"Turpentine VC" | Venture Capital and Investing
00:00:02 · Erik Torenberg, Usman Hanif, Brendan Foody
[00:00:02] S0: Welcome back to Turbantine VC, the podcast where we discuss the art and science of building successful venture firms, VC to VC. This week, we're airing something a bit different in light of a significant $2,000,000,000 valuation announcement from Merkor backed by Felicius, GC, and Benchmark. Six months ago, Turpentine's show one to 100 host Usman Hanif interviewed Brendan Fudi, Merkor's CEO. At that point, the company had grown from zero to mid 7 figure revenue in just one year. This interview is a glimpse into the foundations on which the company is scaling and the massive market opportunity Merkor is going after. Please enjoy and check out one to 100, which also features similar interviews with the CEOs of Brain Trust, Foundry, Clay, and Modal. [00:00:48] S1: Brendan, thank you so much for doing this. [00:00:51] S2: Thank you so much for having me, Usman. I'm excited about the conversation and have listened to a few of your episodes. So so thrilled to have a conversation. [00:00:59] S1: Awesome. Well, for starters, you guys have gone from zero to mid 7 figures in revenue in a year. Clearly, you have something that people want. For listeners who may be unfamiliar with what that is, I'd love for you to explain what is it that you guys have built that people are dying for.
00:00:59 · Usman Hanif, Brendan Foody
[00:00:59] S1: Awesome. Well, for starters, you guys have gone from zero to mid 7 figures in revenue in a year. Clearly, you have something that people want. For listeners who may be unfamiliar with what that is, I'd love for you to explain what is it that you guys have built that people are dying for. [00:01:16] S2: Yeah. So a basic way of thinking about the business is that there's this huge industry around staffing or where people will manually review resumes, manually conduct interviews, and manually match people with jobs. And Merkur builds and trains models that automate all of those processes. Reviewing resumes, just like how humans can go about doing that, joining a Zoom room and conducting interview that's far more in-depth than a human is even able to do. And our most important technology is in facilitating the matching process, where we train very large models that understand all of the characteristics that predict why someone is likely to do well at a given job or task. And an interesting application of this technology is that many of our clients are hiring high volumes of expert contractors to build out human data teams, leveraging all of this infrastructure to better find the best experts in the world. [00:02:14] S1: I see. And so concretely, if someone was to log on to Merkur's website, what does the experience look like from the candidate's perspective, and then from the perspective of the employers?
00:02:14 · Usman Hanif, Brendan Foody
[00:02:14] S1: I see. And so concretely, if someone was to log on to Merkur's website, what does the experience look like from the candidate's perspective, and then from the perspective of the employers? [00:02:25] S2: Yeah, so they can go on to work.mercur.com as a candidate where they upload their resume, they'll join an interview where they can talk to an AI that will ask them questions about their projects about the nuances of their work experience, they'll get feedback on those and then get potentially matched with a job. On the client side, they can go to team.merkur.com. And they can query for any role that they want to hire for where we have an AI powered search and evaluation system that will recommend the candidates that have applied to our talent pool and are the best fit. So we have over 300,000 candidates that have gone through that process on the applicant side and can surface all of the best profiles for employers and companies that are looking to hire. [00:03:13] S1: Awesome. And so what do you think it is about your guys' experience that makes it 10x better than, say, other competition? Or what companies are used to doing in the status quo? Is it speed, quality, user experience? You guys have a good amount of traction and some large companies using you? Why are they picking you?
00:03:13 · Usman Hanif, Brendan Foody
[00:03:13] S1: Awesome. And so what do you think it is about your guys' experience that makes it 10x better than, say, other competition? Or what companies are used to doing in the status quo? Is it speed, quality, user experience? You guys have a good amount of traction and some large companies using you? Why are they picking you? [00:03:34] S2: Yeah, so the dimensions that customers tend to care about the most are speed, quality of the candidate that you're surfacing, and then also the price. And all of these dimensions are usually constrained by manual processes. When you're hiring a software engineer remotely, you might need to conduct, you know, a dozen interviews to find a candidate that's good. And even then, it might have not been a good pool to start with. And the candidate might not have been exactly what you were looking for. But when all of a sudden, you're able to leverage technology to assess and interview hundreds of thousands of people at the cost of software, you're able to find candidates that are far more relevant, you're able to have candidates that will start immediately tomorrow and achieve that all in a cost structure that is dramatically lower than what people would ordinarily find with human processes. [00:04:28] S1: And so when we're talking human processes, you're referring to actually hiring recruiters who sit and you know, browse through different talent networks, say LinkedIn and reach out, do initial phone screens and interviews. Is that what you're referring to?
00:04:28 · Usman Hanif, Brendan Foody
[00:04:28] S1: And so when we're talking human processes, you're referring to actually hiring recruiters who sit and you know, browse through different talent networks, say LinkedIn and reach out, do initial phone screens and interviews. Is that what you're referring to? [00:04:45] S2: Yeah, that's part of it. But also even internal human processes, right? Right? Like most founders, I in building a company and hiring people have to go through a huge volume of manual manually reviewing resumes and manually conducting interviews. And certain elements of that are awesome and need to be maintained such as the human connection of making sure that you really want to work with someone. But the unfortunate consequence is that lots of people that are an extremely good fit for a given role might not be considered. And there's not the right sort of wide net that's cast on evaluating candidates, which becomes possible when you're able to lower the cost of assessing each new candidate to the cost of software. [00:05:33] S1: Yeah. So I completely see it from the perspective of if you're doing this at a human level, the net definitely is smaller for what you can manage. But what about in terms of, you know, other preexisting talent marketplaces with pre vetted professionals? I'm thinking like Toptal, Turing, you know, both, at least I've seen to be pretty popular for contract software engineers at least. How do you guys stand out from, say, some of those players?
00:06:01 · Brendan Foody
[00:06:01] S2: Yeah, so I'll start with Toptal. And then we can get into Turing. Toptal started really as an arbitrage on Eastern European developers, where they would hire, they would hire people there, they would manually interview them manually match people with jobs. And it was a much better experience than Upwork, which they were competing against at the time because they could assure a certain level of quality. The flip side of that quality is that they would charge an insane fee structure, oftentimes averaging 40% and not having the same wide net that you can get with automated processes. And so our thought process was, what if you can take this, you know, high service quality of Topdol and finding really high caliber candidates, but instead of having huge margins and huge fees, because all of those processes are manual, doing that with software, assessing not just like 1000s of people, but hundreds of 1000s of people finding better candidates at a lower cost structure, and delivering them to candidates much faster. So that's the comparison to Topdal and automating those processes. Turing started with the premise of doing a lot of automation in in the late 2000s and 10s. But the challenge at the time was that LMs were not capable enough to achieve what they wanted to or models in general. And it wasn't until, you know, chat GBT and GPT four, and all of those models being possible to fine tune that it actually became possible to assess talent at the cost of software. And so Turing built up their entire business as a manual business that was manually doing all of these processes with a lot of AI product marketing, and really, really doesn't have complete automation. So multiple executives that have left Turing are now advising us are excited about the approach of of completely automating these processes, and really building the best models to predict human ability is the most impactful thing on just ensuring that clients are having good experiences.