[Bonus] Ohio VC Fest — Investing and Building with AI

Jeffrey Stern [00:00:00]:
Welcome to the Lay of the Land Podcast. I am your host Jeffrey Stern and today's episode was recorded live at Ohio VC Fest where I hosted a panel on AI, joined by an incredible group of investors including Peggy Roberts, Managing Partner at the Riverside Company, Candace Matthews Burkin, Founding Partner at Lightship Capital, Hardik Desai, Managing Partner at Jumpstart Ventures and Jamie Weston, Managing Director at Spring Mountain Capital. Together we walk through where real value is being created with AI, how founders can best leverage it in their business and in raising capital, and ultimately we explore the vast opportunities and downstream implications of AI. Looking forward. So with that, please enjoy this panel conversation. Lay of the Land is brought to you and is proudly sponsored by Roundstone Insurance. Headquartered in Rocky River, Ohio. Roundstone shares Lay of the Land's same passion for bold ideas and lasting impact from our community's entrepreneurs, innovators and leaders.

Jeffrey Stern [00:00:57]:
Since 2005, Roundstone has pioneered a self funded captive health insurance model that delivers robust savings for small and medium sized businesses. They are part of the solution to rising health care costs, helping employers offer affordable, high quality care while driving job creation and economic growth throughout Northeast Ohio. Like many of the voices featured on Lay of the Land, including Roundstone's founder and CEO Mike Schroeder, Roundstone believes entrepreneurship, innovation and community to be the cornerstones of progress. To learn more about how Roundstone is transforming employee health benefits by empowering employers to save thousands in per employee per year healthcare costs, please visit roundstoneinsurance.com Roundstone Insurance built for entrepreneurs, backed by innovation, committed to Cleveland

Jeffrey Stern [00:02:26]:
Thank you Sean hello everyone, I'm Jeffrey Stern. It's wonderful to be with all of you here today to discuss a topic that is the topic du jour for for Better or worse occupying an increasingly large amount of our brain capacity. And we're going to add to that allocation today by having a conversation about AI And I want to frame what we'll talk about here today in the context of technology adoption life cycle, which tends to follow a normal path of the innovation itself, some inflated expectations about where that technology might go, a leveling often called the trough of disillusionment, and some a realization in the middle between the hype and the reality, in the plateau of some actual realized benefit to society through the, through the technology. We're going to talk about the Delta, you know, where, where we are in this life cycle where we're actually seeing AI play out in practice, where the innovation is actually manifesting into improvements into, into companies across all the different life cycle stages. And we have, I think, the perfect panel to explore what that delta looks like and what those implications are across the different life cycle stages for, for companies from startups all the way through to mature operating businesses. So with that I'll introduce the panel. Here we have Jamie Weston from Spring Mountain Capital, Hardik Desai from Jumpstart, Peggy Roberts from Riverside, and Candace Matthews Brackeen from Lightship. Again representative really of the whole life cycle of companies here.

Jeffrey Stern [00:04:06]:
So we'll get a great perspective on and really the whole landscape. So to kick it off, I kind of want to just start with signal and noise. Obviously it's, I mean it's hard to go a day without talking and thinking about AI. So there's certainly an amount of hype that is there. Where are you all collectively seeing the delta in your minds? Hardik, maybe you want to kick us off?

Hardik Desai [00:04:29]:
Yeah, I'll begin with data. I just provided this data to our LPs a couple days ago. Well, PitchBook does this sort of Venture Monitor report that they put out and we just looked at the data for the last six months or so for 25 and 65% of all capital that went into venture had something related to AI and ML, at least in sort of how PitchBook compared that and aggregated that data. But that was across 35% of the deals. So now you look at the reverse of it and 65% of the companies who weren't AI in any capacity got 35% of the capital. So the capital clearly is going more and more towards AI. The other interesting data point was round over round increases in valuation for AI companies versus non AI companies. All AI companies were sort of significantly higher increases in valuation compared to non AI companies.

Hardik Desai [00:05:31]:
So whether we call it hype or not, that's where the money is going, that's where all the trends are. And I don't think any of us can ignore them.

Jamie Weston [00:05:41]:
I'll just add a couple more data. Well, another data point to that. Something like 20% of all dollars invested now have been invested in what's known as sort of traditional VC. But 80% are now invested in much larger, more highly priced rounds that were traditionally led by Fidelities or Wellington Managements of the world. Candace can talk to this a little bit better than I can. But in, you know, as Hardik just said, really driven by all these, these later stage large investment rounds around AI.

Candice Matthews Brackeen [00:06:12]:
I'm not.

