The Exchanges

Q And you mentioned you already have a hundred customers, which is sort of amazing, um, considering you're a seed stage company and you just launched, um, any, any sort of tips, strategies, lessons that you could offer to other, you know, seed stage companies about like how one gets from zero to a hundred customers?

A Yeah, it's a great question. Not sure if it's repeatable, but happy to share my two cents. So, um, before we launched, we had about a hundred customers. After our launch, we've, you know, since, as you can imagine, gained a lot more, um, but pre-launch, one of the things that we were very specific about building, unlike I think a lot of, um, sort of SaaS companies today, is we wanted to really understand exactly the end state of what it is we wanted to build. So I, I recently said this on online somewhere, but I think a lot of the opportunities for AI startups is to find something that is traditionally very difficult to scale, but very easy to sell. And then you try to scale it with AI. And I think recruiting is exactly that. So, you know, we work with an industry where there's, there's more than a hundred recruiting agencies in the United States that each make more than a hundred million in revenue a year. They're all basically human capital businesses. They're services businesses. Um, And for us early on, one of the biggest things that we did was just invest in actually understanding how all of these very human-oriented businesses operate. So we actually built out our own basically end-to-end services business. Um, takes a lot of work early on. Um, but from there, you know, you get so much of the product learnings. We now have a feedback loop where everyone on our team is ess…

AI assessment note: “we actually built out our own basically end-to-end services business.”

Q actually introduced the two of us. Thanks, Richard. Uh, Day One Ventures, Susan Vajiki, Ramshiram, And so on and so forth. Mike Volpe, Christopher Ray, Chris Ray, uh, so, um, you know, awesome group of, uh, investors and sort of, uh, AI folks. Um, uh, anything you can share? Like how many of you, uh, are there in the company? Uh, who are you recruiting for? Like any, any, anything.

A Yeah, absolutely. So we are, um, we just actually just announced this, uh, with our launch that we have raised about ten million so far. Um, we raised around earlier last year, and then we, um, had a lot of interest over the summer in raising around pre-launch, so we did something small internally. Um, so as part of that, you know, um, the team at Coastal has been incredibly helpful, um, folks at GV, you mentioned AIX, Um, we also recently brought on Mark Benioff as one of our larger investors as well. Um, so from Salesforce and, um, we're looking more and more as we scale towards, um, working with larger enterprise customers. And so, you know, of course, Mark has been really helpful there, um, as well as, uh, some other additional investors, Barry Eggers, um, who was one of the founding partners of Lightspeed, Mike at Index, as well as some other folks there. Um, in terms of where our team is at today, so we're 15 people, we're trying to keep the team lean, um, but we are actively hiring. So, of course, internally, you know, we're looking for ML engineers, full stack engineers, recruiters, designers, PMs, a lot of the sort of functional areas we're all hiring in. Um, and I will say, as a company that works on AI in the recruiting space, we actually dog food our own products, so we don't have, like, a specific internal recruiting team. We essentially act as customers to our, To…

AI assessment note: “we're 15 people, we're trying to keep the team lean, um, but we are actively hiring.”

Q know, either vertical or sort of like discrete Problem, but like in a full stack way is the, the, the way to go where like you do work at the model layer, uh, but like ultimately you deliver an application. Is that, is that where you think most of the opportunities are like including some of the healthcare stuff that you've invested in stuff, great companies that you've invested in?

A I, yeah, short answer is yes. You could have known that. Um, I've always been a big believer in vertical companies, and I think, especially in today's, where you see the open AIs of the world, it's very hard to compete in a platform, with a platform level company, unless you have substantial funding, and, um, Chris, I, I'm not sure if I ended up publishing this, but my, my PhD advisor, Chris Ryan, I actually wrote a piece together, maybe four or five years ago, and just like, Building vertical AI companies and, uh, some of the sort of common, common, uh, how do I call them? Like, uh, challenges that they face and how, how you should sort of think about them. But I've basically, my entire career has been one big bet on building vertical AI agents. Like the company I worked at before my PhD at Casa, also a great company where we were building, um, essentially AI to automate back office healthcare. So revenue cycle, amazing company. Um, During my PhD, Chris and I worked a lot on sort of these foundation models for more agent like workflows. You know, we have some papers from like custom automating customer support and all these other vertical applications, but I do think for a startup to be competitive today, a lot of people talk about, oh, there's these new AI moats and what are moats in the AI era? Um, I think there are definitely some moats that arise that are different from wh…

AI assessment note: “yeah, short answer is yes. You could have known that.”

Q That's very interesting. And is that, is that you think, um, what the business is going to look like in five years from now, or is that a first stage as you keep training the AI?

A That's a great question. So our ambition is not to build another, I think there are a lot of companies out there trying to build tools, like recruiting tools. Our ambition in the long term is really to build the AI powered end-to-end recruiter for you. Um, and so that's what we have offered from the beginning is an end-to-end recruiter. Um, and today, you know, the AI can't do all five stages of the process I mentioned to you. It might do stage one, stage two, We'll probably get to stage five by mid next year, but in the interim, you have a human who's actually helping you with all those other stages and making sure you have a seamless, um, end to end process. And so when companies, they work with us, it's oftentimes they're looking for that end to end recruiting experience. Um, and so quote unquote, our competitors tend to be these like 50 year old legacy organizations that, you know, many of them still have fax machines and it's, it's a total human business basically.

AI assessment note: “We'll probably get to stage five by mid next year, but in the interim”

Q to pull internal data from, from the customer. Okay, uh, really, uh, really interesting. And then just to drive it home, the, the, uh, sort of the execution layer of this is, uh, is what you send an email automatically to the candidate, or you connect with their through LinkedIn, or like, how does that work? Or you make a recommendation, and then people should do their own reach out.

