deals sells into this sector. I think with generalists, you know, there's varying levels of both software knowledge and just business model knowledge where, like you said, these are opportunities when there is a big earnings move. I think You know, there are certain names that were— I just remember this earnings season, we're down 20 or 30%, and it's not uncommon for those same companies to be up or down another 20, 30 on the next print. But, you know, then, then you also have kind of— I wouldn't call it forced selling, but people have different supply and demand constraints, and they have to cover 40 software names, and they have a very tight risk mandate, and they're, you know, they're kind of If the company doesn't hit a certain number of, you know, they're bogey or, you know, that kind of consensus bogey, it's, that's why you see such outsized reactions because not everyone can say, hey, this is, this is a great buying opportunity. You know, for some people it is like that quarter is all that mattered. Really, really good commentary. And we can kind of divide the next segment up into two things. And some of this is kind of like approaching this a little bit from like an outsider perspective, or you could just, you know, take an independent view, right? That particularly in software, like, everyone should manage expectations, and, you know, that's a CFO's job. But in this sector, right, like, the almost overdependence on beats, right, where everything is just turned into, well, it's the magnitude of the beat, and, you know, it, it's just a little bit ridiculous from an outsider's perspective. Like, do you have any thoughts on like whether companies have hamstrung themselves like with a good idea, but at this point, you know, everything is really in outside of the guidance, so it almost serves no purpose, and we're really relying on bogeys and positioning that a lot of people can't really see, and that ends up contributing to a lot of volatility. So I've totally mangled that question, but if you could elaborate a bit on your own viewpoints on like both management guidance and then, you know, how that interplays with buy-side positioning. Sure. I'll start with guidance and then get into positioning and bogeys because it's definitely a long answer. But guidance, yeah, I think most people give a revenue guidance and an EBIT guidance and maybe EPS, but there's very few that get into the actual, you know, SaaS metrics you would care about, like CRPO growth. And I think for revenue, these companies know it. Everyone knows that, you know, given a certain level of backlog, you know, the backlog and the billings and bookings are all published. So it's not that hard, you know, to know, you know, before the quarter even begins what their revenue number is going to be for the following quarter, because it's not very dependent on, you know, new business. So the fact, you know, every software company beats by revenue by 4% or 5% or whatever the number is historically, you know, and then they only beat by 3.5% this quarter and that's why the stock sold off. You know, I don't think it's because of that. There's— I really do think it is. I mean, yeah, it's a tough topic, but yeah, the revenue side and the revenue beats isn't, I don't think, the main story and reason why. It's more of that bogey positioning where now there's, you know, something that sits outside the actual consensus or guidance numbers. Around, you know, what's the CRPO growth bogey? You know, what's, what's the positioning in the stock? The pods, fast money, you know, there's kind of a— I'll go into a little more detail, but there's like a, you know, these sell-side salespeople will put out a survey where it's like you'll try to get an idea of, okay, what's positioning, with 1 being everyone's short and 10 being everyone's long. And they'll send out the same survey, hey, what's your estimate for CRPO growth for this company? Company doesn't guide it, but you know, and then they're going to publish, you know, maybe a few of those metrics out to people the week before earnings. And a few things: the, the survey on positioning, I think, you know, you'll get different stuff from different bank. You know, some of these larger funds have their own, can kind of back into positioning, and these factor models can as well. And everyone has a, you know, a z-score or positioning score for something. That's important when you see something that, you know, is up, you know, for 2 weeks in earnings and, you know, they come out and say, hey, it's 8 or 9 in positioning. Cool. Like, you know, you can— we can see the price action that it's outperformed, you know, the benchmark the last 2 weeks in earnings preview season. And, you know, we can see from the bogeys, you know, it's well above, you know, Street. So CRPO growth might be, you know, the bogey might be 21%. The thing about bogeys is when I'm submitting that survey, If I'm long the stock, I'm going to submit a number as low as I can possibly think of. And if I, if I want to be short that stock, I'm going to try to influence the survey and submit a number that's really high. So I have a friend who used to work, you know, sell-side research at one of the big shops, and he saw the actual survey