Vista Equity Partners' Robert F. Smith - Who will benefit from AI

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>> Welcome back to the Algo's mainstream podcast. [music] Today's episode

podcast. [music] Today's episode welcomes a pioneer and visionary in enterprise software investing to Alco's mainstream. Robert Smith is the founder,

mainstream. Robert Smith is the founder, chairman, and CEO of 100 billion AUM Vista equity partners. He sits on [music] the firm's investment committees for Vista's flagship foundation endeavor

and perennial funds and serves as a member of Vista's executive committee.

Vista's portfolio spans 90 enterprise software, data, and technology-enabled companies that employ over a 100,000 [music] people worldwide. He's also

heavily involved and committed to the firm's wealth channel efforts as the chairman and investment committee member for Vista 1, the firm's evergreen [music] private equity vehicle. Since

Vista's founding, Robert has supervised on over 600 completed transactions that represent more than $330 billion in aggregate [music] transaction value.

Robert founded Vista after a career at Goldman Sachs in tech investment [music] banking where he was co-head of enterprise systems and storage executing and advising on over 50 billion in M&A activity with companies that were

foundational players in the early days of the internet and technology such as Apple, Microsoft, Texas Instruments, eBay, and Yahoo. Robert has an innate understanding of technology and trends shaping the [music] way that companies

and people interact with the world and conduct business. It's no surprise that

conduct business. It's no surprise that he was early in seeing the rise and impact of AI because he was early in seeing the dawn of the internet in the 1990s. Robert and I had a fascinating,

1990s. Robert and I had a fascinating, thoughtprovoking conversation about the evolution of both enterprise software and Vista as a firm. We covered the early days of enterprise software and what Robert saw early on that gave him

conviction [music] to focus on enterprise software as a banker and then as an investor building Vista. The

investment [music] characteristics of enterprise software. The power of

enterprise software. The power of product superiority in enterprise software. Why sovereignty and dominion

software. Why sovereignty and dominion of data are so [music] important and why it matters for AI. Whether the rule of 40 can become the rule of 50, [music] 60 or 70 with AI. What aspect of AI is most

impactful for companies. How Vista

approaches value creation. what it took to scale Vista to a hundred billion dollar investment platform [music] and why the wealth channel is so core to Vista's business and to the firm's DNA.

Thanks, Robert, for coming on the show to share your expertise, wisdom, and passion [music] for enterprise software and building businesses.

>> Robert, welcome to the Elks Mainstream podcast.

>> Thanks, Michael. Good to see you.

>> Likewise. Pleasure to have you. I think

we have such a fascinating conversation on tap between your career, evolution of the enterprise software landscape, what you've seen from the early days of your

career, now AI becoming a big part of that, how you've built Vista. So would

love to start with your background.

>> You started in engineering.

>> Yeah.

>> I find that fascinating because it feels like a number of investors in enterprise software have started with an engineering background. So I want to

engineering background. So I want to unpack what's instructive about that when it comes to thinking about the enterprise software landscape.

>> Sure. Yeah, it's a great question and congratulations on building a wonderful platform and informative and sounds like you are delivering unique content to your subscribers which is great and

frankly something you know stick with it keep doing it because uh more investors are going to need what I think you can extract and deliver to them. You know,

my background again, chemical engineer.

I went to Cornell. My I'll call it generation of chem focused on a couple things. One, I call it weren't a whole lot of new unit operations or, you know, the creation of

of new [snorts] processes. Uh, but we were in a focus on automating processes.

We had just discovered that we could utilize these, you know, this compute technology for controlling systems. And and one of the early projects I worked on was implementing a programmable logic

controller into a at that time a 40-year-old plant. And these plants were

40-year-old plant. And these plants were typically like all these processes were more manually or or you know analog controlled where you had you know operators who would go through an

episodic observation of an event and then an episodic intervention. And it's,

you know, to the extent they were paying attention and not talking about, you know, who's getting drafted in in football, you would have certain control paradigms that would would occur in these processes. Well, once you

these processes. Well, once you introduced a compute uh system into those environments, you're observing an environment, you know, 60 times a second and you're intervening, you know, whatever is

applicable for the process. So, you have a much tighter control paradigm. And

what that led to at least in the case the first deal I worked on way back when our company implementing one of these is a 26% increase in the productivity of a plant that was 40 years old. So if you start thinking about that in a broader

scale compute actually brings massive productivity and massive efficiency into almost any environment that it's placed into and you take it from the process industries and then move it into

different environments like an office you have that same sort of productivity.

And you know when I look at today you know at Vista you know we're I don't know fourth fifth largest enterprise software company in the world. We've

kind of add all our revenues up just under 33 billion in revenues. What

engineering and chemical engineering in in particular informed me about doing in in managing a business was how do you think about creating a systemic process to

improvement as opposed to an episodic intervention to a problem. And those are really the frameworks that I like to to think about. I call them engineered

think about. I call them engineered solutions. How do you think about an

solutions. How do you think about an engineered solution that when you fix it once, it's fixed forever. Of course,

there's tuning that that that is that's associated with it. And so investing in enterprise software, if you think about it, is it is indeed that I like to say, you know, chemical engineers are they're we're designed to create elegant

solutions to complex problems. And if you take that paradigm of thought and then infuse it into investing, that's really what Vista is. It's an elegant solution to a complex problem.

