E446: Vista Equity President: The Death of SaaS? AI Agents and the $3 Trillion Opportunity

Is AI destroying the software industry, or creating its biggest investment opportunity yet? David Breach is President of Vista Equity Partners, a firm managing approximately $100 billion in assets. In this episode, David explains why the recent selloff in software stocks may be missing a much larger opportunity: AI-powered digital workers. We explore Vista's repeatable playbook for creating value in enterprise software, why mission-critical products and proprietary data remain competitive advantages, and how AI could expand software's addressable market into trillions of dollars in services. David also shares how Vista evaluates software companies amid AI disruption, why incumbents may be better positioned than startups to deploy AI agents, and the career lessons that took him from selling juice to leading one of the world's largest software-focused private equity firms.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  We're now entering a phase, we're all referred to it as software as the worker. We have the ability to actually create and deliver effective digital workers to companies. So we could never do that before. So that is a whole new opportunity for us as software vendors. And it creates a whole new TAM. Some of the major consultants, BCG as a, for example, they've estimated that there is a $3 trillion services TAM that is available to agentic software solutions.

• 0:33David Weisburd?

  Has AI made software category fundamentally uninvestable as investment class?

• 0:39Speaker 00

  No, I think.

• 0:40David Weisburd?

  So you met Robert Smith, founder and CEO of Vista in 2003. Fast forward to 2014, your senior partner at Kirkland Dallas, one of the top law firms in the country. And Robert asks you to leave to join Vista. Why did you say yes?

• 0:57Speaker 00

  Fundamentally, he gave me a very compelling case on the value and opportunity in software and why, you know, Vista was very uniquely positioned to really take advantage of that opportunity. Yeah, Kirkland, I had been part of, you know, growing a business, building out a team and the opportunity to do it again with Robert. I think he's just a phenomenal human being. And so being partners with him and being on this next leg of the journey was very interesting to me. I'll be honest, I didn't just jump right at it because I was frankly, super happy. I really defined my life and evaluate my life on, am I happy when I wake up every morning or not?

• 1:42Speaker 00

  And I was waking up, you know, pretty happy. But I saw this as like the next great challenge for me. And, and so I made the decision to make a big career pivot and, and join the world of investing.

• 1:57David Weisburd?

  And Vista at that point had $13 billion AUM, 100 employees. What was the sales pitch on how Vista was going to get that much bigger? Cause at that point, it was one of the larger firms already.

• 2:08Speaker 00

  Yeah. So what was interesting, he told me like his goal back then was, you know, David, if we do this well, we could get to be 50 billion of AUM by 2025. And, you know, here we are, I think about 100 billion in 2026. So we exceeded plan as they say. But Robert, his design around the firm was very unique. I'd seen a lot of firms in my prior career, represented a lot of different private equity firms and met some really, you know, just talented, interesting people. It's a really, it's a really dynamic, interesting industry. But Robert's approach to investing, I thought was just very unique. He was really focused on how do we make value creation something that's, you know,

• 2:52Speaker 00

  replicable, predictable, repeatable. You know, he's an engineer by training and he really kind of thought as an engineer, like what can I do to build that, you know, we can repeatedly create value? And so he really focused on, you know, what are the core tenants of doing operational transformation of these businesses? And what are the resources that Vista could build to help support these companies in those transformations? And so I thought that approach was just super unique. He had had obviously a, you know, record of successfully transforming and creating value.

• 3:32Speaker 00

  And I was also obviously just very fascinated by technology and, and I could see how from the build out of the internet, you know, I was a lawyer during the build out of the internet in the late 90s and early 2000s and saw the whole, you know, build out of broadband and whatnot, which frankly has some analogies to AI today. And just really, you know, saw that there's massive opportunity and, and it'd be, it'd be quite exciting to be part of it.

• 3:59David Weisburd?

  And I've spoken to Eric Berry, who started Triple Bite, who you guys purchased for over a billion dollars a handful of years ago. And he really stresses this Vista playbook. He's a true believer in the playbook and the value creation. Maybe you could double click on that. What does it mean to provide value add as a software private equity investor?

• 4:21Speaker 00

  It's evolved over time. So when, you know, when Robert started investing in 2000, most software was on premise software. And, you know, you would literally send discs out to your customers. They would download updates and whatnot. And so the playbook in an on premise world started to evolve as, as the model moved to software as a service and to a cloud delivery model. But, but fundamentally, we've developed a set of best practices that what do really well run software companies do well? And we think about four big buckets of a company. So what do they do from a product and technology perspective? What do they do from their go to market and sales motion?

• 5:05Speaker 00

  What do really well run companies do from a talent perspective? How do they pay people? How do they recruit and hire people? How do they evaluate them? And then what are the other operations that need to be optimized? How do you do, you know, contracting with your customers? You know, how do you do a quote to cash system and what? You know, how do you organize your, your functional operations to support good decision making? So within each of those four big buckets, we developed a series of best practices. And then we built resources within Vista, what we now call our value creation team that can go in and work with senior managers or senior leaders and really talk to them

• 5:47Speaker 00

  and teach them in some respects, what these best practices are, how to implement them, how to measure their efficacy. So we're not, it's kind of the old analogy, we're teaching people to fish, not giving them fish. We're helping them optimize their opportunity, whether it's on the, you know, revenue growth side or on the, you know, cost optimization side of things. And as we underwrite a business, it's really through that lens. It's like, okay, we're not financial engineers, you know, we're operators of businesses. So as we look at a particular business, there's lots of things about that business that we like. Otherwise we wouldn't want to buy it. But where do we think there are opportunities for this company

• 6:30Speaker 00

  to really optimize its opportunity to go to the next level? Cause in most cases, we're buying businesses that have growth prospects. And how do we help them accelerate those growth prospects? And how do we help them do that profitably? And so, so we will have a thesis around here as areas that we think we can be helpful. We'll get aligned with, you know, our CEO and their senior management team about that. And then as we close the transaction, we have a very clear plan. Our value creation plan in terms of things that we want to accomplish with the company to help them again, optimize for their next phase of growth and opportunity. And I think that the, what I'll call like the secret sauce of Vista

• 7:13Speaker 00

  over the course of time is number one, you know, having these deep operational perspectives. But then importantly, building and evolving our resources to help support those operational transformations. And again, as we move into this next phase of innovation in software around, you know, AI, we've dramatically evolved the resources that our companies need to thrive in the world of, of an AI world versus, you know, what we used to do back in the on prem days. And so that, that's I'd say fundamentally how we, how we approach our investing in value creation.

