Episode 153: Blair Garrou of Mercury Fund
on Operational Audits, Lean GTM Teams and Liquidity Planning

Episode Transcript   

00:02:48.260 — 00:02:50.100 · Shiv Narayanan

All right, Blair, welcome to the show. How's it going?

00:02:50.220 — 00:02:53.700 · Blair Garrou

Hey Shiv. Great to great to be here. It's going great. Thank you.

00:02:53.740 — 00:02:58.270 · Shiv Narayanan

Yeah. Excited to have you on. So why don't we start with your background and Mercury and let's go from there.

00:02:58.350 — 00:04:08.650 · Blair Garrou

Yeah. No problem. So Mercury Fund is an early stage VC firm. We are based in Houston with offices in Austin, Detroit and Chicago, and we were the first institutionally backed firm to consider regions outside of Silicon Valley, kind of a region unto itself. Um, we are getting ready to raise our sixth fund.

We have just under a billion under management, and we have focused predominantly on B2B software and vertical industrial markets for the last two decades. Um, my background before that, I grew up around technology. My dad was in semiconductors in the Boston area. Uh, went to school in Virginia, worked in D.C. at Deloitte for a while, and then moved to Texas in 1996, a long time ago, uh, did investment banking and then started building some of the tech infrastructure in Houston.

Uh, incubators, angel networks joined, uh, one of the early VC funds here in Houston and then partnered up with the Draper Network. Tim Draper's Fund Network, back in 2005, did a couple of funds with them, and then spun Mercury out in 2013 as an independent fund and have been at it ever since.

00:04:08.930 — 00:04:20.650 · Shiv Narayanan

Awesome. Yeah. That's fantastic. And one of the interesting things that jumps out is that you guys have a proprietary approach that you're taking your companies through. It's called the Mercury Method. So why don't you dig into that a little bit deeper and help us understand what that entails?

00:04:20.690 — 00:07:25.200 · Blair Garrou

We do. If you think about the time of doing deals outside of the coasts, right prior to Covid, there weren't a lot of, you know, quote unquote, you know, multi-billion dollar exits, right? The software. And so you had to find other ways to make your funds really valuable. Um, one of the ways is if your fund is only going to return maybe 3 to 5x in max and not 10 to 20, well, it's lower the loss ratio.

So we started looking at ways that private equity kind of manage their loss ratio. And part of that was done through majority ownership. And then basically just controlling the companies and driving change through those companies. So we thought to ourselves, hey, is there a way that we can do that? So we have built over the last 15 years, kind of four methodologies that we use post investment to really work with the entrepreneur and kind of drive better outcomes.

The first is what we call operational hygiene or ops hygiene for short. And this is a 100 point audit of the company that we do after we invest, where we try to pull out the white hot risks of what that company needs. But we bring our service providers, our advisors, other CEOs and founders to the table, and we just figure out what are ways that our network can be put to work to help the company during its earliest forms of kind of product market fit.

Now, from that report, we then have a team of fractional executives that we deploy into the weaker areas of the business, and we do that up to the point that those fractional execs hire their replacements. So again, much like an operational executive model that you use in private equity. But we do that with fractional execs in key functional areas kind of across software's business.

And then third is we have an AI research lab. We've had that up and running now for about three years. It's called Quicksilver. And this is an in-house group of six machine learning and data scientists. They both will do assessments, both of groups existing architectures, as well as help them build fractionally or go out and hire really good data scientists from some of the top labs.

And that's been a real differentiator for us. And then the last piece we've kind of put in place is what we call liquidity planning, but it is a formal liquidity program that we take every one of our companies through. Call it 2 to 3 years prior to them being ready for an exit. Um, being in venture for so long, I got kind of tired of the old adage that venture companies are bought, not sold.

If you think about how private equity runs it is, they get the company to a certain point, but then they need to be ready to be sold at any point in time. And so we've hired a head of capital markets. We're one of the only early stage funds that we know with that role. We probably spend 2 to 3 hours a week on liquidity planning.

