Episode 158: Alex Slusky of Vector Capital on
Diagnosing Growth Levers and Building a Defensible AI Edge

Episode Transcript   

00:00:00.000 — 00:02:27.190 · Shiv Narayanan
Hey, everybody, before we get started with today’s episode, I want to take an opportunity to tell you about my new book, AI Marketing Blueprint. It just hit number one bestseller status across multiple categories on Amazon, and it walks you through a proven framework that we are taking our clients through here at, How To SaaS and how to stay relevant and visible in a world with AI platforms, one of the biggest problems that we have seen across clients and companies is that the traditional channels that they relied on, like paid search or SEO, have declined significantly or are getting more expensive and competitive.

The reason for this is that a lot of buyers are starting to self educate themselves and research on AI platforms, so a lot of that volume that was previously going to those channels is now moving to these new AI platforms. So to stay relevant, the book walks you through seven new rules that companies in all kinds of verticals and industries need to consider so that they can continue growing their pipeline and revenue and ultimately their enterprise value.

So if this applies to your business or one of the companies that you’ve invested in, I definitely recommend that you pick up a copy. It’s available on Amazon, on Apple Books, on Barnes and Noble, and even share it with your teams. Grab a bunch of copies so that you can kind of consume the content together and start to apply it to your business.

And with that said, let’s move on to the episode. Welcome to the Private Equity Value Creation podcast, where we interview leading investors, operators, bankers, and advisors to help you answer one question how do we increase the enterprise value of our companies? My name is Shiv Narayanan and each episode I will dive deep with a guest to help you become a better value creator and capital allocator.

So with that said, let’s jump right in and let’s get started with today’s episode. My guest today is Alex Slusky, and he is the founder and managing director at Vector Capital. And what’s really interesting about this episode is that Vector Capital is doing a ton in the world of AI, and how they’re leveraging it to help their portfolio companies, how they’re thinking about product in particular and how they’re approaching go to market differently.

And Alex has been doing this for decades, and there’s a ton of experience that he brings to the conversation and talks about a bunch of great examples from their portfolio in terms of how they’re leveraging AI to grow their respective companies. And I’m sure, like given all PE firms are thinking about this, there are going to be a ton of portfolio companies that you could apply.

A lot of his insights into. So with that said, I’ll leave you to it and enjoy the episode.

00:02:34.510 — 00:02:36.750 · Shiv Narayanan
All right, Alex, welcome to the show. How’s it going?

00:02:37.390 — 00:02:38.430 · Alex Slusky
Thanks for having me on.

00:02:38.710 — 00:02:44.430 · Shiv Narayanan
Yeah, excited to have you. So why don’t we start with your background in vector and then let’s go from there? Sure.

00:02:44.470 — 00:04:06.380 · Alex Slusky
Well, I started my career in technology in the early days of Microsoft in the late 80s, and that that led to, you know, with some education to a venture capital job at New Enterprise Associates, A venture capital firm is still still very much around and eventually evolved into me joining a family investment office called Ziff Brothers Investments.

And the way they lured me from NEA was to make me one of those offers you can’t refuse, which is come work for us for a couple of years, and if things work out, we’ll help you start your own firm. And I was, you know, given that offer in my late 20s, uh, did not refuse it. And things worked out quite well. The Ziff family was a key supporter and investor in the early Vector funds.

And really that decision, and my work with them has led to the creation of Vector Capital in the late 90s. So the firm is, uh, 28 years old and has been focused on a fairly consistent strategy ever since. We focus on being value investors in technology industry more broadly, but software really has made up about three quarters of our investing over our, you know, almost three decades of history.

00:04:07.660 — 00:04:22.380 · Shiv Narayanan
And so first of all, that’s that’s great. And that’s a great way to, um, set this up. And interesting how you got the firm going. Talk about what the focus of vector is now. Like, what types of companies are you investing in and what stage and what amount of revenue, etc..

