[0:00] what is B2B dataThe guest today is Chris Pisarski. He is the founder of a seed stage startup in New York City, and he's started a bunch of companies before as well. So hi Chris, what does your startup do? Hey Tony, I'm building a data startup and we aggregate B to B data so agents and models can use that data mostly for B to B purposes. But what is B to B data and how do people use B to B data? O we help mostly sales, recruiting and investing agents and teams use our data to perform functions. So basically we power a lot of the popular sales and recruiting platforms you see out there. And now we're starting to power even the sales teams and recruiting teams within big companies. What's example of a data point and what's example of how that data point would lead to like a real world impact? Yeah. So basically sales teams or sales platforms, they need to find the companies and people that, you know, they want to recruit or sell to. And so we aggregate all that data from all these different sources. You can think of us as sort of a Google but for AI agent data, right? So we're going to pull like, you know, your profile, where you worked, where you went to school, your personal website, your GitHub, right, posts, social posts that you made. We pull all that information together and deliver it to companies via API. So they can now sell to you or recruit you or invest in you, right? That's what we do. Gotcha. So there's all this information out there that the company before didn't have access to. Exactly. Interesting. So like when you say agent, are these like AI agents then? Like how do AI agents use this different from from a human agent? Exactly. So before AI agents, right, humans really gather data. And the way we gather data is we go to Google, we type in something, we hit search, we find the result, we hit click. So if I was building a profile on you because I wanted to sell to you, I'd have to search Tony Chen, then there would be a million of them. Then I have to search Tony Chun Cornell. Then I maybe that gets you your profile. I go into your LinkedIn profile, I copy and paste into a Word doc. Then I go to your Twitter. Then I go to your GitHub, then I go to your personal website. Then I see whatever else is out there on you, right? Agents are going to consume data much faster, right? And they're going to consume a lot more data and then condense it and, and get only the important information. And so the way they're going to do is they're not going to go to Google to get information. They're going to potentially use our APIs, right? And that's going to be a lot easier. And so AI agents, we think are going to do most of the commerce in the world starting maybe, you know, in the next few months. And so we're very primed to be able to service them when they're looking for data. So the AI agent would be able to use all that data to make the decision. Yeah. And so basically what you're going to have, Tony, OK, you're going to have like think of any role right now. Let's think of a SDRBDR. It's very popular role in sales. What they do is they they find people so the company can sell to, to, to the right company in person. They just make, yeah, well, an agent's going to do that a lot faster, a lot better. And it, it's just going to be able to do next what a BDR does at a fraction of the price. Now, I'm not hoping for lost jobs or anything like that, but that's just where the world's going when the AIS, when the BDR or STR agent is doing it, they're just going to need data to be able to do this. And they're not going to use Google. Google's made for humans. Cross data becomes the Google for AI agents. That's what we're hoping for. Gotcha. In the process of a job interview they would also the human would also input data about themselves when they apply for a job. So that's separate. Then you're just like before. That could be another use case,

[3:35] example recruiting websiteright? So say you run a recruiting website or you run a a company and you get it like we're talking to a Fortune 100 company right now, right, right. They get 10s of thousands of applicants a month right now a human has to go through all those applicants all throughout history. A human has to look at each one, right? Maybe they use some sort of algorithm to cut it down, but it's not efficient. A human source. Look, basically what an agent will do is they'll look at all the profiles really quickly. Within a few minutes. They're going to use our AP is to power that to see any other information about that they're going to be able to see, OK, this resume came in, but this applicant came in. Let's look at the LinkedIn profile. The LinkedIn profile only has three connections, fake. For example, a ton of recruiting teams are faced, are bogged down with a bunch of fake applicants. We cut that out. Maybe now we can, you know, we can also see are there any like arrest warrants out for you, right? We can do a search for that. We can look at all your social media tweets to see if there's anything offensive, right? And an agent will do that over our data, using our data. And it'll just be much faster, much more efficient. You know it's going to be better on in so many vectors, in so many ways. So there's a before there was like a standard operating procedure, the recruiter what to do these 10 steps and then you're able to shrink that. Yes, yes. Gotcha gotcha. So like what if they just typed the person's name into like a ChatGPT or something? How does that compare with using crust data versus using like a generic AI? Machine.

