What's Really Happening in the B2B Data Market? AI, APIs & the New Data Economy ft Tom Blue

Speaker A: It's a behemoth, you know, trying to tackle 250 million contacts and keep it clean all the time. It's so hard. They're using probably the exact same service and they're just selling it at different, different prices. But I mean just the sheer demand of it, it makes it profitable, you know. The Next Gen Sales Leaders Podcast Coffees for closers.

Speaker B: What's up guys? Welcome back to the Next Gen Sales Leaders podcast. My name is Benjamin Reed. I'm here with Tom Blue. He is the CEO and founder of Quick Enrich. There is a massive change happening in the market right now. Everyone is flooding away from ZoomInfo and Apollo and these big box data providers and there are more, newer, nimbler, better, cheaper, uh, uh, you know, data providers. And Quick Enrich is certainly on the cutting edge of data providing within the B2B lead gen sales, uh, and marketing space. So welcome to the show. Would love to learn a little bit about your background around how you got started and why Quick and Rich is on the cutting edge.

Speaker A: Yeah, yeah, great. Um, yeah, so, um, yeah, long story short, um, this probably actually won't be too short. But um, yeah, so, um, I have previously had a company called Lead411 and still do. But that's not um, kind of the thing that I'm focusing on. I'm focusing on Quick and Rich because um, like you said, things are really kind of changing uh, in the market. I, I believe, I think that the two things that I'm seeing the most is that um, you're able to verify data, um, much easier now than you could have like four or five years ago. It's just a lot simpler. Costs have come down, um, so it makes it a, um, lot easier to have quality data. Um, so I think that's like probably the biggest um, shift that's happening. Um, and because of that, like you said, there's all these other kind of providers coming out and so um, we know, we noticed that too. Um, you know we have lead four in one and lead 401 is like a, is more kind of like a web app really. It's just kind of focused on compiling a bunch of different data pieces together. We partnered with Bombora for the intent data. We track a bunch of different news events. Um, you know, we have the funding in from, for uh, funding information, the tech stack data, all that kind of stuff and it all kind of compiles in to kind of um, bring kind of like a lead score for people. And it's really just more for um, it's really just been more for sales people and, and, um, now that the market is kind of shifting and all these other people are kind of using clay or they're using Claude code, um, or they're just kind of building their own kind of systems, um, because it's so much easier to build your own system. Um, and they just want access to the APIs and they want it simple, they want it quick, um, and they want it accurate. So, um, that's what Quick Enrich is all about. It's a completely different brand than Lead 401. It's totally focused on, um, supplying data to those particular people.

Speaker B: This is a break in the podcast to bring you our sponsor, ReadyOps.com that is R E V Y O-P S.com, the number one GTM data management platform form for your GTM and revenue operations data on planet Earth. If you are using the modern stack of clay and instantly an email Bison and RB2B and Trigafi and all these various tools scattered across the wind, we unify all that data all into one place so you don't have any duplicates. A unified view of every single contact so you can see all the data and all the activities across all the platforms all in one place. It will allow you and your team to move faster, to get better attribution across the entire funnel and to make a heck of a lot more money for go to Reviops.com, check out how you can get started today with me on a call where we'll learn about your data management problems and how we can get you guys to the next level of growth. See you guys at reviops. Com. Go there now. It will be in the show notes as well down below. Let's get back to the podcast. Yeah. And so in lead 441 1, correct? Yep. And, and so what's the backstory of that, that, uh, company?

Speaker A: Yeah, so I had a recruiting business previously. So I started this so long ago. So it was like kind of a side hustle that I had, um, for a long, long time. So I had other jobs while I had, I had this while I had lead for one. So I was recruiter previously and the way that I found all of our new clients was just going after companies that got funded. Right. And that was like before people were, were really doing that and before people were really, you know, sending that much cold email, you know, is very, very limited amounts. Um, and so, you know, because you could get a kind of a bad name at that point for doing it, it was kind of like a, a. No, no, in some ways, in some circles I guess. Um, and then, so I built this newsletter. It was all it was, was just like this kind of newsletter that just had the funding events, um, you know, for the country. And this is back when the web was, you know, way smaller. Funded companies were much, much smaller. But um, people would just paid to get access to that newsletter. It's just an email, right? And then they would get paid access to get that. And then um, I would just look at like the company management pages and then pull the contacts from there. This is like way before LinkedIn, you know. So, um, people would just look at that and then um, we would give them the email addresses too of those like top people. But you know, the database was tiny, right? And um, so people would use that just to kind of go out and sell their own, their own, um, you know, businesses. It was ide, um, initially just for recruiters because that's what I was doing. And I thought it was like perfect because you know, once a company gets funding, they're going to be hiring a bunch of people. And so that was the initial target. But then it kind of expanded and other people started using it. Um, but it was like I really, I didn't have much of a business for a long, long time. Um, and then it finally kind of grew and um, I ultimately was able to get um, you know, a lot more data. There's just, there was just a lot more data that was available that you could, you could get access to. And so I just started, you know, we built crawlers and did these different things to be able to, to collect that information. Um, so, um, then it kind of expanded and then I, you know, we started adding on different data pieces and then you know, just kind of putting it inside the platform.