00:04:22.540 — 00:05:07.850 · Alex Slusky
Sure. Well, we tend to acquire companies. Over 80% of our investments are control investments. Where Vector is part is the lead investor, typically the only investor in partnership primarily with the management team of the company. The vast majority of what we do is focused on software. And the scale is probably 75 million on the low end.

About 500 million of revenue on the high end. And enterprise value is anywhere from a couple hundred million to a few to a few billion. That’s that’s where we focus. And the firm manages a bit over $5 billion of capital.

00:05:09.490 — 00:05:17.890 · Alex Slusky
Most of that is in the private equity strategy I described. We also have $1 billion credit business also focused on on tech.

00:05:18.890 — 00:05:24.730 · Shiv Narayanan
And so walk us through like what percentage of the firm is focused on the debt side versus the investing side.

00:05:26.170 — 00:05:34.890 · Alex Slusky
About 80% of our capital. 80% of our staff is focused on private equity and the remainder is on technology credit.

00:05:35.130 — 00:05:47.010 · Shiv Narayanan
Got it. Okay. And so on. The private equity and the buyout side. Like help us understand what types of companies are you looking for? Like what are some of the characteristics that really help you feel like, okay, this is one that Vector would want to deploy capital in.

00:05:47.050 — 00:06:02.330 · Alex Slusky
Excellent question. So we’re looking for companies that have hit a crossroads and ones that we can make a positive contribution to their future. So where are those companies found?

00:06:04.370 — 00:06:07.609 · Alex Slusky
They’re usually in a few buckets. The first one

00:06:08.930 — 00:06:35.529 · Alex Slusky
are what we call venture capital silver medalists. These are companies that grew to significant size as a venture capital venture funded software companies. But they’re not no longer growing at rates where the venture backers can make the kind of IRR on an ongoing basis

00:06:37.210 — 00:08:17.040 · Alex Slusky
because they’re not, you know, they’re they’re maybe growing at 10% or 12%, but they’re not but they’re not growing at 50, which is what you need to continue to deliver venture returns. And these are companies that will certainly make money for their early stage. Investors may get the money back for the late stage investors, but don’t belong in venture portfolios.

And so we’ve partnered with some of the most preeminent venture firms in the Valley in in acquiring it from them. Occasionally some of them will roll in to our structures, but we’re not a late stage investor. We will be a control investor in those situations, and we will partner with the management teams to manage these companies as private equity owned assets, not as venture capital funded assets.

And there is a distinct difference between that. That’s kind of the first bucket of what we do. And obviously go through a number of examples. The second bucket are smaller public companies that aren’t loved by the public market. We call them kind of fallen angels. Maybe there are hot ones, but they were not able to be successful in the public markets, and we will take them private and refocus their business.

That’s the second bucket. The third buckets are a little more troubled companies, which borrow too much and are now either controlled or owned or about to be owned by their lenders. And we will step in and recapitalize the business and try to fix the capital structure and fix the operations.

00:08:18.400 — 00:08:42.999 · Alex Slusky
That’s the third bucket. And last but not least, our corporate spinouts. The typically divisions that are orphaned inside of a larger corporation. And we will come in and buy them from from the corporation. Often the corporation will retain um, like 19.9%. So they don’t have to consolidate. And then we will build up a management team and focus that business on

00:08:44.400 — 00:09:30.110 · Alex Slusky
the the needs of this division, not trying to optimize for the larger whole, which is what large corporations are supposed to be doing. So these are the four buckets of companies that we tend to pursue. What’s common about them? It’s usually companies that are not able to execute crisply, or the management teams are often, um, hamstrung by outside issues.

Not that have nothing to do with their market and companies that maybe set themselves up for higher growth than they’re actually seeing, and they don’t know how to recapture it. And we have a number of playbooks across our business of what do you do in each of these situations?

00:09:30.430 — 00:09:38.510 · Shiv Narayanan
Um, and so walk us through that. What what do those playbooks look like, or how are you evaluating which part of the playbook to focus on inside these businesses?