[5:05] more specialized data than foundation modelsSo with GPT, the data that it collects or has is very limited. So if you went in and you wanted information on all your tweets, it's not going to be able to pull that right. You need to specifically go get certain information structured in a certain way. PPT is just, you know, all these models are limited in terms of the type of data they can get. And so that that's where we come in. Gotcha. So you have like much more specialized information about each person? Yes. Like does it have access to more data than the human would have access to? Like before the human would Google or do like background checks. Does the startup have more data than the human would get as well it? Shouldn't have, let's say, more access. It shouldn't have more access than Google has. It's just gonna get it in a way that an agent or people can use programmatically, right? Again, when you access things via Google, it's on websites that are made for humans to do it programmatically at scale. That's where we come in. Gotcha gotcha. When you want to do a programmatically at scale. So with the like the human recruiter, would they use your software directly? Where will another AI agent company use your data? Right now it's it's both right. So we you know, most of our customers are human and they're building platforms and they're building internal tooling and using our data, right. But the future is agents we think are, are going to use it right. And and that's where the world is going. And so we're built really for for agent use. For agent use gotcha and then like so it'll be an agent using your agent data. Like is there another agent using that agent? Like how deep does it go until humans start to use it? Well, right now, I mean, again, a human can can use it right now. They just have to call our API. But I think in the future, well, it'll be agents, you know, maybe you've heard this already. But and again, I'm, I'm not some crazy, you know, tech person or anything like that. I'm just, I'm, I'm a normal guy. But I do think that that the future is agents buying from other agents. I think that's very much in the near term, right? Basically, Tony, when you say you're going to want a pair of new shoes, your agent will know everything about you. Maybe you give it a little prompting, but it'll go out. It'll search for the type of shoe that you want, it'll negotiate if necessary, and it'll make the purchase. It'll have its own credit card. It'll make the purchase, enter all the the address information and that you'll, you'll get the, you'll get the and it'll do this all with another agent, right? And it'll, and it'll also go up chain. It'll be more complex. You'll run a business, you'll run a team, a procurement team that needs to buy. Let's say you're, you're out of business and you need to buy insurance for the business. Basically you would tell the agent or the agent will already know what type of insurance you need. It'll go out, it'll get quotes from all these different other companies, probably through an agent. It'll negotiate with the agent. I'll do the legal, it'll do all the negotiations on legal side, the finance side, all of that. It'll know what to do and it'll make the purchase and you literally won't have to do anything. So that's the future of how commerce is going to go, both on the consumer side and the business side, though you know, commerce will pick up, there'll just be tons of interactions all day, every day, like things will just move much faster. Gotcha, gotcha. So, so when you're like deciding when you're building AAI startup, for example, how deep, like how, how vertically integrated you try to become. Are you going into the recruiting layer of like picking up the actual candidates themselves? Or are you saying, I'll stop at this point and let another AI agents build A next layer? And like how deep down do you go to the foundational model layer as well? Which How do you decide? How How? How did you grow it? Great question. I think that's going to be decided by the market and how things evolve. As of now, we're happy being the data infra layer. So we wouldn't build our own model or foundation layer or anything like that. Basically we aggregate the raw data and we're adding a machine learning sort of team to make the data a little more usable, get a little more insight from the data. So we're not just giving you the raw data. Maybe we'll tell you if someone's searching all the, you know, job postings in New York City, right? Maybe we give them all the job posts in New York City, but they can get more specific with what they're looking for, right? So we give them a derived data. But as far as going beyond that and actually doing the recruiting and building the recruiting agent, you know a lot of our customers are already doing that. We want to support them doing that for now. Gotcha, gotcha. So, so like what are examples of

[9:24] listen to customers when market changes rapidlylike how you decide what the market needs? Like you just listen to your customers, you'll say, oh, it's pretty easy to build this part. I'll just add it on as well. Like how do you decide I? Think it's a combination, right? You want it to be listening to customers, but things are changing so fast. You got to listen to customers and kind of keep an eye into the future and data, Tony, it's hard to develop a Moat or a differentiation, right? Because data is this finite universe of information. And yeah, you can be a little fresher, you can be a little lower latency, you can have a higher rate limit, you can have better infrastructure, but it's hard to super, super, super differentiate, right? And so to differentiate, you got to be innovative. And I think if you just listen to customers, yes, that helps a lot. And that's what you should do. We also kind of have to keep an eye out at what could be different and kind of think on your own and try to be innovative. Gotcha. How about like coverage, like there's like 6-7 billion people in the world? Like like do you have data on all six 7 billion now? Like how important is the coverage of it? Yeah. So coverage is super important. You know, we want to cover all the relevant people and companies out there. And that's where we shine because we're one of the only real time data providers. Real time doesn't just mean fresh, it means being able to get all the current data that's out there in terms of breath. And so we like to say we cover every single person or company that matters. And our definition of matters means they have a certain presence on social, one of the socials, right? It could be LinkedIn, it could be other socials. And that's what we're able to provide. And so when you use our product, you know that you're gonna cover all the relevant people and companies that essentially matter. Interesting. So well, in terms of these cases, you said that there's a sales, there's also the human resources. How similar are there use cases? Did you consider just building one data company just for recruiting, another data company just for sales? Or is it just similar enough that you could combine with the 2? It's similar. I mean, I think that the data that you need to conduct research for sale is the same data just in a different form or different parts of it that you need to recruit somewhere, right? Like I want to know where they're working now, where they went to school, you know, their picture, right? That the things that you know, you would have in a certain platform that you're, you know, like like LinkedIn or Twitter or you know, any of those platforms. So it's pretty similar. I think that there's certain benefit to maybe focusing on one of the verticals, but for the most part the data overlaps so well and for different use cases that it's it's OK to just kind of attack data as a whole. So what about the marketing and sales of the data platform over time? You said a lot more agents will be using it. How do you reach more agents for marketing to humans?

