The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:40:33 · Harry Stebbings, Daniel Dines
[00:40:33] S1: Of course. I mean, OpenAI doing Jalapeno and Anthropic are doing their [00:40:35] S0: own chips. Exactly. I don't think Janssen will be doing so well if they are the single biggest providers, and the source of truth and light of God will come from only Anthropic and OpenAI. So therefore, the open source should succeed. [00:40:51] S1: I absolutely agree, which is why I think Jensen is doing the open letter, which everyone signed [00:40:55] S0: Absolutely. [00:40:56] S1: Encouraging open source. [00:40:57] S0: And I think it was a great move buying Hugging Face. Hugging Face, yes. Why? Because I think it's... It encourages. It hosts all the open source model. It's putting the money where the money is for his company. Totally. [00:41:11] S1: It does also make a neutral provider not neutral anymore. Bias. Yeah. Obviously, have Neematron, and they have their own models now as well. You could say there's a loss of independence now that it's owned by NVIDIA. [00:41:23] S0: I think Nematron is still a small cog in the picture. I think it's valuable, but I would not call it... It's not the same cheap size as the others. [00:41:34] S1: Do you worry about the round tripping revenue? You know, everyone talks about NVIDIA investing here, buying here, the circular economy that come from Oracle and OpenAI. Do you think that's overblown?
00:41:34 · Harry Stebbings, Daniel Dines
[00:41:34] S1: Do you worry about the round tripping revenue? You know, everyone talks about NVIDIA investing here, buying here, the circular economy that come from Oracle and OpenAI. Do you think that's overblown? [00:41:45] S0: It can be because every every major infrastructure in history has been overbuilt. And I I think it's a simple explanation because I was thinking why every infrastructure is overbuilt. Because you have to to make sure you get, you know, biggest piece of the opportunity. If the opportunity is big, it doesn't it doesn't matter. You build a little bit more than it's necessary. So it's clearly that now everybody... That there is only 100% of the pie, and people are building right now 200 of the pie. There will be losers. [00:42:19] S1: Do you not think that this is the first innovation where we are significantly underbuilt? And, if you look at the constraints now, you're you're right. Actually, in prior technology cycles, we overbuilt supply side and demand side was lagging behind. Now we have energy that's being a massive constraint. We have water. We have data centers. We have regulation and policy. We have a significant hindrance to supply side, and we are underbuilt, not overbuilt, which is why every ounce of compute is taken.
00:42:19 · Harry Stebbings, Daniel Dines
[00:42:19] S1: Do you not think that this is the first innovation where we are significantly underbuilt? And, if you look at the constraints now, you're you're right. Actually, in prior technology cycles, we overbuilt supply side and demand side was lagging behind. Now we have energy that's being a massive constraint. We have water. We have data centers. We have regulation and policy. We have a significant hindrance to supply side, and we are underbuilt, not overbuilt, which is why every ounce of compute is taken. [00:42:47] S0: Yes. But are we underbuilt to the extent of this trillions that comes into the infrastructure? I don't know how to answer this. Everything happens on the premise that AI is gonna replace human work in a really large scale. We need to see the timing of this replacement and transformation. It's a big difference if it's coming in ten years versus next two years. So I don't think it's overbuilt for next decade, but it might be overbuilt for the next three years. And stock markets and capital, you know, can be moreciless.
00:42:47 · Daniel Dines, Harry Stebbings
[00:42:47] S0: Yes. But are we underbuilt to the extent of this trillions that comes into the infrastructure? I don't know how to answer this. Everything happens on the premise that AI is gonna replace human work in a really large scale. We need to see the timing of this replacement and transformation. It's a big difference if it's coming in ten years versus next two years. So I don't think it's overbuilt for next decade, but it might be overbuilt for the next three years. And stock markets and capital, you know, can be moreciless. [00:43:23] S1: Maybe I'm a childish optimist, but I saw, you know, Andre Kapathi say that he used coding tools for 20% of the work. And then six months later, he said that it did 80% of the work, and he helped it with 20%. We're investors in Legora. I interviewed lawyers when we did that deal, and they all said to me, you're such tech bros. You think you can replace us? We went to law school. And I was like, okay, cool. I interviewed them two weeks ago. Every single one of 15 said they would be severely unhappy if it was taken away, with most of them saying they hadn't written a document in six months. [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
00:43:58 · 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.