This Week in Startups
01:12:18 · Alex Wilhelm, Brendan Foody, Hon Weng Chong
[01:12:18] S1: Does the AI interviewer you guys have built in its current iteration, does it work with multiple languages? Because I presume that if you're only doing English, you're still constraining to a small portion of the overall possible job takers. [01:12:30] S5: Yeah. We started out predominantly with English. Now now we're starting to do others, working with a lot of our customers to try to make sure the models improve their multilingual capabilities so that the the interviewer [01:12:43] S3: is well set [01:12:44] S5: up for that. [01:12:45] S1: And, I I presume that India is a market of choice for you guys. But what... Are there any other hotspots around the world where you're seeing a lot of, like, talent saying, hey. We really wanna be part of this new marketplace that you're building. [01:12:54] S5: Yeah. Well, so actually the majority... The vast majority of our hires come from The US now, over 60%. India is the second largest geography that we hire from, but also seeing lots of Eastern Europe, lots of South America. One thing to note is that the average pay rate in our marketplace is over $90 an hour. And so it's very different from most labor marketplaces in that way. It's, you know, a totally different league from what you would find with the crowdsourcing platforms like Scale and Surge that sort of built these legacy labor marketplaces with more in the range of $10 to $30 an hour pay rates.
01:12:54 · Brendan Foody, Alex Wilhelm
[01:12:54] S5: Yeah. Well, so actually the majority... The vast majority of our hires come from The US now, over 60%. India is the second largest geography that we hire from, but also seeing lots of Eastern Europe, lots of South America. One thing to note is that the average pay rate in our marketplace is over $90 an hour. And so it's very different from most labor marketplaces in that way. It's, you know, a totally different league from what you would find with the crowdsourcing platforms like Scale and Surge that sort of built these legacy labor marketplaces with more in the range of $10 to $30 an hour pay rates. [01:13:31] S1: But you guys are currently targeting a much more educated and verified employee pool, again, as your starting point, as your wedge. So I wanna talk about how good your system is at finding and placing people because it sounds good in theory, but in practice, of course, that's what matters. So what's the right metric that you guys track in terms of finding the right person for the right job and then having both sides of that, equation be happy? Is it repeat business? Is it successful completion of a contract? What's the KPI there?
01:14:01 · Brendan Foody, Alex Wilhelm, Hon Weng Chong
[01:14:01] S5: Yeah, I think about it as a two sided retention problem. And there's leading indicators on each of those retention problems. So the applicant side retention is, of course, if they come back looking for work. And so we have models that predict what is the probability that they're going to be interested in a particular job opportunity that we present them with based on all their prior experience so that we can ensure we're retaining candidates very well. And then on the demand side, it's how well are those candidates performing? Where we collect all of the performance reviews of who's doing well for what reasons and use that as our eval set, as our benchmarks to go internally on how do we predict, based on the interviews, the kinds of people that are gonna perform well, that are going to translate to a lot of value for our customers? And that's why our demand side retention is so large. So now we're working with six out of the mag seven. We have 1605% net revenue retention on an annual basis. And so it's it's sort of nuts. Wait. [01:15:05] S1: 1605%. So it's 1600%. So 16 x net revenue retention. For folks out there who don't know why that's funny, mature software businesses will often turn in a net revenue retention number of a 115% or or plus 15. [01:15:19] S3: Yeah. [01:15:20] S1: So the number that he put out there is humorously large and implies that customers are buying not just a little bit more of the services, but 16 times as much over a one year basis. [01:15:29] S5: Yeah. The two side retention problem is working well. So...
01:15:29 · Brendan Foody, Alex Wilhelm
[01:15:29] S5: Yeah. The two side retention problem is working well. So... [01:15:32] S1: Yeah. Two... No. Two side retention solution. I don't think problem is [01:15:35] S5: the right is the right word. [01:15:38] S1: So to me, AI companies that need to hire experts is a very... It's a market that has a lot of reason to do well, to invest in new ideas, to try to find the right people because they're in a massive race to build the best stuff and therefore capture the market. How well do you think the Merkur model is going to translate when you go wider and you're dealing with people that might be less individually resume wise impressive and are more average folks, applying for more average jobs? Because to me, difficult job, difficult person might actually be easier to match than finding the right person from a more general pool for a more general gig.
01:16:17 · Brendan Foody, Alex Wilhelm
[01:16:17] S5: Yeah. Well, we do hire a lot of people from general backgrounds as well. It's really, really broad across literally every industry in the economy. But maybe stepping back a little bit, the background of the company is initially we were automating the processes of hiring people for our friends. And then Scale dot ai came to us and they used our platform to hire over a thousand people. And what we realized was that there was this huge transition in the market away from the crowdsourcing paradigm of low and medium skilled talent towards this medium and high skilled sourcing and vetting problem, not only with higher caliber people, but also people that work directly with the AI researchers to help them interpret evals and push the frontier of model capabilities. And that is one of the core reasons that the performance data we collect and all the things we learn and how we facilitate better matches is actually much more similar to a general work environment than, most people realize on face. [01:17:16] S1: When you say, Brendan, that you're collecting the evals, my impression of how this worked was I would come to you, resume, interview, job, and I would go work for someone else for some period of time. But if you're collecting the evals to ingest back into your process, does that mean that when I land a contract or a gig from Merkur that I'm actually working for you guys?
01:17:16 · Alex Wilhelm, Brendan Foody, Hon Weng Chong
[01:17:16] S1: When you say, Brendan, that you're collecting the evals, my impression of how this worked was I would come to you, resume, interview, job, and I would go work for someone else for some period of time. But if you're collecting the evals to ingest back into your process, does that mean that when I land a contract or a gig from Merkur that I'm actually working for you guys? [01:17:36] S5: So technically, the work product that our contractors are producing ends up going to the customers. What we own is just the performance review on, like, did this person do a good job on the project? [01:17:50] S1: So the end customer tells you how the employee did, and that's part of the overall arrangement. So the feedback does make it even if they're working for someone [01:17:58] S3: Yeah. [01:17:58] S5: Yeah. And and one interesting thing is we facilitate the entire payment stack. So we learn from who's getting bonuses, who's getting raises, who's getting dismissed for what reasons, all as as part of the data flywheel. [01:18:10] S1: Okay. So people love to talk about the the usefulness of data and how the more you have, the smarter you can be. So how much better have your systems become, after ingesting increasing amounts of performance reviews, bonus information, contract retention, and so forth. All the stuff we've talked about. How quickly does that filter back in and actually generate a material and measurable improvement in your ability to place candidates?