results, and it's, it's the inverse of a bell curve, essentially. So people that are long, you know, send in really low bogeys. People that are short send in really high bogeys. And, you know, I guess it all equals out, but there's that. And then the stock ends up more trading on that, that kind of, you know, positioning angle and where the actual numbers come in versus that CRPO, you know, bogey. And I think that's why you might have something that, you know, CRPO growth comes in at 20.5% versus 21%, but position is 8 out of 10. You're like, okay, well, they missed by 50 basis points. Why is the stock down 15%? Yeah, it's, it's because of that. It's just historically volatile sector. You know, these software companies aren't exactly cheap. They may— most of them trade off revenue multiples. You know, now there's a little bit of a clearing event that people had this thesis on where CRPO would, would it hit that bogey or not? And it didn't. And, you know, now we get to go— I think now we get to move on and play the next earnings. Yeah, it's fascinating, like, just thinking through, like, even, like, the, the sell-side survey and, and the gamesmanship they're in, and then how all of this nets out, and then, you know, what's the third derivative of that, right? Right. I'm sure someone's heard your, your response and is staffing someone junior to like, all right, how can we come up with the next level of the gamesmanship and infer the positioning of the positioning, like third derivative of all of this stuff, and further the arms race here. This is— it's just fascinating to me. It is funny. I know people build a lot of these sector dashboards, and it was one thing we really helped with that Alpha repo, is if I want to get You know, there's CRPL, you know, at kind of the highest level you can think of it as probably the best thing that approximates ARR for a lot of these companies that are public. And then you get to subscription, you know, CRPL bookings, and then you might get to like a net new CRPL bookings. And people want to compile the year over year growth rate of net new CRPL bookings across 40 different software names they cover and what that looks like, you know, both historically, but then to their future numbers. And what that looks like against consensus. So it's one thing we help with at AlphaRepo, and a big reason why I started it is because I do want to see across the entire industry what's that third derivative, that net, you know, the year-over-year growth rate of net new CRPO bookings across my coverage look like. And we make it very easy to pull that data out of Excel in a way that doesn't interfere with an analyst or PM's daily Excel workflow, but then compile that for people, you know, for people in a PM seat or analyst seat to view it and actually, you know, kind of make your decisions, you know, off of. You know, realistically, just give us the elevator pitch for AlphaRepo, alpharepo.com, because I think there will be a lot of learnings in simply like what you've built, why these features are there, that will tell you like kind of the state of the market. So what's the elevator pitch for alpharepo.com? Sure. So at a high level, if you think of it Everyone in buy-side investing lives in an Excel model, or public markets investing. That's the source of truth on that name. Every piece of work you do in research in your, you know, doc file or OneNote file feeds into some sort of estimate in that model. And there's varying degrees of like model complexity. Some people, I just want to get the high-level drivers right, the 2, 3 things that really matter for this business and put a reasonable multiple on it in 5 years. And understand my risk-reward. Like, you know, when's a good time to buy? When's it— what's, you know, my margin of safety? And is this something to buy? Versus people that will model, you know, just religiously, you know, 1,000-line models, really detailed on every, you know, quarter, trying to get in and find these like mechanical beats. So they might have pulled— I think a good recent one like I saw was, you know, they're pulling a slide from, you know, Google's GCP's like conference before this last earnings., and was trying to understand the token revenue because they'd given last year's number and it had grown 50x. And they were trying to throw this in the model, put like a token cost per token estimate on it. And they were showing like, you know, 4.5% beat. I think GCP beat by, you know, 4.7. So you have varying degrees of this, but the main story is Excel models are very siloed. You live in, you know, Google Model V25, but when you go to V26, you have no kind of view of how stuff changed from V25. So public markets are really about, you know, revision, how you've revised your numbers, you know, but also keeping track of things like your IRRs, risk-rewards, and things like that. What AlphaRepo does is essentially like this Excel plugin that, you know, sits on top of Excel that'll read every line item and everything you want to track within your model, and it gives inversioning and like history to it. So you can then send data, either view that revision data or send that data across the organization. So we kind of go from this very siloed and