>> Such a fascinating explanation of enterprise software and I think we'll get to a lot of what you talked about in the context of Vista and how you think about both investing but

also post deal creation around how you think about implementing processes to help these companies. But first I want to get to >> it's interesting you talk about your background.

>> You actually started at consumerf facing companies. Goodyear is one of them.

companies. Goodyear is one of them.

um before going into banking at Goldman >> and really were at the early days of the internet.

>> What did you see in the '9s? It's easy

to see today that companies and people want to buy more software as a way to >> augment processes.

But in the early 90s, what did you see that made you say enterprise software is the place to be?

>> Yeah. So going to Goldman Sachs. So you

know again worked in in essence I call it applied research and development broadly speaking for about 6 years before I went back to business school and that gave me unique insights as to you know how things are made and how

things are marketed and how things are sold and all those sort of things which was fascinating and fun and I I encourage all young people to do that you know how do you build things as opposed to observe things and talk about

them from that and then joining Goldman and having a chance to really work in the M&A department which then became the tech group Right. Uh

>> you actually had to you got a chance to see how capital is deployed across the planet. How you think people think about

planet. How you think people think about utility of capital, different capital stacks, capital structures and you know where where's the highest and best use of different types of capital depending upon where it's formed and how it's

delivered. The thing that became very

delivered. The thing that became very clear to me was there's a massive arbitrage of productivity that was now

starting to infuse into businesses.

again my early career actually writing code programming these you know programmable logic controllers and seeing how effectively you can actually make a change by having tighter controls

think about that paradigm tighter controls on on operations and then as the world started to disagregate I'll call it hardware from software and back in the day you used to sell it as a whole solution

>> and you had kind of hardware and middleware and then software and all that kind of evolved ult ultimately where the application layer of software actually became frankly the most

versatile in terms of value creation.

You didn't have the hardware or the refresh cycles and so you could actually you know increase the frequency of innovation as a result of the the construct of how software actually works and of course we got you know higher

higher level languages which gave it more more you know flexibility and more capability associated with it. But then

as you actually started to look at software companies which I had the great occasion to when I was at Goldman I got to advise some pretty interesting companies little company called you know Microsoft one called eBay one called

Yahoo and in evaluating those companies what you saw is everyone approached their market differently and not just in terms of the go-to market approach but

actually how they did things like their product development, how their customer service worked, how their you know support worked, how their you know development all all the aspects go to market back office infrastructure all

those sort of things were all so different but yet you would see you know best practices in one company that if you applied that best practice on one company to another company you say wow

man there's a 5% 8% 10% improvement so if you think about it as the paradigm of how do I take best practices out of one place and apply them to the next not rocket science but you know just something that could create that massive

arbitrage this company's operating at 12% ebidal margin if you apply these these five things can operate at a 30% ebid margin by you know being effective and then not

not having to change the product at all and ultimately focusing on call it the execution capacity.

The next layer of that is of course how do you create uh the way I think about it is it is some product superiority which means really evaluating what it is

that the product does for the customers what it could do better for the customers and you have to then listen listen to what customers have to say what is it that your product does for me

what is it that if your product did something else would be even more valuable to me that I'd be willing to pay for and So that is the next part of the equation. And so the way I like to

the equation. And so the way I like to think about enterprise software kind of pre-AII and we'll we'll get to that in a second. Well pre-agentic not AI because

second. Well pre-agentic not AI because AI actually enables that. It is all about product superiority and execution excellence. Okay. So how do you build

excellence. Okay. So how do you build systems at scale to deliver both of those things? Okay. So part of it is you

those things? Okay. So part of it is you know figure out what systems actually work for different components of enterprise software product development customer support go to market back office administry all that sort of

stuff. Build out sets of systems and

stuff. Build out sets of systems and then take those sets of systems and as you buy companies or as you evaluate companies if you applied that system into that company you're looking to

underwrite what does that company now look like? And that tells you what you

look like? And that tells you what you can pay for the business. and then you look at what everyone else can do and say well this is what they should pay for the business and there's some arbitrage there in the process. So

that's the whole dynamic.

>> I think that brings up a really interesting question which is in part because of the process that you have at Vista in terms of what you bring in

terms of operating playbook the the understanding of enterprise software businesses that you've invested in. When you underwrite a business, how

in. When you underwrite a business, how much do you look at here's the business today on its own versus here's what we can do if we work with the business and

take those operating processes and then can help scale the business. Do you wait one more than the other? Um like all

things it isn't as linear but the framework is what are the critical factors for success as an investment that we have under our

control and that's how you have to think about it. Okay. What is it that we know

about it. Okay. What is it that we know how to change or we know how to encourage support

underpin that may also already be in place that in doing so now decreases the risk in that business and enhances basically the convexity of the upside

and that's how to think about it. So

it's it's it's a gradient and it's a function. In some cases, you may say,

function. In some cases, you may say, "Hey, look, this particular management team or this particular individual in this particular area is doing a great job, better at this than anyone else."