Peggy Roberts [00:06:13]:
It's a, I think, I think we're in experimentation mode when you, when you think about the actual application of AI technologies in businesses. So the money is going after the dream, which is appropriate money. Money should go after the dream. But when it comes to what we're seeing actually enabled on the ground in companies, I think we're still very much in the experimentation phase, which is, which is exciting and a lot of what I think we're going to talk about today as well.

Jeffrey Stern [00:06:38]:
Yeah, so I think a lot of the early hype, if you will, was kind of around a lot of the conversation and the questions that I heard from founders and investors alike was where is defensibility in this space? Like what is actually a durable business model? And you know, you had at kind of the original go of startups in the wake of AI wrappers around models. And then I think more over time the conversations kind of converge much to how every company today is a technology company. AI is a layer within a company that's going to help, you know, enable them to do and solve whatever problem they're trying to solve better. So I want to have a conversation as part of this broader conversation about actual AI companies versus products versus like features that are AI and just where you see AI actually manifesting within startups and where it's actually driving.

Candice Matthews Brackeen [00:07:31]:
Yeah, I would say like you have to kind of weed out right away. And we talked a little bit about kind of building a team. So what I look for is are you leveraging AI? Are you building artificial intelligence? And there is a huge difference between the two. So that's the layer you're talking about. So we immediately go to the team. I am looking at the team and who they are, what schools they went to. PhD, do you have multiple PhDs? How many PhDs do you have on your team Matters? Because if you tell me you have an AI company and I ask you who's building it and you can't answer who they are, what lab they came from, who they Studied under. I am not putting as much money or any money as you think I'm going to because it matters to me as an AI investor.

Candice Matthews Brackeen [00:08:18]:
Because you can't defend that. Because the science matters to me as an investor. It's not so much about whether or not you're using it a little bit to make an incremental change because you don't own that technology.

Jamie Weston [00:08:29]:
And let me take the other side of the investment focus. I co run a early stage investing vehicle. It's about $130 million. And I only mentioned that because we are not, and because of our size and early stage focus, we're not investing in LLMs or infrastructure. We may look at, at some of the compliance or governance tools in the middle, but we're really looking for companies that are utilizing AI into their workflows, that are experimenting and, and, and making the most of it. Let me just take a very simple analogy. You know, let's say you're building a hole, a pretty big hole, and you've got a small backhoe, right? You got a small backhoe and you have a, a shovel. So what do you do? You walk past the shovel, walk past the backhoe and you pick up the shovel and that hole is going to take you weeks to, to, to, to get done.

Jamie Weston [00:09:21]:
Whereas if you use the backhoe, could take a couple hours, could take a day maybe at most. And why are you not using the backhoe? Well, you're not using it because you don't know how to use it and you're afraid to use it. So you really have to figure out how to use that tool or any tool that's available for you. So the way to think about it or we do is like, what are the holes in your business that you need to build? That's not the only hole that you're going to have to build. I mean, there's going to be lots of holes in your organizations you're going to have to build. And so I'll, you know, later on I'll talk about some of the holes that, that we're, that we are digging, you know, with different tools in our organizations.

Jeffrey Stern [00:09:53]:
So Peggy, in, in your world there is, I'd say the companies you're dealing with are companies that have, they're not necessarily trying to figure out product, market fit. They, you know, they're beyond that life cycle. They're companies that have already figured it out. How are you seeing those companies? They've already dug holes. They probably dug 10 holes already. So like, how, how are they thinking about AI as it applies to their own businesses where they're not really probably starting from scratch, but are thinking about how to best leverage it.

Peggy Roberts [00:10:19]:
Right. And I think I love Jamie's analogy. So at Riverside we have, we invest in a variety of businesses on the, what we call the lower end of the middle market. So starting with businesses as small as $2 million in AR SaaS software companies who have, you know, discovered their product market fit at that early stage and we can back them and partner with them all the way up to businesses with 300 million in sales across diversified industries. So we've got over 100 companies in our portfolio today globally, and we see everything when it comes to where businesses are on the AI spectrum. And I think the hole digging therefore is highly variable depending on the business and the model. Recent example in our acceleration capital fund, which is again dealing with some of those smaller SaaS software businesses, is a company called Circle Link Health. And they introduced an AI enabled tool that basically can help onboard patients who have chronic care conditions.

Peggy Roberts [00:11:22]:
So a nurse's time is not taken up in that onboarding process. An agent can actually perform that work. So they have now they've expanded, you know, for a medical practice that would once subscribe to their software that was looking for this tool to help the nurses on board, now they can sell a much bigger opportunity set to those medical practices to say, we can solve your labor problem.

