He raised a $10M seed with no revenue—then grew 30x to $30M in year two. | Bobby Samuels, Founder of Protege

Public market fit is when you're feeling the market really pulling you. And there's a sense of, we've done this before, we can do it again. Where it feels like there's sort of this momentum behind you. Obviously that romanticizes it, like it's not necessarily this like one moment. It's like the game never ends, but that's how I think about it. We got an email from the lab saying, Hey, the day you have doesn't work for us. We're really, you know, we're sorry, but we'd love to work with you in the future. And I basically ignored the no. And I was like, Oh, okay. We actually, thanks for giving us this feedback. We can do these things. And they're like, Oh, okay. And then I went back to the drawing board and I ended up doing it. But I do think there's that's like, you gotta be scrappy. In general, for talent, the difference between A and A plus is like just so massive that it's usually worth it to spend the extra money to get that person. That's product market fit. Product market fit. Product market fit. I called it the product market fit question. Product market fit. Product market fit. Product market fit. Product market fit. I mean, the name of the show is product market fit. Do you think the product market fit show has product market fit? Because if you do, then there's something you just have to do. You have to take out your phone. You have to leave the show five stars. It lets us reach more founders and it lets us get better guests. Thank you. Bobby, welcome to the product market fit show, man. Thank you so much. Excited to be here. So, I mean, you know, AI is like, as we know, changing literally every single thing around us. And what I think we all understand is that especially these big LLMs, they're all built on data. Frankly, any AI is built on data. You've built a company that is able to access real world data from a bunch of different data providers and effectively give it to these LLMs, provided to these LLMs so that they can, and all their kind of AI model builders so they can build on top of it. You've raised over $60 million, $65 million to be exact. Last round was like $30 million from A16Z. So clearly you've got a business that's working, that's scaling, growing fast. Let me start by asking you this question because this is the product market fit show after all. Having built this kind of a company in this world, like, how do you think, maybe let me ask you this, like, what is product market fit to you? Product market fit is when you're feeling the market really pulling you. There's a sense of, we've done this before, we can do it again, where it feels like there's sort of this momentum behind you. There's sort of this, like, magnetism. There's two sort of things that maybe were repelling each other, it's like suddenly click. We've talked about this before, like, obviously that romanticizes it, like, it's not necessarily. It's like one moment, it's like, there's this, and then you get the line, and then the world changes, especially in AI, and then, like, the game never ends. But that's how I think about it. And then when you think about your journey, when was the time, when was the moment, maybe take us to that story of when you felt that switch, that click, where all of a sudden it was like, okay, we're really onto something, we're feeling that pull. Maybe I can do, like, the quick company history, because then that can sort of lead into it. So, we started the business in early 24, focused on healthcare. And, like you said, we connect holders of rich data assets with model builders. We're looking for data all in a very privacy compliant IP focused way. We started in healthcare that was where we built the first data network. Then we started commercializing, had our first bunch of deals in 24, but it was sort of, okay, we did this thing, we patched this thing, and how repeatable any of this is, it's still TBD. Q1 now, so Q125, that was sort of the moment where we worked with multiple companies across multiple domains, healthcare and video and others. And there was clearly this massive appetite for what we were doing. It was a combination in a B2B large enterprise type customer base. And so, it's not consumer where you can iterate and iterate and iterate. A lot of it was, okay, we got to the right people, we figured out how to get to the right people. And we figured out how to pitch it, we figured out what data is going to be most compelling. It sort of went from like a trickle to a flood, so to speak, very quickly. And that was the moment where it went, oh, like now this is this worker. And why was that? Was it just a matter of time? You're always pitching and saying the same thing and finally got to the right people at the right time and they really cared? Or are you saying or doing something wrong earlier that you then changed and then started seeing kind of the eyes light up? So, we work in a bunch of different verticals, healthcare and video and audio. And I think one of the real leading indicators of success of how quickly we're able to land some sort of product market fit is the speed of iteration. And so, we sort of iterated everything. We iterated how we talk to customers and how we display certain data and some of the product marketing that we put together, like