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Is there a single company that Toma Bravo is invested in today that doesn't involve AI? No. We're about to talk to Holden Spate, who is a managing partner at Toma Bravo, which is one of the largest private equity software investors on the planet. You ready to talk about AI? Very interesting time to talk About software and Holden is on the board of 10 different companies. So he can talk to you from the perspective of an investor, but also as an executive who is watching companies go through change in the age of AI. Welcome to the latest episode of The Bridge by iCapital. I'm Shanali Basik, the Chief Investment Strategist at iCapital. And today I am joined by Holden Spate. He is Managing Partner at Toma Bravo, which is one of the preeminent private equity firms in the space of software, of artificial intelligence. What a time to talk to you because we're getting past 2026 and the psychology is changing around artificial intelligence? What are the biggest things investors need to know about what 2027 shapes up to look like? Yeah, well, thank you, first of all, so much for having me. It's great to see you again. I'd say the mood, you know, we have, first of All, we have about 70, over 70 companies, enterprise software companies that mainly sell to enterprise customers. So complex enterprises. And the mood has definitely changed at the customer level. So the mood at the beginning of the year was very much about just use as much AI as you can. Everybody, right? All the employees, everybody started by let's use as much AI as you can. We told our investors in March at our annual meeting, we said, you know, at some point the bill will come due. And I think customers are going to start to focus more on outcomes and what are they really getting for all this spend and all these, you Know, or they'll realize how much they're spending. And that moment is upon us. So the way we say we're moving from trials to outcomes. You know, that has been the case that it's been actually kind of expensive for a lot of companies to be using AI just in mass form, right? To the extent that they wanted to. But token costs are coming down. Do you think that this will still be this expensive of a proposition? Token costs are Coming down, but I don't think people are using it very token costs coming down, But the but the cost of the frontier seems to continue to go up. Right? So tokens are becoming much more efficient, but people are using a lot more of them. And so I think the bottom line is, I think people are realizing mid-year that they're over their token budgets. They're wondering what they're getting for all this spend. And I think our companies are now realizing we can do 80 to 90% of what we need to do to deliver value using open weight models. You just question enterprise customers. Anaplan is a company that we have that does a lot of planning, and it makes deterministic calculations about inventory. They have supply chain use cases and other. Why would a company pay per token to use a language model to do a calculation that Anaplan can do for zero marginal cost? These are the kind of conversations that we're now having. And CFOs are in the room now and CIOs, and they're getting very little predictability too. They don't really realize. So there's a lot OF software that's being developed sitting around these models to help people understand their spin, to help it become more predictable. And frankly, what we really lean on and what we think is really important is to have deep domain knowledge. We've been talking about that for a long time. But because we have that in our workflows, we think we can deliver inference to customers much more efficiently and less expensively than what's happening right now. And that's kind of the breakthrough that we're expecting to see going into 2027. Yeah, let's double click on that in just a second here. But first, I want to talk to you about what 2026 looked like from inside the SaaSpocalypse. Because realistically, a lot of things jumped back. So in this new AI world, in software, what works, what doesn't work? Yeah, I mean, first, I hate that term. But you're right, that was the term. And I think the idea and nobody had of crystal ball, right? And this stuff is moving very quickly. But the idea when that happened in February or March is that all enterprises are just going to be vibe coding their core systems of record. They're going to vibe coding Salesforce, Workday, and there's no real need, which is a really a misunderstanding of what software is. It's not just code. It understands your business processes your compliance obligations that understand your employees your customers years and years of of context that sits inside these systems but it was a it was a scary time I mean we tried to frankly buy a lot of things because having over 70 companies gives us a pretty unique perspective into what's happening like most of the software universe is private not public and so we tried but you know most boards, understandably, were not that excited to engage with us when the stocks were down 50% or 60%. I think that sentiment is largely behind us, right? And I think when Salesforce makes an announcement with Dario and Anthropic, it's an acknowledgment that LLMs aren't going to own every domain. Technology has never done that. When we started in the cloud, everybody thought Amazon was going to earn all SaaS and cloud. And that's just not how things work. So I think that sentiment is largely behind us, which is why you see these stocks go up. I mean, Workday, Salesforce, SAP, ServiceNow, they're having very good earnings announcements. There still are some lingering questions about how this stuff gets priced. But I think the days of, you know, nobody's going to, you know. They're all going to vibe code new systems of record. Those days are behind us. What makes a software company sticky? Realistically, what's cool about your