[12:04] marketing to AI agentsThere's the three C's and the four PS. Is there a similar 3C4P for models as well? I think people are still figuring it out, right? So we think agents they're going to they might use, let's say GPT, right, or or clod and try to figure out what's the best data for this particular use case that I'm that I'm doing right. So you want to make sure that you're appearing as a mention or recommendation on these models, right, on these on these platforms. So it's just like a SEO, right? You're, you're putting out information that the agent can go and find. Maybe they're looking at a blog post or something like that, right? They're gonna do their research. You're gonna collate a bunch of information and then decide based on that. And so you need to create really good content and make sure that your content is visible and is authoritative. So very similar in terms of marketing from that respect. As far as down the line, I think there's companies dedicated right now that are being built right now to help solve this problem. And so this will be a huge, huge space going forward. People are just kind of starting to figure it out right now. Especially if it's like an agent on top of Asia on top of Asia, then there's no human that making any decision along the way. I think that's probably what it's gonna be. I mean, the human will you, you know your, your, your model that, you know, whatever you use personally is going to know a lot about you. And there's going to be agents built into that there. They already are, right? And it's just going to know that when it's time to buy your groceries, they're just going to buy them. When it's time to, you know, restock on whatever you need, it's just going to buy it. And, you know, even if you have a wedding, right, it'll say, oh, I, I need this. And you know, it'll just buy it for you. Do you still have like the pilots where you have like a pilot try out this data and then if it's good then they buy it like it's was that too complicated infrastructure for models? We we do so we have a a pilot you can come and get a demo right. You can see how it works, how an agent will use it. Yeah, will have a free tier where they can come and just use gotcha, use the data and decide I want to pay for more credits. That's so interesting, just like the buying cycle wouldn't be like months, then it'll be like 3 seconds and then they can make the decision. Yeah, it could have huge implications for commerce as a whole. Tony, where people are buying from each other, businesses are buying, and all this commerce happens and revenue gets stacked and then it leads to earnings and GDP growth and all this stuff. When Asians are making the buying and selling decisions, it could be a lot faster and it could have crazy implications for, you know, earnings and GDP and all that. I I, yeah, who knows what happens? Yeah, things can happen so much quicker without humans. Like how? How about like your company? Do you buy data from other agents as well? Do you buy stuff from other agents too? Like how does the downstream? Look for we mostly don't. We're a primary indexer of data somewhere to Google, where Google indexes the data themselves and they hold the data. And a lot of our competitors buy data from each other or buy data from a singular source. I think obviously there's, there's down, there's pros and cons to them. Yeah, downsides. So we index most of our data or there's one or two data points that we do buy, but 99% of our data we we index ourselves. Gotcha. Interesting. So, so of all the businesses that you started, like how did you decide to start this business? At what time how did you decide what was the right time to start this one? So as far as timing, I had stopped another company, took some time off, worked on an agency for a bit and I just missed building things at scale, right, Building a team and going after a big problem and it was just missing. It was just, yeah, I'm getting up there in age. I'm in my mid 30s now and I was just like, OK, I know how hard it is to start a business. I really want to do it. Now is the time. Don't waste another second. So in in mid 2023 I started, I actually started looking for a Co founder. I know I can't do it alone. So I started looking for a Co founder for anyone listening that that's a huge problem people solve or face. They can't find a good partner or Co founder. I found mine on Y Combinator Co founder matching platform. I found actually a good amount of people on there. And I went through the whole interview process and took it slowly and went through different work trials with people and ultimately found someone that I really jived with. And here we are later two years coming up on three years later from our first meeting. Wow that's a long time. What do you look for in the Co founder? For example, when someone wants to start a company, they want to find Co founder. What should they look? For I think first match and temperament, right, you know, I think you guys have to be on the same page. I mean you can definitely complement each other in terms of skills and personality, but ultimately, right you you want to just kind of have the same overall vibe and temperament, right? You want who thinks big? You want someone who's action oriented, not someone who just thinks I met so many people on there that, you know, we're afraid to actually start building, afraid to take the jump, afraid to quit. You want someone who's already building right, or who's already built right, who's ready to go. So action oriented thinks big, if you're a thinker, right? You have to be on the same page. Do you want to build bootstrap? Do you want to go after a big market? I think a good temperament, whatever you decide that is, I think those are maybe 3 things to start. From Gotcha, Gotcha. And then how did you build up those temperament yourself? Like where you like this in high school or you like this in college? Like how did you build this temperament up yourself? Yeah, I think I've been described as generally relatively calm, relaxed, right. But I appreciate that and other people who are able to kind of control their emotions and probably developed in, in childhood, frankly, I think some turbulence and chaos growing up. And you just kind of try to ride through the storm and develop that. And it just, it kind of took me, took me along and you know, at certain times it served me really well. But I think that's how I developed it, starting in in in childhood. How about like the thinking big? Thinking big also probably comes from something that happened in in in childhood, but maybe a need to be recognized, right? And need to have some sort of yeah, I don't know if respect is the right word, but but something around that. And that's probably how it developed. I look at it now more as of a opportunity, right? Like you kind of one shot at things and why not go, why not go big? It's it's exciting to go big. It's easier to recruit when you're going big. It's funner to go big, right? You go slay a big dragon. So it probably started off as some sort of thing in childhood, but now it's like, OK, yeah, go after something big. And also from a business perspective, bigger market means more winners, more revenue, right, More opportunities, it's more exciting. So I'd say probably morphed into that. Gotcha. Very cool. And then so you said YC helped you found found find your founder as well.