even just representing that differently actually made a big difference and how we talked about things. So, there's a lot of like just a thousand little micro tweaks. So, this is the key. Like first of all, I fully agree. I mean, when it comes down to, I mean, sometimes you kind of get lucky. You put out a product like Facebook, you know, things just work and people pull it and then you just build on top of it. Most of the time, you have an idea. It's kind of right, mainly wrong. You start tweaking things, changing things, changing things. Sometimes the big pivot, sometimes like mini little changes and at some point things end up kind of working out. So, that's cycle speed between putting something out, whether it's a new product, new messaging and you go to whatever it is and getting that feedback back from the customer or the market and then changing on that. Like that flywheel, I think is the key. Maybe walk me through and this is getting in the weeds, but it's I think where the rubber hits the road, so to speak, like some of the changes that you made, if you remember any of them, that whereas like before we were saying things this way, then we were saying things that way or we were selling this way, then we were selling that way. I mean, whatever changes you kind of, as you think back to that from 24 to 25 period, some of the changes that you made that show us, you know, what it means to iterate. Yeah. So, there are two things. I'm in New York. A lot of the buyers are not. And initially it was, okay, let's try to do this remote and all that. And what I learned is you have to go see people and that just was a huge deal. And like even in an environment where it's like a B2B type thing, you're talking about data, like the personal relationships still really matter. And I think one thing we did was we showed people like, hey, we like care about what you are doing and we actually take a lot of pride in helping to accelerate the work that you are doing and showing up is a great way to show that. So, I think making the effort to come and just be there a lot. One of our early partners was after I sort of put this together, was like, do you live here? Like you're here every week. I don't, but I am there a lot. And like that was huge. And then the litmus test that I used initially and then I used for other folks on the team is are you on texting terms with them, with the like buyer? If you were not on texting terms with someone, I am worried we don't actually have the relationship we need. The amount of business that happens over text is like crazy to me. But that was like both practically very helpful, but also a litmus test. And so getting to learn much more about, okay, here's how people buy. And yes, there's an emotional component, but we're also in an industry like AI at large and data with an AI, which is like pretty opaque. So if you show that you are a human, that you care and that you've been really thoughtful about those things, that matters. And the best way to do that is personal relationships. I think our investing in those really accelerated what we were doing. What size, like what kind of ACVs are you selling? I would say the deals we did in 24 were all pretty much like five and six figures. The deals that I'm talking about in 25 were like mid and high seven figures. I ask that because obviously the in-person component, I think for what it's worth, I'm seeing more and more founders have success within-person sales. I mean, events and conferences, I think have always been a thing, but for whatever reason over the last few years, I'm just finding more are not just doing it, but getting success, doing it and getting success. And I think part of it has to do, I would assume, with AI and all sorts of automation that even happened pre-AI, has made the kind of normal outbound, digital outbound, it's such a crowded market. So anything that differentiates you, I think matters. I think there's also an element to every sale is fundamentally human to human, at least for now. And so, and especially in enterprise, like you can't, it's just fiduciary-wise, like it's going to go through some sort of decision-making piece where somebody's going to have to sign off for a long, long time. And the relationship, I don't know how much it is about that you're a friend or whatever, but there is a credibility piece, like for somebody to bring in a new partner that's a startup and put their stamp of approval on it, they've got to feel that you're going to get things right. And I think spending time to build that trust matters. In an environment like AI, where there is, to your point, so much automation, so much money swashing around, the true, true thing that is the constraint is time. One of the things is like, look, I'm hauling my way all the way over from New York to be here and to see you. That is a clear investment in what you are doing and the relationship and all that. And I think that we have built a team that is like a genuinely good group of people who care about what folks are doing, and I think that also comes through. So, I think these are fundamentally human relationships. Yes, part of it is like you like working with people you like, but to your point, a lot of it is very rational in the you have a demonstrated track record, like trust you and trust is still a massive currency in the