perspective is not just that you're talking about this from an investor's point of view. You're also on the board of 10 companies. I mean, no one's on the Board of 10 Companies. It's a lot of companies. So you see a lot about how these companies are changing to meet the moment. How are they changing most? Yeah. Well, that's a great question. I mean, in the past, and we've been at this for over two decades now as a small team, and we started in sales and services and optimizing that. Then we went into R&D. And then when the cloud happened, we had to get much smarter about hosting and cloud costs and gross margins and things like that. And now if you sat in our board meetings, you know, 90% of the discussion is about AI, which we have a pretty unique, again, because we have so much data coming in. And what we've started to realize is the real value over time, the model layer is just an API call. I mean, that's not, that'S pretty straightforward and it can do a lot of, you can build and we have companies building agents that are simple and sit on simple workflows like if I want to sort the leads I get from my marketing automation system and figure out which were the best leads we can build that on bedrock or amazon using that but context and domain knowledge are really hard to replicate you know and I would just use a couple of case studies like Jeppesen which is a company that has been helping commercial airlines fly planes safely for like 90 years, like that's a lot of context. So if you're a dispatcher for United and you're trying to decide whether a crew can fly from Heathrow to New York, I'll use a real example that hopefully listeners can understand. A general model, if you ask that question, they'll look and they'll connect via API to the flight record and they will look and they have some general aviation knowledge from scraping the internet and give you a general answer. But if you use Jeppesen's AI, they'll look at the exact FAA regulation that it governs, which is not normal language to read the FAA documents. They'll look AT the weather patterns. They look AT how long you have to be on the tarmac. They ll look AT what's happening at the home. They will look AT your union contracts. They look at all that to deliver an answer that's auditable, that the regulators will accept. And we've started to realize that is really the barrier for all these companies is the context and domain, which, you know, the reality is these general LLMs can't own all that. Well, it's interesting. You write about this quite a bit as well. And recently, I was looking on LinkedIn at a piece that you had written about how there's kind of a limit, right? The example that you gave was with the transportation industry, where you could have AI to explore route optimization, explore traffic, but it gets a lot harder when you put it into practice. Explain that. You know, how far does the AI actually go? And Jeppesen's an interesting example because you're on the board there, too. Obviously, we're moving from systems of record to now systems of intelligence to systems of action. And that last part is really hard. Because it involves automating human judgment in some ways. So we have companies like Jeppesen that are releasing intelligence platforms, which are kind of being released now. But it takes a while to get them production ready because you're dealing with highly regulated industries where the answer has to be absolutely precise. And the right answer is generally the cheapest correct answer, right? So we're releasing these intelligence platforms, but that last 20%, you know, you need a human in the loop to really understand the business context of what, right? There are a lot of decisions that are made. You get a lot OF information, but somebody is deciding whether to reroute a supplier order to a customer based on the value of that customer, based on how risky it is, whether they have inventory nearby. And so we're delivering basically all the software around the model is what's hard to replicate. It's the harness, it's the permissions, it'S the compliance. That's what we deliver. But that last 20%, customers, we're going to get a lot more value as customers start to make those decisions. And then the context, our software will record those decisions, so it gets smarter every time. But the bottom line is, you know, Jeppesen flies, helps planes, 60 million flights a year. You know, Dayforce pays hundreds of thousands of hourly employees across different regulatory bodies in different jurisdictions. Another company you're on the board of. Another company I'm on theboard of. And so we think these intelligence platforms are the next big breakthrough. And we think our software companies, because of the domain and how long they've been doing this, are uniquely positioned to capture value. Well, I mean, is there a relationship there between intelligence companies, you're calling them now? It's a kind of a category, it sounds like, that's really building within the space where it's the data that also makes them valuable. And to your point, the domain expertise in that is a specific. Exactly. Exactly. And we have so many examples of that. We have a general framework, but it's like it leans on workflow complexity and embeddedness. It leans on stakes and precision, right? How important is it that this answer be 100% accurate? And then it leams on proprietary data and domain knowledge, right. And I think of, to use one more example, sorry, I'm on so many boards, and we have some discussions, but I'll use Coupa, which is a company we've talked about in your prior life, but they are a network that connects buyers and suppliers. And over 25 years, the company has accumulated $10 trillion of transactional data around those transactions. So we have over 3,000 buyers. We have hundreds of thousands of suppliers on the network. You know, if you're a head of procurement at an enterprise company, a general model can