[18:46] how YC is helpfulHow? How else did it help you? Yeah. So we met on YC Co founder matching platform, which is not part of the the actual program. It's just this, it's just this tool they have for people not at YC. So we met when we weren't in YC, we didn't intend to go into YC. We started just building our product, but then we saw that, hey, there's something big going on here with agents, right? The data market could get a lot larger. So let's apply. And we applied and we were two of the oldest folks in the batch, right? It's, it's a, it's a young person's game now. But we were, you know, I was again in my mid 30s and so is he. And so, but why is he helped in a ton of ways? I mean, it's, it's, it's countless ways. If I had to kind of give you a few points of where it really helped, one is it actually taught us to think really big, even bigger, right? It's, it's like, no, no, no, think of it. Think of what happens at the market's maximalist view, right? What happens if agents completely take over or, you know, data just becomes this completely undervalued asset and it's, it's so valuable everywhere, right? It's like, what happens then, right? It teaches you to think big in terms of speed. However fast you think you're moving, you can move 10X faster, 10X faster. You can get to where you want, you know, next week, but think, you know, think 2X instead of 1X what you're going to have, you know what's going to happen next week. So it teaches you that a next level of speed or two, 3 levels higher speed is certainly attainable. It holds you accountable. I think the, the best thing that happened is you just have to publicly, you're publicly accountable. You make a goal to everyone in your group. You have to hit it. You know, there's no excuse. And yeah, I think it's probably those three things. I think it's. Think really, really big and then think even bigger, think faster, and then think even faster than that. And then yeah, like put out these aggressive goals and just hit them. There's no excuse. Just hit them. And I think that's what I I really internalized from YC. Interesting. Did you internalize that through like the dinners that they have? They're like what was like the like you sit there and then you internalize. What was the process of internalizing I? Think I internalized it from two things. One is we have a bi weekly meeting with our group partner. Ours in this case was Gary Tan. And you just kind of learn. We'd set a goal and he'd say way that's too small and that's too slow. And he'd push you to think bigger or faster. And then you'd say the next one he's like, no, that's still too small, that's still too slow. And so it just keeps leveling up. He's seen the best. He's seen what great looks like, he's seen the best of the best. So he can say no, no, no, no, like to to really reach escape velocity as a startup, you need to just be fast and you need to think big and you need to constantly go out. So I learned that there and then, yeah, we had like AI don't know, once a week or once every other week dinner where they would bring in in our case, you know, Sam Altman came in, you know, the founders of Stripe came in, the founder of Airbnb came in, right. All these successful founder of Twitch, all these successful entrepreneurs. And you know, you learn from these stories of how fast they moved, how big they thought, how they never took no for an answer. And that also gets internalized from those dinners, all those, all those lessons and learning. So it was a combination of those two things. Like did it help with like the like the business specific ones, like finding customers, finding like employees and finding investors like to help with those business specific? Ones it did for sure on the finding customers and finding investors, right. So they have something called the sales conference and it's I think it's one day long. They bring in all these really successful YC founders who do a good job selling and running sales teams. And I learned a ton. I learned so much on that day specifically from one person they brought in. I just, I thought, you know, I've, I've been building companies for 14 years at that point. And I thought he just illuminated so many things that I just didn't know or I learned a long time ago. So I learned a lot about sales and then investing. Yeah. They, they teach you how it,

[16:29] co-founder matchingFor I think first match and temperament, right, you know, I think you guys have to be on the same page. I mean you can definitely complement each other in terms of skills and personality, but ultimately, right you you want to just kind of have the same overall vibe and temperament, right? You want who thinks big? You want someone who's action oriented, not someone who just thinks I met so many people on there that, you know, we're afraid to actually start building, afraid to take the jump, afraid to quit. You want someone who's already building right, or who's already built right, who's ready to go. So action oriented thinks big, if you're a thinker, right? You have to be on the same page. Do you want to build bootstrap? Do you want to go after a big market? I think a good temperament, whatever you decide that is, I think those are maybe 3 things to start. From Gotcha, Gotcha. And then how did you build up those temperament yourself? Like where you like this in high school or you like this in college? Like how did you build this temperament up yourself? Yeah, I think I've been described as generally relatively calm, relaxed, right. But I appreciate that and other people who are able to kind of control their emotions and probably developed in, in childhood, frankly, I think some turbulence and chaos growing up. And you just kind of try to ride through the storm and develop that. And it just, it kind of took me, took me along and you know, at certain times it served me really well. But I think that's how I developed it, starting in in in childhood. How about like the thinking big? Thinking big also probably comes from something that happened in in in childhood, but maybe a need to be recognized, right? And need to have some sort of yeah, I don't know if respect is the right word, but but something around that. And that's probably how it developed. I look at it now more as of a opportunity, right? Like you kind of one shot at things and why not go, why not go big? It's it's exciting to go big. It's easier to recruit when you're going big. It's funner to go big, right? You go slay a big dragon. So it probably started off as some sort of thing in