00:32:03 · George Sivulka, Alex Immerman
[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. [00:32:54] S1: Yeah. It's like if you had all the software deals from, like, license maintenance mode to switching to subscription, the license maintenance ones are helpful to some degree, but what you actually care about is the subscription business model. And maybe, like, as in this transition we're undergoing right now with AI, there's questions around business model. Like, subscription is a default. A lot of companies are charging per seat, but some are starting to charge different things on an outcome basis, on volume. For Hebio, like, you've got one model today. How do you think that evolves over time? How do you... How have you thought about pricing?
00:33:25 · George Sivulka, Alex Immerman
[00:33:25] S0: Hebio right now prices per seat because we're trying to build a product that incentivizes usage. And I think that as you start to have different AI agents that can do different things, it's very clear that you'll start to have people that are paying AI agents salaries or people that are paying AI agents on a consumption basis. And there'll be lots of pricing arbitrage of how you want to wrap and package agents and agent SaaS, if you'll call it. And I think that we're still too early to do that arbitrage. And the reason is that you need to get to adoption first. You need to get to the usage first before you charge on consumption. Because the minute you charge on consumption or you charge on, you know, number of agents and times of salary, you actually are disincentivizing the change that is required to build an AI native company, an AI native firm. And that's a bad thing. So I think San Francisco, like, everyone's, you know, talking about, like, different pricing strategies for AI agents. And I'm like, you know, outside of San Francisco, no one knows who an agent is at this point in time. [00:34:24] S1: Heavy today. A B2B company. You work with some of the largest asset managers you mentioned. As you think about where to build features, how do you maintain a great UI versus becoming the next Salesforce?
00:34:24 · Alex Immerman, George Sivulka
[00:34:24] S1: Heavy today. A B2B company. You work with some of the largest asset managers you mentioned. As you think about where to build features, how do you maintain a great UI versus becoming the next Salesforce? [00:34:41] S0: I commonly feel like a tension between just building what the customer wants right now and building for the future, building the product that I think solves all the customers' needs. But, you know, maybe the the Henry Ford quote of of, do you want a car or do you want a faster horse? And everyone would always say they want a faster horse. And Salesforce is a faster horse. You know, it's got components and a library for everything and everyone. And everyone at my company will tell you that when we prioritize features or when we ask for a feature, it's not something that we add on, but we actually look at the entire piece of software as a painting or as a composition, and we figure out how it fits together. And we continuously redesign versus append. A lot of b to b enterprise AI apps will have a billion different apps, and they'll just be, you know, here's this feature. Here's this feature. Here's here's here's this feature. And it's already starting to look a little bit like Salesforce. We're we're taking a very different approach. We're saying, how does this feature integrate into this other feature? And how does that make sense to the user? At the [00:35:42] S1: end of the day. Alright. We got to wrap things up. Final question. As we think about AI into the future, what does success look like?
00:35:42 · Alex Immerman, George Sivulka
[00:35:42] S1: end of the day. Alright. We got to wrap things up. Final question. As we think about AI into the future, what does success look like? [00:35:49] S0: For the industry writ large, there's a lot of models that are creating disinformation. They're they're generative, and so they're naturally creating a lot of noise. And I'm I'm very hopeful, and I think... Yeah. I I hope to be a voice of AI that doesn't only create information, but finds the signal in the noise or or actually helps distill and make more concise and more cogent the information that we receive. At the same time, I think you'll start to see the workforce change drastically. You'll start to see... One of my big predictions is that over 50% of the global GDP will be contributed by AI agents sometime in the next decade. And I don't think that's work that's replacing humans. I think that's actually net new value creation, and I'm excited for that. And I think that you'll start to see humans... And and my hope is that you'll start to see humans love work more. If you look at the jobs where people are the least satisfied, it's almost always these jobs where there are robotic automatons doing a task over and over again. And I think that humans, when they're taken out of the mundane execution and put in a seat of thinking and deciding and creating and discovering Yeah. Moving from [00:37:04] S1: the objective to the subjective and focusing time there.
00:37:04 · Alex Immerman, George Sivulka, S2
[00:37:04] S1: the objective to the subjective and focusing time there. [00:37:07] S0: Exactly. I think that is that is my goal for the larger industry. Yeah. And I... I'd be happy to live in a world where AI saves 1% of the world's population 1% of their time. And if I can contribute to that, and I think it'll be much more than one percent, I'll be a very happy man. [00:37:25] S1: Prosperity. Thanks for spending time with us, George. Thank you for having me. [00:37:28] S2: With that, another episode is in the books. If you enjoyed it, please do rate and review the podcast or at least share it among your friends, colleagues, and network. We'll be back with a new episode next week.