It's probably one of the more important things we do. But right now, that's led to the fact we've had four transactions over the last year and a half. We have 12 companies in market with bankers. And even though the valuations the markets are giving right now aren't very great, just having the companies ready to transact just has made all the difference of getting them ready and getting them out to market.

00:07:25.240 — 00:07:34.440 · Shiv Narayanan

That's awesome. So I'd love to go through each of these. Why don't we start with the ops hygiene piece? Like what does that entail? What are some of the areas that you really focus in on?

00:07:34.640 — 00:09:28.260 · Blair Garrou

Yeah. So what we're focused on there is we're looking at what processes, what people and what tooling they use for each element of their software business. And we kind of start it from where leads are created all the way over to where things are reported. So we start with marketing, we then move into sales, we move into customer success product.

We end off with engineering. We now have a separate data science component for it. And then we have finance and administration. And so we will bring in experts from our portfolio. And then my partners and others who are experts in those areas. And we give the companies basically 10 to 15 questions per per element, right, per functional building block.

And then we go through it probably, you know, knowing your program of how you work with companies, probably very similar. Some of the questions that you go through kind of on the go to market side, we kind of do that throughout. The company gets about 2 to 3 weeks to put all the information together. We then share it out with our team, but we may have 20 to 30 people sit in on these meetings that are all in person.

We do it here in Houston, and we spend a day going through everything, and it's both a way to onboard the company into the Mercury Fund platform so they know the resources that we have. But it's also to really understand, hey, what are they doing? Unique. What are they doing? We think wrong. What can be more efficient?

Um. And it really gets I think the relationship off on the right foot. But but we look at. All right. If we did a seed round, what's it going to take to get to Series A if we did an A, what's to get a ticket to get to Series B, we expose them to value creation methods that we use later in the company's lifecycle, like how to do business development to corporate development was liquidity planning meeting.

We may have some investment bankers joined for dinner just to kind of give them that full spectrum, because we find too many things. We're just left to chance in venture, and it's not something that we would like to do.

00:09:28.300 — 00:09:40.860 · Shiv Narayanan

What are some of the biggest gaps you find at that stage, like you mentioned, like starting from marketing and sales and product, like, what are some of the core areas that you're digging deeper into or you see, okay, like across the board, all companies need to work on some of these things.

00:09:40.900 — 00:11:47.260 · Blair Garrou

Yeah, I think in the go to market section, you know pre AI, what we saw when it was pure SaaS um early stage companies, customer success is almost an afterthought. Customer success was being applied almost in a reactive way rather than their proactive way. And so really trying to get them to think differently about that.

Also, this whole notion of product marketing is just lost on so many companies. Um, because you have very, uh, data driven, uh, marketing executives that are usually very young but not very seasoned. And so it's like, who like, where are those connective tissue points to pull what they're finding in product market fit to then empower the sales team and understand that, you know, your ideal customer profile and your customer journey map is all done appropriately, right?

And these are things that typically when you go out and you hire a director, a VP level, as you know, they will want to improve. But when you go hire those higher level people, they usually are playing whack a mole, trying to close the leads, drive sales. So we're trying to make sure like our whole mantra is let's go slow to grow fast and let's get everything right in the first place.

But now, with the advent of AI, everything has kind of shifted. So now it's really getting people up to speed on where they should be using AI tools, where they can find leverage and efficiency. You know, especially in go to market around things like qualified leads, if they're using account based marketing, um, you know, what are the better tools for business development resources to be agentic.

You know, we still haven't found good tools for those. I don't think they're going to be replaced anytime soon. So forth. And then the whole notion of managing product and how do you think of product engineering go to market with this whole notion of forward deployed engineers and these longer pilot proof concept cycles, understanding where the company is and being able to provide some frameworks for how other companies are finding success, because we're finding every single AI company in every single vertical is different.

And and the more we do these, the more we learn to shift, right? And we're able to kind of pass those learnings on to other companies.

00:11:47.260 — 00:11:55.220 · Shiv Narayanan

Where have you guys seen the biggest impact that AI is able to make in terms of efficiency gains and optimizations on the go to market side?