business that we're in. So, just to keep kind of peeling back at this, Ani, and like when you're Q1 2025, you're kind of finally getting true product market fit, signs of true product market fit. Tell me a bit more about kind of the problem solution and the ICP at that point. Like what exactly are you selling into? What problem are you solving for them just so we can get a little bit more into kind of why there was product market fit? I think another piece for us was we moved, so we'd build this massive data set and where that is, I'd say uniquely differentiated is on some of the pre-training deals, and those are with some of the labs and those took longer to get to. And so, we have a variety of ICPs, but the ICP in question for Q1 was labs, both bigger and smaller, better doing, primarily pre-training, need a ton of data, but need it in a very high quality fashion with demonstrated distributions and things like that, because these are really sharp buyers. And so, I think it was, the ICP was these large foundation model builders who needed data for pre-training, but they rejected the trade-off of large amounts of data, low quality or high quality, small amounts of data. They were like, no, we want a lot of high quality data. And because of the way we are architecting the business, we were in a really good position to just blow over on that. Yeah, tell me more about where do you get this data? How are you providing it to them? If you rewind back to Q2 2024, which is when we started, we founded the business Q1, we built the data network Q2, started to commercialize in Q3. In Q2, we were like, okay, the data that's going to matter most is unstructured data and folks want to create these longitudinal multimodal journeys of patients. I worked in the health data world before, I ran a privacy business and so had some of that credibility and trust already in place. Is that why you started in health? Yeah, it was a combination of where our networks were as well as where we knew, they felt very confident there was opportunity. And so, we started there and then continued to expand, but we had a thesis on here's the type of data that's going to matter. And so, it'll probably the largest multimodal longitudinal dataset that was commercially available, reasonably quickly through those relationships. And then as we got more feedback from the market, we then said, okay, great, we can augment our partnerships in these ways. And so now across the business, we probably have 250 partners, my guess is that more probably grows 50 to 100% this year. And so, it was like doctor's notes. It is, it's imaging, it's claims data, again, all de-identified, all in a way that is compliant with both the spirit and the letter of HIPAA. Can you walk me through? I mean, this is key, right? Because obviously, if you have this level of unstructured data, it's going to be valuable and you got to figure out how to make it valuable. But like getting that to begin with is not trivial. Like, who do you go to first with this idea of give me all this data and how do you kind of even just clean it out? I mean, how does that world work? Yeah, there's the sort of truism or, you know, the belief of like, what's your unfair advantage when you're starting a company and there's some truth to it. For us, it was, we knew the health data world and we had the relationships where folks trusted us. And that dramatically accelerated us. I think it would have taken a year plus to build that trust in those relationships. It took us, you know, in most cases, we already had the relationships. We can move extremely fast. We launched a video vertical later in the year and the thesis of that, or excuse me, the, it was a company started by some ex-Hollywood licensing execs who had gone around, gotten hundreds of thousands of hours of video for training, but they also leveraged their networks and had a lot of trust and they're very genuine people and all of that. And so that is a playbook that we've run of, like, you know, how do we create these networks or leverage these preexisting networks? But that's how we were able to move quickly. And I want to know who, just to make it specific, like you're going to the CIO of a hospital, like who are you even going to? We went to labs today. We have some hospital system partners, other aggregators, software providers who get de-identified data rights. So it was a pretty broad array of folks. Most of them were already, if not all of them were already in the data world somehow and so it was a sort of shorter sale that it otherwise might have been. And then you go to them and yeah, you'd walk me through like, so getting in the room is easy because you know these people or they know about you or whatever. What exactly are you pitching them at that point? Today, your business is focused on working with primarily pharma. We can open up this whole new channel for you. We'll take a rev share, submit the rest back to you. You should trust us because you know me. I ran this privacy business and you have total control over who used the data for what use case and how it's priced. And so you can opt out of anything you want. And so if you trust us, it is a sort of a no lose for you because you get this interesting upside that resonated and folks were excited to participate. And so we were able to move pretty quickly, but I do