• 7:52David Weisburd?

  This episode is presented by Juniper Square, the operations partner for private markets.

• 7:59David Weisburd?

  It seems like only recently we had the SaaS apocalypse, which was, which was a very viral name and also coincided with a big drop in software, both public and presumably private companies too. Although obviously private companies are marked day to day.

• 8:19David Weisburd?

  Has AI made software category fundamentally uninvestable as investment class?

• 8:25Speaker 00

  No, I, again, I think there was a moment in time in February where at least in the public markets, there was this, we don't know how to evaluate, you know, what the prospects of a particular software company are or aren't. So we're just going to like rerate and trade out, right? And it was not, there was no particular metrics or fundamental financial, you know, performance data that would have said all these companies became less valuable. There was just almost like a concern of the unknown. Like what does this technology really mean for the opportunity? As we sit here in October, you've seen, you know, most of that valuation reset recover in the public markets or certainly a lot of it.

• 9:09Speaker 00

  Because I think people now understand that, you know, as a, as a fundamental matter, software still remains critically important to business and enterprise. Cause we invest in enterprise focused software. But importantly, a lot of these enterprise software businesses are well positioned to thrive and to really help companies leverage this technology and incorporate it into their businesses. So as you think about, you know, businesses that see this powerful technology, AI, and in some respects are struggling with, well, how do I implement it in a way that's effective? Where I'm getting real ROI?

• 9:52Speaker 00

  We as the incumbent software company are very well positioned to bring, you know, new solutions and it's, it's actually really opened up a whole new TAM for software. I mean, if you step back, software was originally helping humans manage workflows between humans. Right? It was, we're going to automate, you know, manual processes and workflows. And it was incredibly productive and is, you know, arguably one of the most, you know, productive tools introduced into the global economy over the last 50 years. But it was really, you know, there was on premise software. Then there was software as a service. We're now entering a phase where all we're all referred to it as software as the worker.

• 10:35Speaker 00

  So we now with these AI solutions and the ability to create agents, we have the ability to actually create and deliver effective digital workers to companies. So we could never do that before. So that is a whole new opportunity for us as software, you know, vendors. And it creates a whole new TAM. Some of the, you know, major consultants, BCG as a, for example. They've estimated that there is a $3 trillion services TAM that is available to agentic software solutions. So that didn't exist before. So, you know, we're in a, I'd

• 11:18Speaker 00

  say we're in a major innovation cycle. The same way we're in, we were in an innovation cycle, you know, in the early 2000s, once the internet became a thing and people started to realize, oh, wait, we can use the internet to deliver software. So you had on-premise software and there was a rerating in the, in, you know, 2014, 15, 16 time period where people are like, you know, how should we think about, you know, this new SaaS cloud model? Is that a good thing or a bad thing for software?

• 11:48David Weisburd?

  Because before you would pay $100,000 or sometimes a million dollars for a software package and then you would have to rebuy it a couple of years.

• 11:55Speaker 00

  Exactly. And so, so there was this whole question, okay, is this new model, this subscription model and cloud delivery, is that actually a good thing for software or not? And so there was, there was a rerating and, but ultimately people realized, no, this cloud delivery is actually an accelerator of opportunity, an accelerator of growth. And so you saw companies that is an on-premise company might trade at two and a half, three times revenue if you converted them into a cloud business or a cloud delivery model and a SaaS model, they were fetching six, seven, eight times revenue. And so there was

• 12:36Speaker 00

  a rerating if you could show, now there are still companies, like what's interesting, almost 50% of all software is still on-prem. So here we are, you know, what, 20 years into the cloud migration and there's still a whole bunch of on-prem software. Right now, you wouldn't pay the same value for an on-prem only solution that you would with a cloud solution. That same thing is going to happen, you know, we believe now. If you're a SaaS software business and you don't really leverage this technology and create new value propositions for your clients, you know, your growth prospects are probably going

• 13:16Speaker 00

  to dim. You might even have an eroding base and you're going to get a valuation that reflects in some respects, your lack of opportunity. Now, maybe you can enhance your margins with, you know, code, you know, code gen and other AI operational solutions, but your fundamental growth prospects are going to look less attractive. There are a whole bunch of companies and, you know, part of what we at Vista really focus on is how do we help our companies innovate their products such that they can bring these digital workers, these agentic solutions to their customers, open up that new value proposition and get paid for it. And I think if you're successful at doing that, which is kind of what our job is, is

• 14:01Speaker 00

  to help our companies be successful doing that, I think you're going to see a major rerating of the multiples up for the companies that prove they can do that. And that's really what we think the opportunity is.

• 14:11David Weisburd?

  Fascinating frame. It's very much like the large LLMs. I've been investor in Anthropic since 2023 and they've really all evolved along with OpenAI into this disruption of services. Why? Some would argue cynically that's the largest TAM, but there's a lot of early, early signs of it disrupting services. You see software as the way to implement this disruption of service, at least some portion of the overall services TAM? Or do you believe that software and vertical software will capture all the TAM?