childhood, but now it's like, OK, yeah, go after something big. And also from a business perspective, bigger market means more winners, more revenue, right, More opportunities, it's more exciting. So I'd say probably morphed into that. Gotcha. Very cool. And then so you said YC helped you found found find your founder as well. How? How else did it help you? Yeah. So we met on YC Co founder matching platform, which is not part of the the actual program. It's just this, it's just this tool they have for people not at YC. So we met when we weren't in YC, we didn't intend to go into YC. We started just building our product, but then we saw that, hey, there's something big going on here with agents, right? The data market could get a lot larger. So let's apply. And we applied and we were two of the oldest folks in the batch, right? It's, it's a, it's a young person's game now. But we were, you know, I was again in my mid 30s and so is he. And so, but why is he helped in a ton of ways? I mean, it's, it's, it's countless ways. If I had to kind of give you a few points of where it really helped, one is it actually taught us to think really big, even bigger, right? It's, it's like, no, no, no, think of it. Think of what happens at the market's maximalist view, right? What happens if agents completely take over or, you know, data just becomes this completely undervalued asset and it's, it's so valuable everywhere, right? It's like, what happens then, right? It teaches you to think big in terms of speed. However fast you think you're moving, you can move 10X faster, 10X faster. You can get to where you want, you know, next week, but think, you know, think 2X instead of 1X what you're going to have, you know what's going to happen next week. So it teaches you that a next level of speed or two, 3 levels higher speed is certainly attainable. It holds you accountable. I think the, the best thing that happened is you just have to publicly, you're publicly accountable. You make a goal to everyone in your group. You have to hit it. You know, there's no excuse. And yeah, I think it's probably those three things. I think it's. Think really, really big and then think even bigger, think faster, and then think even faster than that. And then yeah, like put out these aggressive goals and just hit them. There's no excuse. Just hit them. And I think that's what I I really internalized from YC. Interesting. Did you internalize that through like the dinners that they have? They're like what was like the like you sit there and then you internalize. What was the process of internalizing I? Think I internalized it from two things. One is we have a bi weekly meeting with our group partner. Ours in this case was Gary Tan. And you just kind of learn. We'd set a goal and he'd say way that's too small and that's too slow. And he'd push you to think bigger or faster. And then you'd say the next one he's like, no, that's still too small, that's still too slow. And so it just keeps leveling up. He's seen the best. He's seen what great looks like, he's seen the best of the best. So he can say no, no, no, no, like to to really reach escape velocity as a startup, you need to just be fast and you need to think big and you need to constantly go out. So I learned that there and then, yeah, we had like AI don't know, once a week or once every other week dinner where they would bring in in our case, you know, Sam Altman came in, you know, the founders of Stripe came in, the founder of Airbnb came in, right. All these successful founder of Twitch, all these successful entrepreneurs. And you know, you learn from these stories of how fast they moved, how big they thought, how they never took no for an answer. And that also gets internalized from those dinners, all those, all those lessons and learning. So it was a combination of those two things. Like did it help with like the like the business specific ones, like finding customers, finding like employees and finding investors like to help with those business specific? Ones it did for sure on the finding customers and finding investors, right. So they have something called the sales conference and it's I think it's one day long. They bring in all these really successful YC founders who do a good job selling and running sales teams. And I learned a ton. I learned so much on that day specifically from one person they brought in. I just, I thought, you know, I've, I've been building companies for 14 years at that point. And I thought he just illuminated so many things that I just didn't know or I learned a long time ago. So I learned a lot about sales and then investing. Yeah. They, they teach you how it, what it is to to raise from a position of strength, which 99% of founders kind of go in hoping or they're nervous. They're not. They expect Octa raise. It's like, no, no, no. Like it's almost like seeing behind the curtain in the Wizard of Oz, right. You learn that actually most good deals that you see out there, there's multiple VCs chasing one deal. They're chasing the founder. We have it in our heads, especially in New York City or the East Coast. Hey, we have to get that investor. We have to chase the investor to kind of, you know, have all these meetings. No, it it really is the other way around, where you build something great and investors chase you and you have to choose which investor makes sense for you. That's how it really should work. Yeah, once you have that mindset, it makes things a lot easier. Yeah, Yeah. Those are the they would chase you then. Interesting. Is that because San Francisco has so many and like YC has so many investors around it that it helps? No, I just think they see what great looks like. I think they, they've seen so many companies raise so easily and at such high valuations that have to say no to so many investors that they know that's, that's what happens all the time. And they kind of tell you that And they, they tell you also obviously tactics and strategies to, to do a good job. But but that that's the most important part is just knowing that you have to think of it as like an inevitability, just like your revenue growth, just like the fact that you're going to, you know, once you start thinking, you know, instead of helping things will work, or once you start thinking in inevitabilities, it changes so much. Interesting. Well, all I got like earlier you were talking about your company, about the crust data, like what makes the company great? Like when a VC or employee is thinking of where to go next? Like what makes the company great?