Candice Matthews Brackeen [00:11:49]:
Right.

Peggy Roberts [00:11:49]:
We can not only give you the software and the guts to onboard people, but we actually have the agent who can help do the work. So that's a huge enhancement in value proposition. To use your question earlier, where is the value? That's a great example of where the value is.

Candice Matthews Brackeen [00:12:03]:
Yeah, And I think we're seeing it in radiology, you're seeing it in dentistry quite a bit with telehealth and you know, your dentist. Just reviewing what the AI has said, we're seeing that in fertility with women and it's helping them to get pregnant. So there's a lot of ways that people over the next 10 to 20 years will really be able to leapfrog others by enabling their company.

Hardik Desai [00:12:25]:
So we are probably at the earliest spectrum, at least on this table. So we are investing seed, early series A or late seed, whatever you want to call. And Jamie is a great partner because everything that he said are the sort of businesses that we are sort of building and then groups like Spring Mountain Capital are then coming as follow on investors in those businesses. But we are also really, really hyper focused in the Midwest and predominantly in Ohio. Historically, for us, that presents an opportunity to look at all the legacy industries, whether it's logistics, whether it's healthcare, whether it's manufacturing. How are those businesses thinking about AI? We are looking at a company currently in the healthcare space as an example that's in prior authorization. And we hear tons and tons of what's happening with prior authorization. And I'll just throw this statistic out for this particular company.

Hardik Desai [00:13:22]:
That customer is a health system. A health system is waiting. A large health system is waiting on 30% of that monthly revenue being struck in prior authorization queue. Because human beings are doing that work, AI can easily automate a significant portion of that work and accelerate the path for revenue generation for the health system. That's just one example of hundreds of examples where there are very specific pain points to be solved. Historically, it would have taken a long time to build the software, build the moat around it, or build a product around it. But AI helps accelerate the pace of development, accelerates the pace of launching an mvp, you know, launching new features quickly and showing value quickly to the customer.

Jeffrey Stern [00:14:05]:
I want to revisit the defensibility idea. So with all this, with the increased velocity with which companies can actually build product because of AI, with the amount of things they can now automate because of AI, how do you think about what makes a business defensible when every company can kind of innovate at this speed?

Jamie Weston [00:14:25]:
Well, not everybody is innovating at this speed, I probably should note. And you really look for companies that are on the cutting edge of trying to figure things out. So once again, not to be too simplistic about things, we can get into more details about tools and a real focus of the companies that we invest in. This is a difficult but simple business. There's a team of people that are producing a product that are serving some particular marketplace. Your customers don't care how you deliver that product or what you, what are the things that, that happen in between to get to what, you know, what you're providing them? They just want a better product, they want a better solution, they want a better service or whatever it is. So you know, everything that you're doing to improve your workflow, your internal efficiencies, to improve the product offering, like they don't care, they just want it to be better. So in that lens, you know, how do you evaluate businesses to try to keep on that, that cutting edge? And as I said, you know, we're not investing in the core technologies themselves, but in the software companies that are utilizing these things? So you have to find people that are willing to take those risks because there's always going to be somebody from a defensibility standpoint that, you know and we can get into it more, but that, that could use some of these tools and kind of leapfrog you and I would add.

Peggy Roberts [00:15:43]:
So in our acceleration capital fund, where we're investing in, usually these are critical software systems, right? So it's B2B SaaS software. And since the launch of ChatGPT, our customer retention rates have actually gone up by two points. And so when you think about defensibility, you know, our companies, to Jamie's point, they're delivering a service, they're delivering a product, whatever it is, as long as you continue to make them happy and don't give them a reason to be shopping around. I don't think customers want to uproot a relationship they have unless they're sort of being let down by, by the vendor that they're working with. So we're leaning in with these businesses on how do we develop in the infrastructure, how do we develop in our product or our service to, to AI.

Candice Matthews Brackeen [00:16:33]:
And I would also say there's a, there's a bit of a talent war right now. I mean, we're seeing people making $100 million because of what they have going on up there in their heads. And. But the customer doesn't care. Like you said, the customer doesn't care that somebody's gonna go from OpenAI to Facebook to Google. They just want the best possible product. But it's hard to defend that. And the fundraising that's happening in that space is absolutely incredible to watch.

Hardik Desai [00:17:03]:
But I guess, you know, going back to Jamie's comment, distinguishing between infrastructure and applications, right, There are going to be a few winners in the infrastructure space. I don't know that there are only going to be a few winners in the application space. It's no different than in the late 90s. Everyone was talking about Cisco being sort of the dominant player because they were an infrastructure company. And then eventually that sort of changed into a whole bunch of Amazon and everyone else came after them and they built the products that were solving specific problems. In some ways this is sort of a repetition of that 30 year cycle again, which is how you started this with, right? Which is, yes, there are going to be a few winners in the core application or infrastructure, but there are plenty of problems to be solved and there are plenty of opportunities to solve those problems.