So, how do we underpin that person, that team to be very successful on that part of the business? And part of that is it may be incentive comp, it may be the way that you actually approach the marketplace. It may be how you

marketplace. It may be how you distribute knowledge uh so that you have less risk in the business. We then will look at other things and say okay they actually don't have a go to market

strategy that includes this channel why and what can you do there or they may not have their pricing right or they may not have their contract administration right or they may not have their you know their ability to scale

infrastructure which might be people getting enough developers to do the work at the pace and that you want which we'll get to it on the Gen AI thing I'm sure >> all of those are elements that you

underwrite to and you say okay what do we know how to do. What do they know how to do? What do we think's required for

to do? What do we think's required for this to be a successful outcome given a market today? You can project in some

market today? You can project in some cases where you think the market or what the market for that particular asset might be in the future that if you get from here to there, this will give you those those sort of returns and how do

you derisk that process. So that's

really the the methodology that we undertake. When you talk about d-risking

undertake. When you talk about d-risking process and you talk about something like product superiority, when it comes to enterprise software, where my mind goes or at least one area where my mind

goes is these contracts tend to be pretty sticky when it becomes something that becomes hard to replace because it's a in some cases mission critical software.

What do you look for when it comes to product superiority? Is there are there

product superiority? Is there are there things that you're looking for to discern says this is this is product superiority that a company has?

>> Yeah, there are a few things around the product superiority but as a general category you have to look at it relative to its competitive set. That's an

important part. So it isn't oh man this is this is a great product but relative to what everyone else has relative to what the customers want. So you really have to do some deep dives into why do

people buy this software in the first place? What is it that makes this

place? What is it that makes this unique? What is it that makes it

unique? What is it that makes it differentiated? And what is it that

differentiated? And what is it that makes it color protectable? Is there

anything that's really protectable? You

know, the world of geni changes some of that for a lot of things. If you if you don't have I call it sovereignty and dominion over workflows and data sets, you don't have a right to exist as an enterprise software company. If you do,

then you have a right to dominate. Okay?

If you enable and you know identify that that business more effectively. And so

what was a very important part of how you underwrite is now an essential part of how you underwrite because you also have to understand the impact of agent

solutions in the world of enterprise software. Um if you are just repurposing

software. Um if you are just repurposing content then you don't really have a right to exist because these agent solutions ultimately will I mean they're

the best search engines on the planet and they can aggregate refine define and deliver and dispense you know content much more efficiently than you know many

other things that are out there. Okay.

But if you actually have the ability to say listen I pro I have a unique workflow I have a unique data set that only you know less than 1% of the enterprise data actually lives in these you know public you know call it public

environments for LLMs you know broadscale LLMs to train on and if you have the ability to protect that and utilize it effectively then you have a unique advantage that no one will have

for a long period of time and so there's a different underwriting criteria you have to think about from a product superiority now versus what it was in the past in the past there was a lot feature functions and do you have the

capability? Do you have data sets?

capability? Do you have data sets?

Remember, by having unique data sets and unique workflows, you can actually go to your customers and understand, I call it the derivative products that only you can deliver to them because you're the

only ones with the workflow, the only one with that data, and the only ones who can use that to create a new product that creates a unique set of advantages that you can actually price relative to their utility.

Okay, that is a that is a a a concept that is very different in enterprise software than almost any other industry.

And I like to say it's the only business I know that every day you can develop a product that only you can sell to your customer. Okay, that's a dynamic that

customer. Okay, that's a dynamic that will be enhanced in an agentic world. On

that point, is it the scaled players that will benefit more from the application of Agentic AI than smaller companies or startups that may

have the ability to use AI to help companies?

I got to break your question up a little bit. [snorts]

bit. [snorts] It's not necessarily scaled players, but it's incumbents who actually have sovereignty and dominion over workflows and data sets. There may be scaled players and there are some scaled

players out there today that don't have sovereignty and dominion over the workflows and data sets. They actually

don't have a right to exist. There are

smaller companies that may not have very rich data sets but have some utility in using their capabilities and actually some other data sets that actually can propel them to be quite successful. So

it's a you got to discern some of this stuff. It's not as easy as big will

stuff. It's not as easy as big will survive. But there are some advantages

survive. But there are some advantages to incumbents if you have actually been managing your business well up until now. And that

managing the business and I I can't underscore this enough is really that sovereignty and dominion over workflows and data sets. You don't have to own them. You just have to have sovereignty

them. You just have to have sovereignty and dominion over them. sovereignty so

that you can protect them and dominion so that you can actually utilize data sets that are unique to your business and often are created from the workflows

that that are unique to you as well.

That will create a [clears throat] depends on the industry and what the value is a massive economic advantage in an agentic world. And so there's an underwriting view that you have to have

on how that works that actually is going to change the way that enterprise software is being fed by AI. And the way I like to say it, you know, there's, oh, people, oh, AI is going to eat software.

No, AI is going to feed software that's going to eat services.

Okay? Because what agentic systems actually are, they're agents. They are,

if you think about it, they're workers.

They work 24/7, 365. If so long as you I call it build, tune and enable them the right way. If they're fed

right way. If they're fed with the right kind of data sets in the proper workflows, you can actually create a massive workforce of you know

these agents who will do work that you otherwise couldn't do or otherwise couldn't do at the speed or the pace or the volume with you know human workers.

So there's a dynamic there but not the largest of the software companies out there don't necessarily possess the knowledge of the workflow of the data sets and there's some small ones who might have some incumbent advantages in

that regard. So you got to look at them

that regard. So you got to look at them individually. How do companies make sure

individually. How do companies make sure that they can get that either competitive or comparative advantage in terms of having the data sets to be able

to then use Gent AI to have the workers to make sure they they own that data and it becomes valuable to the customers that they're working with either through software andor helping them with their

services.