[24:19] building momentumWhat makes a company great? Yeah, yeah, like your current company. I think it's momentum. I think momentum is the most important thing in an early stage startup. It's so hard to get. Once you lose it, it's very hard to get back. Momentum attracts customers. It attracts referrals, it attracts candidates that want to work with you. It attracts investors, obviously, right? Maybe something a little more practical. What makes a company great? I think it's attacking a big problem in an exciting market. If you can say, hey, we're trying to be the X for Y, we're trying to be the Uber for whatever, right? We're trying to be the Google for X, you know, So simplicity in describing a big problem really attracts a lot of people, sales, talent, investors, right? And then if you have momentum paired with that, it's a really exciting company. Got you. Interesting. So, so so far like the bigness of a problem, is it like the dollar of the market cap or and there's like the momentum is that like dollars per day? Like what's the metric? It's both, it's both a quantitative and qualitative feel, right? So obviously it's a big, big market, right? But it's also, how does it feel, right? When, when let's say this company called Figure, which is building humanoid robots, right, started, I don't know, five years ago, you know, the market, who knows? It wasn't, there wasn't really a market, right? There was an estimated market, but there's this feeling that you're kind of building part of the future, right? And so like, well, people want to join that. They want to be part of this crazy grand vision. And so I think it's both. I think you obviously want to say, hey, I'm building a big market where there's a lot of dollars and sales people care about that. But you also want to say you're building the future. And it doesn't matter how big the market is. We believe that the future is going to be this and it will be a big market. That's what engineers care about. So you need to tell both sides of the story. Like, there's no robots in this room at all. But in, like, 10 years, there might be a lot of robots. So that's like the future Loki. Yeah. So future. How about momentum? Like how do you demonstrate momentum? Momentum is, I mean, 90% of it is your, your, your revenue, right? It's if you're, if you're AB to B company, it's revenue. If you're a consumer, it's, you know, maybe users or retention, but it's revenue, right? You revenue Trump's everything, right? When when we're trying to go recruit, we tell our recruiter, you know, the recruiter leads with, hey, we grew from X to Y in 12 months, OK. You know, that's what draws a lot of people in. And I think it's it's revenue. You know, you could say it's hiring great talent and all this, but at the end of the day, I think it's it's traction. It's traction out there, right? It's like what what has a tremendous amount of revenue right now Anthropic, right. OK, yeah, it's revenue. But are a lot of people just seeing the revenue? No, they're just seeing anthropic everywhere. It's just like you become ubiquitous. You become ubiquitous in your market And and that's what leads to momentum. Interesting. So there's revenue and there's also ubiquity. Like how do you, I think this feeling of ubiquitousness? Do you go on Tech Crunch a lot? Do you have a big press team? I think social media, I think for example, here at Crust Data, we encourage everyone to post everyday and a lot of those posts do really well. And once you just you keep appearing over and over and people keep just seeing you, seeing you, that's ubiquity, right? I mean, if we at a larger scale, right, you have a big ad budget and you are on TV commercials and you're on billboards and right, But at a small company or a smaller company, I think ubiquity means just being in front of your buying based and to be in front of your buyer base. I think it's just putting out content. Gotcha. So, so then that creates momentum. Interesting. So, so, so tell us more about your entrepreneurial journey until like before you started cross data like the other startups you've built up And what like did you like? Do you learn a lot from the previous ones that brings into this one? Like is it easier the more startups you start? I do truly think it's a lot

[28:04] starting multiple startupseasier the more startups you start, especially if it's more than one. I think if you've done 2, you learn so much. Yeah quickly on the entrepreneurial journey. I, you know, joined up with a company called Privco was the first non-technical hire and that was in 2011 as a data startup, believe it or not, I was there for seven years. I did everything there, you know, first product person first first, you know, ran the sales team for a bit, ran marketing, ran OPS COOCEO for about two years. You know, took it from pre product to millions in revenue. So learned a lot there, just earned a ton. And then I, you know, started a consumer app, right, called Chat and raised a little bit of funding for that, Built a small team, didn't quite hit product market fit, but I did that for a few years. I started a, you know, ran an agency, a software development agency, started a few small companies here and there and then ultimately press data. So yeah, I've been through a lot of 0 to 1 journeys. Gotcha, gotcha. And did you always say in the same industry like I guess the data one from before the data now like what were the similarities in between all of? Them, yeah, data for sure is something I've learned a lot about in 2011 and from 2011, 2017. So I knew what a good business data could be and I took some learnings and ideas from there. And yeah, then it did a consumer startup, which I think is really hard. I don't recommend