Speaker B: And so now is the platform a database? Kind of like Apollo esque or is it, what is the current version?

Speaker A: Yeah, it's, yeah, it's like an Apollo. I'd um, say, um, you know, similar to Apollo Zoom Info.

Speaker B: And then, and then Quick Enrich is API only. Or what's the difference between quick enrich and 41 1?

Speaker A: Yeah, right now it's basically an API. Only I am, I am planning on doing some other stuff. Um, but right now, um, it's just um, API only. I mean you can go into the, the web app, um, you know, and do like simple kind of stuff, like just enrichment, upload a list, you know, bulk upload, you know, do, do verifications, bulk upload, get More enriched, um, phone numbers, emails, that sort of thing. Um, but it's primarily built for like, gtm, you know, engineers or vibe coders.

Speaker B: And just for anybody who's new to the conversation, there's a lot of these micro data providers that are kind of popping up all over the place. I mean, Ocean IO is kind of like the lookalike one. And there's obviously, uh, it used to be Panda Match. I can't remember what. It's disco, like, is the one. Um, Blitz API is one that people are trying now. There's AI arc. I mean, there's so many of these guys. And then there's, um, tools like, um, you know, like Deep Line, uh, you know, there's tools like REV os, there's tools like, uh, Rev Code. I mean, there's a bunch of these tools out there that are kind of aggregators of the various tools, um, including, you know, what we have, which is REV os. So there's. There's a big market evolving around, especially because in the last three months when we're recording this, maybe this is so obvious in the future, but as we're recording this, there's a fundamental shift in how people are digesting data, which it always used to be through the interface and now it's more through the command line, through these terminals, through these AI agents. And so the interface is not as important it would seem. Um, and so I'm just, I'm painting that picture because I'm curious. You know, I'm sure, you know, as an entrepreneur, you're looking at the market dynamics, you're looking at the competition, you're looking at what's coming out. You're weighing the pieces of the puzzle, trying to say, okay, you know, where do we have a competitive moat? What do you think about that? I mean, how do you think about the positioning of your offer within that market?

Speaker A: Yeah, I mean, I. One of the biggest things that we're focused on is just completely making it unlimited. I think it's like one of the things that, that it's been a problem for a lot of people over the years is just this constant credits. And then it's just a kind of a, A needle in people's sides. So they're not able to just kind of use all the data that they need to use because they're constantly like, oh, oh, you. I need, I need to, uh, make sure that I don't waste any of these or whatever. Um, so our goal is to just give access, you know, unlimited access to, you know, all these GTM people as much as they possibly can. Um, so they can just use it and wherever they feel like, you know, they can use it inside their own platforms. You know, if they have their own tools, they can use it inside of their own, you know, GTM systems that they built just, you know, for internal purposes. Um, but it's just something that they don't have to worry about, you know, it's not a big bill, you know, so it's just here, here's the data you, you have, you know, have at it.

Speaker B: And what do you think in terms of, I've seen posts online that are claiming like, oh, data is a commodity now. And uh, and, and you know, anybody can go get data and there's people who are, you know, I, I, I can think of four or five guys who are just actively scraping the world at this point, to your point, you know, how do you, how do you think about that? Do you think there's such a big market, such a unquenchable thirst that it doesn't really matter how many of these guys come around and you think there's a piece of the pie, uh, for you guys or how do you guys think about that long term?

