The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:31:33 · George Sivulka, Harry Stebbings
[00:31:33] S0: Yeah. I'm less worried about that. I think, ultimately, one of the best things about being, you know, having the years of experience is is actually having the depth of knowledge about investing. So for example, if I'm a junior trying to price an asset, I haven't seen that many other companies that look like this company. And so I might say, hey. I think it should be priced at x, y, or z. And then someone in the IC IC meeting will be like, hey. No. I've seen 20 other companies in my forty years of career that look exactly like this, and they all went nowhere. And I'm I'm leaning on my prior experience. With Hebia, now juniors, who are really smart themselves, can say, okay. You might have remembered 20 deals, but I'm looking through every deal in our company's history in a giant matrix that anyone has ever seen. And and I actually can tell you quantifiably that when a company is performing here, it's it's ninetieth percentile across all this investing criteria. We should actually pay 90% premium to market. And I'm actually using more deals than you've ever seen because I know your name is on this many IC memos. And that type of structured thinking or that type of of additional information that you can now give juniors in their career actually think makes better investors. I I I don't I don't think that that takes away [00:32:44] S1: Do you really think it will be a tool for usage, not a tool for replacement? [00:32:48] S0: I genuinely believe it makes humans better. I genuinely believe [00:32:53] S1: years' time, do you not think that is a different story?
00:32:53 · Harry Stebbings, George Sivulka
[00:32:53] S1: years' time, do you not think that is a different story? [00:32:55] S0: I think that it will change the way that people do work, but I genuinely believe that it'll actually increase the AUM of the firms that use it. I think it'll actually drive more employment. I I think there will be some jobs that change. Hey. There's no more bookkeepers that do, you know, tabulations in in spreadsheets on two sheets of paper. [00:33:13] S1: But What changes most and what stays the same? [00:33:15] S0: The cognitive tasks that are lower in cognition, like more back office, kind of middle office, maybe even some of the more junior front office tasks, I think will start to be... Start to move into, okay. How can we manage AI juniors rather than, you know, actually do this ourselves by hand? But I don't think that, you know, just as Excel didn't end up taking away jobs from people or just change people, you know, to having to learn Excel, the exact same thing will happen with AI. [00:33:45] S1: So you don't think that we will see team sizes reduce as a result of agent integration into enterprise?
00:33:45 · Harry Stebbings, George Sivulka
[00:33:45] S1: So you don't think that we will see team sizes reduce as a result of agent integration into enterprise? [00:33:51] S0: You know, there's there's all these stories and, you know, you have, like, Klarna that's positioning for investors that they're firing half their staff, and and and no one really wants... And I think a lot BS. I think it's BS. Yeah. There might be some reality to it, but I think that it's it's an amazing marketing story. And so anytime that I ever hear something that's put out as a marketing story, I almost negate it in my head to actually think about, like, what the implications are. When you're saying something and screaming it from the rooftops, that almost always means that inter... Internally you're freaking out about something. I think I look at that really loud behavior, and I think that behavior itself really negates the content. That's maybe my my positioning on on this sort of stuff. [00:34:27] S1: How do you feel about competition? What are your lessons on competition? There are several players now in the heavier slipstream. How do you feel about that?
00:34:27 · Harry Stebbings, George Sivulka
[00:34:27] S1: How do you feel about competition? What are your lessons on competition? There are several players now in the heavier slipstream. How do you feel about that? [00:34:35] S0: I think that if a $100,000,000,000,000 of economic value will be created by AI and agentic applications, that there will be so much room and so much opportunity for a ton of different players. I don't think that when Excel came out and then Mark released Salesforce and then, you know, people created TurboTax, you know, all these unravelings of Excel actually were were produced later, but that made Excel any less valuable. I actually think it made Excel more valuable. I view Hebia as this platform as something that will actually get better the more people get inspired by it and build increasingly verticalized applications. What models do [00:35:10] S1: you use? You sit on top of what? [00:35:12] S0: We are completely model agnostic. We use all of the major model providers, some of our own models. But ultimately, the foundational difference that Hebia is capitalizing on right now is fundamentally new and and very important difference, which is I actually think on the order of creating RAG and and creating agents and decomposition, is this idea of us in the last year or so having pioneered scaling at inference. [00:35:35] S1: Talk to me about this.
00:35:35 · Harry Stebbings, George Sivulka
[00:35:35] S1: Talk to me about this. [00:35:36] S0: Right now, you actually... So OpenAI is starting to do this with o one, where they'll have a model recursively think about a question over and over and over again before it produces an answer. And so instead of training a larger model, they're using effectively a similarly sized model and just telling it to run multiple cycles, I. Compute more before answering. Hebia is... Actually pioneered something different where a year or almost eighteen months ago, we said, hey. We can't wait for these models to catch up. What we'll do is infer simple single question. Let's actually run hundreds or even thousands of sub models of the best models in the world to compute over every single document to answer the same question. And so ultimately, if you can't train larger and larger models fast enough, you could take whatever state of the art or cutting edge and run it more times to get more compute, I. More computational power, better decision making for the same user right now. And so this is an idea that we pioneered. It doesn't matter if you're using Claude three five or if you're using o one itself, I. E. Scaling at inference, at the orchestration layer with something that is scaled at inference with the training layer, but you get way better results. And it's, it's a it's a way to to drive to more accuracy. [00:36:47] S1: Find provides the best results. We had Des Trainer on from Intercom recently. He spoke about the movement away from OpenAI to Anthropic.
00:36:47 · Harry Stebbings, George Sivulka
[00:36:47] S1: Find provides the best results. We had Des Trainer on from Intercom recently. He spoke about the movement away from OpenAI to Anthropic. [00:36:53] S0: We've seen that for certain types of documents, like the dense legalese or or or more colloquial documents, Anthropic works better. But for other types of documents, like o one or OpenAI four o works better. And it's always trade offs between accuracy and speed and all kinds of things. Actually, a lot of the time when we're decomposing a task, we'll use mixes of OpenAI, Anthropic, even Gemini. [00:37:15] S1: Do you think we live in a world moving forwards of many models that are specialized in different things? As you said, some do legal, some do whatever we wanna talk about, and that's the world we live in, or there's generalist monolith models which really kind of own the whole stack. [00:37:30] S0: This makes me think of the story of Bloomberg, which has the best financial services training set of all time. And they trained a GPT 3.5 class model. It was called the Bloomberg GPT. And they released an archive paper, and everyone on LinkedIn was like, wow. Bloomberg is, you know, cutting edge, and they're gonna steal finance AI. [00:37:47] S1: Why did they not? They did have all... They've got the best data in finance.