"Turpentine VC" | Venture Capital and Investing
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.
00:27:04 · Usman Hanif, Brendan Foody
[00:27:04] S1: How do you kind of show to these companies that the technology that you have built for vetting is, you know, up to par, it's up to standard? I'm curious what that sales cycle almost looks like. [00:27:15] S2: You know, it's it's funny, because there's so many companies out there now, especially that say that they're AI, right, and how they're using AI to help solve a customer problem. And we took this really unique approach, which is instead of just saying we're an AI company, and then sending them a resume, we showed them what we were doing, we showed them like, why we're selecting candidates, we show recording of the AI interview that's being conducted of the candidate. So clients can watch that interview, see that it's a thorough interview, and decide, you know, whether or not like our vetting was thorough enough based on that we show them the, you know, analysis of the work experience of that individual that has the GitHub contributions that we're assessing, as well as a summary of why we selected that individual for their query. And so I think that in demonstrating technology, rather than just talking about it, it becomes much more powerful and customers believing that you're leveraging things at the cutting edge to create good experiences. [00:28:14] S1: So if you're if you're offering AI, you're also citing your sources to almost ensure that the AI is working.
00:28:14 · Usman Hanif, Brendan Foody
[00:28:14] S1: So if you're if you're offering AI, you're also citing your sources to almost ensure that the AI is working. [00:28:20] S2: Exactly, exactly. It's, it's really, really important, right? Like, think customers over time can build a lot of trust in us. But it, it makes sense that they would want to double check, especially as they're getting started. And so we really facilitate that versus other players in the space, which we talked about earlier, you know, have so much branding as AI companies. And we've talked to, you know, executives at all of these companies that corroborate that they're not AI companies, they just talk about it. And they're not actually automating these manual processes because they've built their entire businesses around services, not around product. And so I think keeping this, like, maniacal focus on complete automation and showing to customers the automation that we're doing is really important. [00:29:08] S1: Yeah. And you mentioned sort of the international talent space almost being a niche for you guys. Would you say that is sort of the wedge right now? What are most people using you guys for? Is it to hire folks internationally? And then if so, what type of roles are they hiring?
00:29:08 · Usman Hanif, Brendan Foody
[00:29:08] S1: Yeah. And you mentioned sort of the international talent space almost being a niche for you guys. Would you say that is sort of the wedge right now? What are most people using you guys for? Is it to hire folks internationally? And then if so, what type of roles are they hiring? [00:29:26] S2: Yeah. So we hire. We initially hired a lot of people, particularly in India, software engineers in India, then we expanded to all countries. And now we're expanding to all domains. So while majority of our business is still software engineers, and we've hired well over 1,000 software engineers, It's now expanding into many, many other domains, all sorts of business roles and to medical legal roles, etc. And the reason is that the technology is really broadly applicable. When you are able to conduct a really good interview of a candidate, and you're able to evaluate the resume really well, it's it's applicable to a lot more than just soft registering. And actually, the other thing I'll mention on this, which is like a really interesting technical point, is that there's a lot of transfer learning associated with making predictions across different kinds of models. And that if we make a model really good at predicting performance reviews for software engineers, it's actually much easier for it to learn how to predict who's going to be a good lawyer or a good doctor with far less data. And so this really goes to show how interesting the usage data network effect is, as we expand all these new roles, all these new disciplines within the economy.
00:30:40 · Usman Hanif, Brendan Foody
[00:30:40] S1: Yeah. That was actually gonna be my my next question about sort of how easy it is to move across domains. Right? If you're fine tuning these models, do you have to keep it specific to, say, software engineers? And I'd also be curious for you to touch upon that a little bit. I mean, it sounds like if you're taking into account data points, like, how nice... Like, how well their camera's working, it maybe speaks to intangibles more than even something specific. How how do you see that sort of playing out for all industries? Like, how do you expand to hiring lawyers on top of software engineers? [00:31:13] S2: Yeah. So we have to create labels and new domains such that we're, like, well positioned for them. And it's difficult to, like, say we wanted to add a new entirely unique domain without any labels, the models would probably struggle a little bit. However, if there's some overlap in domains, or we add a small amount of, like fine tuning data to the models at different parts of the stock, it's able to unlock a lot of new abilities. And so that can be really, really exciting. And yeah, it's super contextual to the So there's, like, scaling laws around all of this kind of stuff. And the more data that we get on software engineers, the better the model gets at evaluating all other roles. [00:32:03] S1: So serving a new role would sort of require collecting a bit more data on land and perhaps fine tuning again.
00:32:03 · Usman Hanif, Brendan Foody
[00:32:03] S1: So serving a new role would sort of require collecting a bit more data on land and perhaps fine tuning again. [00:32:11] S2: But oftentimes, not not as much as people would think. It... It's hard for me to give a really precise answer because it's so dependent on the role. Right? Like, as an example, say you have a lot of data on what makes a good full stack engineer. And now you want to predict what makes a good backend engineer, there's, you know, not too much nuance that it's like pretty easy for the transfer learning to apply to such a similar role. Versus if you want to predict what makes a good designer, it's going to really struggle. And so it's very dependent on like how related the attributes that you'd look for are in a given role to the attributes that you would look for in that new role, with some roles being quite challenging and others being much easier. [00:32:59] S1: We've touched upon this a bit when I asked how our company sort of vetting your vetting, like, making sure that you're doing it right. But you mentioned, like, you need data on what makes a good designer. Right? Say you've trained the model, you get an output. How do you just internally validate like, hey, this model is doing well or not doing well. This is actually a good designer. This is actually not a good designer. Is it pretty manual? Like, do you yourself just need to kind of do it by inspection? Or how do you do this systematically?