The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:14:02 · Harry Stebbings, Brendan Foody
[00:14:02] S2: Dude, I had the founder of Cohere on the show the other day, and he said that we are absolutely seeing the reaching of scaling laws being questioned and that GPT five focusing on efficiency really is an embodiment of that. Do you agree that we're hitting scaling laws being achieved and we're reaching a period of plateauing, so to speak, in terms of progression? [00:14:22] S0: I don't think that models are plateauing. Like, if we look at the last twelve months of progress in models, I've been blown away. To his point, we're definitely seeing a difference in the way that people improve model capabilities and that it's no longer shoveling a lot of low caliber medium scale data into the model. Right? It's much more these curated datasets with extremely high caliber people that are built in a thoughtful way. And I think that that transition towards our own environments and all this, like, high complexity data has been one of the most important things underpinning the trajectory of Merkor. [00:15:01] S2: When we think about the supply side of that data, you're obviously one of the providers. There are many providers now, it would seem, including your Turing's, your Handshakes, and surges. And how do you differentiate on the supply side of data in this way?
00:15:01 · Harry Stebbings, Brendan Foody
[00:15:01] S2: When we think about the supply side of that data, you're obviously one of the providers. There are many providers now, it would seem, including your Turing's, your Handshakes, and surges. And how do you differentiate on the supply side of data in this way? [00:15:15] S0: We saw the market shifting dramatically away from crowdsourcing towards sourcing and vetting. And once this happened, there were all these other labor marketplaces that caught on to that transition. Right? They saw our growth, and they wanted to chase after that saying the same things in podcasts and trying to position themselves in a similar way. But I think one of the largest things we've realized is that the outcomes of data, and the people that contribute to it are extremely power law, similar to a company where if you have 100 people on a project, oftentimes, majority of the model improvement is coming from the top 10 to 20% of people, right, just like sort of majority of the value in a company will often come from the top 10 to 20% of people. And what that means is that when we're able to build proprietary advantages in the way that we have not only our supply base in the referral network to access them, but also the way that we match those experts with the opportunities where they're going to do phenomenal work, it creates so much value for customers, that it's extremely difficult to compete against, right? When we're able to find those people that are the 10x contributors, it's very difficult to recreate.
00:16:28 · Harry Stebbings, Brendan Foody
[00:16:28] S2: A lot of people have cited a criticism of the space being they're very good at facilitation, but not great at measuring the efficiency of the data that's produced. The challenges of being first on a show is you say all the quotes, and then I can use them. Edwin said that none of the competitors have algorithms to measure the quality of the data that they're producing. Is that right? [00:16:48] S0: That's not true at all. In fact, we use all sorts of models and algorithms to assess the quality. We train on data to see how it's improving model capabilities. And we do function as a deep research partner to our customers. I think the difference is that I think about our business as at the intersection of labor marketplaces and AI research. And how do we leverage our core competency in finding world class people and pair that with the fact that we work with all of the top research labs at the frontier of model capabilities. And we're not like the crowdsourcing companies, and that we try to hide all of the people on the platform, pay them low rates, etcetera. [00:17:29] S2: One of my friends is on the board of one of your competitors. And they said that labs are incentivized to ensure that no one company dominates. And so they intentionally spread business around to ensure no one becomes too powerful. Is that true? And can you just help me understand that dynamic?
00:17:29 · Harry Stebbings, Brendan Foody
[00:17:29] S2: One of my friends is on the board of one of your competitors. And they said that labs are incentivized to ensure that no one company dominates. And so they intentionally spread business around to ensure no one becomes too powerful. Is that true? And can you just help me understand that dynamic? [00:17:45] S0: I think that that has definitely happened in some cases. But ultimately, the thing that labs care about the most is how do they improve model performance? How do they get those top 10 to 20% of people that are driving the vast majority of the model improvement? And so that's how their spend allocation and investments, you know, ultimately get allocated is what are the vendors and strategic partners that are able to deliver those outcomes? And how do they work as deeply as possible with those partners. And we've definitely found those stories of customers where I think they start out multi vendor and working with a bunch of different vendors, but ultimately get to the point where they realize that they're going to be making a trade off in the performance of their model and the performance of the data sets if they are trying to diversify too much and lean very significantly into moving almost all of their work to us. [00:18:38] S2: That's so interesting. So you expect like a multi vendor approach that then concentrates over time. Is that how you think about like spend?
00:18:38 · Harry Stebbings, Brendan Foody
[00:18:38] S2: That's so interesting. So you expect like a multi vendor approach that then concentrates over time. Is that how you think about like spend? [00:18:45] S0: I do. And if you look at a lot of the analogs and markets, they often start very fragmented with many different players, but consolidate over time. And so much of that reason for consolidation is that there's structural advantages and economies of scale to being the first player and having this fixed cost investment in having the best professionals in the world, the Goldman bankers, McKinsey analysts, the network associated with them, as well as all the matching infrastructure of understanding exactly what tasks and jobs are these people going to do well at. And so it doesn't make sense for so many different companies to be making those redundant investments. And I think that the market being hot is what gives a lot of those companies more funding and more fuel. But consolidation generally happens as markets come back to earth a little bit and levels up. [00:19:38] S2: One thing that I worry about often is concentration of revenue. You saw it with Nvidia, where I think it was like 51 of revenue was two clients in one certain segment of their business. I think it was 36% in another segment of their business. What was your largest customer in terms of concentration of your revenue?
00:19:38 · Harry Stebbings, Brendan Foody
[00:19:38] S2: One thing that I worry about often is concentration of revenue. You saw it with Nvidia, where I think it was like 51 of revenue was two clients in one certain segment of their business. I think it was 36% in another segment of their business. What was your largest customer in terms of concentration of your revenue? [00:19:54] S0: Our largest customer, I can't share the exact percentage, but the breakdown is relatively similar to Nvidia. And part of the reason is that concentration is relevant, but the high order bit is like building a phenomenal business that's creating a lot of value for the most important customers, right? And like, ultimately, NVIDIA is worth trillions of dollars. And some of the best empirical evidence that it's okay to have a business that leans into a handful of customers, especially when those customers are the best customers in the world. [00:20:26] S2: You don't understand, Brandon. I... I'm the Brit who basically takes incredibly talented Americans with insanely great businesses and then critiques them. It was when I said to Benioff the other day, Mark, the single digits growth is just not good enough. And Mark was like, dude, I have a $42,000,000,000 company. What do you have? And I'm like, you know, that's a very fair response. That's... They... I I I think you're right to respond with that. Can I ask you, when scale got bored, did your phone just go off the hook? Did demand just go through the roof?