>> Their best answer is to call us. It's

interesting. I'm actually shocked to be frank with you in c certain companies that we'll meet with and CEOs we'll meet with uh that they actually haven't done a good job preserving that

sovereignty and dominion. I'm shocked on the one hand I'm shocked. I guess on the other hand I'm not as shocked because many of them were on a ARR race. What I

mean by that is they're chasing growth that they in a way that they've actually leaked intellectual property. They've

leaked intellectual capacity. they've

leaked ownership in some respect of the of the very thing that that frankly would enable them to be more successful in an agentic world. It wasn't

necessarily something they were focused on and a lot of people weren't. You

know we because of how we focused on product superiority and product development. We

had a natural motion that enabled us to have more sovereignty and dominion over workflows and data sets than we otherwise would. I'd like to stand here

otherwise would. I'd like to stand here today and say, "Oh, that was all conscious and deliberate, but sometimes you just get lucky, right?" Uh, but I think that luck and all in all in

sincerity came from our desire to utilize data and uh workflows that enabled our customers to use that to create new products. And so as a result

of how we approached that contractually when we bought businesses, it enabled us to actually have a higher utility of the data and the workflows than we otherwise would. I've met with some CEOs of some

would. I've met with some CEOs of some very large software companies. They

didn't do that. And you know, it's just a matter of time before 24y old who can command 6 8 10 agents can eat the lunch of those businesses.

brings up a really interesting point around making sure the executives or the founders of a firm, it could be an investment firm too. I I think I I I would actually group this question both

as investment firm Vista itself or the companies that you're working with and investing in. But what type of culture

investing in. But what type of culture needs to be created by the executive team founder to be able to really think

about and make sure that you as a firm have that sovereignty and dominion over your data and workflows so that you can do all the things that you want to do.

There's [snorts] a component of no [snorts] there are many components of culture that make that effective.

Uh one of them actually has to be an openness to understanding what it takes to be successful in the

world of enterprise software. It's not

just product. Uh it's not just your go to market engine and your sales and it's not just you know better developers.

It's things like your back office administration.

Okay, those are the things that people don't pay a lot of attention to that ultimately define what is the economic you call it opportunity of the business.

I like to describe it this way.

enterprise software companies um economic position is defined by their contracts and you're only as good as the quality

of the contracts that you have signed.

But if you will talk to 99 software executives, CEOs and you tell them to name five most important things, most of them won't name contracts, the quality of their contract.

>> Why do you think that is?

>> They just don't think about it. you know

they think it's all about oh let me sell products you know fast let me sell products get more customers provide support those all important things but that is all defined by the contract and

so you actually have need to have exceptional contracting capabilities that you have to constantly refine I say it this way Michael it's kind of funny

let's take an average salesperson an average salesperson is focused on their commission and so you will say, "Here's your

territory. Go sell this." And they will

territory. Go sell this." And they will spend and know, "Oh, man. I've got a great client, a great customer."

And that customer has a, you know, really strong contracting organization.

Might be a, you know, Fortune 500. And

then they you give them their standard contract and they just eat it up. And

your salesperson now spends 95% of his time trying to convince home office as to why they should take this contract as opposed to spending 95% trying to convince him that the contract that

we're giving them is better for them in the long run. Okay? And remember, say saleserson sells and they toss over there and they're gone. Give me my commission and move on down the road,

right? Um and so you have to have

right? Um and so you have to have business processes that manage that intentionally. Otherwise you will have

intentionally. Otherwise you will have every salesperson and every regional sales manager and every you know national sales manager you'll just have

variance that actually ultimately is the underlying construct of the economic opportunity is compromised by the variance in those contracts.

So those are the dynamics that have to be thought through intentionally and built culturally in an organization as to why that's important. How malleable

do you think that is? And what I mean by that is maybe there's a great company, good product, but doesn't have the

discipline around how they're thinking about their contracts or how they incentivize their salespeople to create the outcome that you just mentioned. How

much of that is malleable and how much can be changed by processes that if you were to come in as an investor at Vista that you could change something like that or is that such a cultural phenomenon that it's hard to change?

>> That gets back to the first part of your question which is you know what culture makes you successful? Openness,

thoughtfulness, openness and willingness to change. So often what happens is a

to change. So often what happens is a product person comes up with something, they build a product, they sell it, it's great, it solves a problem that nobody else solved and it's flying off the shelf and now they're focused on

selling installing customer support. Okay, the last thing

customer support. Okay, the last thing to think about is what contracts have been signed. So part of when we come in

been signed. So part of when we come in it's like okay now we'll look at all that say ah okay here's where we can actually buttress

what I call your the real economic expression of that company by changing the way your contracting process works.

You may not see it, but that is one of the most important things ultimately in a sale process, an IPO, whatever it is, because it now gives you layers of, you

know, foundational layers of of economic support that now enable you to grow off of a a strong base as opposed to dealing with low retention rates because you had bad contracts and people are doing

different [clears throat] things in the contracting process that ultimately two years from now, oh, there's a great client, but they're leaving now or they want to change it because you didn't have a good strong contract for them. So part of the

best practices approach that we take is that let's go in and let's you know you got to go in and segment all of the different contracts. You know which are

different contracts. You know which are the ones that are in great shape. Great.

Which what's the standard that we want?

