Buyers and Builders
00:20:59 · Cyrus Hessabi, Mikk
Because sometimes it means you're over complexifying thinking about many things that might be too far out of scope or, you know, I think at some point depth triumphs over breadth on some things. So I think it's it's kind of a balanced stick, but I certainly like having that bridge background because I think it's defying you for so long now. [00:21:18] S0: You mentioned this AI risk. I think it's interesting. Maybe we can go more specific, because thanks to your experience, you see things which many other investors who back searches don't see. So what else? You mentioned one thing is the healthcare staffing industry. What else? What do you see? You look at different companies, different industries. You see like, okay, here is a big, like, could be a red flag in the future. [00:21:44] S1: Yeah, for sure. So you're thinking about what, where is the biggest AI risk in terms of job growth? [00:21:50] S0: You have people coming to you with with deals, some things under LOI, for example, and you immediately see that, okay. It's too risky. And you go back to your team saying, okay, guys. We should maybe skip this one and and move forward because of x, y, and z. Maybe you can give some examples, or how do you think, how do you view these opportunities?
00:22:09 · Cyrus Hessabi
[00:22:09] S1: But, you know, I'll tell you this. I don't know that many examples where we said no because of AI risk per se. AI is usually... I don't mean to be cheesy or kind of, you know, setting a narrative, but the I think in many cases, it's more a consideration and an opportunity set versus a risk per se. It's more, okay, this is how... This is what we should probably implement in this business to keep it competitive and and even do better versus, okay, okay, don't go there because we're asking to replace everyone, you know? Unless you go into some specific businesses, but they have already underlying poor economics, right? I don't know, you look at a low margin call center, we might think, okay, labor's in, you know, there's too much labor intensity, or some people have tried automating their call centers like Klarna, and then they have to bring back humans. So I have some, you know, there's maybe these types of considerations, but more often than not, the AI piece, we're looking at something like, I don't know, I was looking at some, like marketing automation services. I think there you have a lot of AI opportunity as well, right? Matchmaking, campaign generation, Copilot for creativity. I think more often than not, it's can you easily leverage a solution that exists off the shelf and bring it to the business? Much like in the past, PE firms might think, you know, what's kind of the best ERP or the best CRM I should implement in my business? Here's kind of what is the best AI strategy I should bring in versus saying completely backing out of industry just because of that. Yeah. That's just from my my own experience, my memory today. I'm sure I'm sure there's industries that missing in my examples.
00:23:46 · Mikk
[00:23:46] S0: But what else when it comes to it's interesting that you said that you see those opportunities, many maybe won't again, because based on your experience, has there been any deals or, yeah, deals coming to you where you see immediately that, Okay, this is it's like screaming how good of an opportunity it is if we implement AI in what they do on day to day basis. And that will be the value add.
00:24:15 · Cyrus Hessabi
[00:24:15] S1: That for sure. So I looked at a few definitely, right? And I think some of the, I mean, of course, the companies that we backed, right, in various industries, think those were definitely kind of in that realm. I think there's other industries that I'm excited about from the AI perspective. You know, I think you can see online there's quite a few businesses that have raised in the insurance space, insurance TPAs and back office. I think that's very interesting. I think the main reason those businesses would be interesting is because of two things: high share of labor as a total cost, right, and repetitive, therefore automatable work, and work that also fits what AI can reliably do today. So processing documents, summarizing documents, being able to parse information, being able to synthesize and find gaps in information in long streams of data. So I think typically, those are jobs that I think also, I don't want speak on behalf of everyone, but I think these are maybe jobs that I think create burnout as well, right? Or work pieces where, I guess, people wish they didn't have to do that, previously. So I think these are also opportunities where you can say, I'm going to make my employee base way more fulfilled. I'm going to be able to keep them focused on things that matter the most. And then we can use AI to automate the pieces that are of low cognitive load perhaps for them or less less fulfilling. You know? I think I think that's kind of the main the main point here. I'm trying to find an example because I'm trying to find examples where, like, software changed the way that we work. You know, how how we as just everyone works, right? Like, with every kind of new productivity tool or software tool, you know, a whole piece of people's jobs changed, and then we focused on a higher value piece of that job, right? I think that applies to, you know, when you start using software in a company or when new technology comes for engineering design or whatever. Just think about how people used to do things forty years ago and what you do now for a given job. You know, it just let's say we're we're recording this this podcast. Right? It took a few messages and now a video call on on the laptop. Right? But the podcast is still happening. It's not being replaced by AI per se.
00:26:33 · Cyrus Hessabi, Mikk
I think it's a similar example where the core value of the workflow is being kept, but the menial work around it is being removed over time. [00:26:42] S0: And when you joined Shore Capital, did they give you their current portfolio companies? Hey, now we have this guy, Cyrus, joined us. He has this excellent background, as you have. And now please go through the portfolio companies and please find opportunities where we can implement AI. Like, did you did you have to do a work like this as well, or it was all about those new opportunities, new companies, new new searchers? [00:27:06] S1: I think new new for now. I think, like many fans, you know, we think a lot about AI and AI for the portfolio companies, right? If you see on Shore's website, and there's a lot said, and Shore is very famous, in fact, for having all these different centers of excellence and pods, right, that are kind of unique in bridging actually. Another theme for bridging between Fortune 500 and kind of accomplished large scale businesses and the management practices they're in, connecting those with emerging roll ups and roll up platforms of SME businesses in those industries. And I think in the same way, I think our approach with AI is going to be thinking deeply what is the bottoms up way to create operational value and improve companies, improve people's lives with that. Where can AI fit? Basically, much like we have these centers of excellence in other areas, other functional areas of the business. [00:28:01] S0: Was there anything you had to unlearn when joining Shore?
00:28:06 · Cyrus Hessabi
[00:28:06] S1: I mean, I'm still kind of transitioning. So the... I think, yes. But I kind of... I'm I'm gonna tell you what it is, but I think it it was also already in my mind as I made the transition. But I... I'd say, I think in VC, incentive structure is around optionality, around long term vision, around, you know, winning the deal right now and and, you know, kind of valuation is whatever. I don't want to be too simplistic in my view, but I think it's true, right? Typically, the amount of really, really good assets or high quality companies to buy isn't that high, especially vis a vis the volume of funds that exist in venture. So I think the unlearning bit is to go from that environment to an environment where there is almost the reverse. There aren't that many ETA investors. There's lots of high quality people to back, lots of high quality things. You have to kind of parse your time a little bit, be able to say yes, no kind of fairly fast. And I think keep yourself accountable also. I think it's very these are industries that are very bottoms up. Like when you buy a services business, when you're acquiring a kind of traditional economy business, it's real in a way from day one. It's not a J curve type of investing. It's not kind of vision driven as much. The value has to be delivered in very near term. You have to value the company properly. You have to be quite clear on exactly what is gonna be creating value for the business versus not. So I think there isn't that much room for narrative versus execution, and I think unlearning, the if I may say, is kind of that. Although what I was going to say earlier with my caveat was kind of with the AI enabled services thesis, it was already like that, because the last thing you want is to come in with a very top down simplistic view of the world. AI is going to do this, AI is going to do that. And then the day after you, you know, are invested in the business, you realize, wait a minute, there's structural barriers to actually implementing any AI into this business. And so now we're stuck with with a bad investment. So I think it was already kind of like that over the past year, but definitely if I had to give you a comparison, small cap PE to VCA is really around that dynamic.