00:11:55.260 — 00:12:36.740 · Blair Garrou

Yeah, yeah, easily. On the marketing. We're really trying to teach our companies that everyone should be a one marketing employee firm, where you take someone who is pretty good at market ops. Um, but they also have really kind of strong kind of maybe product marketing chops that everything else can be done, you know, agentically and then all the way up to the demo and then that demo getting handed off to an AI, and then it's like, okay, how do we leverage those AEs? And then how does customer success mold with the product managers of the future who are doing those forward deployments? And we're still learning all of that? I mean, even though it's been a couple of years, everything is just really changing.

00:12:36.780 — 00:13:10.830 · Shiv Narayanan

Yeah. Can you expand on that? Because, I mean, on our side, we work with a large and small firms and their portfolio companies, and we're definitely leveraging AI at different parts of the value chain. But there are some things that you can't leverage AI for, like content creation is an example of that, where you use AI to create content.

It kind of just goes to the mean or the average, and the quality is not at the level you'd want it to be, or the thought leadership isn't the way you'd want it to be. And so how are you deploying that inside these companies where it's like a one person team? Or is it because the companies are smaller and they still need to build foundational elements like product marketing and website content and things like that?

00:13:10.910 — 00:15:09.370 · Blair Garrou

Yeah, it's the latter, right? Because when we get involved in AI companies, they tend to maybe have 3 to 6 customers, but their revenues are well below a billion. Right. Because we're coming at that seed stage. And so the content creation at that point, it's more about how they're handling their site for, uh, GEO, GEO versus SEO, um, how they're kind of building out some of their sales, you know, programs and tooling.

And like you've said, we want everything kind of written by someone who's product marketing oriented, like, like a human and understanding, like what their ideal customer profile is. And so that's one of the fractional executives we have. So we have a person on staff. Lisa Roberts she's our chief growth officer.

But she has spent 20 years in product marketing and everything it touches. So a lot of times she'll come in and do an assessment. She has a stable of people that she uses in. She's out of Boston that she'll bring into these companies. She'll she'll kind of get people going on the right track. But we find just like some of the companies that we backed, um, in native AI platforms, that there's this whole notion of brand voice.

Right? And so if we can have brand voice built through a human early, then we can teach an agent how to build that content later. I think what's hard is when you have the agent build the content from the get go, and it's not learning a voice right of that product marketer or of that company, then it gets to be pretty stilted.

But we're finding that you can do that pretty well. And then we're basically showing people ways that they can, like repeat the success of like a sales robot type of a tool, right? Like if you know your ICP, you started LinkedIn. You can build agentically to get people in conversations, to get them to schedule meetings, and to get them on the phone with real people to talk about your product and then drive the go to market that way.

And then when you flip the switch, it then becomes really interesting. Once you hand it off to product like those customer, you know, account people.

00:15:09.410 — 00:15:16.050 · Shiv Narayanan

What about for some of your larger companies where they're doing five, ten, 15 million in revenue? How do you handle those situations?

00:15:16.130 — 00:16:18.150 · Blair Garrou

Yeah, they tend to have a few more people. But like, you know, we have one example of a company that's in the veterinary software space. It's one of the leaders there out of Austin, um, that we deployed our chief growth officer, Lisa, our fractional exec there for, you know, 50% of her time for for about a year.

Um, they've gotten down to about two full time marketing, uh, you know, equivalents. And they have spent a lot of time on figuring out, you know, Exactly where you can tool and where you can't. One of those full time equivalents. Um, is is a human right, and they are driving content both for their using the agents and their having the agents write things, but then they're going in and modifying things and they keep on, you know, tweaking and prompting to drive that.

But it's also the same person that's working directly with events, because events is one of the best ways that they drive leads, like in their in their campaign book. So we were finding it's different for every company. But but we don't have any companies that have more than maybe three, you know, marketing professionals even scaling out to 20 to 40 million.

00:16:18.190 — 00:16:20.230 · Shiv Narayanan

What's the largest portfolio company you have?

00:16:20.510 — 00:16:22.110 · Blair Garrou

Uh, 500 million.

00:16:22.150 — 00:16:25.430 · Shiv Narayanan

Okay. And even then you're saying it's only a handful of marketers?