What do we need to change? How do we change it? What's the process in order

change it? What's the process in order to change it? Because you just can't come in with, hey, guess what? Everybody

gets a new contract. Okay. No, you've

got to actually go through in a systemic way to build capabilities and ultimately to build these layers of support uh for the business.

>> One thing that's interesting in in what you just said is that >> only one thing >> and that that specific thing I at least I picked out enterprise software

seems like it could be construed to have a slightly different risk return profile than other parts of private equity. Even

yes, it's still an an investment in a company, so it's still equity. But some

of these contracts feel a little bit infrastructure-l like. They could be

infrastructure-l like. They could be long-term contracts, maybe not the same length as an infrastructure investment contracting with a government entity, but they have a different risk return profile. I think that I want to unpack

profile. I think that I want to unpack that because I think that's so instructive to enterprise software as an investing category. And I want to go

investing category. And I want to go back to when you started Vista early on.

>> Obviously, there's been multiple waves of software that have since changed the market. We'll get to AI in this context

market. We'll get to AI in this context bit, but to start, what did you see in enterprise software at the time that you started Vista that made you say this is this is a market that I want to be

investing in and this is where I wanted to deploy capital?

>> Yeah. So if you look at a very high level, if you do this well, enterprise software has mission critical and you know

business criticality.

What's that mean? That means that you look at renewal rates and retention rates and you look at oh this person's been a customer for eight years. They

are completely reliant on this software.

Can you build contractual you know obligations with that class or group of customers that actually gives you certainty and remember these are 95% gross margin products at the end of the

day right no capex negative working capital right no inventory build it once sell as many times as you can right so to a great extent that sounds like a

very stable investment opportunity which oh by the way sovereignty dominion workflow data sets you can now create new products and now create a new set of economic rent capture opportunities,

right? Cuz I'm now selling a new product

right? Cuz I'm now selling a new product that only I can sell you because I'm the only one with your data and understands the utility of your data utilized in your environment or the in this in this

product that I'm going to now sell you.

So you think about you can base load these businesses with some convexity in the upside. So from a risk return

the upside. So from a risk return perspective, these are great businesses to own.

And then it's a matter of how do you use newer waves of technology ultimately to capture more economic benefit. Okay, how

do you take this product that was onrem back in the day legacy to hosted?

What's the advantage of that? Or one of the advantages is when you move to a hosted environment, your clients no longer have to go through hardware refresh cycles, right? It's a browserbased interface in

right? It's a browserbased interface in essence. And so now all of a sudden you

essence. And so now all of a sudden you had, you know, millions of dollars that you were paying in hardware refresh to go away. Well, I should capture some of

go away. Well, I should capture some of that value, right? I'm selling you a product that decreases your cost of support. Okay? And you your capex goes

support. Okay? And you your capex goes down, goes to opex. I should get some my fair share. Okay? Oh, by the way, in

fair share. Okay? Oh, by the way, in that environment, I can now make changes more quickly to the product and update the product more quickly and actually give you feature function benefit that you otherwise wouldn't have in in in

your environment. So I should capture

your environment. So I should capture more of that benefit as well. So you

have this ability to what I call stack economic rent curves for existing customers over long periods of time which actually create a more unique advantage to you know capturing economic

value as an investor. So that's the point of it.

>> I think it's a great segue to something you've said more recently which is rule of 40 in software is kind of the the gold standard for how people look at investing. You add something like aentic

investing. You add something like aentic AI maybe that takes it to rule of 50 60 or even 70. Can you unpack what you mean by

even 70. Can you unpack what you mean by that and what that means for those who are investing in enterprise software?

>> Yeah, let me break that up into two separate statements. Okay. Rule of 40

separate statements. Okay. Rule of 40 was a gold standard. I will argue we coined the uh phrase uh not going to get many arguments against that is my guess.

And for those of you in the audience, it's really a combination of EBID margin and growth rate. Uh 25% Ebidow margin, 15% growth rate. Great company, 40%, you

know, rule of 40.

Genai enabled. Before we get to Agentic is utilizing AI to make that rule of 40 more efficient.

In enterprise software 30% of our cost in essence are product development. Okay. So you think about

development. Okay. So you think about the first instances of a you know genai enabled product. It is a developer who

enabled product. It is a developer who now can operate three or four agents who can be as effective as five or six developers. So all of a sudden you go

developers. So all of a sudden you go from well wait a minute I was spending 30% of my cost on development. I can now spend 20% of my cost on development more

effective on new product and code writing than older. But you know again there's a tipping point dynamic that's going to occur in our marketplace where

>> ultimately the genai enabled um or the genai generated code will ultimately you know supplant what was the legacy code and so then you're going to see a

massive shift in the cost dynamic. So

now all of a sudden you're increasing your your your margin without doing selling one additional product because your cost of support cost of development goes down. Point one development

goes down. Point one development customer support as you think about utilizing these tools for customer support calls you know 40 50 60 70% 80%

90% deflection of these calls with agents higher NPS scores higher net promoter scores higher customer service that you can do with fewer people. And

now all of a sudden you're reducing your support cost. Go to market. Okay.

support cost. Go to market. Okay.

If I talk to my CEOs, well most of them, they're going to tell you that their sales people spend 100 100% of their time selling. If you

actually do the work, they're only spending 15% of their time selling. 85%

of their time, it's the administration around selling, sales support, you know, capturing information, putting it in some repository, using that to, you know, to make a more effective sale and all those sort of things.