Jeffrey Stern [00:17:53]:
You introduced, I think the fundraising idea, which we haven't talked so much about a lot of folks in this room, I think are, if not explicitly working on that right now, might be in the near future. And I think Hardik, you kind of set the stage with some of the statistics about how the landscape for fundraising is changing. How would you counsel people, founders, specifically entrepreneurs, thinking about fundraising, where we are today in this life cycle and how, if at all, does AI change how they should approach fundraising?

Jamie Weston [00:18:28]:
Well, I, sorry, I don't think the answer is, you know, putting AI into the title of your deck. And I'll just come back to basics again. If you're not showing the results or you're not really conveying utilization of AI to fundamentally change the way your product is being provided to your customers, why does it matter? So just get back to basics and tell a good story around why you're different than others. And sure you can utilize AI into that conversation around how you're, you're, you're, you're deploying it, how it's making more efficient, how you know you're not using it, burning as much cash and all the cool things that you ultimately can do with your customers, you know, at that nirvana state, you know, that's an interesting conversation.

Candice Matthews Brackeen [00:19:12]:
Yeah, I would just say with AI, you can build something early. So build something before you come talk to us. That's, I mean, I think that's the advice. You've got free code, you can go out there and build it in advance and just then go, come and talk to us, spend time with us, build a network, go sell it. Right, you can go sell it. So come to us after. People aren't writing early checks to ideas unless you have exited multiple companies. That's the only time I'm doing it.

Candice Matthews Brackeen [00:19:47]:
I'm not putting money into a first time founder with zero experience for the very most part.

Peggy Roberts [00:19:54]:
So, so the lens I'll share with you again, just keeping in mind, we're looking at, you know, very early stage SaaS, software companies all the way up to very well established, still lower middle market, but you know, more diversified businesses. And we've developed what I, we call it an AI scorecard. Not very inventive, but it's a tool that we use during due diligence when we're looking at a business to evaluate them along two main dimensions. And I'll just share that with this group. It just, it's a simple rubric that, that we have. The first dimension is looking at you sort of inside your four walls. Right. So how are you using AI, Whether it's in your sales, marketing, operations, finance, accounting, you name it.

Peggy Roberts [00:20:38]:
How are you building that in? Secondarily, it's how you're using AI from a product or service perspective. Are you building it into your product, which is a lot of what Candace is talking about. If it's a software company, how are you building that into your offering? If it's a services based business, how are you building it into that offering? Like the Circle link example I used earlier. And then third, what are the governance structures you have around it? So if we go in and meet with a company and they're like, I.

Jeffrey Stern [00:21:08]:
Don'T know, we don't really have a.

Peggy Roberts [00:21:09]:
Policy for that and we don't have those security, whatever, it's a red flag. So that's kind of bucket A, what is the company doing about the company's business? And then bucket B is looking at the market, the market you're in. And how do you answer those questions about how the competition is embracing AI? Is there a significant market opportunity opened up by AI because of what we can now do? So really spending time thinking about that before you meet with an investor on both the inside the four walls and the outside the four walls impacts from.

Hardik Desai [00:21:45]:
AI, I guess, you know, maybe if we go back just to the very, very basics. It all begins with trying to solve a problem. The entrepreneur identifies a problem that he or she feels needs to be solved, do some research, do some validation, build first version of a product, get some early traction. There are a whole bunch of things that need to happen just to get to a million dollars or $2 million in revenue. You know, how does the customer buy? Who is the right buyer? How do you go through a sales process? All of those things can. There are great tools from an AI perspective that can accelerate the journey for the company to get to that product market fit quickly, which enables them to continue to then, you know, raise more money.

Jamie Weston [00:22:25]:
It's pretty funny. I mean, ChatGPT has been out for less than three years now. And particularly over the last year, the experimentation and the utilization within businesses is just widespread and it's pretty amazing. And now, you know, we're at the point where we're talking about, well, okay, that's great, but you know, let's just talk about the business again. And it's a tool to accomplish a particular goal. So it's funny that we sort of gone, you know, full circle. I just want to just touch on, you know, engineering and code for one second. Right.