00:16:25.470 — 00:16:26.150 · Blair Garrou

Three?

00:16:26.310 — 00:16:35.550 · Shiv Narayanan

Yeah. How does that work? Help us understand that? Because I've seen these companies where like, they're very sales lead. So as marketing's role more traditional or are they still driving pipeline and revenue.

00:16:35.590 — 00:17:34.720 · Blair Garrou

They have got agents driving leads like and they went through a massive AI transition, right? They at one point in time had 18 marketers. Um, and as they went through their AI transition, they just kept chopping away, chopping away, chopping away. But they had so much content that they could train their internal agents on.

It just became a lot easier. And so there are three people. They've got one that kind of manages the whole account based marketing program. That's all kind of done agentically. They've got one person that manages like events that's more of a marcom-like type individual, and then the last person is more than marketing ops, where there's all the leads that they're handing off, you know, to those AI's.

But this isn't a this company is a tools company, right? So it's not a high volume of transactions. Um, the highest volume of transactions company we have is probably 35 million. Um, but yeah, that's at about 3 to 4, like FTEs kind of marketing wise.

00:17:34.920 — 00:17:59.730 · Shiv Narayanan

Yeah, it's certainly a trend we've seen in a lot of companies where they're able to bring down their staff. But at 500 million, three marketers like that is definitely something that is much rarer than what we've seen. So it'd be interesting to dig deeper in there. Um, but I want to I want to talk about some of the other ones you mentioned.

You talk about the fractional side. Like how are you deploying them, or how are you making a decision on which areas to focus on and the transition plan to someone full time?

00:17:59.770 — 00:20:01.910 · Blair Garrou

The most obvious place for us is the companies need help in defining their customer journey map and their ideal customer profile. Um, even on the tech side, right? And because the more they do that, then the more they can just keep diving and diving and diving in. Um, that is where, uh, you know, our fractional exec, Lisa Roberts, fits in.

Um, when people are building out their customer success team, which from an agentic perspective tends to start with these quasi account manager or customer success product marketers. But when that starts to build out, they start to get to some scale. Um, we have another individual. Her name is Danielle Quinton, uh, who is fractional, and she will dive in putting best practices in like classic customer success about what that looks like.

Um, and then the third piece is, you know, Lisa, Danielle and a few other people we have have people that will work 25 to 50% of their time kind of getting, uh, and sales stood up. Right. Both from a sales ops perspective. Like, how do you think of HubSpot or how do you think of Salesforce, how do you get people going?

So we can then bring in junior salespeople, figure out the type of person, the type of persona of the salesperson that works and kind of drive that forward. You know, a lot of people like to bring in like one original AI and have them kind of build things out. We just find that's just fraught with issue. So we would much rather just have, you know, some more stability in place and just saying, hey, here's if you're going to use this kind of sales process, here's how this this piece works.

I would say on the AI side, we're still trying we're still trying to figure out what is the most normalized path from a sales process perspective with some of these enterprise AI deployments. Like, we still don't know the answer there because everything is happening so differently. Every company is reacting so differently.

And so it's not like solution selling or Sandler. You can't put it all into a box. And so I still think we're learning there a lot. But I've got to stop there.

00:20:01.950 — 00:20:22.470 · Shiv Narayanan

Yeah yeah yeah. Interesting. We do a lot of this work ourselves. And we're coming in in a fractional CMO capacity where you have to put in the foundational elements in place, and then at some point, then you can when you can hit the ground running, you can onboard someone and they kind of have a lot more to kind of stand on and work faster.

So, so totally get that model. And we've seen it work well for ourselves.

00:20:24.190 — 00:21:49.380 · 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 could scale a lot faster to drive more pipeline and 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 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 [email protected] 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. I'm curious on this AI side, like walk us through that.

What is the value add there? Or how are you leveraging that inside your portfolios?

00:21:49.420 — 00:24:37.210 · Blair Garrou

Yeah. So in the first piece, when we diligence a company, they'll come in and do an assessment of like what the company is trying to accomplish and how they're going about it. We found that it was really difficult for us once ChatGPT dropped, um, the market was moving so quickly. Right. And then there's big abstract movements in the market, right where you've got the gen AI, frontier LLMs, and then it moved to a RAG model, and then that moved to an orchestration layer which became like a harness.