think it was contingent on we trust you. And then just a little, the side maybe on this data piece, like data being the new oil, like that's been the cliche for a long time. And I would almost argue at least until AI was, in my perspective, a little overhyped. I mean, you know, people worry about the Google data and the Facebook like selling your data. And like really, they just kind of leverage their data to just bring ass to your face. I mean, that's really like the most valuable use of data. But in this context, help me understand these players that were capturing data for a bunch of different reasons. Were they in most cases already monetizing it? And you're just saying, hey, come monetize it with me too. Or were you kind of zeroing to one where it's like, you've got his data, you sat on it, you don't really do anything with it. Let me help you make money off of it. More of the former, more of them are already doing some sort of data licensing. Our take was this is a market that's moving extremely fast, figure out the path of least resistance. So don't optimize on take rate. Don't optimize on getting the biggest systems in the world. Figure out the path of least resistance. So you go, you have the data. This is like over what Q1 and Q2 2024 is just getting these data partnerships. Yeah, exactly. And then Q3, Q4, that's when you start selling. Commercializing. Yeah. You had relationships on the data side. Did you have relationships as well on the kind of go to market side? None. Let's go deep on that. Like going to market is always, well, not always, but most often I would say the hard part of the equation. And especially with an offering like this, it's like in theory, everyone should want it. And you would think like it seems like once you got to the right person with the right message, they did want it. But getting theirs is not trivial. What steps do you take? Who do you go after? How do you go after them? Walk us through all of that. Yeah. When we raised in Q3, Q4. And how much did you raise? We raised a $10 million seat. We had a, basically a DAC analyst of the partners we'd signed up and some very tenuous like demand conversations. But it was weak. We hadn't, in part because I'd spent very, very little time there. I also had like my first child in March and that meant it was, you know, harder to do a bunch of things than I otherwise might have. Surprising how many people start a startup while they have a kid. And I'm always like, who isn't there? No better time. But it is what it is, man. It just happens how it happens. Yeah. My wife's pregnant was number two now. Oh, buddy. There's never a good time slash, yeah, all this stuff. So we started the commercialization conversations. I mean, it sort of was like there's no good way to do it other than just like grind. And I like, that's not helpful, but like, okay, great. How do you network to this person? You should do it. You should go to the events. You should do a bunch of cold outreach and then just like network network network network network. Did you have clarity at least of who exactly in, okay, take that one of your financial models, like this is the title I'm going after or these are the 10 titles that I'm going after. Like, did you have that clarity when you started or you just kind of said, let's talk to whoever could be relevant and kind of will figure it out. Sort of. There are some labs we've had it took us 18 months to get to the right person. Now, they are not the ones we sold to in Q1, but the sort of organization of these companies also needed to figure out. But so for us, what did we do? We were very fortunate to work with CRV is like, who's a great seed who made a bunch of introductions. We brought on a bunch of angels. They made a bunch of introductions. We had a list of companies we wanted to go after some of them we knew some of them we networked into some of them we reached out to cold. We traveled and visited the early customers. We did that, you know, but don't worry, just get points on the board. We did the things that don't scale and then, you know, iterate, iterate, iterate. And you only need a couple of deals just to get the ball rolling. But that's really how we did it. So for us, you know, if I think about sort of different flexion points in the business, doing our seed was one of them. Yes, the capital, but also we had a lot of great investors come in who helped get us in front of the right people. And in doing so helped us understand the structure of how these deals happened, which is unusual. I imagine an AI environment like the way products are bought may also shift. And so I think understanding who's the buyer, who's the decision maker or like the influencer, whatever, all the different sales again, I'm not not in sales. Those are some of the different things you really need to understand. I'm going to ask you for a small favor, a tiny little favor. In fact, it's not even now that I think about it's not even really a favor for me. I'm actually trying to help you do a favor for you. Just hit the follow button. You won't miss out on the next episode. You'll see everything that we release. If you don't want to listen to an episode, you just skip it, but at least you don't miss out. I think one of the things that you're alluding to is like you have to bootstrap your way to credibility. Once you have