• 14:47Speaker 00

  I think there's a couple of things embedded in your questions. One of the embedded questions that you're asking, are these large language model companies going to be a platform? Or are they going to evolve into specific products? If you look at the evolution of software, you know, back in the day, there were, you know, early software companies that became platforms upon which a whole bunch of other products were built. And you could say, well, why didn't the platform build all the products? It's because it's hard. It takes a lot of work. It takes a lot of understanding of individual verticals, individual customer needs, and it's just hard to do that at scale, right? So then a bunch of other companies grew up. And so there's a reason why today there's roughly

• 15:29Speaker 00

  100,000 software companies because of, of the need to develop expertise in a particular vertical, a particular workflow, a particular data set, what have you. I think you could make the argument that you, you should see the same thing here, which is the LLMs are going to largely become large platforms upon which applications sit. You've also got this whole other dynamic, which is you're going to see more of a proliferation of what, you know, we call the open source, open weight models. So you've got, you know, what get referred to as the frontier models, you know, the, from Anthropic and OpenAI, you know,

• 16:09Speaker 00

  Chad and whatnot. But you're going to see a number of open source models because if, if you think of these models like broadly as just intelligence, you know, depending on what you need to do, you need a different level of intelligence. And presumably those levels of intelligence should be priced differently, right? So, you know, an analogy I use, if, if I needed to teach, you know, one of my kids high school algebra, I don't need to hire an MIT mathematics professor to do that. Hiring the LLM to do a lot of the agentic work in an enterprise is the equivalent of hiring the MIT professor when I could probably

• 16:53Speaker 00

  get away with a high school math teacher, which gets paid at a different rate because of the different credentials and work to get there. So in any event, you're going to see platforms and then I think you're going to see a whole bunch of products. The question is, are those products going to be built by software companies? Or are they going to be built, you know, natively by someone just building a native application on top of one of these models? I think the answer is you're going to see both. Like in any innovation cycle, there are going to be, I think as the incumbent provider of technology, you are in the best position to win, meaning you currently on the customer relationship.

• 17:35Speaker 00

  You currently house all of their important data. You deeply understand their workflows, their, the context of their business, how they interact with their customers. You've got all of this knowledge. You should be best positioned to say, this is where I can build something that will give a new level of value to you. Like I understand your business deeply and this is the agents or the apps that are going to create a new level of value. And so I think if I build it, and by the way, I've been protecting your data security for the last two decades. You trust me to do that because I've done it for 20 years. If I build that, you should want to buy it from me. I'm already there. You already

• 18:17Speaker 00

  trust me. We already have a relationship. I've already got a thousand customers. If I build something interesting, I can push it out to a thousand people almost overnight. That being said, not everyone's going to innovate. Like that's, that's the great thing about capitalism is that, you know, you're in a competitive world. So there will be some folks that, you know, build a native application on a model that didn't start with a SaaS application, that will build something really interesting that will be disruptive to some incumbent players. And so similar to in the days of like on-prem, where someone built a new cloud native application that was great and those on-prem companies

• 18:59Speaker 00

  that didn't innovate got, you know, eaten, you know, that, you know, very well is going to happen. There will be companies that don't, you know, succeed in this innovation cycle. And again, I think our job as, you know, stewards of companies and as investors is to make sure our companies are at the front end of this innovation cycle and that they have the tools they need, the resources and know-how that, and expertise that we can help them with to be on the front end of the cycle. And so I think it'll be a little bit of a mix. I mean, we're in a competitive world. It's going to be a little bit of a mix. I don't personally believe that the frontier companies are going to build applications for every single use case. And I don't fundamentally believe that companies are going to build

• 19:42Speaker 00

  them themselves at scale. I think there's too many data security issues. There's too many worries about data leakage. There's this whole question about, are you going to take your data out to the model? Or are you going to have the intelligence in the model come into your environment for the data? And again, that's where I think as the incumbent software provider, we've got a really important advantage to be the successful provider of this technology.

• 20:08David Weisburd?

  I want to get into data security in a bit.

• 20:12David Weisburd?

  But a lot of people listen to this podcast, they're investing not only in the private markets, but also in the public markets. So much of S and B 500 today is just software.

• 20:23Speaker 00

  Yeah.

• 20:23David Weisburd?

  Maybe outside of the Mac 7s, a lot of them are becoming more hardware, but still a large portion are software companies. If you take away the specific vertical and just the idiosyncratic qualities of a software company, you turn into this kind of generic, very valuable software company. What do you look for? What makes a tier 1A investment for Vista, whether on the private or public side?

• 20:50Speaker 00

  You have to start with, are you providing something that's mission critical? And, you know, people sometimes confuse like an ROI with mission criticality. So I could sell you something that has really good ROI to you, but it's not mission critical. And if times get tougher or whatnot, you'll quit buying it because I just, I gotta cut costs somewhere. If I'm selling you something that's mission critical, you can't turn it off because I'm core to how you run your business. So we look for something that A is mission critical, B is deeply embedded in the workflows of your business, C is working with your most important data.

• 21:30Speaker 00

  And then we historically would look at a number of metrics to validate, you know, those views like, is this really sticky? If it's really sticky, then your customer retention rate should be, you know, 95%. If you're losing 20% of your customers every year, it must not be that sticky, right? And so we will look at, you know, financial metrics to help validate what otherwise appears to us to be something that's, you know, sticky and mission critical. So that's, you know, ultimately what we've historically wanted to invest in because we feel that those are very durable businesses. Because again, there's a, there's a very high value proposition. We, we historically studied what's the ROI on like core software and it tends to be a little over 600%.

• 22:14Speaker 00

  So the ROI payback on investing in software was historically very high. Again, I think in this agentic world, we have a chance to create a new level of ROI in our solutions, which is very exciting.

• 22:27David Weisburd?

  This is from the customer side. They put in a hundred thousand dollars, they get $600,000 back.

• 22:31Speaker 00

  Yeah, yeah, like quickly, quickly.

• 22:34David Weisburd?

  And so do you think there's a Jevons paradox coming to software in that as the cost of it goes down, usage might actually increase and it's always?

