00:25:07 · George Sivulka
[00:25:07] S0: I'd say that 2023 and 2024 were really the year of experimenting with AI. And 2025, it's now like, hey, the boards are saying, what's the P and L impact of the, you know, sometimes $100,000,000 investments that we're making on this experimental technology? And it's put the onus on Hebia and the way we engage with our customers to always have value cases and to always prove out value cases. And you start to get stories to go back to the credit agreement example, where firms are saving, you know, if every single credit agreement, you know, they're reviewed multiple hours and it costs $2,000 per hour for a lawyer to review them. And now they can review them in house. They're saving on tens of thousands of dollars on a per deal basis, maybe hundreds of thousands of dollars for a significantly complex deal on credit agreements, and they're looking at hundreds or even thousands of deals a year. That's meaningful ROI. There's other examples where Amlaw 50 clients are onboarding new customers for their own their own clients. And they're basically saying, hey, what used to take five to eight hours a customer to onboard and pull up all the right data, we now can understand instantaneously. Right? Lawyers sometimes will have to look at their client's data room, figure out all the red flags, problems, everything else. And now they can do that in Hebia and know everything about their customer out of the box. And there's examples of private equity associates that are saving, you know, four to eight hours a week because they're building out libraries of their portfolio companies, board decks, and investor updates. And they're able to benchmark their portfolio much more accurately.
00:26:39 · Alex Immerman, George Sivulka
[00:26:39] S1: You just mentioned the private equity associate. To get ROI, humans are in the loop. Humans are working with the software. They're involved in Hebia. They're there every day. Talk about the relationship between human and software and where that is today, where that's going. [00:26:57] S0: I think, ultimately, there's a lot of software and a lot of AI that seeks to replace humans. And the way that we design, the way that we conceive of new interfaces, and I think the way that I I hope the larger industry does is is in ways that empower humans, in ways that make humans better. And ultimately, I think Steve Jobs always talked about computers as bicycles for the mind. Right? It's a way to just go down and and and basically explore or do whatever you wanted to do. A simple machine that was a very a very elegant solution. And I think that it's easy to to stop at chatbot and say, okay. This is our bicycle for the mind. But when you can start to explore other interfaces and still empower the user, that that is empowering. That is software that is in in in service of the human and and the human that's appreciating the software for what it can do. So, as we [00:27:52] S1: think about humans in the loop, those humans can be senior investment professionals who may be reading someone else's work. But the majority of your users, they're early career professionals. They are what you referred to earlier as AI literate, this next generation. How are you building your product for them? How is it designed? What's special for that demo?
00:28:16 · George Sivulka
[00:28:16] S0: I think that the demographic of of folks that are early in their career are... You know, they're the ones that, for the first time, are AI native. They are the the first analysts that are learning how to do the role where they're they're actually using, you know, technology that can do the mundane parts of it. And I think that, you know, it's it's funny because you you... You'd think that only Gen Z would be really AI native, but there there are even some, you know, MDs, like the one off MD that will will go in and actually check their analysts' work with AI. So they'll create a matrix and, like, pull out red flags or inconsistencies over something their analysts sent. But I think it's I think it's it's special because when you catch people early on in their career, obviously, the banking analyst sometimes will go and become an investor, and the investor will sometimes go and work in a large corporation, or maybe they'll become, you know, the founder of their own firm one day. And I think that the change starts with the people that are the early adopters, with the people that have the most mundane workflows, and then it has to bubble out through the organization. But that can only happen with a lot of the C suite executives and MDs and CIOs that support Hebio. I mean, they love it. They use it themselves, and they also have a change management mandate. And so it's kind of two sided, and our best deployments are those where both the senior folks and the junior folks are aligned on using AI.
00:29:30 · Alex Immerman
[00:29:30] S1: As you think about, like, the finance industry in five to ten years, hebia, AI, a lot of change coming the industry's way. Do you dream the dream? What does the industry look like in a decade's time?
00:29:44 · George Sivulka
[00:29:44] S0: I think my... This is my favorite question in the world, because I I believe fundamentally a few different, like, big things will happen. First, when AGI is here, there will be a massive correction in the financial markets. That is actually my Turing test, is if AGI is here, will it actually be able to make significantly more money than a human investor, I. E, better than human investing? And I think part of that will be uncovering fraud at massive scale, uncovering market inefficiencies and, like, human behavior that is mispricing assets. And you'll actually start to see what happened with quantitative investing start to happen and really are about all of the qualitative, you know, alpha that exists or does not exist in the world. And everything will just turn to beta. Levered beta, if you will, and if you have leverage. On the other side of things, I'll... I also think that private investing will change way more than public investing. Why is that? If you look at public markets, you've got like a Bloomberg terminal. Right? You can go in and you could look at every single number, and it's all perfectly form formatted. And every quarter, you have to file with the SEC and you have all your numbers perfect. In the private markets, you have a data room. And the art of private market investing, especially in, like, the later stages, especially, like, private equity, private capital, is basically building a Bloomberg terminal for the private company. You want all the numbers. You want the charts. Your IC memo is effectively the Bloomberg terminal for the private company. And there's... There might be some qualitative pieces, but you're trying to structure unstructured information. And what Hebia is trying to build is the Bloomberg terminal for private companies. Not like a PitchBook or Crunchbase, which is really just for sourcing, but actually to go out and take all of the information private and public and prestructure that. So the minute you get to a new company or you you can actually perfectly understand what it should be valued at, all of the numbers that you would look at on a Bloomberg terminal, like, pulled out and ready to go for you for the private company.
00:31:36 · Alex Immerman, George Sivulka
[00:31:36] S1: And I [00:31:36] S0: think that will change the way from a speed perspective, from an accuracy perspective, from the ability for LPs to want to invest in the asset class, from the ability or the proclivity for private companies to want to go public. All of that, I think, will change, and I think AI will really transform the capital markets. Does that mean [00:31:54] S1: that older firms who have the most data will be advantaged? Like, how important is data, like private data, towards winning? [00:32:03] S0: It's interesting from the perspective of firms that have lots of data, like mega funds. When they come to Hebia, they're able to say, hey, I have this history of 10,000 sims. And so Since 1975. 1975, and and Hebia can OCR them, pull out all, know, look at all the graphs and tables. Yeah. In an HP 12C. No. But it can basically turn into this beautiful, like, SIM library. And everyone's like, wow. This is amazing. But at the same time, I also think that as the market changes, you'll actually start to see new opportunities that, like, don't have comps. Don't like the historical stuff won't actually matter. And so perhaps, you know, spending too much time thinking about the historical deals that were pre ChatGPT or pre generative AI or pre LLMs are not actually going to help you with the deals post LLMs. It'll be even better if you don't have that much information.