stale, like Excel models to something we kind of make it global and dynamic for public market investors. So understanding, you know, we met with management, we're revising our numbers down, consensus is flat. You know, if you think about that, you know, with a team of 5 analysts that are covering 40 models each. You can't possibly see, you know, across 200 Excel models as the PM, you know, the information in those revisions that you care about, especially as you connect consensus data, alt data, and things like that. And then, so that's more of like our, what I'd call like our pod and shorter-term type client. For our longer, you know, kind of duration clients that are going to own something for 6 months to 5 years, it's really about building those sector sheets, CIO dashboards, in pulling that data out. That's really important to on a per model basis in just understanding their entire book and their entire coverage of where's their best risk reward, where are we biggest, you know, where are we furthest above street in data. And then two, how do we share this or like data across the organization? So in the prior example of building that net new CRPO bookings dashboard for my entire 40-name coverage, I'd have to store everything on like SharePoint and then reference all those files individually. Make sure my calendar matches were correct with all the different fiscal calendars. And it would probably crash my computer trying to reference 40 different Excel models. And I'd have to try to like figure out which one was the latest and always be referencing that. And we sit in your Excel workflow today the way it is with no templating, no changes, and allow you to do kind of anything you would want with those numbers outside of that model in creating history and attribution to it. Wonderful. And what type, like, as always, like, more people listen to these things, like, looking for the next thing and effectively, like, want to know what they can buy and who should be buying this. Like, what's your ideal customer profile? Yeah, we deal with a few. So it's, there are some things where we've, they've attempted to do this before, and this is probably your top 20, 50 byside public equity firms. And it sits in like— it's a separate template page today, and it takes, you know, a lot of time out of the analyst workflow, or back office is updating these numbers manually to send them to the CIO, and then to sync it all back into the CIO's dashboard. It takes a lot of time, it's cumbersome, and it doesn't cover the entirety of the Excel model because it has to fit into a template. In those kind of conversations, we work with the tech team, usually CTO or the head of data, the head of fundamental equities engineering for those type of conversations where we're ripping something out or replacing something that was really cumbersome before. There are other kind of line of businesses. We just go straight to the portfolio managers or CIOs and they can get off and running out of the box, you know, really quickly within a few minutes and they can get everything in the web app and dashboarding. And, you know, the Excel plugin very quickly. And it's, you know, so if you have problems that you want to see your revisions, you want to see how accurate you are, you want to see how accurate your analysts are at different metrics. They're really good at, you know, mid-cap internet incremental margins, but they're not so good at, you know, MAU-type metrics and large-cap internet, things like that. And even stitching together these giant sector sheets. So I want to see my divergence against, you know, Visible Alpha consensus across all my same-store sales metrics for all of my restaurant coverage. And I, yeah, I just want to build this so it's always live. And that's where we go to the PM and kind of work with, you know, understand their workflows. There's a wide range of what we do, but it's kind of understanding that what resonates with them, what they're doing today manually, that's a real pain and pain point for them. And, you know, you wouldn't expect, but maybe one of these, you know, PMs at a large shop might be even spending, you know, 8 hours a month, you know, doing something manually and their analysts are even spending more. That then we're then freeing up where they could do actual fundamental research. And can you give any commentary on how, like, on the public company side, the IR side, the CFO side, their sophistication in their forecasting? I, like, I know it's more outside in, but compared to this buy side sophistication, right? Yeah. I think IR, there's, there's a few things. And I've read this somewhere. I forget where there's someone that says like Morgan Stanley does a pre-IPO kind of teaching for CFOs on guiding. So yeah, they just always guide low because companies that always beat guidance go up. So I don't think anyone expects any software company that becomes, you know, comes public to start guiding, you know, more realistically, like numbers they actually think, you know, they're going to hit or hit by a small margin. The one thing I'd say on IR is they're either a little too good, so they know that the key debates coming into that call, and it's all scripted for management, you know, pre-queuing