Speaker A: Yeah, I mean, I definitely, I mean, you're 100% right. I mean, it's, you know, way more of a commodity than it once was. I mean, tremendously, just in the past five years, um, that's just 1, 100%. Um, but the demand for it has just been so much larger. There's all these other tools that are, that are coming up that I've never even, you know, you know, I've never heard of, you know, and they're, you know, coming to us and saying, you know, we're interested in putting, you know, your data inside of our platform. And so it's just, I think there's just a larger appetite. The price has, you know, has definitely, um, gone down, you know, um, as far as like per record, I'd say, um, overall. But I mean, just the sheer demand of it, it makes it, you know, profitable, you know, so it's still very,

Speaker B: very profitable in terms of like the, I've heard from different people and I don't know much about the, the working is behind the scene of these companies. Right. So I, you know, I'm more on the aggregation side, not on the distribution side of data. Meaning my platform is a cdp, so it collects data, but we still, we don't provide data, you know, and we're not an Apollo, we're not a zoom info, um, So I talked to people that are in that business and they say that there's like several main kind of large wholesalers of data and then a lot of people are essentially buying and reselling that data, but maybe adding a twist or a cleanup mechanism to it. Uh, what is the business? I mean, where do you guys find the data? How much of it's proprietary? How much is it resold? Uh, I'm very curious about how that works.

Speaker A: Yeah, um, we're not one of those that does that. We do have one data provider, um, that kind of gives us access, um, to anything that they can kind of find on the web that they're crawling. Um, so we do have one data provider that does that. But it does, they do not, you know, bring in the emails, they do not bring in the direct dials. You know, they don't, they don't bring in all of that, that stuff, all those added pieces. So we've built out our, you know, custom solution to fill in all of those gaps. Um, but yeah, there are, I mean for sure, like, you know, there's a lot of um, people that are using the exact same provider, they're using probably the exact same service and they're just selling it at different, different prices, you know, um, so, um, yeah, so there, there's definitely, you know, overlap there with a lot of different providers. Um, I do know that, you know, with a lot of our, the data that we have there is, we're able to get m. Better coverage for people especially like in the U.S. canada and UK market. Um, you know, it's not perfect obviously. You know, we have, you know, data issues just like every single data provider out there, there's, it's like, it's a behemoth, you know, trying to, you know, tackle 250 million contacts and keep it clean all the time. It's so hard. Um, but we're, we really have been focusing on these kind of core English speaking countries. We're really good at those three countries. But in addition to that we also are any, any country that has like an English speaker, they have an English title. Um, then, then we hammer in on those. Um, so our coverage is really, um, solid. So there's, there's definitely a lot of stuff that we have that other people don't have. And, and sometimes there are things that they have and we don't have to. Um. But uh, but yeah, well, you know,

Speaker B: there's, it seems like there's categories of data providers. If you had to like create like a taxonomy of the types of Data providers. You're seeing like there's signal data providers and then there's like kind of these lookalike, ocean IO type of providers and there's more of like the standard big box Apollo type data providers. Then there's these resellers and then there's the enrichment providers. Uh, I mean is that comprehensive or would you order it in some different ways?

Speaker A: No, yeah, that's pretty comprehensive, I'd say. Yeah, I mean there's always been kind of like, like niche kind of um, like different data providers I think. You know, you do have to be careful because you don't want to just be pigeonholed into that one particular thing. You know what I mean? Like, okay, we're just look alikes, you know what I mean? So I think, I mean I know you can expand your brand and, and you know you're able to, to change things, but I, I do think that that is something that people have to look out for. Um, but yeah, I mean I think you're right. There's, there's some people that are well known for, really good at, at this and really good at that and you know, trying to kind of piece that all together, um, you know, can be hard. But yeah, it's, there's a lot of different, you know, a lot of different little sectors and within data period in

Speaker B: oil, uh, and gas. So I come from the oil and gas background and I uh. Man, you can't find data uh, there easily, let's put that way. So you have to, there's something called Don's Directory. Shout out to Don's Directory. You know it's like all the entire founding team at this point is probably like 90 years old. Uh, you know they're, they're, it's all like a paper book. I mean they have like a 1990s style website. It's fine. People are there for the data. Uh, but they have all of the like literally all of the oil and gas people on that, on that data set. Right. And then you, there's a different one called Rex Tag, you know and it's like a very specific oil and gas database. Which other types of data sets. Uh, we, we got into the business of scraping. So I'm chief revenue officer for something called Wellsite Navigator, which is the, it's like the top oil and gas GPS solution. So it's like how do you find someplace in the middle of the desert without roads? You know, so they had to curate this data set across all 50 states and in Canada of all of these. Well Sites with specific names and operator names, which is like oil and gas names, and, you know, these things called API numbers. And every data set has a different scraping mechanism and curation and cleaning mechanism, and you have to have a whole little process for each one. Um, so, yeah, I mean, I, I specifically encountered those very niche data sets and there's just, uh, uh, it, it is, it's wild how niche you can go. Um, um, so, you know, that's where it kind of gets back to one of the key things that we see a lot of people doing is there's no one perfect data set, right? It seems like there's. You have to go and you have to look for coverage in different ways, especially dependent upon the market you're in. Um, um, what do you think is the ideal strategy around, like, you're, you're advising somebody, somebody comes to you and says, hey, I need to pull this list. Now there's very advanced GTM engineers who kind of know this, but if you're advising somebody, you know, how do you think about that? What do you think is required to get a good list these days?