• 22:43Speaker 00

  Well, so I think the other interesting element of AI is that the cost of writing software and the cost of operating a software business should come down. The question will obviously be like in any market, like what part of that cost reduction benefits the company? What part of it benefits the customer? It tends to be a little bit of both. But I think, I think fundamentally, if you're selling something that either fundamentally has value and ROI to the customer or it doesn't. And if it has value, they're going to pay you. And if I can deliver it more cheaply or I can deliver it faster, I can expand the number of people that want to buy it from me more rapidly.

• 23:25Speaker 00

  So I do believe that as the cost of creating software comes down, again, I mean, software proliferates every element of our life today. I think it's going to continue to proliferate. It's going to proliferate in newer ways with again, these agentic solutions. But yes, I don't think the, the consumption of this technology is going down. I think it's actually going up and somewhat changing in the sense of this new agentic opportunity.

• 23:50David Weisburd?

  And I know you're not a macroeconomist, but intuitively, when I'm going through my workflows on my business, everything's gaining efficiency. And now in my superhuman app, which I use for email, no financial affiliation, unfortunately, but it, it responds back with a crafted message to pretty sophisticated emails. And I look over and I could edit it or whatnot. But none of those AI efficiencies seem to be showing up in the economic data. Why is there a disconnect there? One thing I've learned from talking to hundreds of investors is that great investment firms aren't built on investment returns alone. The firms under are great at the things most people don't see.

• 24:33David Weisburd?

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• 25:18David Weisburd?

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• 25:25David Weisburd?

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• 26:34Speaker 00

  It's a good question. I don't think there's as much efficiency being captured relative to the cost as people think right now. And again, that's where I think there's a massive opportunity. So you've got, you know, you're hearing a lot about token usage, token maxing, you know, all the spend on compute. And I think what a lot of enterprises are seeing is that they're giving access to these, you know, what I'll call, you know, generic solutions and letting people in some respects play, experiment, you know, what have you. And the layer of what I'll call friction that they're taking out of their lives is, you know, not that material relative to the cost.

• 27:17Speaker 00

  If I can actually create two of you versus, hey, I can write emails faster. What if I could create two of you? Like that would be super powerful. And you'd probably pay me a lot of money if I could make you, your wife might pay me a lot of money to create two of you. And so that is, I think, where the future is. And, and then I think from a business standpoint, every business is going to have to like, and again, even with our businesses, as we create efficiency, your real question is what do you do with it? Right? Do I reinvest in my business because I think I've got more growth opportunity? Or do I just harvest the cash flow? Right? And I think there's different answers for different businesses.

• 27:57Speaker 00

  If I, if I think I've got a lot of opportunity to go get, I should be taking my savings or my efficiency and reinvesting it in that opportunity. If I don't feel that, then maybe I should just harvest cash and, and, and return cash. And I think in our space, there will be versions of, of both of those. And I think in, in just broadly corporate America, I think you'll start to see more of it. But, but again, I think, again, I'm not a macroeconomist, but if you look at

• 28:29Speaker 00

  how well, you know, the US is positioned competitively relative, you know, let's put China on the side in the whole AI race. But just broadly, like we are very well positioned to, you know, continue to have a level of growth that exceeds Europe, exceeds other developed countries. And so I think you might see a lot of efficiency, but you might still see employment gains because we're able to just, you know, sustain growth at a faster rate without as much inflation.

• 28:57David Weisburd?

  I want to go back to what you look for in software companies. You answered it extremely succinctly, which is stickiness of the product. Given that AI is changing so quickly, are you just zooming in and checking out the stickiness on a month-to-month or week-to-week cohort today? Are you overweighting that versus the historical stickiness?

• 29:19Speaker 00

  Certainly, you know, retention rates from three years ago are not as relevant as retention rates from three months ago. And so we're certainly, as we look at, you know, our go-forward underwrites, I mean, you know, we have a deep understanding of the technology. So we, on the one hand, we somewhat objectively look at, okay, given the nature of this solution, given the data that it works with, given the workflow that it operates within, do we think it's more or less durable in a world where you've got agentic competition? We also look at, and by the way, does it have agentic opportunity that we can help it go harness? So we're going to both look at the durability of the existing solution,

• 30:03Speaker 00

  and we're going to look at what is the, you know, opportunity going forward for it to expand its own TAM? And again, certain companies, we think there's significant opportunity, others, you know, less so. But then we're then going to, you know, validate our views by looking at, you know, the data. And so we will look at, you know, more recent data. Renewal rates, right? What is the renewal rate on customer contracts? What is their contracting cycle? And when customers get a chance to decide whether to renew or not, what are they doing? And so we'll look at, well, what's their renewal cycles? What's their renewal data? And the more recent renewal data is certainly going to be much more relevant in an environment like this where there's innovation going on,

• 30:46Speaker 00

  you know, versus, you know, four or five years ago when this wasn't that dynamic. So yeah, we're certainly indexing to more recent data, again, to try and validate what our perspective, you know, we might think something's durable and then the data doesn't bear it out. We're going to obviously, you know, look at the data.

• 31:03David Weisburd?

  And outside of mission criticalness and the stickiness of the customer set, is there anything else that you care about there? If you run a business, you probably start your morning with way too many tabs open. You're checking revenue, ad performance, email, inventory, spreadsheets. And before you've made a single decision, you've already spent your morning just figuring out what happened yesterday. That's what I like about Perplexity Computer. Instead of jumping between every dashboard yourself, Perplexity Computer could connect to the tools you already use and build a command center for your business. It could pull together things like revenue, ad spend, margins, inventory, and email performance, then show you what changed, where you might be losing money, and what you should look at next. But what makes Perplexity Computer really interesting is that it could actually work on projects.

• 31:46David Weisburd?