And now you're talking about, you know, world models and JEPA world models. And it's just really hard to like keep up with everything. So they will come in after we kind of say, hey, they're really on this top thing, and they'll do an assessment and they'll come back and basically say, hey, this is super interesting.

Here's what they're doing. Here's what they're really thinking about. Things uniquely based upon what we know. And here's some areas that we think we can be helpful. And so if you think about our strategy of doing native AI deals outside of Silicon Valley, we got to be sure we're right. Right. And we got to be sure that if someone is building a unique world model for a customer journey map for a website, right.

Um, and we backed a company that's doing that, that they're doing it the right way, and they're doing it in a way that there's not West Coast groups that one of the bigger research labs that's figuring those types of things out. Um, so that's the first thing to do is assessments. The second piece is sometimes the companies want to partner with them almost an incubation type role and say, hey, would you mind just giving me the resources I need, um, engineering wise, and work with the 1 or 2 developers I have, and we'll build this out for the 6 to 12 month period of time.

And then there may be companies that get to a bigger scale. And this has happened with some of our classic vertical SaaS companies that have become AI enhanced. They will go out and they'll hire someone to lead the AI transition. If they don't have people internally doing it, and then Quicksilver will come in and they'll basically say, okay, here's how we would do it.

And they work with those companies to provide some fractional support or to actually carve off an actual project and to go work on it with them. And because they're kind of on a first name basis with the big AI labs on the West Coast, they kind of know what's happening there. They've got a lot of credibility.

Uh, sometimes they actually help us win deals. You know, by talking, you know, directly with the company. But it's real. It's been a real differentiator. And prior to that, we had a data science lab that was called aAcr Data Science. And so we had done that for about six years prior to launching the AI lab, because it was just different people.

And so we've we've had a history of launching these independent consulting firms that we fund, like a portfolio company, and allowing them to work with our companies so that it's hands off, because that gets kind of difficult to write. If you've got resources internally, you're trying to charge for it.

00:24:37.250 — 00:24:40.010 · Shiv Narayanan

How do you look at the ROI on that type of an investment?

00:24:40.050 — 00:25:37.860 · Blair Garrou

Yeah. It's more. Um, the ROI comes from all the companies that they, they touch. So with our data science group, we we've had seven exits built around, uh, machine learning and data models and platforms that they help build that that had real great returns for the firm. So we saw that firm like, do really well and kind of move forward on the Quicksilver side.

They've they worked with five of our vertical SaaS companies on helping them transition. And we have a warehouse line of credit for our next fund. So they've already worked with two out of the three deals in that grouping to kind of help get them launched. So we see it more as they're going to help our overall return and the investment that we make in them.

I mean, they may end up getting bought by a consulting firm, or they may turn into a product company someday. But we try to kind of balance it out, kind of work with our LPs and say, hey, this is why we're making this investment in this firm, but we think it's a real benefit to the to the fund. Mhm.

00:25:37.860 — 00:25:48.820 · Shiv Narayanan

Mhm. That's very interesting. Yeah I definitely think more and more firms should be leveraging AI in this way and having some sort of an internal consulting model around it, because the ROI is definitely there if you can help all your portfolio companies.

00:25:48.860 — 00:26:12.380 · Blair Garrou

It's and it's no different like what private equity groups are doing. Right. I think the larger ones like Blackstone and all I mean Blackstone's had like, you know, 8 or 9 people on staff for a while. Um, we just because we're a smaller fund, even though we have a billion under management, our funds are like 150 to 200 million apiece.

We have to find a way to basically have it outside the fund where it could be an investment, because our management fee just couldn't support, you know, that kind of talent, you know, being up and running. Yeah. Yeah.

00:26:12.380 — 00:26:22.700 · Shiv Narayanan

What about on the liquidity planning side? So I totally get that on the VC side. Like a lot of VC backed companies don't think about this. So I'm curious like how you approach this and prepare these companies for exit.