Speaker A: Yeah, I mean, I definitely feel like you need to get a sample from them, you know, like, and, you know, give them your icp, you know, and your specifics on, on every single thing and get a sample, I mean, before you, you know. But I, but I will tell you, a lot of people never do that. They just hear like, a particular brand and they're like, okay, you know, I got, you know, recommended from so and so about this brand, you know, and, and they'll just kind of pop in into a service and a lot of times they'll get into some contract that, you know, sucks for them. Um, uh, but yeah, I always, always, always tell people to. I mean, you got, you have to look at it. You have to look at every single thing. And the problem is it's kind of a pain in the ass, right? You have to like, go in there and then you. And then basically you might need to dial a few people, you know, to see how, how good the data. Data is. Right? It's not just the emails. And the emails are the easy thing to verify. It's like the, the phone calls are the, the harder kind of thing. So, yeah, I mean, we, we always tell everyone, you know, to test and test and test because, I mean, it is like, I don't, I've never heard of that company, you know, um, Dons or whatever. But yeah, I mean, like, they would have data that we would never have, you Know, and, um, but I mean, I find that all, all interesting. I wonder how often you, like when you guys were doing all that crawling, like, how often were you gonna update that, that information to get that other data? Remember you were saying you were, you were crawling.

Speaker B: Oh, you mean for, um, well, say navigator. Yeah, yeah, that's a good question. I, I think it was bi weekly. Sorry, bi monthly. M. Um, I honestly haven't asked that in years. Sorry.

Speaker A: Yeah, so you just kind of automate that and just. And then.

Speaker B: Yeah, yeah, yeah. So there's a whole, um. Uh, in the beginning, this is like putting me back ten years ago. Uh, I didn't know what a data engineer was and I didn't know what the process of ETL extraction, transformation, loading was. I didn't know any of it. So I was a guy who, through the twist of fate, got, uh, thrown into the oil and gas industry, ironically, because I worked for Clean Water Action, which was a nonprofit environmental group which was fighting against anti fracking legislation in Pennsylvania. So it was very ironic I ended up in the oil and gas industry. Uh, and I, uh, ended up in Midland, Texas. There's a long story of how I got there. Um, and we, we were running a, of all things, a water marketplace. In the oil and gas industry. We were selling and buying water, which is why do you need water in the oil and gas? That's a whole story. There's a lot of water and oil and gas. You're basically pulling oil out of the ocean, or, sorry, oil out of the ground, which was an old ocean. So there's water down there like three to ten times the amount of water than oil. People don't know that. So there's a lot of oil and gas, um, problems around the data there. And so we had this marketplace that helped curate some of that data around buyers and sellers and the available water. And we were brokering stuff. Um, long story short is it turned out to be more of a data, uh, a database of like, analytics around oil and gas water than it was a marketplace. Turns out there's like very bad liquidity in water markets. There wasn't enough buyers and sellers. Um, and that's again another hour webinar. But what, uh, I had to do is we bought one of our competitors, which is a crazy story because they were bought for $30 million, and then the acquiring company ran them into the ground, Just really screwed the original founding team, gutted them, um, from inside, and then sold the guts to us for $300,000. So it was a wild journey and we adopted their entire data analytics platform and it became clear we had to get very good at pulling a lot of data. So there's all these like very niche forms that we had to pull. Like something called P18s and H1s and W1s and uh, H10s, all these different file types from various uh, state bodies. And so we had to do stuff like ocr, this is before AI where we had to, you know, take pictures of the things and extract all the information from the PDFs. And uh, we actually used Mechanical Turk which uh, is a, I think it's an Amazon service at this point. And we'd have like three different people fill out like extract the information and then cross reference it. And if um, one out of the three didn't match, it was crazy. So we had all these ways of extracting the data. But it was my job in the beginning and I was the head of sales to go find what data types we needed. Uh, so I was the data wrangler. Then we hired a data scientist, not realizing data scientists don't do ETL typically. And so then we got introduced to data engineering and that was a year of bullshit and we hired the wrong guy. And so it was a journey. And then we finally came up with this system and we then built a whole data engineering team and the rest is history. But uh, that was my first introduction to. That's actually part of the reason I have reviops because I turned out to be. Most of the products I sold were deeply oriented around this data ingestion, um, management side of things, but very difficult stuff for sure.