an enterprise, especially, right? So once you have many customers, case studies, this and that, it just things take care of and solve. Then you're in a more normal enterprise sales cycle. But at the beginning, when you're effectively nobody with maybe like some seed round or whatever, it's not like you can't get in cold. I'm not saying that. I'm just saying you will go way faster if you don't have to go in cold. So how can you like say you're trying to sell to Microsoft just making it up if you just can find somebody who has credibility with somebody senior ish in that enterprise in the right department and can make that intro. You just like leapfrogged all because these guys are getting bombarded all the time with different things they could buy. So you're just trying to find ways to like kind of hack that and just get a little bit ahead of the curve and speed that, which is I think what you're talking about with CRV and all these intros that the angels are making is they're effectively lending their social capital over to you. And it doesn't mean these guys are just going to buy just because that happened, but they'll take you way more seriously than if you're coming in cold. It'll move way faster. Yeah. Yeah, I think that's right. And coming at it from a multi-pronged approach where you are able to get to a bunch of different folks concurrently and just try them, try them, try them. I think that's also sort of what you have to do. I think it's like what we did and it works in like a B2B like enterprise type sass model. I think it's probably different set of things and consumer or in the mid market. You mentioned there was different inflection points. Let's touch on some of those that ultimately to that moment that we talked about Q1 2025. I imagine one of those has to be either your first customer or like your first, you know, important, notable customer. How did that happen? So we signed one of the labs in 2024. And one of the ways we did that is we built a bunch of really, really good relationships. And one of our partners said, hey, we hear this lab is trying to do this thing. We don't have enough to service them. We think you do. You should reach out to them. And so I wrote this guy cold and he responded and that turned into a deal. I do think also, you know, on this deal, so they didn't exactly know what they wanted. They had budget they needed to use by the end of the year. They didn't exactly know what they wanted. The researchers, they had sort of desires that shifted all the time. And at one point in the deal, we got an email from the lab saying, hey, the day you have doesn't work for us. We're really, you know, we're sorry, but we'd love to work with you in the future. And I basically ignored the no. And I was like, oh, okay, we actually thanks for giving us this feedback. We can do these things. And I look, oh, okay. And then I went back to the drawing board and I ended up doing it. But I do think there's that just like, you got to be scrappy and hustle and we're like all of these cliches, like the cliches. Did you change your product in order to be able to do those things or would they just misinterpret what your capabilities were? They misinterpreted the capabilities. Okay. Or rather, it was like, hey, we needed these two types of data together, but we really need three or more. It's like, well, we can do this and bring in this and bring in this. We can get you five. And they're like, oh, okay. There are a bunch of nuggets there, which is just make friends in the industry, try to be helpful genuinely and be good person. And they will try to direct you one way or the other. And then, yeah, you just got to try a million different angles. And then when it comes to kind of structuring that deal, like there's many different approaches. I mean, how did you do it? Did you some try to bake in, you know, go to legal, fight the hard fight at the beginning. Others are like, no, just do like the smallest pilot possible. Stay below the line where it's just like easy and just get going, you know, or try to kind of figure out what the KPIs are so that you can have a whole commercial roll out if things go well. I mean, how did you kind of structure those first few deals? For us, it was basically get points on the board. So for some of the deals, we actively try to make them smaller. It was like, hey, we think we're biting off a lot. We think you guys are biting off a lot. I mean, like, no, this is what we need. Okay. So just like, okay, great. Get points on the board. Be very easy to work with from a legal perspective and just try to push on things there. So those are some of the things that we thought through. And so, yeah, how are we ultimately able to move quickly on that? And then now that we've got like kind of moved through the story, I mean, walk us through in more detail like Q1 2025. Was it what makes you feel like that's when the scales tipped? Was it just the size of a deal, a specific lab that you signed or something in the product that changed? Like, what was the thing that makes you feel like that's when things clicked? It felt like our message was resonating. And the output of that was these deals. It was like a bunch of different signals, a bunch of deals across a bunch of labs and non labs of different sizes. It felt like, okay, we had this thesis and it's playing out