Genai enabled agents can do a lot of that work and so all of a sudden your salespeople now spend 40 50 60 70% more time selling as opposed to the back

office administration part of it. All

those dynamics right there I just lifted your margin from 25% Ibid margin to 40% Ibid margin. Okay. So now you go from

Ibid margin. Okay. So now you go from rule of 40 to rule of 65. And now you say okay now how do I enhance my go to market part of this that I can actually be more effective in sales lead

conversion. So now all of a sudden

conversion. So now all of a sudden you're lifting your growth rate. Okay

now your rule of 70 right so that's a dynamic which is what I call genai enabled enterprise software. The second

part is agentic where you say wow I'm now actually able to develop agentic tools agents that actually are delivering a completely different set of

products utility of those products or actually addressable market going to a different addressable market that actually has [snorts] an exponential opportunity. What is that exponential

opportunity. What is that exponential opportunity at a high level? It is AI enabled software that now can go and displace services because you now have workers which are agentic workers who do

the things that an individual might do and especially you create them in an orchestration layer. Those are the

orchestration layer. Those are the important parts of how you mo you know move from rule of 40 to rule of 70 to agentic and

some agentic may not necessarily be the you know the or some rule of 70 may not be agentic in terms of their products but you can still operate them at almost two times the ebidal margin that you had

them operating a year ago. That's what

we're pivoting all our companies towards. Are there industries where

towards. Are there industries where services are going to be impacted by Agentic AI, where software companies are

going to be the ones that eat into those services or generate revenues from capturing part of that market?

>> Yes.

Which which industries or categories do you think it'll be? And if I tell you, Michael, then everybody on this this this podcast will then go buy those companies. So, I'm not going to tell

companies. So, I'm not going to tell you. [laughter]

you. [laughter] >> We'll have to find out then.

>> Where watch and see.

>> On the flip side of this, where do you think AI still falls short?

>> You've probably seen, you know, we we've announced this deal with Microsoft to in essence build out our capacity to create a gent solutions. We got their first company through the factory. We got

another 12 going through now and flights of them. This is a lot harder than what

of them. This is a lot harder than what people say. And if you look and you

people say. And if you look and you probably read the MIT study, oh, you know, 95% of this stuff is in personal productivity. You're not seeing much in

productivity. You're not seeing much in the enterprise cuz it's hard. Uh, and

you actually have to to create highly precise agent and agent action in the enterprise. So, here's the way I like to

enterprise. So, here's the way I like to characterize it without going straight down that rabbit hole. Um,

hey, JTEX solutions for consumer, we can deal with, I'll call it the fuzziness of outcome.

You know, the hallucinations in essence.

Okay, you and I decide, hey, this has been great. Let's go have dinner

been great. Let's go have dinner tonight. Let's go to a Thai food

tonight. Let's go to a Thai food restaurant in New York, tell my agent, book, you know, dinner for two, Thai food restaurant, 7:00. Great. 95% of the time you and I would show up and it's a

Thai food restaurant and the restaurant in the and it's booked at 7:00, all that sort of stuff. Within that 5% [snorts] will show up and it actually is a

restaurant that actually had a Thai dish but not a Thai food restaurant but a Thai dish or that restaurant may have not been in business for three years because of where they're actually

drawing the information, excuse me, the information from, right?

that's not adequate for the enterprise.

So you think about every month someone's getting paid and a wire transfer goes into your account and every month you you know give your

daughter or son you know thousand bucks and so an agent in an enterprise may say well why am I doing that why don't you just send a thousand bucks to the son or daughter right and in some cases you may

say that's okay but for the most part no right you still do not want to change that workflow even though it can be quote unquote perceived to be more efficient. So that's a different use

efficient. So that's a different use case in the enterprise that still would deal with call it a reasoned answer with a fuzzy, you know, fuzzy outcome that may or may not be sufficient for you.

And in certain environments, certain cases, certain enterprise businesses that's completely unacceptable. And so

you have to manage that in a context of building out enterprise agentic systems, which is what we're doing across our infrastructure with our partners. So

that's an important part of what we're doing.

>> On that point as well, I think it's a good segue to living your ethos as well.

Internally, you've thought a lot about AI from an investment perspective and applying it to your companies. You've

also used it internally. Where do you feel like you get the most impact or benefit from using AI at Vista?

The most across the board for me.

I've got an agent, my agent's name is Q, that I use every single day for conversations like this. I'm about to go sit with an investor. I'm [snorts] about to go sit with a CEO or someone who's

interviewing me. I'll type in the five

interviewing me. I'll type in the five questions, eight questions, 10 questions, whatever it might be, and it will come out in my voice. Here is how Robert would answer that question in

bullet point and in text and pros. We

should try that afterwards and see if what your your answers were relative to what Q's answers were. How similar do you think >> Q is to you now? And I'm sure it trains

and gets better relative to what it >> what it was >> what what it was and what you and how you would answer.

>> It's it's multimodal now. I mean, so we've changed the modality a few times now because in the early days it was, you know, pretty it was oh, it was efficient. it would take somebody

efficient. it would take somebody in my team, you know, a few hours, whatever it might be, to prepare for this interview.

