The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:48:46 · Harry Stebbings, George Sivulka
[00:48:46] S1: Are you more excited in the post Trump? [00:48:48] S0: I think the thing that I care most about in the world is that we, as an industry, have very clear guardrails that we can follow and and understand to build the best possible tools to get our tools out to the economy to to make sure that everyone transitions in the best possible way. So I'm ultimately... Regardless not [00:49:06] S1: thrive on a better financial system, and we're seeing now a financial system in The US from afar that would seem to be thriving. Objectively Yeah. It would appear that Trump is good for business. [00:49:16] S0: I I won't make a comment here. I I I think that there's a lot [00:49:22] S1: funny. Think it went very viral before, the election because they said it's so interesting. [00:49:27] S0: There's [00:49:27] S1: 99% of CEOs come on the show, and they either shut up or they say they vote for Camilla, and then it ends and then it ends. And they're like, by the way, I'm so Trump. I am so Trump, but it's fascinating. [00:49:41] S0: Yeah. For sure. For sure. [00:49:43] S1: So I totally understand they're not answering. Yeah. You are not alone. It's okay. But the one question I wanna ask, you mentioned NVIDIA before. Sure. That's a really big question around their ability to to sustain that monopoly. You've seen Google. You've seen Matter. You've seen Amazon all wanna move into the chip play. Yep. How do you think about NVIDIA's ability to sustain their pretty unwavering monopoly so far?
00:50:05 · George Sivulka, Harry Stebbings
[00:50:05] S0: So so NVIDIA has, you know, I think the the the best moats aren't technological moats. They're not data moats. They're actually people moats. People and networks have the most friction to change. One of the things that NVIDIA does best is is is the fact that they made this early bet on machine learning. They created CUDA, which is the way that, as I mentioned before, almost everyone learns how to train models. Like, they learn how to, you know, how to interface with NVIDIA chips for training. And as you're starting to see, maybe that that prediction that I made earlier, the shift away from from training to inference as a fundamental, like, almost macro shift in how people deploy AI, actually I think that will destabilize slightly the dominance of NVIDIA chips. You can start to actually use AMD chips or even custom architectures, which which all the major model providers are also currently exploring to do inference. So you have your academics and your researchers, you know, training large models on NVIDIA chips, but the minute they deploy them, they can deploy them, on cheaper infrastructure. And that and that actually, I think it will be a big change. So I... I'm actually still bullish on NVIDIA, but I'm even more bullish on other chipmakers and and custom ASICs to do inference because I think there will be a larger shift to inference moving forward. [00:51:16] S1: Is that other chipmaker paradigm existing incumbents, Google, Meta, Amazon, you name it, or is it a new generation Cerebras style?
00:51:16 · Harry Stebbings, George Sivulka
[00:51:16] S1: Is that other chipmaker paradigm existing incumbents, Google, Meta, Amazon, you name it, or is it a new generation Cerebras style? [00:51:25] S0: Probably be large tech providers and and AMD. I I don't know about Intel. Right? I would probably bet on them. There's definitely an opportunity in the market, but chips are hard. [00:51:36] S1: Before we do a quick fire, do just wanna kind of resurface back up to the Asian layer? [00:51:40] S0: Sure. [00:51:40] S1: For sure. Are we out of the experimental budget phase? [00:51:43] S0: I think that 90% of the market is still in experimental budget phase, but we're starting to see early promises of actual value. And my entire business is focused on on just those repeatable use cases. [00:51:55] S1: Everyone thinks they're a master of agents and agentic workflows. What do they think they know that they actually don't know?
00:51:55 · Harry Stebbings, George Sivulka
[00:51:55] S1: Everyone thinks they're a master of agents and agentic workflows. What do they think they know that they actually don't know? [00:52:02] S0: Like, I think ultimately the people in the enterprise that are most excited about AI and, like, positioning it so strongly are CTOs and information technology people. And maybe the thing that we've... That Hebbe has always said is that the CTO or the IT folks are actually the people that know the least about the business. The people that that actually understand how to use AI in a business context are those that are closest to the business. And so we're jumping the gun a little bit with the CTOs trying to build the CRM before it's been invented. And, you know, you actually need business people to build the CRM and excel first in kind of order of operations. And so there's a lot of unbundling of AI applications or CTOs trying to go out and build, you know, a very specific, you you know, vertical application. But I actually think that building this platform, HebbiaMatrix, is the thing that will unlock users' ability to discover what they can use AI agents for. [00:52:53] S1: What will be the pricing mechanism for the future of agents?
00:52:53 · Harry Stebbings, George Sivulka
[00:52:53] S1: What will be the pricing mechanism for the future of agents? [00:52:57] S0: It's a good question. There's, like, four canonical price... There's, like, consumption based pricing, and there's per seat pricing. There's, like, hey. Rent a salary. So pay a salary for an employee, which is... Seems a little bit ridiculous, but will be less so. And then and then maybe there's, like, flat pricing. And and I think it it ultimately depends on how you're driving value. Because Hebia is building human centric AI, the human layer to how you orchestrate an AI agent staff, that that scaling at at inference, we do Perseat because it's ultimately always back to the human. I think you'll see all of these new business models and pricing mechanisms. [00:53:29] S1: Do you do Perseat because it's back to the human or just because it's what they know as a buying mechanism?
00:53:29 · Harry Stebbings, George Sivulka
[00:53:29] S1: Do you do Perseat because it's back to the human or just because it's what they know as a buying mechanism? [00:53:33] S0: I I actually think that we are human first. We're business user first to to the point where CTOs like to pay for consumption or API, etcetera. And, like, you know, business users like to pay per seat because it's how they how they map back to value. But also we wanna incentivize change. Tech is not the hard part of all of this. It's hard. But the hardest part of AI change management, no matter what company you are, are people and, like, actually getting people to use the software. When you charge for consumption or API pricing, you're disincentivizing the change. You're saying, okay. Well, I'm gonna penalize you in a monetary way for every time you use an AI application. What the heck? Versus here's a per seat fee. It might be expensive, but use it more. You could you could run more LLM calls on on Hebia effectively for free than any other platform if you if you actually are driving real chain, and that's what I love to see. [00:54:22] S1: Are you ready for a spicy round? [00:54:24] S0: Let's... Give me the spicy round. We got the tissues out here too. [00:54:26] S1: We got the tissues and tissues. Right? This is the... In in case you need them to hide behind. So this is a spice round. So this is questions from friends of yours. [00:54:35] S0: Okay. We got we got some changing colors up here. Love it.