it and it's like that after, you know, might go through a few years and it's always a little too on point to every little debate and questions answered or there's too little. So it's really impossible to understand from public filings or even talking to them, you know, the key kind of drivers that they view the business on, like in-market exposure, if you're really trying to dig deep into those kind of answers. But I don't think you're ever going to change the guidance philosophy for for many of these companies. The one thing I would say is, especially given some of the SaaS sell-off recently and what I remember from '22 on is, you know, free cash flow per share is the north metric. And if you can reliably grow that, it's always good to make the right investments long-term for the business, you know, when those are needed, but really focusing around free cash flow per share and making sure you're growing that and, you know, you're not you know, if you get too bloated, that there's the right long-term focus on that is, I think, the biggest thing, because you're not going to change guidance philosophies. There's varying degrees of how forthcoming certain IR teams are willing to be with information. I don't think, you know, that's either a DNA thing or, you know, you're very intentional to either be very forthcoming or, you know, ahead of the ball versus, you know, more closed off. And the public comments are all we're going to comment on. But just making sure I think investors know there's a strong focus on free cash flow per share and growing the business sustainably over the long term. Because I think right now in public markets, or at least the stuff I follow after some of these sell-offs the last few days, you know, these are like, you know, a lot of these are very good businesses. They're very attractive business models. And, you know, they just need the right, you know, people, you know, it's tough to bring generalists into this to get excited about software. If revenue growth is slowing and profitability isn't, you know, it's kind of wavering, you know, you're not growing free cash flow per share at a higher clip than revenue, even though revenue is decelerated, you know, 20 or 30 points. Yeah. And two things there. One, like for a number of the IR folks or CFO folks listening, they're like, well, we don't have any free cash flow. So that's a challenge. And then two, like kind of from the generalist side or just just, you know, being a valuation-aware investor. Like, you can look at some of these price targets and it's, you know, 34 times 2032 free cash flow discounted back to today, and you're like, you know, 34 times and way out, that's, that's a lot. That's a big, pretty big multiple, particularly for how durable these businesses might be. Like project management software or observability software or so many forms of infrastructure, like the amount of change they go through every 3 years, 5 years, that, you know, 2034 might as well be 2070 in a lot of scenarios, and you're still paying 34 times free cash flow to get, you know, a 12% IRR, and you're going to deal with an incredible amount of volatility, you know, every damn quarter. It can be a challenge to dip your toe into some of this. Yeah. And yeah, no, it is. And I am sympathetic to the IR people and CFO that have to deal with the varying degrees of, you know, extremely short-term modeling questions that they hear during conference and callback season that, you know, probably doesn't sound too relevant to the business for them, but given the business models of some of these larger funds that they just have to, and who pays the commissions for these conferences, it's, you know, they just have to answer those things. But also, yeah, there, there's all, there's a wide range of businesses and there's a wide range of even within software. I think, you know, there's a lot of terminal value questions right now, but it's, you know, there's a wide range of companies out there people can be investing in. And unless you've got a really good story of why, you know, you are investing, you know, today, you know, not growing free cash flow or don't have, you know, free cash flow to really kind of understand and get right about that, you know, the days of everything trading above 15 times, 20 times revenue or or over for a while. Yeah. Look, this has been great. This is— I love the level of detail and sophistication we've gotten into. And I think that would be great for a lot of people. And then, you know, podcasts in many ways are for learners, right? And for someone who's in college or is an investment banking analyst or junior equity research associate and wants to get to the buy side, what are some advice you'd give them both to break in and then be, you know, hit the ground running and really be a great analyst? Like, what are some— yeah, yeah. The biggest thing is getting at-bats. I don't think anyone's a good analyst their first year to 18 months. To answer the last part of the question, just get at-bats pitching, like asking people for feedback. And people are very open. If you send, you know, if you send 100 people your idea on, you know, something that, you know, maybe they own in their 13F, I'm sure you'll get, you'll