A Yeah, it's a great question. So, um, if you log in today, you'll be presented with what we call our sourcing AI, so conversational interface. You talk to it, it'll answer strategic questions, and then help you find candidates that are relevant. Once you find, let's say, a hundred candidates you think are a good fit for your project, um, what you can do from there is reach out to those in a somewhat automated fashion. So you can write email sequences to them. The AI will help you craft email sequences, um, and then our I, I should comment by saying our product is not a, um, it has, it's, there's the core AI, but we also augment it with actual expert recruiters behind the scenes as well, um, to enable you to do end to end. Um, so customers that work with us, they're using the AI to source and to help outreach, but if they don't want to do that, our human recruiters can actually help them with those components too, as well as helping you screen the candidates. So our recruiters will actually hop on calls, screen the candidates for you, give you the data back. Help you, you know, prepare data for compensations and offers as well. Um, so we do offer that end to end and match. Traditionally, you know, I think a lot of people for us, we see our main sort of the, the biggest parallel to what we build is probably the external recruiting agencies of the world. So if you've ever worked wi…

AI assessment note: “reach out to those in a somewhat automated fashion. So you can write email sequences”

Q And you think eventually the end-to-end includes, uh, interviewing? Uh, I, I, there's, there's a screen I think you mentioned, but like, do you think the AI can, like, ultimately determine, um, you know, in a few years from now, okay, this is the best candidate for the job?

A I think in a few years from now, we'll definitely have AI that Is smart enough to do it, but I think the question is more so whether or not candidates will be, if, how much it'll impact the candidate experience so that people actually want to deploy it, right? And, um, for us, if you look at what today you might engage an external recruiter for, external recruiters actually don't do any of the interviewing for you. They help you screen, they help you source, and so we think that's the really big opportunity, and we look at what that particular segment of work does today, Um, we think that is something our AI plus really talented experts on the team can, well, we've shown that they can do it together end-to-end very effectively already.

AI assessment note: “we'll definitely have AI that Is smart enough to do it”

Q Yep. Are you sensing that, like, this big wave of hype we've had for the last nine months, is that starting to, like, slow down a little bit, like, from the, from your perspective, or is that just as vibrant as ever?

A Yeah, it's a, it's a great question. I think there's the hype from, like, the investment perspective, and then there's the hype from, like, what I call the talent perspective, and they're very correlated, but not the same. So we have so much talent data that, in the last couple of weeks, we've actually been releasing what we call, like, talent insights. So, for example, our team has mapped out where all the ML engineers in the US are, and it turns out the majority of them are in the Bay Area, and that's why you should, if you want to build an AI company, you should probably go to the Bay Area, right? And I think another fun one that we've looked into recently is just this, ah, title of prompt engineer. A lot of people want to call themselves prompt engineer because it's, like, the cool thing on the street to be called, right? And, um, if you look at the data, it's, like, basically until twenty-twenty-three, no one called themselves a prompt engineer, and then twenty-twenty-three, all of a sudden, a thousand people in SF are now a prompt engineer, and, and that number's actually, like, relatively stable since in the last couple months it wasn't, like, a You know, everyone's trying to become a prompt engineer. Maybe they're going for something else. I don't know. And maybe now it's like a rag rag expert or something. Um, but it could be something like that. I think on the investo…

AI assessment note: “I think the excitement is still there, but I think there is more of a reservation”

Q Is part of the idea that, um, you can one, uh, sort of accelerate the process of what a manual recruiter would do, but also to perhaps find people that would be less obvious. Is that part of what you're trying to do?

A That's a great point. Yeah. Today we work with about a hundred companies. Um, actually before we launched, we worked with about a hundred companies helping them hire and scale their teams. So some of our sort of early customers pre-launch were companies like Inflection, Anthropic, Sandbox, U.com, um, public companies out there as well, as well as some early stage startups. And these companies work with us because we help them hire at half the cost three times faster. And this is enabled by giving them a lot of the Sort of the AI, um, gives them control of running the process. So if you've ever used an external recruiter yourself before Matt, you'd know that, you know, normally you, you wait a week. You finally get on that call with the recruiter. They ask you some questions. Two weeks later, they send you a couple of candidates. Turns out they're not a good fit. And then it's a month rolls by. You're finally starting to find maybe a couple of people that you think are good. And I think the real power of AI for, um, In, in the next one or two years is enabling these non-experts to be able to participate in the workflows that traditionally experts will do. So at MoonHub, what we do is we make it possible for you as a hiring manager or recruiting leader to talk to the AI without knowing how to use Boolean searches or sort of the traditional lingo and, you know, complexities of rec…

AI assessment note: “we help them hire at half the cost three times faster.”

Q What does Replicate do maybe for, for anybody in the audience that may not have ever heard of them?

A Yeah, for sure. So Replicate essentially builds like these, um, AI models that, um, they allow you to run open source ML models. Uh, so a lot of folks will go to them and run their own ML models for some of the more recent models that are coming out, and they've been really good at helping, um, run ML models in the cloud basically immediately after they become open source. Um, and, and then we have some other companies. I think you mentioned you.com. They're an incredible company building consumer search and everyone should try them out. Uh, and then, you know, some more companies on more like the healthcare side, for example, ambiance. Um, so it's a team that's building, uh, essentially AI powered medical scribing, um, for the telehealth and healthcare industry. And that team, Michael and Nikhil, both incredibly brilliant. And the company I think is really doing a lot of good in the world today. Um, And yeah, the list goes on, but I think you can, you can probably find online somewhere the list of companies that I've invested in. I will say nowadays I'm very much doubling down on the companies we've invested in already. Um, and so opportunistically investing in some, again, good friends that less active in sort of investing in new companies.

AI assessment note: “they allow you to run open source ML models”