The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:24:38 · Harry Stebbings, George Sivulka
[00:24:38] S1: What makes you say that? [00:24:39] S0: I think, like, 90% of enterprise AI right now is almost like this vaporware. We swear it works. Like, look at this amazing demo where we ask, what does the CEO say about the investment? And the minute that they actually go to, you know, try to use it in a real world example completely just fails. And so I actually think that the majority of AI usage, a lot of these usage statistics are all kind of, one of my favorite phrases is fugazi fugazi. And one of things that Hebio really tries to put forth in the market is is to say, hey. Change will take time, but we have a system that that is actually starting to drive real measurable value over very specific defined use cases. And our tagline's always, hey. Stop experimenting with AI, which everyone's experimenting. They're all really excited about it. Start driving value, like, getting value out of it. [00:25:26] S1: To what extent is this RPA versus Ajanti?
00:25:26 · Harry Stebbings, George Sivulka
[00:25:26] S1: To what extent is this RPA versus Ajanti? [00:25:29] S0: I actually am not a big believer in RPA. I think RPA is a is almost not an AI application in the in the new sense of AI. It's like AI in the old ten years ago sense of AI, where RPA is effectively, like, very simple computation. But some of the things that people are asking Hebiah are over 800 page credit agreements or 230 page, you know, SIMS, confidential information memorandums, this marketing material. They're not actually asking for things that... Like copying numbers. They're saying, hey. Tell me what are inconsistencies in this document. Tell me where there's an event of default that we can trigger. There's almost this like open endedness or this new level of computation that people [00:26:10] S1: can do. I think Daniel Dines, who we had on the show, comes out on Wednesday, he said it very well. He said like, listen, RPA is low skilled, low level cognitive processes and agencies, high skilled Yep. Ambiguous Great. Decisions. Yes. I think that's a nice phrasing. [00:26:24] S0: I think it's it's incredibly clear. Yeah. And I think that we very much are capturing the the agent, the the high level ambiguous decision making and trying to trace it all the way down back to individual citations or individual characters that led the model to that decision. [00:26:39] S1: I thought about actually Satya's statement the other day that he made, which is the notion that business apps that exist today will all just collapse into agents. Do you agree with that, and will apps be the predecessor to agents?
00:26:39 · Harry Stebbings, George Sivulka
[00:26:39] S1: I thought about actually Satya's statement the other day that he made, which is the notion that business apps that exist today will all just collapse into agents. Do you agree with that, and will apps be the predecessor to agents? [00:26:51] S0: I actually don't don't agree with that at all. I think he's completely wrong. I think it depends on how you define business apps, but I actually think that if the new business apps are platforms, you'll you'll actually start to see those platforms really take hold. Like, Hebby is a platform. It lets you build whatever agent that you'd like. And so here's a bit of a mind fuck. When when building Hebby or when building all of these foundational primitives for how people use AI over the last four and a half years, Heavier has always asked ourselves, what are the apps that AGI would want to use? Or what are the apps that agents would want to use themselves? I e, what are the tools? Because these AI applications are really good at using tools that we could build that would assist LLMs or these really smart foundation models, whatever they are in the future, to get to an answer more quickly. It's quite interesting. You know, Hebian matrix orchestrates lots of smaller LLM calls. It's actually scaling at inference, I. E. It's running massive amounts of compute at the orchestration layer. And we think that, you know, an AGI system would prefer to use HebiaMatrix to diligence a company or to look through thousands of documents versus to read them all by hand and they're in a really long context window.
00:26:51 · George Sivulka, Harry Stebbings
[00:26:51] S0: I actually don't don't agree with that at all. I think he's completely wrong. I think it depends on how you define business apps, but I actually think that if the new business apps are platforms, you'll you'll actually start to see those platforms really take hold. Like, Hebby is a platform. It lets you build whatever agent that you'd like. And so here's a bit of a mind fuck. When when building Hebby or when building all of these foundational primitives for how people use AI over the last four and a half years, Heavier has always asked ourselves, what are the apps that AGI would want to use? Or what are the apps that agents would want to use themselves? I e, what are the tools? Because these AI applications are really good at using tools that we could build that would assist LLMs or these really smart foundation models, whatever they are in the future, to get to an answer more quickly. It's quite interesting. You know, Hebian matrix orchestrates lots of smaller LLM calls. It's actually scaling at inference, I. E. It's running massive amounts of compute at the orchestration layer. And we think that, you know, an AGI system would prefer to use HebiaMatrix to diligence a company or to look through thousands of documents versus to read them all by hand and they're in a really long context window. [00:27:58] S1: So is the future of business apps... Not business apps, but business platforms? We've got platforms, agents, or apps. Yeah. What is the future?
00:27:58 · Harry Stebbings, George Sivulka
[00:27:58] S1: So is the future of business apps... Not business apps, but business platforms? We've got platforms, agents, or apps. Yeah. What is the future? [00:28:08] S0: I ultimately think that it will be a mix of of all three. History doesn't repeat itself, but it often rhymes. You know, 60 ago or even longer, the foundational unit of compute, I. E. Doing a calculation on a computer, was effectively introduced to to the enterprise. There were plenty of people that were tallying things or bookkeeping in actual books, and their jobs changed. And there were apps for bookkeeping, and then there were platforms like Excel that let people build better apps for bookkeeping, and then Excel was unraveled again into better better apps for bookkeeping. And I think that, there's opportunity not only in verticals, but there's also opportunity in the entire industry in in in terms of building platforms, in terms of building cooperatives, in terms of even building new types of, quote, unquote, agent employees. And I think that that opportunity is the exact same size. It's... If if there was a 100,000,000,000,000 of dollars of value that was created in the stock market from the introduction of the computer or the fundamental unit of compute, I actually think a $100,000,000,000,000 of value will be created, in the next sixty years from the the introduction of inference or or of AI compute, which is [00:29:09] S1: Will will that be additional value, or will that be value that denigrates from the existing value of alternatives?
00:29:09 · Harry Stebbings, George Sivulka
[00:29:09] S1: Will will that be additional value, or will that be value that denigrates from the existing value of alternatives? [00:29:15] S0: I believe it will be additional value. And maybe I'm too techno optimist, but I actually think the S and P 500 is is completely $100,000,000,000,000 [00:29:22] S1: of additional. I can't quite get my head around that. How does that even exist? Adding in that $100,000,000,000,000 of value, is that just because we will see GDP and productivity grow so much that takes place? [00:29:32] S0: I I ultimately genuinely believe that more than 50% of the GDP will be contributed by what you can call agenetic applications in the next few decades. Yeah. I actually think it'll happen faster than the next few decades. [00:29:45] S1: Do you? Yeah. Because, again, Daniel. [00:29:48] S0: Daniel Hot on take. [00:29:49] S1: Yeah. Daniel on the show was like, listen. [00:29:50] S0: Put me up against Daniel. Yeah. What? [00:29:52] S1: He's my neighbor, so he's literally next door. So we can arrange that. But he said that we consistently underestimate how long it takes for enterprises to adopt new technologies, to get comfortable with data security, to get comfortable with processes. Is that right, or do you think actually we are past the tipping point?