"Turpentine VC" | Venture Capital and Investing
00:20:18 · Usman Hanif, Brendan Foody
[00:20:18] S1: I see. What would you say was the most challenging part when it came to building this technology? I mean, it could either be one of these specific features, whether it be the AI interviewer or the resume analyzer, or maybe it was just at a broader level, maybe data collection, like what was really hard to get right for this to work? [00:20:38] S2: Yeah, I think the largest challenge, which will continue to be the largest challenge for probably a long, long time in the business is in predicting who's going to perform well, for what reasons. It's just like such a challenging problem, because humans aren't very good at it. And there's so many problems within that of collecting the right data, data is like super subjective, and you want to be very thoughtful about, you know, minimizing bias and having peer review on all of that data and having experts that have deep expertise in the domains that the person is discussing, all the way to model architectures to, you know, different approaches and training them. And just optimizing that model to be as predictive as possible is, I think, the most interesting, as well as the most challenging problem in the business.
00:20:38 · Brendan Foody, Usman Hanif
[00:20:38] S2: Yeah, I think the largest challenge, which will continue to be the largest challenge for probably a long, long time in the business is in predicting who's going to perform well, for what reasons. It's just like such a challenging problem, because humans aren't very good at it. And there's so many problems within that of collecting the right data, data is like super subjective, and you want to be very thoughtful about, you know, minimizing bias and having peer review on all of that data and having experts that have deep expertise in the domains that the person is discussing, all the way to model architectures to, you know, different approaches and training them. And just optimizing that model to be as predictive as possible is, I think, the most interesting, as well as the most challenging problem in the business. [00:21:31] S1: And and what would you even start to lay out as potentially a moat? I assume that maybe some of these other players that we previously mentioned might be thinking of, hey, we should maybe use some AI here as well. I I believe I've heard maybe a couple other startups have at least looked at the space, maybe none with as much traction as you guys. But how do you think about that, like, defensibility?
00:21:55 · Brendan Foody
[00:21:55] S2: Yeah, so there's two modes that come with a business. And then I'll give a spiel about like my broader take on on why labor labor aggregation is such a large opportunity. The first one is the marketplace, which is that when you're facilitating transactions, and you build up a large amount of supply side and a large amount of demand side, there's network effects in, you know, strengthening each other. And so as we get larger and larger, that makes facilitating transactions even easier. And it makes it even more challenging for our competitors. The second mode is around a usage data flywheel, where we're able to learn from not all but most of our customer interactions to predict based on you know, the previous 1,000 roles that we placed, who is likely to perform well at the next role. And that can be really, really powerful, especially as we start reaching for placing millions of candidates. Because if others don't have that performance data that's structured in the right way to train an LLM, then they're not going to be even close to competing on the same dimension. I think like at a high level, the reason that I'm so interested in the labor market is that it's the largest market in the world and also the most inefficient. And I really strongly believe that building the infrastructure to collect proprietary data on everyone on earth and why they're a good fit for any role. And matching people with those opportunities is not only the largest opportunity in the economy, but also the most defensible one. Because it's predicated around really strong network effects and proprietary data to support them. And so I I think the defensibility of the business is actually one of the things that made me so excited about Merkor.
00:23:45 · Usman Hanif
[00:23:45] S1: And from the perspective of the employers who are using you guys, beyond just doing the vetting for them, which therein allows them to just, you know, in theory, see a lot more candidates, is there other things you offer on the platform that's attractive and makes them use you guys rather than other talent marketplaces?
00:24:04 · Brendan Foody, Usman Hanif
[00:24:04] S2: Yeah, so we also facilitate all of the payments and international compliance associated with these transactions. The reason that we do that is twofold. The first is that this is a solution customers want. They don't want to have to deal with all of the overhead of using different tools, onboarding people having their own documents, they just want to press a button and have that person ready to go on their team, which is the experience that we deliver for them. And the second thing is that through seeing the entire life of the transaction of how long a customer is working with a candidate, how that experience is going, it allows us to collect really valuable data. And that speaks to the performance data flywheel that we were talking about earlier. If we were to say, just be like LinkedIn, and, you know, have a candidate that a recruiter can search for and and not getting data beyond that, we wouldn't get the most important part of that transaction, which is the ability to predict how well a candidate is going to do. And so really, being with a customer through the complete lifecycle of seeing their problem and the initial steps and how the solution ends up playing out. Obviously, in this case, you know, is immensely important for the data that informs all elements. [00:25:21] S1: And for these companies, where does a lot of the cost effectiveness for them come from?
00:25:27 · Brendan Foody
[00:25:27] S2: Yeah, so reducing the reliance on recruiters is huge. Another big thing is allowing them to unlock a lot of undiscovered talent internationally. One thing we found is that many people like the idea of hiring someone in India or South America or Europe or wherever because of better price performance in terms of hiring a better engineer or, you know, technical project manager that can do a good job or work super hard and not cost as much money. But then they'll have experiences that are more mixed than people they might find in Silicon Valley. And our core belief is that there is incredible talent internationally, but it's not as dense as the talent you find in Silicon Valley. And when you're able to automate the process of assessing that talent, solving this talent density problem, then you're able to find the diamonds in the rough. You're able to find the people no one else is considering and just create incredible experiences for both sides. Like, can't tell you how many stories we've had of people we've hired from, you know, parts of India in particular, that are phenomenal engineers, honestly, like much better than many engineers we work with in Silicon Valley, aren't given opportunities, remotely in the same ballpark. And through allowing them to discover these opportunities, it changes their lives. And the idea of doing that for like a given individual is is really fulfilling. But the idea of scaling that for thousands or millions of people is even more meaningful.