this way. And so I think if it had just been one big deal, that would have felt good, but wouldn't have felt that same pull. It was that there were a bunch of deals of different sizes where what we were saying was resonating. And so I think it is this match of the thesis we have the market is resonating with and we're getting tugged in this direction. And that's when we felt it. Now, I think I mentioned this to you earlier, like Q1 was great, Q2 was not. But by that point, you know, my job is sort of to worry about everything always, but like by that point, it was, oh, we've gotten these signals already. And so we know there's something here. And so yes, like this quarter didn't go well, but we're not like drawing major conclusions about the viability of the business because we have all of these data points of things that worked. Just keep running your play and then that end of the forking and we've gotten to a pretty good spot accordingly. Let me ask you this question. And it's a hard question, but imagine I'm like an early stage founder. I'm trying to sell into enterprise enterprise notoriously. Like it just takes long. How do I know like what signs can I look for? What signs did you experience? Given that, you know, in your case, you did truly get that pull ultimately that I'm actually on to something just going through the motions that need to, you know, happen because it's enterprise versus I'm wasting my time and just like getting stringed along and actually just going to leave nowhere. I will caveat that we are an industry where folks are making bigger acquisitions faster than they historically have made. I think that the deal sizes for the speed of deal is unusual. Which by the way is also a sign of product market fit. Like it's industry related, but it's a sign of demand as well. Like the reason that it's not just happening in a vacuum. That is true. That is true. It's probably a combination of do you feel like you're making forward progress and do you think this is mission critical for them and do they think this is mission critical for them? And I think if you're able to get all of those pieces forward progress and sort of a belief in mission of criticality on both sides, you're probably going to be in an okay spot. It's when one of those three things doesn't work that you can either have something that doesn't go through or it doesn't. It's not that tenable. One of the things that we always want to watch out for is this notion of like companies where they have some dictate from on high of, I don't know what, but just figure out our AI strategy. And if it's that top down, often there's not a substance to it. And so if the buyer may say, I need to go do some AI stuff, you may sell into it, but it may end up being a one time thing because there's not more substance to it. So that's why it's not just do they believe it's critical. You also have to have a thesis for how this is really critical for them to the question. I just thought telling a little bit more about your business model. Like you go to data providers, you get the data and then the model builders, they pay to access to the access. Like it's pre training, right? Get the data, you know, build whatever and then they're done or do they pay like recurring? Like how does it all work? They pay for some license period, which varies a lot based on the model builder. But one of the things we've seen is for high quality data, the model builders don't want to part with it. It's not the we train once we don't need the data ever again. It's we train once and if it's good, we want it forever because if we retrain our models from scratch, which is a common thing, they need it going forward. So you can see that maybe of the scale, like the numbers, like whether it's revenue or GMV or whatever. We did a million GMV in 24. We did 30 last year. We're on pace to do somewhere in that ballpark in Q1. Wow. So continued pretty rapid growth. And then another question I just want to dive into is like talk to me about go to market, maybe the difference that you're finding in go to market before you're like very confident, right? Like Q1 25 where you're very confident that you're onto the thing and now where it's like you have an existing business and it's just about, you know, it's a little bit of that rinse repeat. Yeah. So much of it is about the playbook. And I do think the founder led sales thing is really important because I went in sort of figure out the playbook, what are the things you need to do to go with these customers and these customers. And then it's okay. Great. I did that. I mean, I was running sales till we run out chief course officer last year. So it's probably from the first year, you know, year and a half of the business. And I was doing a lot of the sales personally that we brought in a great head of sales and healthcare in 25 as well. But you have to go figure out the playbook in a lot of ways. What is the playbook now? A lot of his could great get really embedded in these different organizations go deep with the sort of research expertise, invest in those relationships deeply, figure out the multi pronged approach. But I think it ultimately comes down to, okay, great, here's how the model builders, here's a think about acquisition. Here's where you need to push and how, you know, what relationships to focus on. And then