  It could help rewrite an email flow, build an ad campaign, or draft outbound outreach. And unlike most AI tools, you're not locked in into one model. Perplexity Computer orchestrates leading AI models behind the scenes and automatically chooses the right one for each step. For a limited time only, you could try Perplexity Computer for free. Head to pplx.ai/invest. That's pplx.ai/invest to try Perplexity Computer today. Most AI tools are still answering questions while Perplexity Computer is completing full projects. If you run a business, you probably start your morning with way too many tabs open. You're checking revenue, ad performance, email, inventory, spreadsheets. And before you've made a single decision, you've already spent your morning just figuring out what happened yesterday.

• 32:30David Weisburd?

  That's what I like about Perplexity Computer. Instead of jumping between every dashboard yourself, Perplexity Computer could connect to the tools you already use and build a command center for your business. It could pull together things like revenue, ad spend, margins, inventory, and email performance, then show you what changed, where you might be losing money, and what you should look at next. But what makes Perplexity Computer really interesting is that it could actually work on projects. It could help rewrite an email flow, build an ad campaign, or draft outbound outreach. And unlike most AI tools, you're not locked in into one model. Perplexity Computer orchestrates leading AI models behind the scenes and automatically chooses the right one for each step. For a limited time only, you could try Perplexity Computer for free. Head to pplx.ai/invest.

• 33:15David Weisburd?

  That's pplx.ai/invest to try Perplexity Computer today. Most AI tools are still answering questions while Perplexity Computer is completing full projects.

• 33:25David Weisburd?

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• 34:09Speaker 00

  We want to understand what data are you working with? How proprietary is that data? How valuable is that data to your, to the end customers? Those are, you know, those are critical things. How deeply are you in the context of the business? Like the data itself, you know, people talk about what are the different, you know, kind of moats around AI? And it's like, well, is it a system of record? Which is generally connotating something about you're managing their most important data. Like, that is a factor, but that's not the only factor. You can be a system of record but not be durable because there's other elements that you're missing. So we look at what is the data you're managing? How important is it to your customer?

• 34:53Speaker 00

  How deeply are you embedded in the workflow and the relationship that your customer has with either their customers or their other key stakeholders, employees, vendors, others? Because if you've got a deep understanding of the context of the business, the vertical that they operate in, that's hard to replicate. Like, you have to be living in that to know. So rather than someone taking a generic tool and trying to do what you do, it's like, no, I've got a deep understanding of this particular industry and how it operates, how it works. You know, if it's a regulated industry, what its regulatory environment is? What are the consequences of getting something wrong that I can't get wrong?

• 35:37Speaker 00

  And so these are all critical factors. And, you know, there's something that people talk about is something, a deterministic outcome or not, right? So fundamentally, large language models are not deterministic. They're predictive, right? They're predicting the next fundamentally-

• 35:53David Weisburd?

  Probabilistic.

• 35:54Speaker 00

  Yeah, they're probabilistic. They're predicting the next letter in a sequence of letters at the end of the day. Businesses can't generally operate in a probabilistic outcome. Like, if you're an insurance company and I'm using technology to quote you an insurance policy, like, I can't kind of get it right. Like, I have to get the exact right answer every single time. Like, every time you apply, it's the exact same answer. And so, so our company, so we have a company, Duck Creek, that is in the property and casualty insurance space. They are building agentic solutions to really automate elements of the property and casualty insurance policy administration. So for example, if you're in property and casualty, you have an occurrence. You have an insured event.

• 36:37Speaker 00

  The first thing that you have to do is fill out a form called first notice of loss, where you're providing a whole bunch of information to your insurance company so that they can evaluate your claim. And, you know, where did it happen? Who was involved? What other vehicles? If it was a car, then what other vehicles are involved? Was anybody injured? Da, da, da, da, da. And by the way, the insurance company is also trying to figure out, is this a proper claim or a fraudulent claim? Like, are there, right? And so, so we've built agents within Duck Creek that can do 90, over 90% of the, of the intake of this first loss of notice that ordinarily would have been done with humans is now all being done with agents, agentic solutions.

• 37:21Speaker 00

  And those agents are not only populating all the key information and data and giving you as a customer a much more seamless experience than having to deal with someone on a, you know, telephone or whatnot, but is also able to activate, you know, fraud bots to look at, you know, what's going on and are there issues of a potential fraudulent claim? And so they can put in metrics that, okay, if there's personal injury involved in this accident, we want to get a human involved. If there is a claim over a certain amount of dollars, we want to get a human involved. If there's these issues of potential fraud, we want to get a human involved. So you can, you know, you can agenty a lot of the process,

• 38:04Speaker 00

  but then put, you know, parameters around it to make sure you're getting humans in the right kind of part of the loop. But that, you know, these are the kinds of things that, like, us deeply understanding the property and casualty insurance business and the regulatory environment and whatnot allows us to build safe, secure agents that can really facilitate a lot of efficiency for their end customers in a way that someone, two people in a garage, hey, I want to automate property and casualty. I'm going to do it off. It's a very complicated thing to do, especially in regulated industries. And so being the trusted existing partner, you know, puts you in the best position.

• 38:45David Weisburd?

  If you're evaluating a software company that had one of two factors and you could only choose these one factors, one was just being the best understanding of the industry. Let's say a niche property and casual insurance company that understood the space better than anyone in the world, a genius in the space. Another one had just the best relationships with customers in the world, every customer's best friend. Which of those two would you pick? And how would you weight them against each other?

• 39:16Speaker 00

  It's an interesting question, so I'm not sure how you would be the second one without also being the first one. But I think fundamentally, you have to look at how do your customers interact with your product? And do they view the experience as something that's helping them give their customer the best experience, give their employees the most effective platform to be efficient, what have you? So I would fundamentally look at how is the product being used? How is the interface? How is it managing the data? And does it seem like the best solution? Because at the end of the day, like you can have great relationships, but if your product isn't particularly good, it doesn't matter.