probably tell you what a good procurement contract looks like and how to get a contract done with a supplier. But Coupa can tell you whether you're overpaying by 10%, you know, and whether that supplier delivers on time or is generally late, right? So it's got so much intelligence from this data, and they have really broad and good data rights, which is also quite unusual. So you mentioned something earlier about how, you know there's that trade-off being made in terms of how much a company spends on AI. That's, I'm sure, true for your software company, many types of enterprises. But being in the boardrooms, how do you feel that executives are making that trade-off between human talent and intelligence? Talent is critical, you know, and I think one thing that we've seen, I mean, for one broad comment I would make is that the sale itself, especially in this time of more scrutiny and the CIO regaining power and people now starting to worry about data sovereignty and data privacy, the sale itself has become more technical. And so customers now, they're waking up, as we talked about earlier, and they want to know, they have no predictability. They're like, how do I predict what I'm spending on this stuff? How can you show me? And they want to see the AI work with their data in their environment. And not only is there more software that we're building and making because of AI internally, but we also, the sale itself is becoming more technical. So there's a whole change management reorganizing of the way go-to-markets work, which is happening right now, and it's really at the early stages. But we thought counterintuitively that, you know, oh, we've got all these incredible coding tools. We're going to reduce our engineering capability, our engineering talent over time. And it's almost like the opposites happen. There's more software that we're building. There're more software that we re selling. But we also want, we need, like the CIO of a big company wants to have a technical person in there to show them how it works, which is sort of the Palantir model, right? The four deployed engineers. And that's kind of the go-to-market transformation that's happening also. So there's a lot in flight right now, but we think it's really encouraging for companies that are long on domain knowledge. So let's go back to where we started a little bit here, because I want people to really understand what's ahead when it comes to AI and software. We'll go ahead, then we'll go back, and then we're going to go ahead again. But 2027, why is it? What changes really? Are we going to continue to go on the path that we have been, you know, a little bit of fear on the edges of software, AI changing and the value of the frontier labs changing? Or is there kind of something more fundamental underway in terms of how enterprises adopt software or AI-enabled software and the future of AI in corporate America. That's really what we're talking about here. Yep, and I think the reason we're – first of all, I would say, and I am very bullish on software, but I should be very clear that I'm very bullish from the right types of software companies, the kinds that we were talking about earlier, proprietary data, deep workflow complexity, embeddedness, and also those that are innovating. It's true that it's easier to make software in other companies They're AI-native startups that can build workflows, get into the workflow, and start to accumulate data. And so you don't have as much time as you used to. And I think the markets at first were not very smart on this. The debt markets in some ways, as I look at how they're differentiating, when you look at the different spreads and the yields on different credits, they're actually pretty good at figuring out whom we think the winners are and who the losers are going to be. Our portfolio, because we focus so much on quality of revenue, I think is really well indexed to AI opportunities. but I think what what we're seeing right now it's so exciting right it's like I think what's happened is the technology is unbelievable right now it's probably under you know under marketed how incredible it is but enterprise transformation takes a long time and is slow and enterprise workflows are cross-functional they're complex and so really right now all the revenue from these frontier labs generally is coming from coding or like chatbots and customer support and things like that. The real breakthroughs are when you start really automating human judgment and when an agent can safely and in a permissioned and auditable way look across different workflows and make a decision as to whether a supplier delay, how to make that decision, right? And there's years of context and years of human judgment, and those things take a long time. And so I think what's happening is in the coming years, we see this shift back to, it went from let's just use AI to now it's, people are now talking about, they want to know, they're sensitive about their data. They're sensitive about, it's about data sovereignty. It's about data privacy. And it's about really this next phase is all about outcomes. And I think that's what, that's a 2027, 2028 even story. It takes time. To your point, outcomes might be harder to achieve than meets the eye. I think so far it's proving that way. I think we're in this moment, which we think, look, deal cycles in software have probably, they take a little longer now. People want proof of concept. But all that pivot, I think, it's kind of like back to the 80s and 90s. It's like on-prem and data sovereignty and show me before I buy. And I think that's a good shift for the right types of software companies. It's a disciplined market. It is. So, you know, let's talk about 2026 a little bit more here, because Toma Bravo had a very headline-grabbing deal that a lot of people paid attention to, Medallia. Didn't go well, this deal. What does that say, though, about what happened this year? I mean, is