get you know, a few, you know, if not a few, maybe 10 responses and people be willing to talk. Like, just get at-bats and, you know, try to talk to people and people in the seat to understand the line of questioning they ask, what they're focused on, especially if they own the name. You know, most people aren't going to have a massive ego talking to a college kid and try to show you all they know on it. Yeah, they're gonna, they're gonna try to guide you through it. But the biggest thing, I think this is more in I'm very thankful I came about before the ChatGPT era of summary and going through this data. But if it's really a name, you know, within your coverage that you're going to focus on, start with the primary docs and go religiously, you know, through the last few 10-Ks, maybe the S-1, go through the last, you know, maybe 5 Q4 calls and the last 8 quarterly conference calls. Get an idea of, you know, you know, the business. How do they talk about the business over time? What questions are people answering? What's that narrative? Go through the conference transcripts of how they talk, you know, on kind of a broader basis to a wider audience on these things. Like, really focus on those primary documents, then go to some of the secondary, you know, documents like sell-side research, maybe some, you know, primers on, you know, that, that subsector, you know, going into the competitor, things like that. And really writing down, hey, what are, what are the drive— like, what are the, the few, you know, key drivers of this business? What are the, the 3 things that really metrics that matter, how they think about the business, you know, where is growth going to come from? Like, do I have a view on, you know, just the overall industry growth, subsector growth, you know, this company specifically, you know, what really matters? What are the big questions that like we need to answer for this? You know, not for this to be like a good investment, but like for this to be, you know, is this a good company? Like, what do I have to answer? Like, what are the biggest risks to them? The way they run the business today, like what are the biggest risks in their, their like own planning for the next 5 years? Or like, is there— I think in software, especially in small cap, you can deal with something that's $300 million of ARR, and there might be something that's $15 million of ARR that like, you know, we really need to watch, keep an eye out on. So understanding all that primary research first and then go build the model, like understand, you know, how do I flex different assumptions and net new ARR growth and how does that flow through? Am I thinking this is software specific? If you're building that sales and marketing off of net new, or if you're building sales and marketing first to drive net new, how many people do they need to actually hire? And I think this is where a lot of people, especially buy side, it helps to really understand the SaaS business model. Good examples, Shopify. I think an analyst came to me and it was a different firm came to me a few late last year, he's like, I've noticed Shopify has a ton of job application, you know, job applications up for enterprise sales reps. And they have like something like 20, which is like, you know, he's like, oh, I think they're really going to beat on GMV, you know, as they bring over, you know, these enterprise accounts. And you kind of have to walk them through, okay, they've got 20 jobs, you know, applicant, you know, they've got 20 positions up today. What's the period of hiring them, what's their ramp period after that to when they start actually going after deals? What's the time to close deals and how are they going to compete against Salesforce and Adobe on those deals? And do they have the right motion, the right people to actually win that and run this organization? Okay, cool. We're probably 5 quarters past today now from when they hire all these people. Now what's the ramp time of moving over to Shopify and actually ramping the GMV sold on Shopify platform? So. I think if, you know, you need to like have a little bit of an operator's mind to, to be a really successful buy-side analyst and think how the company thinks. But you need to do the primary work first to really answer like and come up with the list of questions you need to answer on what drives that business, what are the risks. And then you can go into the model and say, okay, this is how I think the mechanics of the business work. What's a good price to pay for this business? What things would invalidate my thesis that I've, I've priorly, you know, my prior thesis. And then too, if, you know, if that happens or, you know, then, you know, how do we think about returns from here? You know, maybe they keep beating your estimates. It's going really well. You know, you underwrote it at 12% IRR. It's returned 20% for the last year. Alpha Repo helps you understand, hey, that's because, you know, revenue beat consensus by 8% and they beat our revenue number by 5%. But, you know, we got the multiple a little bit wrong. But, you know, how do we go from here? So as we roll our price target forward, how are you keeping track of that and, you know, having some process and some