The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:08:03 · Brendan Foody, Harry Stebbings
[00:08:03] S0: I'll start with the other story that I like to tell around my side hustle in high school, which tees up why I didn't want to go to college. But I initially was reselling sneakers as a lot of people my age would do in that generation. And I realized that all of these sneaker resellers were eligible for AWS credits, but they weren't claiming the AWS startup promotions. And they were instead just paying big AWS bills. And so I started a consulting agency where I would help the sneaker resellers create websites for their startups, them apply to get credits. And some of those became actually like venture scale companies. And I made hundreds of 1000s of dollars when I was in high school. And so when I, you know, was starting to think about whether I wanted to go to college, I was thinking, why would I go to college to get some job and like a Fang company or in consulting or whatever it is where I'm making way less money, I would love to just like, go full time on the things that I love doing. And so I had a big argument with my parents about whether or not I should go to college. And eventually I appeased them and applied to colleges like ten days before the application was due. [00:09:12] S2: How do you advise other young people stay on the value of college, given what you've seen and experienced now in reality?
00:09:12 · Harry Stebbings, Brendan Foody
[00:09:12] S2: How do you advise other young people stay on the value of college, given what you've seen and experienced now in reality? [00:09:19] S0: So much of the reason that college is no longer valuable from an educational standpoint is that that information is all available online. Like my parents preconceived notions is that they didn't have YouTube, they didn't have the internet and all of this access to information at their fingertips. And so they needed to learn it from professors, right versus for me, I listened to almost every Stanford GSB lecture when I was in high school. And I just like loved consuming information online and like listening to all of your podcasts, Harry. I've been doing that since I was little. And I think that AI only exaggerates that and making it easier to organize that information to understand it to learn. There's still value to college from a social standpoint. Like, I had a lot of fun, but I don't think there's too much value from an educational standpoint.
00:09:19 · Brendan Foody, Harry Stebbings
[00:09:19] S0: So much of the reason that college is no longer valuable from an educational standpoint is that that information is all available online. Like my parents preconceived notions is that they didn't have YouTube, they didn't have the internet and all of this access to information at their fingertips. And so they needed to learn it from professors, right versus for me, I listened to almost every Stanford GSB lecture when I was in high school. And I just like loved consuming information online and like listening to all of your podcasts, Harry. I've been doing that since I was little. And I think that AI only exaggerates that and making it easier to organize that information to understand it to learn. There's still value to college from a social standpoint. Like, I had a lot of fun, but I don't think there's too much value from an educational standpoint. [00:10:07] S2: Listen, I totally agree. I went to university for about four weeks before I dropped out. Oh, I didn't know that. Yeah, yeah, I went for four weeks and then a sponsor offered me a $100,000. And I went to my law professor and I was like, how much do you earn? And he was like, $82,000. I was like, great, I'm out of here. Like, this is not... And I hated law as well. Dude, I wanna start with something that Edwin said on the show. He said that everyone in the space is simply body shops. Is that a fair summarization of the space, or do you push back and say, Makor is not just a body shop?
00:10:43 · Brendan Foody
[00:10:43] S0: I don't think it's fair at all. I mean, we operate as close research partners to all of our customers and helping them to mobilize some of the highest caliber people in the world to push the frontier of model capabilities. But I think so much of our insight on the market is radically different and understanding how important high caliber people are, rather than leaving them out of the narrative. And I'll give the backstory of sort of how we really got involved in the market in the first place. Scale AI came to us, they used our platform to hire 1000s of people. And we realized that there was this enormous transition underway, moving away from this crowdsourcing paradigm that scale and search pioneered of how do you get low and medium skilled people that write barely grammatically correct sentences for early LMs, and very quickly moving towards this sourcing and vetting paradigm of how do you find the Goldman bankers, the McKinsey analysts, the FANG software engineers, the top doctors and lawyers that can work directly with researchers to help them build the highest complexity data on Earth and understand what that data is. Because when we were dealing with undergrad level math problems, it meant that the researchers could easily look at the math problem and understand why the model was making a mistake. But when we're dealing with the kind of work that a Goldman associate would do in their fifth year, it means that the researchers can't interpret the evals, and they can't interpret all of the data that they need to hill climb and ultimately improve model capabilities. And so it's really that trend around a different engagement model and higher caliber work that caused us to take off and really catalyze this meteoric growth.
00:12:23 · Harry Stebbings
[00:12:23] S2: If we extrapolate that out further and further with the advancement of models, your supply side becomes narrower and narrower if you think about it. As models become smarter and smarter, the ability to do what you do requires smarter and smarter people, and they are just by nature less and less. How does that evolve to its ultimate destination then as we run out of really smart people?
00:12:44 · Brendan Foody
[00:12:44] S0: The total addressable market is limited by the amount of things that humans are better at than models. And so I'll give an example that helps to contextualize this. I remember when we started working on a high complexity RL environment project, where the model would use one tool and interface with it in a task that would take a human a few hours to do. We started this became a famous product eventually, but we started out with 100 people. And it was easy to stump the model, it's easy to find mistakes that it was making. And over time, only 20 people could contribute to it, the exact dynamic that you're describing. But then we started adding other degrees of complexity of how do we get the model to use other tools, like accessing your Google Drive, like accessing your calendar, your Gmail, your Slack, all these different things. How do we get it to do the trajectories that, you know, human might spend ten hours, one hundred hours on, and all of a sudden, everyone else could contribute to the project again, because they could stump the model. And what it goes to show is that so long as there's things that the human is able to do, the model is not able to do. And we want those capabilities in the model, whether it's to schedule a meeting or write emails for you or whatever it is, we need humans that help to create those verifiers and help to measure that frontier to ultimately improve model capabilities.