Jamie Weston [00:22:54]:
So a bunch of our companies use GitHub, Copilot, or they use Cursor or they Use Claude or code suggestions, code editing, code refactoring. Once that code's done, then we'll use AI tools for QA for automated testing. Right. Once that product is almost, you know, to be ready to be released, it could take two weeks to put release notes out. Now it takes a couple hours to do. But what Candace said was interesting from a startup perspective and the same thing with our companies around product experimentation. So we can run, you know, 10 different prototypes at the same time now when we can only do one before. And most importantly, that prototyping work can be done not from the engineering side, but from the product side.

Jamie Weston [00:23:40]:
So your leverage ability to experiment, you know, is, is vastly expanded.

Jeffrey Stern [00:23:45]:
Yeah, I mean I'll, I'll add to the experimentation. I mean, I've seen companies on the marketing front, you know, what used to require months to, you know, AB test thousands of different versions and renditions of the same campaign. You can now spin up AB test and create this competitive bracket style marketing where you invest in the winners in a matter of hours and test, you know, a thousand campaigns. But I want to pull on that thread because I, I think one of the things that's changed, I mean Candice, you mentioned at the earliest stages is you can, now there's no excuse for not having an mvp. You can just spin up a, you know, a project that's functional, that you can get user feedback on, that you can begin to sell. How have your expectations changed about how companies are using this and what they need to be doing now that they couldn't before, that it's unlocked that maybe before due to lack of resources or lack of time, they couldn't do. But now there's less of an excuse at, at, at each kind of respective stage of the life cycle.

Candice Matthews Brackeen [00:24:48]:
I don't know if my expectations have changed. I still expect a great company. Yeah, I think they need to be leveraging all of the tools. I think maybe when I was like an emerging fund manager, my expectations were different. But I think just from kind of growing up in this career, I still expect the highest level possible. So I don't know if they've changed. I'm just still looking for excellence.

Peggy Roberts [00:25:14]:
I would say we, and I would encourage anybody in the room again who's entering into conversations with investors is to really have the ROI story buttoned up. So if you have introduced AI into some part of your product, service or your internal operations, what's been the roi? Show me the story over time. The proof is in the pudding and then we can start talking about what the future could look like together, that would, that would be sort of some of our expectations.

Hardik Desai [00:25:43]:
I don't know that any expectations have changed from our end. You know, if anything, you can build product quickly and get some feedback quickly so you will have data points coming back quickly.

Candice Matthews Brackeen [00:25:51]:
Our metrics haven't changed. I still just have to return capital at the highest level possible, so. And you do that, yes or no?

Jamie Weston [00:25:58]:
Well, I mean, from Hardik's perspective, I think it's even more challenging when you have all these companies that can spin up pretty quickly. Getting to your point, Jeffrey, on kind of moat and defensibility, you know what's sustainable? Right. When you have a company that's got customers that you can leverage the technology to build upon and stay ahead, that's a totally different story. But when you're talking about de novo businesses created in a garage, so to speak, I mean, it makes it much more difficult.

Jeffrey Stern [00:26:26]:
Yeah.

Hardik Desai [00:26:27]:
And at least in our sort of overall portfolio, what has pre AI, post AI, what has always been sort of a really, really strong indicator is how much does the founder understand the industry that they are operating in. It's a lot easier to, if you are from the industry, to build the right product, have the right set of conversations. And we use as part of our diligence process, we spend a lot of time trying to understand the background of the founder to have some perspective on are they the right people who can build this product in the market they are operating in.

Jeffrey Stern [00:27:04]:
So in this broader hype versus reality topic for, for AI, I think one of the just, I think human elements to this is, it's, it's kind of exhausting to just like think about AI all day, every day and to stay abreast of all the changes and reconcile with the reality that this is the worst it's ever going to be. It's only going to get better. It's only going to, you know, impact businesses more from here. How personally are you staying, you know, abreast this world? I mean, obviously I think from the investors perspective, you get to meet with all these companies so you get to see how they're experimenting. But I mean, just personally, like, how are you trying to keep up with all of it?

Candice Matthews Brackeen [00:27:43]:
I'll just say real quick because this is, I, my spouse exited a facial recognition company, so he was building AI like 13 years ago. He sold that company back in 2018 to a private equity firm. So we of the nerds, all of his friends, now my friends are, are the PhDs I'm talking about. So they Kind of all know each other and all of that information, whether I hear it on a zoom or we're at a meetup. Like, we spend a lot of time in the. In that world with those people in the weirdest little, like, coffee shops and, you know, the old co working spaces. Like, they're there just with each other. And so that's how we stay abreast.

Candice Matthews Brackeen [00:28:28]:
It's not necessarily seeing it on TechCrunch or out in the news. It's like the actual practitioners who've been building it for a long time.