00:26:22.900 — 00:26:43.269 · Blair Garrou

Yeah. So we have a meeting two hour meeting every other week. And during that meeting we go through each of our three, the last three portfolios. We talk about how companies are performing after every quarter, and we talk about market headwinds and tailwinds. And we also talk about growth like not coming back.

And so

00:26:44.470 — 00:30:37.360 · Blair Garrou

if companies need to exit quickly then we basically put them on the program. Um, if companies we believe are going to be 2 to 3 years from getting unsolicited offers, we put them on the program as well. Right. So it's not just for the companies that are not performing as well as they can. It's for the ones that are really performing.

The only companies that we don't do with this are companies that have grown so fast that they go to the West Coast and they raise from a mega fund, because then they're kind of on their own trajectory, and that's more of a normal kind of venture model. But about 30%, 35% of our companies go to the coast and raise huge amounts, probably ten, 15% after 6 to 9 months we know aren't performing.

So we try to sell those immediately in an acqui-hire or whatever we can. But we feel if we can move that middle from maybe a 2 to 3 x to a 3 to 4 x, that means all the world, right from a fun return perspective. But we can do another turn or two of the fund. So that's where all the time is spent. So we are ahead of capital markets and then the partner on the deal.

They work with the founder first to make sure they're okay working with this free resource which all we're like hey that sounds great. But what what the resource does. He's out of Austin. He makes sure that they have an up to date data room at all times, so gives them feedback on what that data room should look like.

And that helps for future funding rounds. But it also helps. I mean, more often than not, I mean, we've been surprised when somebody comes in with the bid and they want to see a data room, then everyone's scrambling to put the data together. The second piece is they make sure that the CFO function, whatever is in it, is sound, right?

Do you have someone that's either serving a fractional role or a VP of finance or CFO that's actually done a transaction before and can stand up to a PE firm or a strategic if they're trying to buy you. Um, the third piece is working with the partner and the board just to, uh, essentially get everyone comfortable with the fact that this company will be receiving offers in the next couple of years, and it's okay to receive them if you're prepared.

And that goes along with making introductions to the right boutique investment banks so that they can get invited to those conferences so they can get exposed to the PE firms or the strategic who will want to buy them down the road. We found that all of these things were being done, but they were being done at the last minute.

And the best thing about our head of capital markets is now when somebody comes in with an unsolicited offer, they may not be in market, but we can quickly do a market check with a legit banker who's on our payroll, who can then call six different comps, six different groups to see if they have an interest and see whether or not we should actually do a full flight process.

And so all of these things have been kind of put in place. Um, now the frustrating thing is like right now, as you know, we are basically taking the SaaS apocalypse as an excuse to not pay appropriate multiples. Right. That, that, trust me, that will go away. Because what we're seeing is that, you know, these are looking at companies that are growing 30, 40% saying, hey, that's not interesting in this era of AI.

But when you talk about AI companies selling into enterprise, enterprise can only take on software so quickly. So this whole notion that AI and the enterprise is going to grow two, three, 400% is a complete fallacy. That's only happening in the tooling space, and that's only happening with companies like Claude ChatGPT that I would argue, are being used more for productivity use by individuals than they are to actually automate workflows and do things.

I mean, it's the whole probabilistic versus deterministic thing, which we can kind of talk at length at. And so, you know, back on the liquidity planning, we are getting companies ready. We've got over a dozen in market right now. And although some of the bids coming back aren't interesting. We just take them out of market.

We wait and we go back into market. But. But we don't have to go out and convince a banker to take them on, which is what's happening right now. So it's been really, really efficient, really helpful for us.

00:30:37.400 — 00:30:53.520 · Shiv Narayanan

Does that mean growing these companies a little bit differently though, because like in a VC model you're trying to grow a lot faster, right. And you might sacrifice short term EBITDA for long term revenue growth. But you're preparing for an exit. Something like rule of 40 ends up mattering. So I'm curious like how you manage trade offs like that.

00:30:53.840 — 00:33:21.120 · Blair Garrou

Yeah, we would say that our top third of our portfolio, they are growing like traditional West Coast venture. And they've proven that because they've gotten West Coast venture to come in and fund their burn rates for their crazy growth rates. The companies that are in the middle of our portfolio is more like traditional software.