>> Now, I can literally type it in on my way over here, and if I bother to read it, right, it'll likely get get close to it. But there's other parts of utility,

it. But there's other parts of utility, you know, how do we deliver information back to our investors? Okay. Okay. The

data constructs. Okay. There's investors

in this fund, this fund, not in this fund, this fund. Okay. I'm about to go sit down with them. I need literally type in you know put in well I don't have to type it in but you know and it drops down and says okay here's what they're invested in here's what they've

returned here's their cash flows here's they've gotten back in the last you know 30 days 60 days 90 days 120 what you know return that sort of right massive utility what it took people weeks to do

I it's done in you know seconds for me on my way walking into the meeting great utility there okay even you know GPTs that we are now using for analysts

associates in terms of how they're preparing their their memos for investment committee and how I'm contrasting them versus you know well we've looked at 12 12 businesses in this

industry in the last 15 years you know what went right what didn't all those sort of things in the industry what were the presumptions what okay so every part of our business is being affected by

this so when you said okay which one's more I can't say it all depends on what I'm using that day at that moment in that time but I'm seeing the efficacy again this gets back to that personal

productivity is really really really high for that for me being a knowledge worker in the role that I am in but I can see the utility of our industry and

our business and building again I feel confident about what we are building internally and how it is affecting I'll call it the data because of the way we've built it you know the you know

call it the rag architecture the way I like to think about it we bring models to our data we don't take data to our models okay and that's an important paradigm time to think about it in in this environment.

>> Unpack what you mean by that.

>> Less than 1% of the enterprise data actually sits in these LLMs today.

Okay. I think IBM had talked about that a couple weeks ago. Okay.

>> If you now start taking your data and putting it into others models out there gets absorbed in ways and used in ways that you don't get the utility and benefit. But if you

actually take these models and apply them into your construct and utilize them in your environment, you're the only one who gets the benefit of your data.

Sovereignty and dominion over workflows and data sets. I know, right?

>> Yeah.

>> Okay. That's all important and important construct around utilizing AI in the enterprise.

>> If we think about that concept, sovereignty and dominion over your own data, let's take that in the personal concept.

So neither of us grew up with AI.

>> Yeah.

>> Younger people or investors or executives founders now are growing up with AI as a tool that they can use.

What do you think are the skills that they need to have in the age of AI?

Different, similar to the skills that we had to have when growing up and thinking about how we wanted to build our careers?

We had a lot of emphasis on STEM education.

Uh being able to solve come up with algorithms to solve complex problems that isn't going to be as

useful because these systems will be more effective at it later.

So there is one argument that says let me be effective at at managing agents and that is a skill you and I didn't have to have. That is going to be for certain people in their jobs critically

important. For other people it would be

important. For other people it would be more important to understand how to communicate as a human being to another human being to spend time rather than the sales agent worrying about okay how do I pull together this information is

how do I connect with that person as a human being so I can actually make this sale or whatever it might be. So it

depends on your job and your role and what your plan and ambition is as to how you should utilize a a gentic world or a genai world that you're that you're now

living in. You know, I've got couple of

living in. You know, I've got couple of my kids are creatives and I remember two Christmases ago, you know, you should start to use these for,

you know, your creative. No, dad, that's not this. It's okay. Okay. Okay. Okay.

not this. It's okay. Okay. Okay. Okay.

Now I can see how they use it to advance part of the work that they are doing which is still creative but now it's things that would take them weeks to do they can do in minutes which gives them the chance to think about the next order

of opportunity.

>> So I want to make sure we unpack some definitions here. So tell us a bit about

definitions here. So tell us a bit about Gen AI.

>> Okay.

We've been utilizing what I'll call I'll distinguish a little bit you know artificial intelligence for over a decade in our companies machine learning artificial intelligence where you're actually able to create you know unique

algorithms that create highly efficient tools uh to deliver you know very specific outcomes using those algorithms. When you start thinking about genai, it's a

generative dynamic where you're generating not just the next letter, word or phrase, but you know ultimately a a you know an outcome based on what the context is. Okay, which now creates

and gives you a whole different utility of opportunity. And of course, you know,

of opportunity. And of course, you know, you've got some great people out there in in, you know, and you know, Microsoft and Google and OpenAI and all those who are developing very unique models that can do this at size and scale and with a

fidelity that actually creates new products, new opportunities and fascinates the imagination, right?

Agentic actually can start to not just take this generative information but actually reason with it and do something and actually act and in some cases act

based on an instruction or by an an agent an instruction from a person or an agent orchestrator or human orchestrator orchestrating multiple agents to do

series of tasks specific tasks to do work. Okay. As opposed to individual

work. Okay. As opposed to individual task. And so the context of that

task. And so the context of that actually creates a whole another set of dimensions of opportunity in the world of of enterprise software. That is what we've been focused on with our partners

Microsoft Anthropic.

How do we take this agentic idea, make it real in the enterprise with the precision, with the capability that no one else can do. So if you think about

it, I've got 750 million users of our software. Each user is a knowledge

software. Each user is a knowledge worker that you can actually take that knowledge worker's job and break it into a series of tasks that agents can affect

on some will be introduced or managed by the orchestrator a person or another agent which can do it and now deliver that into our customers environment to

create value for the customer. So I tell my team we're going out of the user business into the agent resources business. So 750 million users of our

business. So 750 million users of our software basically equivalates is equivalent to about 8 to 10 billion agents that we are going to actually be delivering into the marketplaces of our

companies and our customers that actually do work and that's kind of creates a really interesting paradigm of opportunity. One question on that last

opportunity. One question on that last point.