Peggy Roberts [00:28:36]:
Yeah, I want to join the valley of the nerds. That sounds like fun. So I'll share maybe just a couple anecdotes from inside Riverside if that'd be helpful. You know, again, we're global, so we've got 300 employees around the world. And we have a fantastic CTO who sits in New York. And he's just been an amazing job building the sort of the culture of experimentation and encouraging people to lean into how we can use AI. This is in our own business now. I'm not talking about the portfolio companies now.

Peggy Roberts [00:29:08]:
You know, we. Evaluating deals, sourcing deals, sourcing investors, you know, everything, how we run our back office, all that stuff. And so that's been really, really refreshing. And, and I think one of the. So we've, you know, we've been workshopping, we've been pulling in consultants, we've been looking at our data models. We've been just doing a lot of creative work from the ground up. The thing that I think keeps us the most honest is our investors. So the folks that give us the money to deploy into companies are asking us all the time, what are you doing about AI? And so that's really kept our feet to the fire to, you know, do the best we can.

Peggy Roberts [00:29:44]:
Nope, nobody has an answer.

Jeffrey Stern [00:29:45]:
Right.

Peggy Roberts [00:29:45]:
I said experimentation earlier on. I think nobody has an answer to what this truly could do for our business. But I'm enjoying the journey, learning along the way. And then I do want to just build on one quick thing that Hardik said earlier about the founder or CEO of a business and the industry. Right. That they know the industry when you're giving them money. I think for us, that also is a predictor of how smart they're going to be in using AI. Because when you think about opening up a new market, a new value proposition, you need somebody who really knows the business that they're in.

Peggy Roberts [00:30:19]:
And we're constantly making tough choices around our leadership teams and making sure we have the right talent in the seat and surrounding our CEOs with the right talent. And that's a big part of the conversation.

Hardik Desai [00:30:32]:
You asked personally. So I'm trying to figure out if my 9 year old, soon to be 9 year old, when he says, let's ask this question to ChatGPT, should I be paranoid about it or should I be excited about it? Let's ask this to ChatGPT, I guess, you know, maybe just on the jokes aside. And we, I used this example in our prep call last week. We invested in a K12 company that's growing well, doing really, really well. Six months ago, nine months ago, when they were going to the conferences of school superintendents, the team everywhere was AI. Three months ago, when they went there, no superintendent wanted to talk about AI. And that's the reality that everyone is trying to figure out. Right.

Hardik Desai [00:31:16]:
And so as you are growing, scaling a business, how do you think about the customer's perspective? How do you think about how your customer who is also getting bombarded and hammered with everything, AI, everything in their life. Right. So how do they react to it and how do you add up your sales cycle, your messaging to those customers?

Jamie Weston [00:31:38]:
Yeah, I don't think that there's any consistency within our portfolio companies around usage or standards. And I think there's a lot of experimentation. You know, some people are more engaged with it than others, some teams more engaged than others over time. I personally think there needs to be like an AI quarterback at a company, particularly when you get to a particular scale.

Hardik Desai [00:31:57]:
Right.

Jamie Weston [00:31:57]:
That AI quarterback will sort of see the whole field. They will understand what's happening within their organization. But importantly, hopefully they will, you know, have their finger on the pulse of the things that are happening in the industry. It's also, you know, critical for licensing. Right. When you, you sort of have these little individual licenses. But you know, we're tool agnostic and, and I, it's one example like one of our companies and a couple of our companies use gong, which is a, a solution to help you record your customer interactions, your customer conversations. And so they flow that GONG data into an LLM and they get incredibly rich feedback on their customer conversation which builds over time.

Jamie Weston [00:32:36]:
It's almost a de facto survey that just sort of keeps happening. Now we don't use that across our company. I just should note that we do that in a separate instance of LLM because it is private customer information. So it doesn't go out. But that should be done across our organization and I'm positive it's not.

Peggy Roberts [00:32:52]:
Yeah, I think that's A great point and just reminded me too. In our portfolio companies trying to encourage the experimentation and knowledge sharing that they're developing with each other, bringing the CEOs, CFOs, CTOs, whoever might be sort of in the lead together into working group sessions to talk through the challenges they're facing in their business. I think the concept of a quarterback is a real one. I think the biggest frontier in my mind, especially for our more established companies, is education and training enabling our workforces to adapt and adopt AI in their workflows. It's a real challenge. It's cultural changes. So if you're trying to battle those headwinds in your organization, having somebody who, that's their, you know, their, their job worry about.

Jamie Weston [00:33:38]:
I totally agree. I heard a podcaster recently say that if you're, you know, putting training in for AI for your employees, you've already lost the battle. Right. I don't, I sort of disagree. You know, you need to have some baseline level of, you know, education.

