The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:43:58 · Daniel Dines, Harry Stebbings
[00:43:58] S0: Harry, you you should read my book, my friend, because it answers When exactly the same the frame that a person operate is really well defined by someone else, like in law, AI can be devastating in the impact. When the frame is not as clear, and there are so many exceptions that are custom made for an enterprise [00:44:22] S1: Dude, you're about to spend two days with a lawyer who is my girlfriend. She will tell you that law is highly ambiguous, subjective in terms of writing styles. She would [00:44:32] S0: only the human language, and AI understands it perfectly, and it understands every freaking nuance of human kind of sensitivities. As long as it's documented and it's well defined, there is a manual for the freaking law, AI is amazing. When there is manual, AI is not amazing, and [00:44:53] S1: it doesn't work. That's the huge difference. But $300,000,000,000 is the legal industry in The US. It's a lot. If you think about how much labor could be replaced by that, I think 30% would be reasonable. That would be $90,000,000,000 of available revenue. [00:45:10] S0: Yeah. But that's not gonna convert in token revenue. Maybe out of 90,000,000,000, companies might charge maybe 10%. Maybe it's 10,000,000,000 total opportunity in tokens. [00:45:22] S1: Am I mistaken to invest in Legora and other Harvey investors mistaken to invest in Harvey if it's 10,000,000,000, not 90,000,000,000?
00:45:22 · Harry Stebbings, Daniel Dines
[00:45:22] S1: Am I mistaken to invest in Legora and other Harvey investors mistaken to invest in Harvey if it's 10,000,000,000, not 90,000,000,000? [00:45:31] S0: What I can tell you that from a law perspective, open source models, frontier models will do just fine. Maybe they do also the custom workflows around the legal process, which is really valuable. But to me, that's also, you know, a big part of my thesis. Models are interchangeable, but the workflow, the map of work and the workflows around the map of work is where the real value is. If Harvey and Laguerre are doing this, they map really the work, they create the workflow and they create the legal department for me. Of course, it's a much bigger value that they capture. But if it's only to get the legal opinion, a call to a model, that's not gonna be a $100,000,000,000 market. 100%, man. [00:46:19] S1: What percent of token traffic do you think will go through open versus closed models in twelve months? [00:46:25] S0: To me, I think the question is different. What percentage of the traffic will go to truly frontier model like Astra or Fable versus a very cost efficient models. For enterprise work, my prediction is that 90% of the flow will go to very cost efficient models. I don't think you need frontier level quality of models for most operational work. Just to [00:46:54] S1: be clear then, so we will actually still use the core provider, which is OpenAI and Anthropic. It'll just be deprecated older models.
00:46:54 · Harry Stebbings, Daniel Dines
[00:46:54] S1: be clear then, so we will actually still use the core provider, which is OpenAI and Anthropic. It'll just be deprecated older models. [00:47:01] S0: I will still use Anthropic and OpenAI AI with their cost efficient model, but I will have a verifiable backup on open source all the time. I should be... As a responsible enterprise, I should be able to switch models. I cannot be locked in. [00:47:19] S1: Can I ask you, we invested in? I'm just checking my portfolio against your brain. I believe strongly in open models, and I think that every company, not every company, but mid to large scale company will have their own model, own their own intelligence, and feed their own data into it. And, you know, I think it goes to the statement of kind of owning your own intelligence, not renting it. That's why we invested in fireworks, and I believe in the open model ecosystem. [00:47:41] S0: You know, I'm a big fan of fireworks, and we are using them quite a bit. [00:47:46] S1: Do you like
00:47:46 · Harry Stebbings, Daniel Dines
[00:47:46] S1: Do you like [00:47:46] S0: them? Yes. We like them a lot. And I'm I'm a big believer that an enterprise should distribute their bets, and one of the bets should be on open source and very importantly, should be on this map of work. Because think about, if I wanna train a model, my own model with who am I, I need to have this who am I very well documented. I need to create this manual because I'm training one model today. But in next two months, there is another better model, base model coming into the picture. How can I do transfer learning from my old model into the new model if I don't have the data and the exact menu? I cannot. Well, there are, you know, terrible losses when I do this. The real investment for an enterprise is to creating this map of work that documents how they actually work. And with this one, they can train their own models. It's in fireworks or other provider, doesn't matter. But this is their IP, and this is their core data. Makes sense? [00:48:48] S1: And so you do, sorry, just so I understand, so you do believe that companies, and a lot of them, will have their own models with their own data?
00:48:48 · Harry Stebbings, Daniel Dines
[00:48:48] S1: And so you do, sorry, just so I understand, so you do believe that companies, and a lot of them, will have their own models with their own data? [00:48:55] S0: I do believe that they will at least have their own models as a backup to frontier models. To me, where I'm not clear, if I can provide the same cost efficiency with my own model versus a cost efficient model from Anthropic and OpenAI. Because I think these guys are in a position to truly optimize large infrastructure. So part of their business model will be to deliver, you know, more intelligence per dollar than even I can squeeze from my own models. [00:49:28] S1: If you have highly specific data that is exact to the request that you have, which is your data, I think you'll get more token efficiency with your own model than you would an optimised model. [00:49:41] S0: Only if you are training your models, that might be true. And only if you can deliver... If Fireworks can deliver at a large scale and in a very optimized way. [00:49:52] S1: Would you invest in Fireworks at $15,000,000,000? [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.
00:49:55 · 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.