it for most people, but you learn a lot there. You learn how every user interaction's very important. You learn sort of the micro details of things, right? You learn retention and you learn about e-mail marketing. You learn think B to C people do things a lot better on the marketing side and you learn just and on the product and designers design side as well. So you learn to just go really deep into the details. But I would say, yeah, to answer your question before that, what are the learnings that I've carried across or you know, I think it's, you know, it's it's cliche, but the number one, it's people. And it's not just getting the best people, it's making, it's, it's continuing to motivate them. It's, it's making them happy. It's not hiring too early. It's not hiring too late. It's not over hiring when you think you have a deficit somewhere. There's, it's all, there's a lot of like psychological biases that come in when you hire people And being aware of that, it's, it's very difficult. I still don't get it completely right, but I'd say that's a huge, huge thing that I've learned. Gotcha. How How about like did the customers from previous businesses follow you on to future ones? Employee, I mean, the time off was too long, OK, I mean, maybe a couple here and there, just contacts of mine, but no. And, and things are changing so fast in data that, you know, yeah, it's, it's it's changing so fast. People move jobs. I mean, from 2017 to 2024, I didn't really keep up what was going on in the data space too, too much. And even now from when we started, the data space is a lot different. So and then so the two two the Cornell days like did any like what were helpful classes at Cornell? Did you pick up any entrepreneurial spirit at Cornell? Like what was the Cornell experience like? I did. I did. I think it was called the E ship

[31:01] entrepreneurship learnings at Cornelllab when I was there, something like that, right? I took I took a course or two in there and learned a lot about how certain people developed businesses, right, and how it got off the ground and there was guest speakers and things like that. I would say I learned how to think at Cordell, right? I took a lot of courses that I took an eclectic, but it was cool is I took a very eclectic coursework or course load. And so you just learn how to think different perspectives and different modalities and, you know, to kind of be your own person, right. And but, yeah, there is an entrepreneurial support system even back in 2010 and 11, right, when it wasn't a huge thing, especially on the East Coast. I think Cornell was a little at the game. I'm sure now it advanced a ton since then. And on, on, on in that vector. Yeah, I remember, you know, learning a lot in those classes. Yeah, that makes sense. And then like the the first job you took out of college was a start up too, the the perfect one. Like how did you decide to work at a start up? So it was because, you know, I remember being at Cornell and thinking about what I wanted to do and a lot of people in 2009, 2010, 2011 were going into finance for consulting and or law, right? That's basically you went to medical school in those three places and that's where most people went, right? And I remember like studying for some interviews or potential interviews using a certain product called Vault. I don't even know it's a thing anymore. But there was like the Vault guide to banking, the Vault guide to finance and consulting, and I used that product. And then I met the guy who started that company. He was starting Pro, the company I eventually joined. So I thought that connection, wow, he built an amazing product, you know, achieved financial independence and wealth from it. I'm interacting with this guy, he wants to hire me or he wants to work with me, I should join up with him. And so that's that story came to be. Was this like through a job

[32:54] finding the first startup job coming out of Cornellapplication or like how did you meet up with a guy and how did you? I met him at a networking event or I met, sorry, I met I think his CTO at a networking event, something like that. And yeah, that's and then we got connected quickly and I went through the whole interview process. They were hiring at the time and I was walking into the room and they subletted an office from a real estate agent. And they're like, you know, I remember going back for my second interviews, like, wow. You know, they've talked to hundreds of people or dozens of people, and you're the only familiar face that they seem, you know, I've seen return. I'm like, oh, wow, this must be a really tough job to get. But yeah, that's how that's how it came to be. Well, A. Lot of students these days, they apply on a handshake, they apply for jobs on LinkedIn. If you are trying to pull something that might be helpful, I would say that I'm a founder of a 30 person startup, right? We're basically approaching 88 figures in revenue a year, right? Well past Series A in terms of traction. And I say all that for context in that I get honestly a couple dozen reach outs, especially when we have an active job posting personal reach outs a day. And I try to review as many as possible, but I only respond to maybe one every other day or one every 3rd day. And that's if someone really writes something that cuts quickly, concisely, and personally. And most people don't do that. They're writing messages. But it's so clearly AI. Or even if it's not AI, it's just not interesting or it takes too long to get to the point. Like you're not going to separate yourself unless you have it in. It's so crazy how low the bar is. You just need to write something compelling and it's, it doesn't take that long. Just no one's willing to