this a one-off deal, or was it under the cloud of the SaaS-pocalypse? Yeah. So, look, we've been very fortunate. We've done almost 600 software and technology deals over 25 years. and you can count on a handful of those that didn't go well for our investors. That is one of them. And it was done at a time where prices were very high. But to me, that's not an excuse. And frankly, for us, it's not a 2026 story. Our investors, we've been very transparent with them that this company is a problem. We're starting in like 2023 and it's not because of AI. We overestimated, you know, the revenue growth of the company. We underestimated how competitive that industry is. And sometimes the industry structure does change a bit. We also made one self-inflicted probably leadership mistake. You learn a lot, you move on, but that is not an AI or SaaSpocalypse story. That's news to people who read the media, but it's not news to our investors. That has been a long time coming. Yeah, that's interesting. I think a lot of people saw AI and said, what is exposed to this in the software world? But you're actually saying this was not an AI story. It was not a AI story, and in fact, I mentioned this to others, and I really do believe it. We have to be sensitive to vintage years, and we think this is going to be a great vintage, 2021 2022 is clearly not going to be the best vintage for enterprise software but we did some of our best deals at the peak of the public software market you know when you buy us you're not buying an index you're buying alpha right and a bunch of years and domain that we have too so adenza which we put axiom and calypso together in the middle of 2021 bottom line which we price late december of 2021 those look like you bought at the peek and those have been incredible deals One, we've already gotten liquidity on. And as you know, we sold it to NASDAQ. We took a bunch of stocks out on the board. Adina did an incredible job of delivering more value. I think it was one of your best deals ever. It was a great deal. It was great deal we made. So I still think business quality and good business plan and good leadership trump, you know when you buy. But we made, you know Medallia was not the same revenue quality that we frankly are used to. So we learn and we move on. I'm glad you brought up how to think a bit more about that 2021 vintage, because if you look across the industry, software or not, there are a lot of private equity firms that were buying in that market. So the idea of deals coming back, it's interesting because to your point on deals, they've actually kind of been slow outside of AI. And it's interested. I'd be curious to know, you know, if I were you this year, I would also want to be buying software companies, too. Right. I mean, you did see the valuation come down. Where are we now? I mean, are you seeing interesting opportunities, and are they in the public markets where we've seen a lot of pressure on prices, or are they in the private markets, actually? Yeah. Well, like I said, at the beginning of the year, we were trying to engage anybody who would engage with us, which we were, unfortunately, largely unsuccessful. Because things got so cheap. Things got so chief, and you're like, look, if we believe what we're saying, we should be buying companies. And now a lot of those stocks, I mean, Workday, we're up 50%, 60%. I mean I go to all the middle market companies. They're all up 30%, 40%. And so the premiums we're offering don't look quite as interesting. And in our large business, you know, our largest fund is $24 billion. We raised $35 billion in new equity early last year across our large, middle, and small market fund. So you've got some dry powder. We have a lot OF dry powder, but we also do a lot to take privates because of the size in our larger fund. And those, frankly, I would say the public markets from where we sit are pretty fairly valued. I mean, it's transactable. You know, leverage is not quite as available or as good. So for the first time in a while, we're seeing more divestitures and more private market opportunities. Because, I mean look, there are a lot of private equity companies with a lot of inventory and really good companies. And the prices have kind of been stable enough to where I think people have a rational view of what companies are worth. And so it's changed a lot. Two months ago, if we'd had this conversation, I'd say we're all over all these public companies. Now it's shifting more to private companies. And I'd Say what's in for us at the moment is domain product-centric leaders and founders, right? I mean, that's, you know, it's good to be a go-to-market CEO. We used to have a lot of success with that. But right now, the way the world is moving, it is really nice to have a product-centric leader who really understands and who, like, you know, we have leaders who code at night and send us things. I mean those are the kind of people you want right now. And customer intimacy, companies that really own the customer relationship. We just did a deal, Accelerant, for our Discover Fund. It was a multibillion-dollar take private. So we're still active, but you're right, 80% or so is uninvested in our funds, so we're actively looking. But liquidity needs to start happening to unwind some of this, right? There's too much inventory out there right now. In the hands of other private equity firms, you mean? In the heads of other equity firms. It's not for our companies that, you know, it's not diminishing the IRR a lot because most of our companies are growing EBITDA, you know, 20% or so or more. So it's not hurting our IRR to hold. On the other hand, you know, investors are, you know, they want money back, right? This has been a years long, but there are green shoots. There are green shoots. There are definitely green shoots, there are green shoots and so it's an interesting time too because, you know, we were talking about Medallia, but there's