Jeffrey Stern [00:33:52]:
There's no better practice for learning than trying it.

Peggy Roberts [00:33:56]:
But I think, and sorry, I'm just, I'm like soapboxing this for a second. But we love investing in education and training companies at Riverside and I think that the use of AI in our workforce, so I'm not talking about PhDs in the nerd Valley, I'm talking about people that are in our companies doing their day to day jobs. They need to think about things differently and that doesn't happen overnight. Right. So we have to help them come along that adoption curve and understand how they think about their job, their data, how are they, how are we structuring data, how are we accessing data, what are great uses for this and that? That's in my mind, that's the current wave that we're riding up.

Jeffrey Stern [00:34:37]:
So in the last two years, I am continuously impressed by folks who've been able to leverage AI to the greatest effect in their companies. And the question I want to ask, I'll ground in like an example that I've seen just to explicitly show everyone here, like how powerful this is in practice, because I do think there was a lot of hype, but I mean, I've seen a company double their EBITDA in one year through a very tactical implementation of AI. And this is not, this is like a, call it seven figure, eight figure business. So these are like material changes and that. I mean, that's not hype. Like I've, I've seen that happen. And so I'm curious from all your perspectives, like what have you seen that is most surprised you about practically ways that, that this is materially altering the trajectory of companies?

Candice Matthews Brackeen [00:35:29]:
For us, it's for accuracy. So we have a company in our portfolio that supports fertility, like women getting pregnant. So it's called prove. It's a progesterone ovulation test. Right now it's a urine based test. So you pee on a strip and you take a photo of it and there's a color change. The accuracy over the last year has increased so much that we're seeing thousands more babies being born. And that's a metric that matters.

Candice Matthews Brackeen [00:36:03]:
That's for us, a metric that matters. It was the AI. Anything else?

Peggy Roberts [00:36:09]:
How are we going to top babies? We're going to top babies. I was going to use like a really horrible example. I'll just keep using it. Forgive me.

Candice Matthews Brackeen [00:36:18]:
We've talked about backhoes, we've talked about nerd.

Peggy Roberts [00:36:22]:
So this is a company called Partium. Partium, which you know is a funny name, but they, they, they were sort of like a ERP system for like sort of complex supply chains. If you're in a repair kind of business and you're having to source a part to fix something, you would use Partium to help you find the part and track down the part. So, so similarly, this is what made me think of it. We're using photography now to just snap a picture of the part and up comes where that part is. And again, these aren't like this isn't just back in my warehouse. This is very, this goes deep into a supply chain to figure out where do we need to get this part to get it out to the engineer. So that is, it's, you know, it's basically halving the time that an engineer or that one would spend to fix a repair issue.

Peggy Roberts [00:37:11]:
So when they sell to, you know, one of their big customers is the railway system in Germany. The engineers on those trains, the trains are down, you know, whatever it is that you can source the part, get it fixed, get it back on its way in half the time that you could before. So it's not babies, but it was very powerful all through using image technology.

Candice Matthews Brackeen [00:37:32]:
Yeah. You know what, let me add one more thing because now I'm excited about my company. Why that matters for us is that that company started as just like a really easy analog. You pee, you make it work. We're trying to test, we're making this thing work. She's now figured out to do how to do it continuously so that a woman will be able to wear this and eventually she'll know right away when it, when it will work for her. And so those changes, especially in healthcare, we're just going to see things move leaps and bounds. And it's just that kind of easy utilization like P and G owns clear blue easy.

Candice Matthews Brackeen [00:38:09]:
And they could have done that too. Right. They had the right to win by being the first mover in that space. Just using technology, just bringing together a small and scrappy team of people who are willing to use new technologies has allowed something really big to happen. And so all of these legacy companies with legacy ideas can really do the same thing.

Jamie Weston [00:38:35]:
Yeah, I think at least in our portfolio it's just a building blocks of incrementality of these things that are kind of, and maybe you don't necessarily appreciate unless you dug in exactly all the progress that's been made. I just want to build a small vision of the future for customer interactions, particularly with software companies. So, you know, you would say, okay, well why don't you attach some sort of text based LLM that you can ask your data questions. Well, we're not there yet because people are uncomfortable. They don't necessarily know what questions to ask and it's not in their workflow. Right. It's just not necessarily right now part of their DNA. So what you need to do as the vendor, as the provider of the technology is to identify the most salient insights from that data that you can for them.