• 40:00Speaker 00

  The best product tends to win. And typically, in a lot of software markets, there might start with a lot of products, but over time, the industry is consolidated into one, two, three, you know, kind of killer winners. And so if you have a great product, it's the industry is likely going to consolidate towards you. If you have a crappy product, but everyone loves you, they might find new friends.

• 40:29David Weisburd?

  Yeah, your crappy product has somebody else's opportunity.

• 40:33Speaker 00

  Yeah, 100%, 100%.

• 40:37David Weisburd?

  You mentioned one of the most boring but underrated aspects in, I think, an age of AI, data. David Freeberg, previous podcast guest, just talked about him putting O'Halloran's information into ChatGPT, creating novel science, and then lo and behold, a couple of months later, ChatGPT was essentially intentionally or unintentionally distributing this data to other companies in the space. How should software companies go about protecting their data, given that it seems to be one of the most meaningful areas for competitive advantage moving forward?

• 41:15Speaker 00

  Well, again, I think that is actually one of the critical issues as to why many enterprises are going to incorporate gen AI through their software applications. So today, the data that our software companies are managing are the customer's data, and we are protecting that data. We are using that data in a manner that is consistent with what the customer's needs are. And we can bring the agentic capability to the data in the secure environment that it's existed in. We're not allowing the large language models, as a for example, to train on our customers' data. It's the exact opposite.

• 42:00David Weisburd?

  Is there a new standard for that or a new methodology for investors to evaluate whether a company is doing that?

• 42:08Speaker 00

  Again, if you look at large enterprise and the struggles they're having with adopting this technology, this is one of the big issues.

• 42:16David Weisburd?

  It's a security issue.

• 42:18Speaker 00

  Yeah, like CTOs and CIOs are saying, I've got to be very careful about unleashing this stuff on my data because I need to know that my data is staying with me. This is like one of my key assets of my business. So if I'm letting one of the frontier models into my environment and letting it, like, is that, is my data staying there or is it not staying there? And so that take this to a different level of regulated industries, you know, you're one of the large banks, you're one of the large insurance companies, what have you, like the stakes are incredibly high, large healthcare companies, HIPAA and everything else. And so that, that understanding where, where is your data and where is it remaining as

• 43:03Speaker 00

  you're bringing agentic solutions to the data is, is obviously a critical issue. And that's where, again, I believe that a lot of our customers are going to turn to us as the incumbent technology provider that is actually protecting their data today to say, yeah, you bring me the agentic solution in a way that I understand how my data is being used and not being used and how I can get comfort that my data is staying within my environment, so to speak, and not being used to train a model that can then disseminate. And so that's again, where, you know, when you think about like, are the frontier models

• 43:45Speaker 00

  going to do everything or are there going to be, you know, a lot of, you know, applications, many of which are going to be embedded within software? That's why I think that's going to be.

• 43:53David Weisburd?

  And those applications are using open source or how are they protecting their data?

• 43:57Speaker 00

  It depends. Some are using open source, some are using, you know, elements of the frontier model, but they're, they're, they're bringing the model in in a manner that's not allowing the model to train and take the data out. They're doing what they call post-train. And so, yeah, I mean, they're using, they're calling for the intelligence to help with a process, but then they're, you know, retaining the data within the environment at the, at the end of the day.

• 44:24David Weisburd?

  One of my biggest thesis for the next few years is cybersecurity as it relates to data. It seems so obvious, but very few people are talking about it outside of people that are cybersecurity experts. A, do you agree? And B, why do you think no one's really talking about this issue?

• 44:41Speaker 00

  Well, I think lots of people are talking about the issue. I just think there, you know, it may not be talked about as widely in the, you know, what I'll call the general, you know, business press or general media. But it's certainly an issue that everybody's thinking about, you know, I think dating back to, you know, when Mythos and people were very, you know, freaked out about that, you had the hugging face incident, what have you. So I think a lot of people are focused on it and thinking about it. And I think the companies that are focused as cybersecurity providers, they're all trying to think about how do we harness this technology to keep up with the other side, so to speak. And in some respects, you're going to have agents fighting agents from a cybersecurity perspective.

• 45:25Speaker 00

  So I think there's, I think there's a fair amount of focus on it. I think that the not like the enterprise level customers are struggling with how to think through all of it and how do they ensure that their data is safe. But I, but I think broadly in the tech industry, there's, there's a lot of, there's a lot of focus on it, there has to be, and certainly, you know, each of our companies. Cybersecurity is one of those interesting things. You have to be right every day, all day. Like you can be right a thousand times and you're wrong once, and it's a big problem. And so it's a very zero tolerance function, a zero tolerance industry. And so it's, it's something we've been focused on, you know, we've been investing in software 26 years, we've been thinking about cybersecurity for 26 years because it's, it's, you know,

• 46:09Speaker 00

  core to, you know, our ability to protect our customers' data is a core expectation that every one of our companies lives with.

• 46:18David Weisburd?

  I was just speaking with the head of Morgan Stanley Cybersecurity, and he made a joke that the reason you're talking to me is that we haven't had a breach. If we ever have a breach, you will not be talking.

• 46:30Speaker 00

  He hasn't had a breach that's right, like, there's always like, I haven't had a breach that I know about.

• 46:34David Weisburd?

  Yeah.

• 46:35Speaker 00

  Yeah, it's, it's, it's, it's scary stuff.

• 46:38David Weisburd?

  It's not a career, not a career I want to be in.

• 46:43Speaker 00

  I am glad that it's not my day job.

• 46:45David Weisburd?

  Yeah. Going back to Vista's investment strategy, you have so many moving parts in the software space. Do you plan to be investing more in individual software companies or less and into more number of companies? How do you think about your portfolio construction, given this changing dynamic of AI and driving down costs?

• 47:08Speaker 00

  Historically, we've always believed in diversification, like when we, you know, we operate a series of closed-end funds, we also have some well-focused products, but, but, you know, within our closed-end funds, we generally. Have targeted, have in, you know, between 50 and 20 investments per fund. Uh, so we, we like, you know, just diversification of assets. We also like, um, you know, diversification on vintage years. So we don't want to deploy all of our money in a particular.