actually a whole flip side story with Toma Bravo here, because you guys also have a private credit strategy. How does that work, right? Because on one hand, you know, you see the way that private credit has been treated around software. But on the other hand, You're in a ripe opportunity to capitalize on these dislocations. We are. I'm glad you mentioned that. Because frankly, as you look across our platform, we're, I don't know, we're 90 something percent private equity, right? That's our thing. We buy companies. We operate them best of breed. We always have. We've gotten better and better and smarter and smarter, but we're underweight private credit. And we have incredible data that would be beneficial, right? We see all these companies and they're not just software companies, right. They sell to end markets. So we have a great purview into what's happening in the global economy because we have so much data coming in from our portfolio companies. So we hired a new leader to run our private credit business, you know, a year ago, and he's been amazing. So Jeff Levin is a great leader. And you're right. There's been a rotation out, right? People feel overweight software at the beginning of the year. So especially in these private BDCs where there's less liquidity and there's some big holders and there are companies that we know well, we've actually capitalized on that already. So we've been buying some really good credits for equity-like returns in companies that we really like. And we're getting more and more money into that strategy, and we're really excited about capitalizing on that. It's interesting. You made a comment a little earlier about software you like versus don't like. Bring us into your head on that, because how do we think about winners and losers in the age of AI? You know, if you look at the whole landscape of software, do you like 50% and the other 50% either gets bought or goes away. You know, how does this whole thing play out? Yeah, I think, and like I said, the market, I think, is starting to, you know, understand it. First of all, all software companies, I mean, their existing vendors, they serve companies. If companies are happy with their existing vendors and they can provide AI solutions that work, I think most companies want to buy from their existing vendors. So everybody has a chance to be successful. But, you If you don't have real proprietary data, if you're not deep in workflows, we buy companies that tend to have 95% plus gross retention rates, so they lose very little revenue every year, which tells a lot about the kind of software they're providing. If you do not have those things and you do have a – I would say leadership is critical right now. If you donít have a leader whoís – I talk to a lot of public companies too that arenít our companies because weíre obviously looking to buy companies. And I do hear a lot of – there are some old-school thinkers that expect, hey, we sell this the way we sell it and we have really good renewal rates. And there's no – there's not that urgency to say, hey. The world is changing. I've got to get an AI platform released fast. I need people using my AI. And so companies that don't have those things, right, they're light on workflow. They maybe don'thave a product-centric founder. They don'thave real proprietary data. And I think it's some of our companies that we see, too, where they're building agents a lot on the front end of technology. When I say front end, like things that are involved in the customer workflow, so on top of Salesforce or something, to sort leads or route calls or do things that the stakes aren't super high in the precision, but you can build it pretty easily. There are a lot of software companies that do those things. I would beware of those. Is there a single company that Toma Bravo is invested in today that doesn't involve AI? No, no. I mean, the story now, the diligence, even is all about AI, to be honest. I mean it's – I think we've gotten very smart about it because we've had so much – our Slack channels have never been more active. I think wir're working harder in the last 12 months than we've worked as a partnership in a long time. and even on the diligence side, having sitting in all these board meetings, I mean, the one thing we've always emphasized is we don't have a ton of people for the amount of assets we have. Part of that's because we lean so much on pattern recognition, right? What do I hear? What am I hearing from the portfolio? Am I hearing the same thing from this new prospect in this new company? But AI is the diligence story too. That's pretty much where we start and where we end. so it's interesting because we were talking about what ai is and isn't you mentioned agentic solutions for a while can you have agentic solutions that are human intelligent without getting to agi um can you haven't human i mean we're not there yet clearly i think there's some work to do here there's some work it's complex i mean all our companies would say the last it's like to me that's the last mile that's like the last 20 percent right? It's like, I can give somebody, we can deliver software with a ton of context because we understand what an ERP database means and practice for certain companies and Jeppesen with all of its flight knowledge. And we have a bunch of pilots on the staff. I mean, they know the KPIs for all the individual users, the dispatcher, the flight operator, they know all those things. But the last 20%, which is sometimes judgment related, right? Like, why did I make a certain decision, right? I had all this data and we're going to serve up AI that gives a recommendation, but ultimately a human's going to say yay or nay, right, and then we'll capture that and get smarter. So I think we're a ways away from that still in the enterprise. Before I let you go, I just want to talk a little bit more about capital markets too. You were talking about exits and opportunities. You're