00:09:38.710 — 00:11:44.140 · Alex Slusky
Well, you really have to look at what the underlying business is. Um, let’s it’s actually be great to use a contrast of two deals that are Vector deals. One, a, um, both database companies, both purchased from their venture backers. One company is MarkLogic that we purchased a few years ago and sold in later on, proceeded to sell to Progress Software and one company’s SingleStore, which we we bought about a year ago from from its venture syndicate.

Both database companies both backed by Vector but somewhat different strategies. So MarkLogic was an older database technology very heavily used in government and financial services. And it was very clear that while its customers were, you know, very committed to the product, the product was deeply embedded.

As we know, switching costs from databases are very, very high. Not something anybody wants to do. And MarkLogic was embedded in healthcare.gov, for instance, in the US Counterterrorism Center. No one’s going to rip it out. But what was the opportunity for MarkLogic, it was not the most advanced in most current database technology.

It was really going to be a three and 3 to 5% grower, and that’s all it could realistically be. Grow with its current customers, but acquire very few new ones. So the strategy there was to obviously focus on costs was really to rationalize, to take a company that previously was losing quite a bit of money and turn into a company that was delivering 35%, 30 to 35% EBITDA margins.

So we took a company with a negative rule of 40 to 1 that was very close to 40. And obviously that which we did in about two years creates a lot of value for all the stakeholders. And once we’ve cleaned it up and made it a much more focused, better executing business, we

00:11:46.020 — 00:11:57.300 · Alex Slusky
were able to attract Progress Software. So we had a number of bidders because everybody looked at them and said, you know, we love databases. They’re very sticky, But we want databases. The big money, not databases to lose money.

00:11:58.620 — 00:12:06.220 · Alex Slusky
So that’s a very straightforward playbook. We can obviously get into the details of how we did it. Now take SingleStore,

00:12:07.580 — 00:12:11.860 · Alex Slusky
slightly larger than MarkLogic still growing in the teens and 20s.

00:12:13.820 — 00:14:44.729 · Alex Slusky
Uh, maybe not enough to to satisfy its venture investors, but actually very much a growth asset in our mind. Now, SingleStore is a database that combines high performance and analytics at the same time. Yeah, many people know it under its old name called MemSQL. Now, combining a high performance database with analytics in one code base is a very hot space.

As you think about what you need to do with AI. Um, the thesis for SingleStore has become even stronger than it was a year ago when we purchased it. So the approach of SingleStore stories. Obviously not to optimize profitability. The approach of SingleStore to be very, very thoughtful about the use cases where we win because, you know, clearly we cannot take on all open source databases and Oracle and everyone else at the same time.

So we have to be very rigorous about the use cases that we have. And there’s about 10 or 12 use cases which where we truly thrive and then put all of our wood behind those arrows, really focus laser focus on showing developers and IT departments of why adopting our database ultimate is going to save them money.

Now, adopting your databases is always a difficult process, and you end up growing with the amount of data and with existing customers more easily than you do winning new ones. But winning new ones is critical to your long term success. One of the things that’s made it a lot easier is some of these very sophisticated, very complex, high performance databases are, you know, reasonably complicated syntax and are somewhat more difficult to implement.

Well guess what? AI riding to the rescue. Claude knows how to implement SingleStore. So does Codex. And so our threshold for being adopted has dropped dramatically. And as a result, the company’s growth has even ticked up. You know, close to the 30s. Again, very different use case. We have to in this case, it’s a lot more about execution and about working with management team to define those use cases.

Optimize sales, bring in more rigorous CAC to LTV analysis, which we’ve done. Bring in a new CFO. You know, we are upgrading and improving the business, but in a way where we’re trying to increase its growth rate

00:14:45.930 — 00:15:00.000 · Alex Slusky
and very, very different than MarkLogic story. So what’s important to emphasize is Vector. Vector is an expert in software, but we don’t run the same playbook on every particular company because not every situation is the same.

00:15:00.720 — 00:15:34.040 · Shiv Narayanan
Yeah. How do you determine which approach to take for a particular business? Because every company like you mentioned, you’re especially you’re buying companies that there’s room for optimization and there’s room for improvement. So walk us through diagnosing that business to like what’s what is a prioritization mechanism to figure out where you actually focus, the management team, where you focus your limited resources, and how you actually think about value creation as a as a operational playbook.