system around that? But that's the main thing. Do really good primary work, synthesize it, ask good questions, build the model, understand risk-reward, and then it's ultimately just do that as many times as you can and do solid work around that and then get feedback and just take as many at-bats as you get. So that's an amazing answer, and it might vary by role, but you, you included AI, and it's fascinating to think about, you know, when you think about filings or digesting earnings or all of the other dimensions AI is going to be capable of or is already capable of. And so the question there is AI for the buy side, opportunity, threat, or neutral? I think it's a massive opportunity, actually. I know adoption's been rather slow, especially at some of the firms we talked to that are larger and have very valid security concerns about those. But there seems to be a turning point of, hey, let's figure out how we're actually going to harness this because it's a— there's— I cover 40 software companies and then I have a pretty wide net within consumer where I just opportunistically look at. Things. And I can probably go summarize every restaurant earnings. You know, if I'm really focused on a few growth restaurant stocks that are on my watchlist, I can go summarize everything to get me— hey, pull out all the cost bucket information from these 30 restaurant names that have reported. I was never going to be able— like, you know, no analyst is really going to be able to do that even that quickly. It's not a great use of their time either, especially if it's not in my core coverage., but I can go understand, hey, how did those cost buckets, you know, what are, what are people seeing right now? Oh, it's going to summarize the breakfast places see this in eggs. You know, Texas Roadhouse looks at risks because they're kind of more, you know, middle, middle of the road and beef prices have gone up, you know, 8%. And this has been mentioned in XYZ's, you know, transcript as well. So I think it's a big unlock in terms of what people can do, you know, and, and kind of you use it as like a research tool. I haven't seen anything yet, and that's a massive, massive, you know, is going to make the analysts obsolete. Maybe, you know, analyst hiring slows a ton in the next 10 years, but you're still going to need humans in the seat. They're going to have to drive that model. They're going to have to do the work. They're going to have to make decisions. You know, there's a lot of human judgment still involved. And, you know, the big part in financial services that I think people we're worried about that's starting to get solved through some of these MCP things like Anthropic's doing with, you know, people like Daloopa and, you know, bringing in, I think, S&P Global and a few other people is actually getting in, you know, the right data to actually, you know, the true data. Because, you know, these things for a while would hallucinate. It just make up, you know, numbers on whatever Salesforce's revenue was. And they were really difficult to use for things that were outside of bringing together a ton of transcripts or podcasts. Summarizing or synthesizing them for you. Yeah, that's a wonderful answer. And I tend to agree with you in so many of those things. And you step back a bit and the sophistication of all of these things, like when I think about like what an equity research report looked like and kind of my earliest exposure to them was 2002, right? And also the work we did in our investment banking group, how we maintain comps. Right. It was pre-CapIQ and it was, you know, and all of these things. And now like everything you're discussing right now. And so layer in AI and the sophistication of these firms and the analysts and PMs involved. It's just going to go to a whole nother level. And what was an 80-page deck is now going to be equivalent of 750, but it will also have like Q&A built in and you know, 5 times the amount of like data processed and all of these things that I think you fundamentally like can't get rid of a smart, motivated human from this. And then when you also think about the dollars involved, right, you're putting $150 million trade, right? But like, why— like, just thinking as a business person or anything, like How can you let a machine kind of do that unsupervised? Just, yeah, you share duty even, right? I think I'm like, yeah, if you think of like investment banking or equity research more as like sales roles, you know, I think the hesitation in public markets like buy-side is, is really, you know, it's not a sales role. It's a you make money or lose money role. Yeah. And the risk that something, you know, the more you automate and the more you trust it, I think there is that trust issue still of— I mean, I know some tools that aren't AI tools that still people don't trust fully, even though they might have 99.999% data accuracy because of the process of, I want my analysts entering these numbers manually and things like that because there is too much at stake for them. Essentially, my job's at stake, my bonus, whether I make money, lose money at the end of the day is You're too important. So I'm sure probably I'm not thinking big enough yet, but you know, I'm sure like we'll