here's the sort of like land and expand type motion, but it really started with figuring out the structure, figuring out who cares about what and why and how and going from there. So it is like, we figured out the way to work with two of the big labs or three of the big labs. It's like, okay, great. Now let's go do it for the 10 or however many, I mean, you get new ones, you're popping up all the time. Actually, you know, how does that work? Like, is there a limited set? I mean, whether there's 10 or 20 or whatever, just not like 2000, like, how do you think about like that market size equation of it? So we talked a lot about the like big labs. Part of our thesis is that there's a huge demand outside of the labs as well. So we work with pre-revenue startups. We work with public companies that are not labs. Like we work well outside of just the labs. Lab is the biggest part of the revenue today, but it's not the majority of our customer count. And the non-lab business is one we're investing in heavily because we think that's just a huge growth area for us. And then outside of health data, what are some of the other verticals that have worked for you? Today we're in four verticals, health care, video, audio and motion capture. Health care is the biggest, but have made pretty good progress in the others as well. And we'll probably launch between two and five more verticals this year. And video is just, it's the same sort, like, you know, if you think about something like Google who has crazy access to like all their YouTube library, you're just providing that kind of video, like those video assets to everybody else that wouldn't maybe have access to a YouTube or something similar? Yeah, I mean, and even YouTube, like, at least for others, they explicitly forbid model training. So, and, you know, if you want certain types of content, if you want high production value movies, that's not really on YouTube, more or less. And so you need to go outside. And so, you know, we work with European soccer leagues and African production companies and fill in the blank for anyone who's looking to build a video model. What are you guys at today, like, employee-wise, for example? They're like 55. How have things changed? Like, just in terms of AI, I'm curious, as an AI, obviously, AI native startup and like with everything we're talking about with, you know, AI replacing workers and all this stuff. It's this weird thing, right? Because on the one hand, you hear about like, AI is going to kill all all software is going to kill all jobs and like the fastest growing companies that I work with, you know, they're hiring and they're hiring this as much and not more than than before. I mean, like, what's your perspective on that? Like, how are you thinking about, you know, do you feel like you're just going to need less people or is it just less people per dollar of revenue? But you're going to grow way bigger and you're going to need more. I mean, how do you think about hiring and AI? We haven't slowed down hiring. I do think there should probably come a point where revenue per employee and a company probably should go up, which is just, yeah, fewer employees needed to support a certain amount of revenue. But I tend to be skeptical and this could be self-serving. I tend to be skeptical of the apocalyptic all jobs are going to be gone. I tend to be a little bit wary of this time is different. I think pretty much every technology ever has led to job growth. I think this will be the same. So, yeah, I think any one company may be able to do more with fewer people. But does that doesn't mean that companies spring up and all that like probably so anyway, I think your question. Yes, I suspect over time, fewer folks are needed to support a company at any given size, but I'm skeptical of the apocalyptic prognostications. So, let's stop there and ask kind of like last two questions. The first one is, was there ever a time and this has been a pretty fast ramp so there might not be, but was there ever a time where you worried that for whatever reason this might not work? That either you'd get stuck or you'd fail or just wouldn't really find the way to make this as big as you'd hoped? I know I'd sort of like said flip like I worry about everything all the time. Like, I think you have to be sort of nervous about like, okay, fine, this worked on the maxed and like, I think if anybody in the AI world says I've figured it out, they are either lying to you or to themselves. Like the environment moves so unbelievably quickly that you just have to be really nimble. And so, you know, one thing we really haven't touched on is like, I think the having an exceptional team is like more important now than ever, giving how quickly the market evolves. And so, in my role, I have to be worried about, okay, great, this has worked really well so far, but the future could look very different. And what are the things we need to do to really position ourselves well about that and create the right team and organizational structures and operational discipline that we can adapt and thrive in these new environments? So, I do think the like notion of we found product market fit and let's scale, which maybe was truer in like a pre AI world, if it exists at all in a post AI world, I think is much more fleeting and you sort of have to be evolving at a