00:15:34.120 — 00:15:43.080 · Alex Slusky
Well, obviously, the vast majority of that thinking, not the work, has to happen during due diligence underwriting.

00:15:44.760 — 00:15:47.080 · Alex Slusky
You need to understand what is your TAM,

00:15:48.280 — 00:16:12.920 · Alex Slusky
how competitive are you in your TAM? How quickly is your TAM growing? You know, you know, what is the realistically addressable market and can I go win in that market given the research I have, because something that $100 million company can do is different than what $1 billion company can do over a $10 billion company.

00:16:14.040 — 00:17:41.030 · Alex Slusky
Uh, SingleStore may have a fantastic TAM, and it’s growing fast. Now, is it going to go head on against Databricks? No, that’s probably not a not a war we’re going to win. But can we identify specific use cases. Definitely. So you do a lot of that in underwriting. And then you decide how important is growth to my investment thesis.

And can I realistically deliver on that growth. Like is it believable. And you have to look at each other kind of around the table and at that table, for us it’s always the investment team, but also the value creation team. We have a very capable value creation team led by Kiran Rao, who joined us from Vista, uh, about just less than a year ago.

And so we all look at each other and say is, can we actually impact growth in this business? And if the answer is yes, obviously you get paid a lot. Um, as a private equity investor, as a management team member, you know, if you can grow a business, you’re going to exit much better. But you need to also be realistic.

One of the things we find is that a lot of venture funded businesses try to grow at all costs, and the growth is value disruptive. I can I can show you business after business that we’ve turned down or the CAC to LTV is actually negative.

00:17:42.470 — 00:18:12.270 · Alex Slusky
So you you know, you need to be rigorous about what what you really expect. And once you’ve decided on that, then you have to relentlessly execute. Now, that execution, I mean, even in growth companies, still means attaining operating efficiencies because there’s no excuse not to operate as close as possible to the efficiency frontier.

Because. But why not? Right. You can either take those dollars into

00:18:13.430 — 00:18:36.190 · Alex Slusky
more profit, or you can take those dollars into more R&D and more sales, more feet on the street. But there’s no reason to waste money in a in customer support. Like make sure those places are efficient, which really today means, um, AI enabled as much as possible and frequently offshore,

00:18:37.830 — 00:18:38.510 · Alex Slusky
you know.

00:18:40.030 — 00:19:16.380 · Alex Slusky
And so once you’ve done that and you’ve created, in a sense, more dollars to spend you, you you decide, do I want to spend them on customer acquisition? which if if the market allows it and your competitive map allows it, absolutely. That’s your highest and best use or to drop them to the bottom line. Like an example I gave you with MarkLogic.

And it can give you many others, um, you know, just to market, we were not going to grow about 3 or 5%. So knowing which world you live in is super important. And a lot of executives fool themselves about which world they actually are in.

00:19:17.220 — 00:20:34.970 · Shiv Narayanan
We’ll get back to the show in just a moment. But before we do, one of the most common and important value creation levers that we hear about on the show, from private equity investors and our own PE partners, is go to market. Yet when these same PE partners bring us into their portfolio companies or new target investments that they’re exploring, we find that the marketing function is quite immature under utilized and under optimized.

And so that’s a huge opportunity that we see inside these companies. And if you have a portfolio company that you feel like it’s scale a lot faster to drive more pipeline than revenue, or you’re looking at a new investment where you feel like that could be core to your investment thesis. But we’d love to explore that with you and figure out how we can partner with you to drive more enterprise value creation.

On the marketing side, similar to the way that we’ve done with major PE firms like TA, Updata, HG, STG, and many more. At this point, we’ve done hundreds of engagements across hundreds of industries and verticals, and we have a ton of benchmarks and frameworks that we bring to these engagements to help you drive as much enterprise value as quickly as possible.

So if that sounds like something that you might be interested in, you can just email me directly at Shiv at How To SaaS, or go to our website and schedule a demo, and we’d love to speak with you about it further. And now with that said, let’s get back to the show.

