The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:16:04 · Harry Stebbings, Brendan Foody
[00:16:04] S1: I buy that completely, what I don't buy is the speed of transition. And what I mean by that is when you look at industrial revolution, agricultural revolution, it took multi decade cycles to implement and train new technologies to do what humans did. Yeah. Now with Nano Banana Pro, I can get rid of all designers in my media company pretty much overnight. [00:16:23] S0: Well, the thing I agree with you is about displacement. I agree there's gonna be a very significant amount of displacement, but I also think that the economy is becoming much at creating new job categories and allocating new labor. Like a great example is what we do in that now we're paying out over $3,000,000 a day in the fastest job category ever created in history. And I expect that's going to continue growing exponentially from here. And I think that there's going to be so many new job categories created across everything within AI, such as training agents for deployed engineering, building data centers, all the way to all of the problems that we otherwise wouldn't have been able to address as a society. Like how do we build solutions to climate change? How do we have more people working on rockets to explore space? Totally get you. You said 3,000,000 per day pay down? What is that in twelve months' time? In twelve months' time, that's probably about triple that. [00:17:22] S1: 9,000,000? Do you think you're being ambitious enough? Maybe it's quadruple that.
00:17:22 · Harry Stebbings, Brendan Foody
[00:17:22] S1: 9,000,000? Do you think you're being ambitious enough? Maybe it's quadruple that. [00:17:27] S0: We internal projections that are always much more aggressive than our external projections, but we almost doubled our projections last year. [00:17:36] S1: What new role will we have in five years that does not exist today? [00:17:41] S0: One of the largest things that people underestimate, both in the context of AI Labs as well as within the enterprise, is how significant of a job category it is gonna be to train agents. Like what we're seeing is that all knowledge work is converging on training agents because it is structurally more efficient to do something once. Instead of having a customer support representative that is redundantly responding to hundreds of tickets, they're going to train an agent how to do that once. Instead of having a lawyer that is redundantly doing dozens of similar red lines on commercial contracts, they're going to train an agent how to automate that. And even probably when you're playing around with Claude, you see that there's so many repetitive workflows of how you prepare for a meeting or draft emails or whatever it is, where it's just much more efficient for you to train the agent how to do that activity so that you can amortize that over the entire useful life cycle rather than doing it redundantly yourself. And so I think that there's going to be this enormous paradigm shift as agents enter the workforce and everyone begins to manage them.
00:17:41 · Brendan Foody, Harry Stebbings
[00:17:41] S0: One of the largest things that people underestimate, both in the context of AI Labs as well as within the enterprise, is how significant of a job category it is gonna be to train agents. Like what we're seeing is that all knowledge work is converging on training agents because it is structurally more efficient to do something once. Instead of having a customer support representative that is redundantly responding to hundreds of tickets, they're going to train an agent how to do that once. Instead of having a lawyer that is redundantly doing dozens of similar red lines on commercial contracts, they're going to train an agent how to automate that. And even probably when you're playing around with Claude, you see that there's so many repetitive workflows of how you prepare for a meeting or draft emails or whatever it is, where it's just much more efficient for you to train the agent how to do that activity so that you can amortize that over the entire useful life cycle rather than doing it redundantly yourself. And so I think that there's going to be this enormous paradigm shift as agents enter the workforce and everyone begins to manage them. [00:18:44] S1: Can I ask you, when we think about that enterprise adoption, I think one of the biggest problems that we have is data structures and data cleanliness? I interviewed a guest the other day and they said we'll have like data cleaner as one of the most important jobs in the next five years. Is data structure and data cleanliness the biggest barrier to enterprise adoption?
00:18:44 · Harry Stebbings, Brendan Foody
[00:18:44] S1: Can I ask you, when we think about that enterprise adoption, I think one of the biggest problems that we have is data structures and data cleanliness? I interviewed a guest the other day and they said we'll have like data cleaner as one of the most important jobs in the next five years. Is data structure and data cleanliness the biggest barrier to enterprise adoption? [00:19:01] S0: Well, I agree in part. I think that certainly the models need to have access to data to perform their jobs effectively. But the caveat is that they'll be able to clean the data themselves fairly effectively as reasoning capabilities go up. The thing that humans will need to contribute to is all of the tacit knowledge within the organization that isn't written down. Because I found that when I try to get agents to do all of these workflows throughout Merkur, there's just an enormous amount of context that lives in people's heads that the agents need to have access to perform effectively. And so much of that is going to be the new job of employees of how do we codify all of this knowledge? How do we train agents so that they're able to perform these tasks effectively across every function in the organization? [00:19:48] S1: I'm sorry for digging down, but you said reasoning capabilities will allow enterprises to clean data more efficiently. Why?
00:19:48 · Harry Stebbings, Brendan Foody
[00:19:48] S1: I'm sorry for digging down, but you said reasoning capabilities will allow enterprises to clean data more efficiently. Why? [00:19:54] S0: Well, the reason is that if a model is able to, for example, read through every message written in Slack over the last six months, the model can presumably structure a table of here are all the different customer conversations that happened in the CRM, etcetera. And so I don't expect humans to be doing like that type of stuff of how do we structure data, how do we classify it, etcetera. But I do think that humans will do the things that models inherently can't do, such as the test of knowledge. [00:20:24] S1: When we look at the market for being a data provider to some of the largest models in the world, it's such a large market that you're seeing the unbundling of it into such verticals. I met the other day a medical, real world medical data provider to them, where basically they have surgeons that kind of, I don't know, have video cameras on and they record all the real world data. Do we see the mass unbundling of the data providing market? Is that how
00:20:48 · Brendan Foody
[00:20:48] S0: it plays out? It's interesting. We're doing a ton of data collection in the physical world as well, especially across skilled domains, where you have electricians and mechanics and scientists strapping cameras to their head to record things. I think that there's always going to be some degree of value in some of these niche vendors that are able to go really deep in a specific vertical. But what we're finding is that there's enormous value to aggregation and economies of scale, and that when we have this talent network of over 5,000,000 people that are able to refer their friends, it's just so much easier for us to find the marginal doctor, because we have that enormous talent network that can refer us to their friends. And even more importantly, that the kind of data shapes that we would build for a lawyer are often very similar to the kinds of data shapes that we would build for a doctor. And so all of the tooling that we build is very, very cross applicable, and that's the way that most labs have been scaling out their data quite horizontally. And so for that reason, we are finding that the labs tend to prefer partnering with a very horizontally capable vendor that is able to flex across all of the different verticals and scale extremely quickly, rather than working with 100 different vendors that they have to train for the same data shape in 100 different domains.