It's some of it may be native AI, some maybe enhanced AI. Some of it may not be as much to enhance as you think. Um, but those companies we know haven't gone through this massive growth trajectory. And so we're like, you know what? We're not going to have this high a burns. We are going to focus on growth. We're going to make sure growth is about 30%.

You know anytime growth kind of flows below that. Then we just sell. We just, you know, get out because there's not going to be a lot of pricing there. But we're kind of kidding ourselves. We believe like if you can't go out and get a West Coast firm to cut a huge check to go grab market share, then that's not the trajectory your company is on.

And that's just not going to happen. And so we look at the rest of it as saying, okay, how do we maximize value here. We're not going to just have these obscene burn rates without a coastal group believing in it. It makes make sense. And so we kind of put them in these different buckets because of that. And we think the market will end up rationalizing all these multiples eventually.

Yeah. I mean by multiples I mean the foundational models aside are just absolutely ridiculous. You know, when we have companies in our portfolio and a million run rate, getting a 200 million pre money on the coast. But we also have companies at 40 million run rate profitable, growing 50% of the year, getting a 4x multiple even under AI enhanced like something's got to give right.

I mean you could probably see it with a lot of your customers and a lot of people you talk to. I mean, the SaaS apocalypse, that was the market late to the game. And so it was baby out with the bathwater. Um, companies, you know, with real data moats, companies with real systems of record. And they're incorporating AI, right, to keep out all the AI incumbents, if they are vertical.

Right, like their industrial solutions. And they have people that really know those industries, they're not going anywhere. And we're seeing that over and over and over again. We we've talked enterprises saying we see no need to rip out the software now if they don't like the vendor, if they feel they're overpriced, if they feel it's junk, if they feel it's not modernizing, they'll take it out.

So it's just one of the more volatile environments that we've that we've seen. Yeah.

00:33:21.160 — 00:33:28.600 · Shiv Narayanan

Yeah, that's that's really great, huh? We're coming up on time here, Blair. But before we close off. If people want to get in touch with you or learn more about Mercury, how do they do that?

00:33:28.640 — 00:33:33.480 · Blair Garrou

Yeah, just email is great. So just Blair Blair at Mercury Fund.

00:33:34.720 — 00:34:04.960 · Blair Garrou

Uh, you can also find me on LinkedIn at bgarrou. It's B-G-A-R-R-O-U. And we'd love to talk to more people about kind of what we're seeing in the market. We felt we were early uh, we started making native AI investments about six months after ChatGPT drop. And so we've watched the waxing and waning of all of that. And we also had 60 vertical SaaS companies that we had to put a gun to their heads and said, evolve or die like you got to get there.

So we've learned a lot, even though we're a relatively small fund and so happy to share our best practices with people.

00:34:05.000 — 00:34:22.690 · Shiv Narayanan

Yeah, awesome. We'll be sure to include all of that in the links in the show notes. And with that said, Blair, thanks for coming on and sharing your process and your wisdom. I think there's a lot of great takeaways for firms that are VC or in private equity that can apply to their companies, especially on the AI side, and the fractional and operational expertise that you're bringing to these companies.

So appreciate you doing this.

00:34:22.850 — 00:34:25.210 · Blair Garrou

Thanks for having me on, Shiv. It's been great. Appreciate it.

00:34:25.450 — 00:35:14.770 · Shiv Narayanan

Thanks for listening to today's episode. Before you take off just a few requests from our side. Number one, if you haven't done so already, please subscribe to the podcast on iTunes or Spotify or YouTube or wherever you go to listen to your podcasts. Number two, if you are in the market for due diligence services, strategy consulting or fractional CMO services, please get in touch with us at www.howtosaas.com and third, please buy a copy of my new book, Exit Ready Marketing. It covers a ton of concepts that we take our customers through private equity investors, B2B companies, CEOs, operating partners and marketers. And there's a ton of great value in there that expands on my previous book Post-Acquisition Marketing as well.

So with that said, I hope you enjoyed today's content and we'll see you on the next episode.