00:20:36.410 — 00:20:39.250 · Shiv Narayanan
Right? Operationally, how.

00:20:39.250 — 00:20:40.170 · Shiv Narayanan
Involved.

00:20:40.170 — 00:20:57.970 · Shiv Narayanan
Are you getting with these companies? And you you touched on a little bit, but just help us understand, like how deep are you getting? How are you partnering with the management teams? Are you focused more on getting the right people in the right seats, or are you getting bringing your own ops team to actually execute what the teams themselves?

00:21:01.330 — 00:21:04.530 · Alex Slusky
It’s a simple question. Complex answer.

00:21:05.570 — 00:21:06.490 · Shiv Narayanan
We have time.

00:21:07.010 — 00:21:07.770 · Alex Slusky
We do.

00:21:09.850 — 00:21:11.250 · Alex Slusky
In the ideal world,

00:21:12.290 — 00:21:22.930 · Alex Slusky
we want to give a playbook to the management team and let them execute. That’s nirvana for us because we can spend our time on other projects.

00:21:24.090 — 00:21:32.769 · Alex Slusky
In reality, most companies that find their way to us have some holes in the management team. Now, if the

00:21:33.810 — 00:21:44.610 · Alex Slusky
company’s Swiss cheese and there are many, many holes in the management team, they they don’t end up in the Vector portfolio because we can’t rebuild something wholesale.

00:21:45.650 — 00:21:53.439 · Alex Slusky
It just becomes very hard. but all but always there are elements of the management team. Maybe a brilliant CEO who needs help

00:21:54.480 — 00:22:24.720 · Alex Slusky
with his direct reports. Maybe a tremendous chief product officer teamed up with a great VP of engineering. We always want somebody to build around some core which has which has been burning the fire of this company and feeding it for for a bunch of years. But we understand that the company may have holes like we often find weak financial management.

We’re a private equity firm. We have we’re demanding. So like we will frequently upgrade the CFO, for instance.

00:22:25.760 — 00:23:42.520 · Alex Slusky
So that’s nirvana. We give them a playbook and they execute. But what often we find is that there’s demand from our management teams for our value creation team to partner with them on discrete tasks. So defining the ideal customer profile, the ICP. But we have experts inside Vector, who were really, really good at running that analysis and helping educate the chief revenue officer, his team, sales ops in how to define it and how to focus it.

Because one of the things we find is a lot of companies, particularly when they’re venture owned, they spread in all directions and they try to go after every kind of customer, and you hit customers who are in your sweet spot, and you get customers that are on the periphery and the ones on the periphery. Even if you win them, they’re expensive to win and they’re expensive to keep because they’re in the periphery.

They’re not in your main wheelhouse. So like defining that and it often takes an outside perspective. And sometimes you have to kind of slaughter some sacred cows that the business has believed in. But they actually aren’t true. And so then you could focus like we’re big believers in focus.

00:23:43.600 — 00:23:53.390 · Alex Slusky
And so that project is often done better outside, but then you hand it over and then have the team execute something you develop together.

00:23:54.590 — 00:23:57.470 · Alex Slusky
You know, another example would be let’s say a company

00:23:58.910 — 00:24:51.510 · Alex Slusky
doesn’t know how to offshore, like never had an operation in Eastern Europe or India and doesn’t know where to start. Then we will bring in, you know, we have operating partners in India or operating partners in the US who know how to do this. Uh, we will we will then kind of bring our expertise to that effort.

Lately, we’ve been bringing a lot of our expert AI expertise to our portfolio companies because we use the same consistent set of benchmarking tools, for instance, to determine what percentage of code is is AI built. And so each rather than having each company define its own measures and its own pull request measures, you know, we’re going to create one consistent framework across our entire portfolio so we can actually show the CEO.

This is where you stand. You know your number one or you’re in the middle of the pack.