actually buying in private markets, right? Not out of the public markets as of now. When does this IPO market open up in a bigger way? The M&A market for companies that are not just giant frontier labs or related AI companies? Boy, wouldn't I love to know. We have, because we have a lot of them. And I think, look, I think investors right now, I mean, AI is the thing. And, I think everybody's waiting for anthropic and open AI and people have been rotating out and they want to see how they perform too. And I think people, there is still from investor accounts, probably some lingering skepticism about the sustainability of software revenue and what happens if we start pricing on outcomes and actions. Is that going to be a success story? We think it opens up a massive TAM. And I think people, I mean, if Anaplan can automate 40 to 50 percent of the labor and Fortune 500 CFO offices, you know, we should find a revenue sharing arrangement that really works for us. I mean it's a massive amount of TAM, but there's not a lot of proof yet on that. And so that's also why I think 2027 will be a good year. I think people will get more and more comfortable that these companies are sustainable. They have revenue models. General language models are not going to do everything. These other companies will be out. And so we'll see how they work. And I think compounding 20% EBITDA growers in great spaces, I think are going to be in vogue again. Is that generally what we're looking at at a software company today? I think our portfolio at scale is, that's about right. I mean, nothing, it's pretty much business as usual. And you see it from the earnings announcements. Companies aren't, and that's one of the interesting things and maybe frustrating things from our seat is that people index so heavily to what's happening in the public markets. And most all of software companies are private. So we see all these companies, they're all performing just as we expected. I mean, Jeppesen, people wouldn't believe the numbers if they saw Jeppesin and Dayforce today, what they're going to do this year. And revenue growth is accelerating, not decelerating. People are finally seeing that in Salesforce, Workday Service now. They go, oh, they'RE not decelerating. So that's part of the reason for the big bump. But I think that story, the more time goes on without some big deceleration, I think the better for our companies. Well, is there a chance also that margins actually improve meaningfully? Because if people were so worried about token costs before, and token costs are declining, and you have more discipline in the boardroom, then doesn't that mean that actually you have kind of a really nice cocktail for software companies who are buying the tokens? Is that off? It's true. No, no. I think people, that's been a big question. You're right, is that how can, what are our gross margins going to be on tokens? Just feels like the first beneficiary, that software companies, the ones that everyone left behind the first part of 2026 are going to win first from lower cost. We think we can make really good margins on inference, basically. That's what, and we can host that and we Can do it in a way that customers have predictability. They know what they're going to spend. They see it, but we think that that story also is overblown. Like we think we can make really good money, especially as right, like you said, we're, we are a route to market for tokens and those are getting cheaper and we can do, and models are getting better and they're getting less expensive. And so we're beneficiaries of all those trends. Yeah. And the last note on that too, it's like, you know, if you're sitting there looking at the future of AI and you're thinking about the future software, then isn't it not such a bad thing that the capital markets are just kind of holding on for a minute and exiting at a better price down the road as people catch on to this. I love it. I know it's like you're sitting in the boardroom with us. I mean, that's right. And again, because we're compounding at these nice growth rates and EBITDA is growing faster than, so we really, I mean I look at my portfolio, this is really, really nice. I mean, you know, mid-teens kind of top line growth on average with 20 plus percent EBITTA growth and more net income growth, it's not diluting our IRR. And so having multiples continue to rebound as people start to realize, wow, this stuff still has real terminal value. And, you know, there's a really, it'S as much opportunity as risk, right? That's what we've been saying. And again, I think as that becomes more evidence in the numbers and the more time goes by, probably the better for multiples, which is great for us. Well, that's the thing. Our investors having to just expect different things of their private equity investors today, because it is a complex environment and actually the technological landscape is completely being overhauled in front of us and the exit opportunities are different than they were maybe a few years ago yeah i think look i joined this firm you know 22 years ago because i believe and i still believe very much that being a sector specialist matters a lot i mean that's how you deliver alpha. We've always looked at not just how are we performing in private equity generally, but how are performing against a basket of public software stocks or however you measure alpha, we've always been really sensitive to that. And I just as I look out at the world and see all that's happening, we have such an advantage by being in all these meetings with all these companies, delivering with all this customer feedback that we're getting in terms of not just how we operate the portfolio, but how we make new investment decisions. And so I think that's still the formula for Alpha. Holden, it's been so nice to talk to you today. That is Holden Spate. He is Managing Partner at Toma Bravo, and you've been watching The Bridge by iCapital.