• 47:37David Weisburd?

  How many years do you like to have on that?

• 47:39Speaker 00

  Yeah. I mean, we've, we've averaged, you know, between four and six years of vintage diversification. And I think that's, you know.

• 47:45David Weisburd?

  That's pretty healthy.

• 47:46Speaker 00

  Yeah. I think that's served us well because obviously we've been through cycles. So I don't know that there's going to be anything that we think differently about that general approach. As we're in the agentic, uh, you know, as we're in this, you know, innovation cycle. I think we've just got to be extremely disciplined. Uh, and we've got to really take the information that we're learning from all of our existing assets and apply them to new investments. And so when you're in investing and you've, obviously we're all in competitive environments and there's lots of people that purport to do what you do and whatnot. And I think you have to be really careful about investing behind momentum or what's the latest thing.

• 48:26Speaker 00

  I think you have to, you know, I think we've done a good job of being very disciplined of saying like, we know what we're good at. Uh, we know where we've got unique expertise, unique resources that we can bring to bear to help companies, uh, thrive. And let's stick to that, you know, if we don't believe that our involvement is going to have material impact on the prospect of the company. Then we shouldn't be buying it because our approach is to be a value added partner and investor. And so if we don't think we have value to add, then we're not financial engineers, we're not stock pickers, we shouldn't be buying the company.

• 49:06Speaker 00

  So I think, I think being disciplined, uh, you know, we have a high level of conviction in our ability to help our companies thrive. I think, you know, we've really evolved our resources. I've got over a hundred people that are focused on how to help our companies with agentic transformation and AI transformation. Whether it's on the operations side or on the, you know, the product revenue side of their businesses. And so we really want to make sure that we've got the right resources, uh, and that we're, we're leveraging our entire ecosystem. You know, given our scale, I think today we own, uh, 90, 91 companies.

• 49:46Speaker 00

  Uh, we see a, we see a broad swath of the, you know, um, technology landscape. And so thinking about how do we leverage all of this know-how, the learnings? We've got some just incredible executives that work within our companies. How do we leverage what they know for the benefit of the platform? I think we're, you know, we're very good at leveraging our platform for the benefit of each of our companies. And that's if we don't believe that all those things are going to matter in an investment, then it's probably not an investment for Vista.

• 50:20David Weisburd?

  Maybe on this, on this, the portfolio company side in terms of use of cash. You give them a hundred million dollars. Do you expect that that use of cash is going to change next year from say, two years ago in the age of AI? And, and how does that mix change in terms of what they're spending their money on?

• 50:41Speaker 00

  For each of our companies, uh, there's, I'll say broadly, two big lenses, you know? Lens one is can you leverage these tools and technologies to be more efficient with the resources you have? So can you generate more code using code gen solutions? Can you provide a higher level of customer service, uh, without having to hire a whole bunch more people? Can you shorten your sales cycle and make your salespeople more effective through the use of these tools? So with every one of our companies, we are, you know, certainly going to invest in and look at how do we make them more effective operators using these tools?

• 51:26Speaker 00

  I think separately, we are going to, we look at each company and say, you know, where can you create agentic or AI solutions or products? That are going to be very valuable to your customers and therefore they should want to pay you to kind of get that value from you. And so thinking about where do we want to lean in on product innovation? Where do we want to spend additional money on, you know, product development? Yeah, those are the decisions that, you know, we're making. Whereas three or four years ago, we were necessarily thinking about, well, how do we build agents to create a new TAM? Because, you know, agentic was, was just an emerging thing. Now that's what we're keenly focused on.

• 52:08Speaker 00

  And, um, and those are the investment decisions that you need to make. And I think one of the issues that you always have to manage in a technology and an innovation environment is are you building things just to build things? Cause they seem cool, uh, you know, they're the latest new thing. Or is there actual value to your end customer that they will pay you for? And so one of the biggest, uh, things that we focus on with our companies is what is your actual strategy? What is your product strategy? And why do you think this is going to be valuable to your customers? And why do you think they're going to pay you for it? And that thinking through where is the value? And, you know, you, you're already deeply embedded in your customer and in their workflows.

• 52:53Speaker 00

  So where are the parts of their workflow that you could bring agentic solutions that would actually be very valuable to them? That, that whole, you know, strategic thinking is a really important part of all of this. Where, um, again, we, we've built a set of resources. We call our agentic factory, uh, that is a bunch of, you know, Vista resources that we, that work with our companies. To help them both think through their product strategy, but then actually help them build the product and so, you know, literally get hands on keyboard and help them with their innovation cycle. And that's where, you know, I would say we weren't, uh, you know, five years ago, that sort of innovation, you know?

• 53:33Speaker 00

  We were certainly involved in product roadmaps and what's the next set of feature, of the functions that will make your software, you know, more useful or a better interface or whatnot. Now we're looking at, okay, how do we actually create whole new Tams?

• 53:48Speaker 00

  Uh, you know, total addressable markets through, you know, effectively bringing digital workers.

• 53:52David Weisburd?

  It's fascinating because in many ways it's the microcosm for where the economy is going. So I asked you, where would you invest a hundred million in a software company? If yesterday you were investing in the product, making the product better, maybe incrementally, maybe dramatically better. Your answer was, well, the product isn't going to eat up the hundred million. You're going to have services on top of it. You have to service and get better customer service. And then once you've invested in those two, now you could go into new products and new product development. In many ways, that's what entrepreneurs are going to do in the economy. What happens when all your customers are happy? Great. Go, go, go get new customers.

• 54:27Speaker 00

  Absolutely.

• 54:28David Weisburd?