00:24:52.630 — 00:25:24.390 · Alex Slusky
Uh, certainly only be a laggard. So there are things that are better done externally than internally. But again, we you know, we aren’t remotely scaled to run every one of our portfolio companies hands on. We’re not a holding company. So the goal is 12 or 18 months in once the company has been fully brought up to speed.

We’ve put in the right management team, have that management team run the company and then have us have our value creation team do projects here and there.

00:25:24.870 — 00:25:33.470 · Shiv Narayanan
What are some of the areas that you currently see as the biggest value creation opportunities in your portfolio.

00:25:34.030 — 00:26:38.940 · Alex Slusky
Well I’ve referenced already AI getting AI embedded throughout our portfolio is maybe one of the most important things we can do for SaaS companies. Kind of going off the name of your podcast. And and that really means two things. First, it means having AI permeate the product. This is the harder part, by the way.

It’s making sure that whatever a some young, talented startup that wants to go take our market away by using modern AI tools, that we preempt them, which, by the way, our portfolio companies and many software companies more broadly are extremely well positioned to preempt because we understand the use cases we already have the customer relationships, we’re already there.

We’re talking to the people at the company, like we’re at a far better position to execute on an AI strategy than some new entrant, but you have to do it. You can’t just talk about it, right?

00:26:38.980 — 00:26:56.900 · Shiv Narayanan
What does that look like? Because we hear this from companies all the time, like putting AI into the product and almost intertwining those two things. It’s not like a separate product, it’s part of the same product. But I’m I’m curious, like, what does that look like in your mind? How have you coached your companies to to focus on this?

00:26:57.020 — 00:27:01.260 · Alex Slusky
Well, that really means trying to understand

00:27:02.300 — 00:27:14.140 · Alex Slusky
how you know what the AI threats are and what the AI opportunities are. Understanding what the startups are trying to do and then beginning to think. Ask questions like where is my data store?

00:27:15.740 — 00:28:09.850 · Alex Slusky
What you know, to run AI, you actually need to run AI on something, right? On some on, on, on on data that you’re an information that you’re gathering. Like, have I been gathering information? I’ll give you a live example. Cybersecurity, obviously, an area that is not perceived as threatened by AI, but has a huge AI opportunity and an AI threat of a different kind.

Not that AI is going to replace cybersecurity, but AI attackers. AI powered attackers are becoming far more virulent. You know, we’ve all read the stories. And and we all know that even if Anthropic and OpenAI succeed in limiting their systems for being jailbroken, some open weight, open source LM won’t be.

So let’s just assume that the bad guys are going to be fully AI empowered. So at our portfolio company WatchGuard,

00:28:11.730 — 00:28:14.290 · Alex Slusky
you know, we looked at the situation and we said, well wait a second.

00:28:15.370 — 00:28:17.010 · Alex Slusky
How our firewalls

00:28:18.090 — 00:31:21.080 · Alex Slusky
upload their logs to the cloud and have been for a few years now. They do this to do some of the EDR and other kind of cybersecurity operations that, you know, we need, we need to do to protect our customers. But what a great database for AI operations. So now we’re implementing AI which can actually monitor all those logs and really process them so much faster.

And you almost have like an operation center that’s going to be AI powered. Now, big companies, you know, have human powered operation centers and increasingly high powered operation centers. But what WatchGuard serves SMEs and SMB cannot. Doesn’t doesn’t have that. But we’re going to give them a kind of a cybersecurity CIO in a box or by AI.

But why are we able to do that much faster than a new entrant is because we have millions of devices already deployed, all uploading data to the cloud that AI can help analyze. We’re really well positioned, but companies that don’t have that need to come up with waster, where their data can begin to get pooled so that AI can operate on that data.

Very, very important. Now, other companies like we have a company called Planful, which is budgeting and planning. Very successful. Growing nicely in the office of the CFO. You know, um, clearly their customers aren’t going to be comfortable uploading all their data together so that we could begin to, like, give them advice across thousands of different, um, budgets.

Right. In cybersecurity, people don’t mind if information is shared because everybody wants to detect and address threats really quickly. In financial operations, not so much. Right? But there where I can do is, you know, AI can power chatbots. AI can give tools where we can take budgeting, planning, not just limited to the department in the corporation, but actually bring our our products into the executive suites.