do it or go through an, an, you know, an intro, but show off what you're good at, right? Like I'm getting, if I'm getting all these like software engineer at Cornell or any other school, you know, average GPA or above average GPA, random internships, like you're competing with everyone. They're all the same, right? You need some sort of angle, some sort of intro, some sort of something to rise above the noise. And it doesn't take that much and you know, no one's really able to do it. Gotcha, gotcha. Well, what makes something compelling, for example, is like how big they think, how fast they move. It could be anything, right? It could be tell me something cool, tell you know, show me that you've done some research on something and write something personally to me that's going to get me. Don't put it in the first sentence. Don't like thank you for reviewing my resume or Hey, Chris, hope you've been really well. I once I see, because I get so many that I know it's not something I care about, right, like hit me early. Don't make it too long, just tell me something really interesting or make it make it so unique, right? Hey, I listen to you on on Tony's podcast. I love when you said this. How would you be open to speaking for 10 minutes, right? Like. That's unique. Even if I'm even if I'm really busy, I'll at least say, hey, maybe not now, but in a few weeks or, you know, you took the time and again, but also with AI, some people might just scrape this podcast transcript and and right. So make it very clear that you're a human writing it, right as much as you can, right? And so, yeah, if you put in the work, you know, it'll open doors really well. And then from your perspective, like did you find, do you use your own software to find good candidates as well? Like are you able to do more outbound now? Yeah, we do outbound for sales. We do outbound for recruiting all through our own product. It's been great. Yeah. Are most of your hires like Outbound now because you need to recruit so many more people? Not yet, not yet. We're just kind of in the early stages setting that up. We work with some pretty great recruiters. But yeah, I think, I think we're, you know, we have started testing our own outbound using, you know, clawed plus our APIs and it's looking really interesting. Gotcha. The overall AI landscape, where do you think like the AI landscape will be shifting over time? Like where is a good place to start startups if like current

[36:32] where to build an AI startupstudents interested in starting a startup? It's very unpredictable, right? Say, you know, you never know when the models or the large, you know, language models are going to just wipe something out that you've built or just do it themselves. I would say, frankly, an infrastructure, right? I think fintech is pretty hard to disrupt, right? So Fintech infrastructure beyond that, it's very hard for me to recommend what is safe right now. I think you should just find something that you really a problem that you're really interested in solving, just just go after it. But I don't know what's a safe bet right now. And as far as being nimble and being able to react to changes in the infrastructure, how do you stay nimble? Just by working hard, common sense, keeping abreast of what's going on, not being too scared by the things that are going on, but being practical. If you see that this is gonna be disrupted, be, be, be ready to go. It's just, I think it's just paying attention. That's it. I mean, you got to put your head down and work really hard and, and ignore distractions, but you got to pay attention, right? And I think customers will help inform you, right? If you're talking to a bunch of customers and they're like, no, we use cloud for this now, OK, if multiple people can say that, then there's a thing. But if customers are, I think the ultimate signal is obviously pull, right? And even if you don't have a product yet and you're at the beginning stages, if you schedule a call and you pitch something and they want to do another call or they keep answering your follow-ups, there's something there. If they're not, hey, maybe they're not interested or maybe they're using something else right. But I think it's just paying attention, paying attention to how customers react, how they respond to things, how they respond to your follow-ups. Interesting. And then for the final question, what's the kindest thing that anyone's ever done for you?

[38:14] final questionWow, there's been so much kindness. I would actually say I'm going to, I'm going to, I'm going to put a cornelian out there. How's that? Yeah. You know, one of my first investors in my previous startup, my consumer startup was a Cornelian and he, he invested in that startup and it didn't work out. And I was, you know, devastated, obviously, when I shut down the startup, not because I, I failed, but you know, I, I had people believe in me and, and put a large amount of money into the startup and I kept putting off kind of telling him. And I finally told them. And he just, he just, you know, with complete grace was like, you know, totally understand. I, I feel mostly for you, Chris. I hope you're OK. And and he just, he totally just showed tremendous grace and and, you know, forgiveness, I guess, but I didn't know how that worked. And and then he invested in cross data. He's just, he's opened a lot of doors. I've been on calls with him where, you know, they didn't go well or I didn't do my best. And he never, you know, he never showed an ounce of disbelief or, you know, yeah, he just he's been very kind. And I think the fact that he saw me trying to chase a dream and, you know, invested, I think that was just a kind. So wonderful. Thanks for sharing, Chris.