  It's one of those things that economists have a hard time plugging into models because all they know is really old fashioned models on unemployment and current TAM. They, they don't see again, this Jevon's paradox. What happens when more customers are happy and businesses have happy customers where they're going to start new, new products? It just never happened before. The efficiency has never been there.

• 54:50Speaker 00

  Right. And again, I think this technology is actually opening up a new value proposition that didn't exist. Like I didn't have the ability to deliver digital workers to you. I now have that ability. So that, that is just a whole different way to think about what is the value proposition I can bring you? And so how do I deliver that to you in a way that is consumable to you in a safe, secure deterministic? You know, manner that, that then you say, wow, I'm like, I'm getting massive value from this that I realize I have to share that with you as the one that's providing it for me.

• 55:29David Weisburd?

  You have one of the most unusual backgrounds of, I think, any guest, certainly any guest to your, to your prestige. You started out in Michigan in the Midwest. You were doing sales for it in the food industry. How did you end up the president of Vista?

• 55:47Speaker 00

  Uh, that is a good question. Uh, my kids will, uh, ask me that question as well. Um, you know, it's interesting. I, I've, I've, I have had kind of an unusual path and, uh, you know, I grew up in a family. My parents, uh, did actually not graduate high school. My dad started life as a butcher and worked his way up as an executive for a food company. And, you know, my first job was sales for a food company. And that was like, what, you know, my brothers and I, we all started in the food industry. That was what you just did. You know, it's kind of like other families that, you know, their father was in an industry, you kind of go into that industry.

• 56:22Speaker 00

  And so I started off in sales, uh, which I think is one of the best ways to start a career is having to go out and sell and learn, learn how to sell, learn how to be persuasive, learn how to take rejection. Uh, learn how to understand.

• 56:36David Weisburd?

  Something some of your lawyer peers may have had troubles.

• 56:39Speaker 00

  Yeah, yeah. Uh, exactly. And so as I decided to go back to law school, I didn't really know any lawyers, but I just had this idea from a little kid, like lawyers get paid money to talk and I like to talk. So I should go be a lawyer. Uh, and I just realized that was, um, something I had an aptitude for, and I had a love of doing. And, um, you know, my prior life in sales actually helped certain elements of being a lawyer, both in terms of going to get business, but also in terms of being persuasive. You know, as a, I was a corporate lawyer and so, you know, I did a lot of negotiation and learning how to listen and understand someone else's perspective. And then how to, can I be persuasive to them?

• 57:23Speaker 00

  I had some of those skills from my prior career. And, uh, in my early legal career, I got exposed to private equity and.

• 57:33Speaker 00

  I found private equity just very fascinating. I started my career as a corporate lawyer for large companies. And you know, what's interesting about the dynamic is most of the employees in that company, they've got much more downside if something goes wrong than they have upside if something goes well, like personally. And so when you're the lawyer, you're a lot of your activities involve explaining, you know, risk and helping them like avoid risk and it's almost defensive in nature. And then as I got exposed to private equity, it was like this, you know, group of folks that, you know, fundamentally take risk for a living and they want to understand

• 58:14Speaker 00

  risk. They want to be able to price risk and want to make sure that they're taking responsible risk. But as an investor, like there's no guarantees and you're, you're taking risk. And so being a lawyer to private equity, I was able to harness my business background and my legal background and give people kind of, I'll say, business informed legal advice. And I just, you know, really enjoyed doing that. I, I ended up being fairly good at it and, and had a really, you know, kind of wonderful career. Was working at the top firm for private equity Kirkland and Ellis and it was loving life. And, and then I got this opportunity with Robert and made, made a

• 58:55Speaker 00

  big pivot. But you know, what was interesting is each step along my journey kind of enabled me for the next step. And, and I would say, you know, in each case, I was going to go, you know, into an area that I wasn't fully proven in, but I was going to get a chance to go see what I could do. And so I really embraced that opportunity to kind of a little bit bet on myself, a little bit like find out where, you know, my, you know, peak is so to speak, or where my peak of competency is. And it's been a wonderful journey that I've really, you know, I think my wife, I'm coming on my 30th wedding anniversary, which I'm pretty, pretty, pretty proud of. But I think my wife would

• 59:39Speaker 00

  tell you that she's seen me, you know, work incredibly hard for 30 years, but have really just enjoyed the journey. And I feel, you know, one of my big things is gratitude and I feel super grateful for the opportunities that came my way and that I was able to take advantage of. So, uh, so, you know, kind of here I am, as a, you know, a senior executive in the finance world, started life selling juice.

• 1:00:05David Weisburd?

  So I'm not going to take you back to when you started selling juice, but if you could go back to you graduated law school and you were just starting at Kirkland Ellis, one of the top firms in the country, and you could only give yourself one timeless piece of advice to carry forward in your career. What would that be?

• 1:00:22Speaker 00

  Don't underestimate, you know, the power of stamina. Like to be anyone that you see that is, you know, had some level of success, they've worked at a pretty high level over a very extended period of time. And that's hard to do. Life gets in the way. You can get distracted. You can have obstacles in your way and being able to push through all that and, you know, persist and just get up every day and work hard and work at a high level. It's, it's not an easy thing to do. And I think, you know, I tell my kids this and I tell them like, listen, I wasn't like the smartest guy that ever graduated law school. I was not the smartest person. I met like incredible people

• 1:01:07Speaker 00

  at Kirkland and whatnot. But part of what allowed me to, you know, kind of rise up was, you know, I had some level of talent and whatnot, but I was able to, you know, work at a very, you know, high level over a long period of time. And that, that ability to stay focused and, and, and persist is I think underestimated.

• 1:01:29David Weisburd?

  Well, David, this has been absolute masterclass. Thanks so much for stopping by.

• 1:01:32Speaker 00

  Thanks for having me. It's great to see, to see and spend time with you.

• 1:01:37David Weisburd?

  Congrats on getting to the end of my conversation with David. If you enjoy that conversation, you're going to love my conversation with Cliff Asness right here.