And as you can think about the future, executives can actually sit with our system and put in natural language queries like, show me the budget this way. What happens if I increase the sales force in this geographic region? What are the implications? And we can give those interfaces. And executives can run scenarios much, much more quickly than was than was capable that the software was capable of, even as recently as 9 or 12 months ago.

But with AI tools and obviously the the value is not is not the natural language queries, but all the intelligence and the manipulation that those can translate into using using modern tools. So those are would be. I’ll give you the two very different examples. But we like our portfolio companies. And frankly software companies more broadly have to get this right or we’re going to get our lunch eaten.

00:31:21.800 — 00:31:43.270 · Shiv Narayanan
How do you think about applying AI or similar principles to the go to market side? Because that’s the other half of this equation, right? There’s product innovation that has to happen. But companies are also struggling to figure out, like how do I do sales better or how do I create better content, or how do I engage with customers as they’re self educating more?

I’m just curious how you go about helping your companies with that.

00:31:43.310 — 00:32:34.509 · Alex Slusky
Well, I’m actually glad you asked because we have a portfolio company called Showpad, which is all about AI sales enablement. So Showpad, circa a few years ago would take your content store would would have ability for someone who’s selling complex mechanical equipment, say, a Caterpillar to um, for salesperson to understand how all the different features work together.

Um, you know, in a variety of, of similar tasks. But the robot of the future actually is working to literally have a virtual salesperson. Not only can we have all the content, but AI applying that content can, you know, customers aren’t comfortable with this yet, but we’re not very far from actually being able to send them an avatar

00:32:35.510 — 00:33:03.630 · Alex Slusky
to a sales call and powered by all of our data. They can just in real time, answer your questions. Now, again, customers aren’t comfortable with that yet, but I can envision a world where our avatar is talking to the buying avatar in some in some not not too distant future. And I’m not trying to get too futuristic on you, but enabling sales is

00:33:04.830 — 00:33:39.310 · Alex Slusky
critical. Showpad is in that business. But for any software company, just generalizing. Um, if you do something as simple as retain the transcripts of your sales calls, you know, some customers may be uncomfortable with that, but many won’t be. You can actually do tremendous analysis and understand, is this prospect real?

Is this prospect far fetched? Where should they belong? In the pipeline. You know, how do I what do I what information I can based on their questions? What information do I need to come back with?

00:33:41.900 — 00:34:06.100 · Alex Slusky
To address their concern. Imagine a world where every salesperson is as good as your best salesperson, because they’re because the tools are so good. I think that’s something that actually is being implemented across certainly our portfolio, because that that those tools are available today. And if you can buy a product, you can absolutely do it yourself.

00:34:07.180 — 00:34:40.419 · Alex Slusky
And using Claude Code like this can be done. This is not a far fetched and a far fetched endeavor, but also there are increasingly third party vendor tools that that do a lot of this. Um, so enabling sales is critical because sales has to get more productive, more efficient as, as we know it. Competition is only increasing.

And for every software company because the barrier for writing software has dropped,

00:34:41.460 — 00:34:46.820 · Alex Slusky
they’ll be more new and there are more new entrants. We have to recognize that I think existing companies,

00:34:47.980 — 00:35:13.420 · Alex Slusky
not just the ones or systems of record, because there’s been a lot of focus, like I’m a systems of record, system of record. Great. If you’re a system of record, you’re in a fantastic position. That’s why, you know, there’s rumors that Silver Lake wants to buy a Workday, right? The quintessential system of record.

But even if you’re not the system of record, if you’re deeply embedded with customers, if you’re deeply embedded with use cases and you know the vertical cold, you have a big advantage.

00:35:14.620 — 00:35:28.740 · Shiv Narayanan
And you have a huge advantage, especially with the data that’s proprietary in these systems. Uh, what advice would you have for other private equity firms? You know, as they’re thinking about AI and leveraging it across their portfolio. Like, where should they start or where should they focus?

