The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:49:55 · Daniel Dines, Harry Stebbings
[00:49:55] S0: Probably, yes. If this hypothesis of open model is true, which I believe is true, I think they are under... Undervalued. I think they will have to get very soon in this big game of securing compute. Because if they don't secure compute, I don't understand how can they give me the inference at the scale that I want. What do you think? Because you invested in them. [00:50:18] S1: I did. I think you're absolutely right that they need to move into the compute layer, and I think Lin is doing that, I'm sure, very soon. [00:50:23] S0: So they will have to raise, you know, Tensor will be the last guy. [00:50:27] S1: Yeah. And she did that at Facebook. I think that's a unique thing to this team. They did compute securing. [00:50:34] S0: Yeah. It's a great team. We really like them. [00:50:36] S1: Great fucking team. [00:50:37] S0: And we worked with them before the big hype. [00:50:39] S1: I also think the data providers are massively underpriced and underappreciated. Macore and Surge in particular. Everyone's like, oh, they're commodities. You're just buying data. Data is the most important thing to feed model quality.
00:50:39 · Harry Stebbings, Daniel Dines
[00:50:39] S1: I also think the data providers are massively underpriced and underappreciated. Macore and Surge in particular. Everyone's like, oh, they're commodities. You're just buying data. Data is the most important thing to feed model quality. [00:50:54] S0: But what's the difference? Think one thing is storage, and one thing is understanding of the data. Because if I have a storage, I can have a tape, and I can put data on the tape. Would we invest in a tape company? I don't think so. You need to invest in the intelligence that understands the data and feed the model and, you know, extract the right data at the right time, feed feed really the model with the data that is needed with the context. Because if you don't have if you have just data, but you don't have a way to create a really good context to give the model when they ask something, it's useless. I I think you would say that they have more data than anyone else across more categories than anyone else. So when the model requests highly specific data, because of the breadth [00:51:38] S1: of their library, they're able to provide it in a way that others aren't. [00:51:41] S0: If it's their own data and is valuable for models, I'm sure the models will buy will buy that data in an instant. [00:51:48] S1: Can I ask you, what have you changed your mind on most in the last twelve months?
00:51:52 · Daniel Dines
[00:51:52] S0: I didn't understand this necessity to have a manual in order to work. That was maybe the biggest breakthrough in my understanding. That every time I'm running a query towards AI, AI should have at their disposition the entire way, you know, my company work or this particular process work. When I realized this, I understood also that this thing that it's the the biggest differential between memory and true learning. This is how we started the discussion. And I'm not sure I really make a point, but it's a huge difference between just laying down, having a scratch pad, or being transformed by an experience. And that's the thing that I realized the most. And I think I realized also what's kind of human for us Because I experienced a lot with AI writing, not code, but writing a book. So I've been through different styles. I I understood a lot how to prompt them. AI doesn't have a style. And you you realize why they don't have a style, because they are an averager of anything. In order to have a style, you need to have kind of a body. You need to have individuality, because we are the choice that we made and the choice that we don't make in a sense. So you need to be transformed because otherwise, I will just ask AI, read this book and write in the spirit of this author. And it's not really working because you need to be transformed by the experience. So to me, this is gonna be the biggest breakthrough in the AI technology when I can have models at the size of mythos being transformed on a job, being, you know, putting in a laptop. And it might be possible, who knows, you know, the pace of technology. Maybe twenty years from now, I can have a 10,000,000,000,000 model that is my own model and is getting transformed along with me. But we need to see. I think there might be a few series of innovations to get there. Because I wanna give you also an interesting data point. AI is solving very interesting math problems that humans didn't solve before right now. But AI still, it's not capable of creating frameworks. I don't know. Relativity is a framework. Okay? And I was thinking, why so? I think one of the main reason relates still with this not being transformed when you are on the job. When I'm writing a book, I am being transformed by the act of writing this book.
00:54:30 · Daniel Dines, Harry Stebbings
Every time I'm writing something down, there is something in me that changes that is not necessarily the memory thing. It's me that is changing. Einstein has been changed by his experience thinking about the speed of light of what happens when you go, you know, behind the light. It's not like Einstein wrote it down. And then every time he fought again, he rewrote a piece of paper. No. He became gradually, you know, a different Einstein than the one that started to think of a problem when he created this thinking, this frame of relativity. Models don't do this way. Even if I put swarm of agents, everything, they have to write down everything. They are not being transformed by the process. So therefore, it's very difficult. In the end, they will have this context, the 1,000,000. It's very hard to go beyond this 1,000,000 tokens context window. A frame might require a transformation as you work on that frame. It's a it's a different way of learning than pure memory. So that's... I wanna make this argument as clear as possible. [00:55:42] S1: Are you optimistic for your children? [00:55:44] S0: I'm extremely optimistic for myself, Fari, therefore for my children. I don't wanna sound like an AI doomer, man, because I I believe I don't think you do. I am. [00:55:55] S1: No. I don't think you do. I sound like a doomer in a way. I I think we'll have a lot more job loss. I think it will happen a lot quicker. I think we're seeing it in real time.
00:55:55 · Harry Stebbings, Daniel Dines
[00:55:55] S1: No. I don't think you do. I sound like a doomer in a way. I I think we'll have a lot more job loss. I think it will happen a lot quicker. I think we're seeing it in real time. [00:56:03] S0: I'm much more optimistic that we won't have so much. Because based on my own experience with AI, I don't think the diffusion is as fast as you imagine, particularly because enterprise have to document in much greater detail their processes. [00:56:18] S1: Can I just ask, sorry, we're both Europeans, and we're both sitting in London? I don't know how to say this, but we don't matter anymore, just being blunt. Do you think that gets better or worse in the next three to five years? [00:56:31] S0: Yes, man. It's hard to admit the reality, but from a technology standpoint, I think we are largely irrelevant. But it's so stupid because the biggest producer of machines that make chips is based in Europe. It's ASML. Yeah. We could have made these chips in Europe. You know, some of the most brilliant minds that build AI, even if you think Dario, Sam, all of them are European origins. Ilya, we had the talent. We have the technology to build the machines, but somehow we are losing it. And it's very stupid. [00:57:07] S1: Do you see a difference in work ethic having a team in The US and The UK? [00:57:11] S0: Yes. I I experience with teams in UK, and at 5PM, they are all, you know, in the pub. [00:57:18] S1: Why is that? Because money matters more?
00:57:18 · Harry Stebbings, Daniel Dines
[00:57:18] S1: Why is that? Because money matters more? [00:57:21] S0: I think culture matters more than money. It's a more dynamic culture. I don't think I would have succeed in Europe the way I did in US. So I... I'm an European, but as an entrepreneur, I am... I'm American. This is what I told to everybody. So I... My formation is an American school of entrepreneurship even if I started my company in year. [00:57:44] S1: Listen, I I get it. And you look at Legora and you look at 11 and some of the best companies to come out of Europe in the last few years. And if you think the revenue machine is anywhere but in America, you're lying to yourself. [00:57:55] S0: It's an easier to access revenue machine than in Europe, clearly. [00:58:00] S1: Faster. It's easier. The teams have scaled GTM functions before. Completely agree with you. [00:58:06] S0: And American companies are making larger bets on vision without waiting for so many proof points as the European companies. And even people in the middle management can make sizable million dollar bets on new technologies in US. I haven't seen this appetite in Europe. [00:58:28] S1: I don't want to ask this, but I am interested. If you were to advise a young European entrepreneur today, would you say to go to The US?