continue to supplement this buy-side workflow process with different AI tools over time. But like we've been on a few of these AI tools for hedge fund lists and AlphaRepo, and I think bar a few, most of them are just ChatGPT wrappers on filings and public, you know, publicly available data. But I think we'll get more and more of these kind of either custom workflows or deeper for your buy-side finance workflows that are, you know, more AI-heavy and built to actually fit in. The other thing I'd say with AI is it makes the services side for kind of how we think about the business at AlphaRepo, it makes that side a lot easier. And we definitely like, you know, do a lot more custom type onboarding and services kind of division here because we're fitting stuff to process you know, we want to make sure our tool fits in with their process and how they want to, you know, view that data. You know, our bread and butter is the extraction out of Excel and then throwing data back into Excel, whether that's from a different model or from an external API. That really fits into the analyst process really well, but we are kind of, you know, how we do the services side, AI really does improve. And I think there'll be a lot of very custom type, and maybe that's agentic, you know, workflows that really solve. Some of these smaller and very company-specific and fund-specific workflows, because they're all— every fund is so different and a lot of these templated solutions just don't work. Yeah. Doing some work on our side, quantitative development, everything, it is amazing, like what is now eligible and open to so many people. And it's going to be exciting to see all this unfold. And I guess we'll just wrap it up with, again, I've noticed, you know, people listen to these shows to learn like the latest and greatest. Just like the final mini elevator pitch at AlphaRepo, how to find you, how to find you on socials, how to work with you, just something for people to, to hang, hang their interest on. Yeah. Yeah. So AlphaRepo, if you think of any issue you have in your current Excel process, whether you're a buy-side firm or even, you know, we can work with anything with a date row, honestly, that you have where you're trying to capture point-in-time Excel data., and then use it for any comparative purposes to other datasets or to itself, or sync data in and out. That's, that's what we do. And we do it in a very seamless and way where we fit in. You don't know, you know, you barely know we're there and there's nothing you have to change about your current workflow for us to fit in. Best way to find me is, you know, we do some LinkedIn and Twitter postings. So it's Joseph Burkert on LinkedIn. alpharepo.com is our website. And then I have a formerly anonymous Twitter account, @SasquatchC on Twitter, but that's, I would say that's mostly used for product marketing at this point. And I will credit that, by the way, through our analyst business, we interact with a lot of CMOs and product marketing folks and we cover those sectors. And Joey, I have to say, you are one of the best product marketers I've seen in the last few years. Like if you guys go follow his LinkedIn or his Twitter, right, he does a great job of What I like about it too, it's, it's true demand gen that you have. Like you always get someone to a demo page or a screenshot. It's not brand, it's like pure product marketing demand gen. And, and I love it. So you, if you wanted to, you could go to any of these software companies and be a top tier product marketer. So, well, thank you. I really appreciate that, Matt. I know we've discussed in more detail before, but it's Yeah, it's, it's, you know, I don't know if we call it FIDE marketing or, I mean, we try to keep it very solution specific, but I think you'll see even more of that for some of these different ICPs, whether it's CTO or PM at the fund. That's, that's, we can break down to, you know, we kind of have 5 or 6 different solutions we actually sell value props. So I think you'll see even more of that over the next month or two. Well, I love it. It's, as I say, it's all gas, no brakes marketing. Right? Like you get the Calendly page in there, right? There's no like— there's always a call to schedule. It's, it's get to the Calendly. Like, I love it. I think more software companies need that. I think we would see better sales and marketing metrics. There would be a lot less, you know, wasted leads or low quality. These are the only people who are clicking on your Calendly page, like, get it and want to talk to you. And so I think maybe for any of the operators or some of the private equity people who have some control over their portfolio, go look at Joey's LinkedIn and think about how your portfolio should be doing that or your own company, like all gas, no brakes, product marketing. So I think we'll close out on a lighthearted note there. And look, I appreciate the, all of the advanced commentary you've brought to the table here. And this has been fun. And, and everyone, as always, the show notes will have everything if you need to find anything or anything about Joey, it will all be there. In the show notes. So thanks, Joey, and see you soon. Yeah. Awesome, man. Thank you so much for having me.