>> When do you think we'll see the impact of all of this on companies? And and I asked that question kind of zooming out and saying you saw the first wave of the

internet.

>> People got excited. It then obviously got overexuberant and then the impacts of the internet started to permeate and reverberate 20

years later. Amazon in 2015 was much

years later. Amazon in 2015 was much different company than Amazon in 2000.

>> Yeah.

>> How do you think about that >> what you saw in first wave of the internet as it relates to AI?

>> A similar paradigm. The first wave went of course to the hardware vendors then infrastructure and then software vendors really started to capture the

opportunity set. Um, I actually struck a

opportunity set. Um, I actually struck a deal with this young man by the name of Andy Jasse back then cuz he had been building a really interesting technology called AWS to support this other

business called Amazon. Had some compute and we were able to come up with some unique opportunities leveraging their capabilities, our infrastructure to now

create a factory to go from onrem to cloud. As far as I know, we're the

cloud. As far as I know, we're the they're converted more business from on-prem to cloud than anyone else on the planet. That was a process that we saw

planet. That was a process that we saw still play out. I think there's still only like 50% or so of the enterprise software companies that are still that are now cloud still on prom on prem. So

it's 20 years later, 16 years. That's

the kind of dynamic we're dealing with.

I think something similar is happening now. Of course, hardware vendors today,

now. Of course, hardware vendors today, Nvidia probably the most prominent of which capturing a vast majority of the economic rent and attention. The

hyperscalers are saying, "Hey, we're going to build out infrastructure and capability because we've got a lot of free cash flow. We were infrastructure providers in now we're going to provide a different type of infrastructure."

Great. Capturing economic rent. Then you

have these hype this you know Nvidia deciding well I'm going to get on that game too. I'm not just going to provide

game too. I'm not just going to provide chips. I'm going to provide

chips. I'm going to provide infrastructure as well. And you got a bunch of sovereigns out there saying I'm going to do it as well. So there's a whole infrastructure dynamic where you're now going to start to see the swelling of

overtime capacity GPU capacity. But I

still believe it's going to be the enterprise software vendors that are going to capture who sag Dominion, right? Uh that'll capture the vast

right? Uh that'll capture the vast majority of the economic rent because they actually have the workflows and data sets that no one else has. We're

seeing it now cuz we're doing it now.

But it is not trivial. This is not something you just plop an agent in.

Hey, go update these databases. Go

figure it out. You've got to now construct it in a way that actually creates the I'll call it the the the high precision outcomes in the workflow

environments. So it may take a while to

environments. So it may take a while to manifest broadly but as far as I know we are on the forefront of making these enterprise class solutions effective

today.

>> I think that's a really interesting point in the context of the evolution of Vista as you've built the business also to now include a new set of investors.

I'm sure you've worked with family offices and and wealthy individual investors for a number of years, but the wealth channel in particular, you've built a business to help them get access

to enterprise software. And I think it's a pretty interesting time to >> think about that because of all the opportunities as you just laid out when it comes to enterprise software, the

application of AI and Aentic AI to that.

>> What made you decide to build a wealth business that would help the broader wealth community access enterprise software? Yeah, you know, like all

software? Yeah, you know, like all things you you sit back, you look our institutional investors have been fantastic and great supporters of our business and we will continue to support their businesses, their ambitions, you

know, to to to expand in this space and many cases diversify what their opportunity set is. We have found however that there are a lot of uh private investors who don't have access

to this. 97% of these companies are

to this. 97% of these companies are private. you know, we have the ability

private. you know, we have the ability to actually, I'll use the word, democratize the opportunity set. And so,

we should do that. I think it's the right thing, frankly, for our country, you know, to to be able to deliver uh a set of investment opportunities that can actually change the retirement

slashinvestment vector of a broad group of investors and not just those who are investing through large institutions. So, that's really

large institutions. So, that's really what the thought and ambition was and I'm pretty excited about where we are so far. The other aspect of the wealth

far. The other aspect of the wealth solutions business you've built, you've really thought about AI and how AI can help wealth managers run their own businesses better. There's a lot of

businesses better. There's a lot of software that helps wealth managers and has helped innovate their own business, but AI is now helping them do that even

more. How do you think about applying

more. How do you think about applying Vista's knowledge and network when it comes to AI in the context of helping the wealth partners you're working with run their own businesses?

>> Yeah, and we get asked that question a lot and we are working with many of them to help them understand how we're using it and what the the utility is. And I

think we'll see over time what I'll call the emergence of very specific applications and use cases of agentic solutions that enable them to be more effective not only in the services but ultimately the sales of their product to

their customer base.

>> Well, one thing that's interesting about wealth managers is they have sovereignty and dominion over their data because their clients that is unique data to them. They can leverage that. That was

them. They can leverage that. That was

something that you illuminated on this entire conversation and you've done it as well at Vista with the business you've built. So fascinating

you've built. So fascinating conversation. Thanks so much,

conversation. Thanks so much, >> Michael. Appreciate the time and good

>> Michael. Appreciate the time and good luck and have fun.

>> Thank you.

>> All right. All the best. Thanks.

>> Thanks for listening to this episode of Alt Goes Mainstream. I hope you enjoyed it. You can read more about alts at my

it. You can read more about alts at my Substack altainstream.substack.com.

Substack altainstream.substack.com.

[music] Thanks a lot and have a great day.