Jamie Weston [00:39:18]:
Let's say the top insights, that's number one. And then number two, you know, data in and of itself has no value unless you, you bring knowledge to that. Number two, you have to say, okay, what does that data mean for you, customer? And then the nirvana part of it is to say, okay, we're going to use AI agents and we're going to take action on that recommendation, or what you're looking to do and then you sort of complete the circle for your customer. I think when you get to that point in time, it's incredibly interesting.

Hardik Desai [00:39:47]:
Yeah, I was going to say we have about 90 companies that are active in our portfolio. Some were funded three weeks ago and some were funded 17 years ago. So everyone is trying to figure out across the spectrum, across multiple industries, manufacturing, healthcare, software. So they are all in every aspect of what they are doing. The founders are trying to figure out what are those incremental improvements in efficiency, which when you add all of those things together can be a meaningful change to their top line, bottom line.

Jeffrey Stern [00:40:17]:
So I think we'll work to bookend the conversation here with a Lightning round of sorts. We're sort of all in the business here of investing in the future. With that comes some prognostication about what you think the future might look like. So to round it out, I think, and grounded again in this hype versus reality conversation in the next five years, what are you most looking forward to actually becoming reality as a consequence of AI?

Hardik Desai [00:40:44]:
I would say just wearing my Midwestern Ohio hat, we are going to be driving multiple industries and multiple applications and use cases of how AI is transforming those industries.

Jamie Weston [00:40:57]:
Yeah, I mean we deal with the application layer so I mean selfishly I guess I'll say we think the most change is going to happen at that application layer. So some of the things that I, you know, and we may not be investing in these areas but some of the things I think are fundamentally going to change. You know, drug discovery so you can run simulations of particular molecules within a simulated body environment and get to answers so much quicker. That's pretty cool. There's personalized and adaptive learning and education side of things that's going to perhaps fundamentally change the education system you've got, you know, from the finance side of things you can work with risk management and compliance, maybe your own financial portfolio to get to a better place. And then Peggy, you may have mentioned, you know, logistics. I think logistics, the optimization of the whole supply chain, logistics could fundamentally just be so different and I don't know, fixed but vastly improved.

Candice Matthews Brackeen [00:41:56]:
Really in general the human experience is going to completely change. I don't know if any of us really know what five years from now is really going to look like. I think we all healthier and a lot more babies. A lot more babies and then we'll have new problems to solve because we may have automated our way out of things including doing today. So years from now it's just really going to be interesting.

Peggy Roberts [00:42:25]:
And Jamie took like all the industry sectors that I was going to say. No, I'm just kidding. We at Riverside we've done I think over 120 now education and training deals. We love that space. Do a ton in K through 12. We do a little bit but mostly it's post, you know, it's corporate training, things like that. So we think that's the frontier to be in. It's an exciting place to invest in.

Peggy Roberts [00:42:48]:
AI is going to have a ton of applications there and we're all about levering up the workforce.

Hardik Desai [00:42:53]:
If I may please, to close this out, we are going to seed a company. Spring Mountain Capital is going to do follow on investment in that company Riverside is going to acquire that business, and Candace is going to fund all of us.

Jeffrey Stern [00:43:06]:
Love it.

Hardik Desai [00:43:07]:
Love it.

Candice Matthews Brackeen [00:43:08]:
And we got a red.

Jeffrey Stern [00:43:09]:
It's perfect. That's perfect. Well, I just want to thank all of you for tuning into this conversation. You know, despite the comment about, I think the exhaustion that we have about, you know, perennially talking about this every day, it's also on the same side of that coin. Endlessly fascinating. So, I mean, I'll speak for myself, but if any of you, you know, want to just chat about it, I don't think any of us have the answer. You know, it is ever evolving. So happy to continue the conversation, but thank you all.

Jeffrey Stern [00:43:37]:
That's all for this week. Thank you for listening. We'd love to hear your thoughts on today's show. So if you have any feedback, please send over an email to jeffreyoftheland FM or find us on Twitter odleoftheland or sternfa J E F E. If you or someone you know would make a good guest for our show, please reach out as well and let us know. And if you enjoy the podcast, please subscribe and leave a review on itunes or on your preferred podcast player. Your support goes a long way to help us spread the word and continue to bring the Cleveland founders and builders we love having on the show. We'll be back here next week at the same time to map more of the land.

Jeffrey Stern [00:44:14]:
The Lay of the Land podcast was developed in collaboration with the UpCompany LLC at the time of this recording, unless otherwise indicated, we do not own equity or other financial interests in the company which appear on this show. All opinions expressed by podcast participants are solely their own and do not reflect the opinions of any entity which employs us. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Thank you for listening and we'll talk to you next week.