00:35:30.820 — 00:35:32.860 · Alex Slusky
General private equity firms or technology? Private equity.

00:35:33.180 — 00:35:34.060 · Shiv Narayanan
Technology.

00:35:35.100 — 00:35:35.820 · Alex Slusky
Well,

00:35:36.980 — 00:35:44.570 · Alex Slusky
I’m going to have to be somewhat edited since there are competitors by definition. But I think the important thing is to.

00:35:46.770 — 00:35:48.810 · Alex Slusky
Is not to panic.

00:35:50.330 — 00:36:23.850 · Alex Slusky
I think it’s important to that we all together educate our limited partners that this is not the end of software. It’s not even remotely the end of software that we are. That as an industry, we have to be very thoughtful about what — AI will save money. And so you have to decide, do I pocket those savings because I’m not as — back to where we started our conversation — because I’m not as sure about the competitive position in the growth rate of my

00:36:25.130 — 00:36:25.970 · Alex Slusky
of my

00:36:27.730 — 00:36:40.610 · Alex Slusky
underlying business or can. Can now can I deploy if AI saves 50% of cost in development? Do I deploy to the developers?

00:36:42.770 — 00:37:23.330 · Alex Slusky
All AI powered. So I can, I can I can build features that much more quickly. And then you need to go through your entire cost structure and then say, where does AI make sense? Where does AI not make sense? You know, there’s been a lot of discussion of customer support. I’m a believer in automating customer support.

But realize, you know, customer support is like 4% of revenue. Typically 4 to 5. So yeah, we put AI in. We’ll save a point and a half. It’s money. It’s it’s worth doing. It’s not going to change. It’s not going to change your life. Um, adding a lot of AI to R&D is going to save more or or either not or either save money or enable you to do a lot more.

00:37:25.050 — 00:37:38.480 · Alex Slusky
It’s your choice. Uh, adding a lot of AI to sales could make you more effective. I actually don’t think ultimately it’ll lower sales costs all that much because just expect a more contested cap, you know, competitive map.

00:37:39.640 — 00:38:00.480 · Alex Slusky
Um, you’ll have opportunities in G&A. You know, nobody loves payables and receivables. Highly automated functions with AI so that. Will software companies look dramatically different in five years? Actually, don’t think so. But will a lot of functions be done by agents? Yeah, 100%.

00:38:02.080 — 00:38:11.680 · Shiv Narayanan
That’s that’s awesome. We’re coming up on time here Alex. But before we close things up and wrap up, uh, if people want to get in touch with you or learn more about Vector, what’s the best way for them to do that.

00:38:12.400 — 00:38:21.160 · Alex Slusky
Um, well, obviously our website Vector capital.com. But if somebody wants to get in touch with me, I’m pretty easy to reach Alex at Vector. Com.

00:38:21.480 — 00:38:39.840 · Shiv Narayanan
Awesome. We’ll be sure to include that in the links in the show notes. And with that said Alex thanks for coming on and sharing your wisdom. Obviously you guys have invested capital in a ton of great companies and you’ve been doing this for so many years, so it was really great to hear your perspective and your wisdom, I’m sure will help a bunch of our listeners and private equity investors that are trying to figure out things with their own firm.

So appreciate you doing this.

00:38:40.760 — 00:38:43.400 · Alex Slusky
Thank you. Thank you very much. Thanks for the great questions.

00:38:44.520 — 00:39:28.840 · Shiv Narayanan
Thanks for listening to today’s episode. Before we close off, if you haven’t already, go grab a copy of my new book, AI Marketing Blueprint. It just hit number one bestseller status on Amazon across multiple categories, and it walks you through a proven framework on how to scale revenue and pipeline.

In a world with AI platforms where customers are self educating and learning a lot more off your website, and where companies are struggling with declining inbound traffic volumes from paid search, paid social and even organic and SEO channels. So if that sounds like you or a company that you’re invested in, go grab a copy for yourself, your team or your portfolio company, and I’m sure it’s going to help you out on your journey with that business.

And with that said, thanks for listening today and we’ll see you guys next time.