historically fast rate. On team actually, let me ask you another question, which is I see, and this is maybe like maybe a false dichotomy, but just to kind of plant the seed, like I've seen two ways of hiring. One is, for example, I need an enterprise sales leader. Let's just make that up. Okay, my budget is 200k. Who can I hire? The other one is I need an enterprise sales leader. I want the best possible enterprise sales leader for my stage and sector and all that stuff. What's that cost? Right. So just two kind of different ways. If that's clear of thinking about how to fill roles, which camp would you say that you're in if those are really two camps and why? I think it is two camps for us where we were successful in raising capital a couple different times. I'm a firm believer in the second camp of find the best person, you know, within the reason and go figure out how to get them. I think that in general for talent, the difference between a and a plus is like just so massive that it's usually worth it to spend the extra money to get that person. Now, you need to be able to look at the rest of the team and the eyes and be like, hey, we're paying this person this much. And yes, that's more than you might but these are the reasons why like you need to be careful that what you're doing is fair by go find great people and like pay them accordingly is very much my mentality. Yeah, for what sort of a fully green I also think sometimes it gets lost in translation in the sense that, you know, then founders will hear this and say, okay, but like how like realistically how am I going to go if the best person is like the CRO at HubSpot making it up like they you know they make millions of dollars like how am I going to get them and and the answer is that's actually not the right person. Like if you've got 30 people, you've got to look at, you know, and you want this enterprise sales leader as an example, like you've got a team of like three enterprise sales people like who is the best person of taking you from this stage to the next and maybe they can take you to the following stages as well. TBD but that person is usually not making an order of magnitude more than what you could pay at some, you know, order of magnitude bigger company because they're doing a fundamentally different job. And so you usually talk about there was to paying something and something times 1.5 maybe times two in terms of the best person for your stage and sector whatever versus just filling that bucket and saying, okay, my cap is this and then therefore you'll just get whatever fits into that box that you drew. Totally. Yeah. If you found someone you really liked and they said you're an early stage company and they said, okay, great, I need a million a comp next year. That itself is like you probably don't get our stage. Yes. It's both like they're probably the right person, but also if they wanted that, then they probably don't get it, which means they're not going to be the right person. Obviously, the go get the best person. There are practical constraints like it's not just at all costs blow up the business like like, but on the margin and much past the margin, I would say go get the best people. Team always matters and pre-pramar fit, post-pramar fit, they matter obviously as well. And but the point is, figure out the things that will move the needle and for those key roles, I think you just do much better if you focus on getting the best people and then you're limited by how many of those best people can you get and how many needle moving areas you could really invest in versus, you know, starting the other way around and ending up with, you know, people that are not as good as they could have been, which by the way has a lot of other costs like management, communication, alignment, overhead, et cetera that might not be measured in dollars, but they're measured in time and effort and could be, you know, worse of anything. So last question. I tend to think about a lot of sort of like metaphors in terms of sports and music and all that stuff. Like if you think about like the NBA draft, the gap between the number one pick and the number two pick is usually massive. And not exactly true for like hiring, but there's some truth to it. And like great talent is almost always worth paying up for. And I think that's especially true early on where these first few pieces are so critical. I think it's usually important. Last question. What would be like your top piece of advice for early stage founders that are trying to find part of market fit? Focus on building relationships. Surround yourself with great people who can support you and be thought partners and then just be relentless and just try every door, push everything and don't take no for an answer. Those are the three things that I think I would give to anyone who's looking to find part of market fit. Bobby, thanks so much for jumping on the show, man. It's been great. Yeah, absolutely. My pleasure. And thanks for having me on. Wow. What an episode. You're probably in awe. You're in absolute shock. You're like, that helped me so much. So guess what? Now it's your turn to help someone else. Share the episode in the WhatsApp group you have with founders. Share it on that Slack channel. Send it to your founder friends and help them out. Trust me, they will love you for it.