Speaker A: Yeah. Well I just find that interesting because I do think there's like you can get a lot of information, you know, um, publicly, um, but I don't know, there's never like an easy, doesn't seem like there's like an easy way to surface it. And I feel like, you know, a lot of us, a lot of these data providers don't focus on the, the more hard to reach data points. Um, and I do think that's something that I want to do as we move forward is just really kind of expanding um, upon that. Um, you know, some of the things I've been looking at doing besides just you know, the normal contact company data kind of stuff has been been also just kind of plugging into like Serper, you know, SERP API and being able to kind of pull different data points, um, and then just you know, enrich it, you know, with our, our contact data or just basically crawl, you Know, crawl a website, be able to pick particular things, and then also just enrich our additional data to it. I mean, not that people can't do that already. I know that there's, there's tools that are out there, um, that are already, you know, doing that and like ap, you know, ampify actors and stuff like that. Um, but I just feel like if there's, I think there's better ways that people can put things together and make it a lot more simple. Um, I think there's, um. You know, we've. We're getting, you know, all of these tools and everything have helped everybody a lot, but there's still a market where there's some people who don't care about building anything. I just want the damn data. You know, Like, I just, I want this. I just, you know, like, it's not worth my time to be able to build. I know that, you know, it's easy with cloud code and all this other stuff, but, you know, I just, I just want this. Um, and I think, um, there. There is a, um, you know, something there where I would really like to expand upon that.

Speaker B: You know, it. It's a really interesting point because I came from, you know, like I said oil and gas, right? Uh, before that I was in nonprofit stuff and I was in politics. Back in, like 2012. I was a political science guy, uh, in school, so I thought I was gonna be a lawyer. So when I got into sales and marketing, um, and I went on this journey, you know, like, all this stuff was Greek to me. I didn't have any clue, you know, I mean, the idea of data engineering was like, what? I mean, the fact that we're even talking about this right now, if I went back 15 years, that person would go, how the heck did that happen? So I kind of come from this place of, um, you know, I've done landscaping and roofing and worked as a waiter, bartender. You know, I've done every stupid job you can imagine. And I've gone door to door, knocked on 30,000 plus doors, and I've now done all these crazy things. So I kind of have all that perspective behind me. So when I come into this world and I just see on LinkedIn every day these 5,000 tool flowcharts, and I use these 20 data providers and I pull them in this flow. It seems a little stupid and crazy to me, to be honest. Like, it's like, how, why? Like, like, what is going on? Uh, and in, to some degree, it made me think, like, I was like, clay is like, the most gen. It's like almost like cult leaders. It's like Clay, Clay turned a bastion of people into these like sucker, uh, fans or whatever you call it the word for like just complete, like zombie, like, like, like followers now the way they are with Claude said, with love again, high community. Um, but the truth is, is like I, I just, it just seems all kind of silly. It's like, okay, the only reason we're doing all this is because there isn't a good curated data set that is fundamentally clean and if it was, we would just go download the damn data set and we would send some emails. I mean, what are we doing? Right? So, so, so there's a deeper question there of like, and maybe I'm the fool because I'm the guy who created the platform to curate all those things, you know, um, so there's irony there. But I mean, is there a way around it? Because it seems to me. And one of the things I bet kind of a lot of money on at this point is that that's only going to increase. There's only going to be more niche data providers with different curations and different levels of sophistication and people are like, especially in this new world of the uh, Clay has created where there's this data or this, this GTM engineer. Is that inevitable? Or do you think it's going to come back and be like more Apollo, like in the future where there's just this like standardized data set that's open source that everyone has access to? I mean, how do you see this go?

Speaker A: I, I think it's going to be that way. I think it's going to go, go back. I mean, I do think it helps that uh, the, the younger people are more familiar with, with like technology and everything than like older generations. You know what I mean? Like, I think that it, that they have that on their side a little bit there. People could just. Because there are, you know, younger people that are used to doing, you know, more technical tasks. So I, I do think they have, they have that on their side. But I just think, you know, marketing and sales people, they're just gonna want. I just want the data. I mean, I just, you know, like, I, um. And it's not. There's my dog. Sorry. Um, yeah, I mean, and it's nice that, you know, that it's been easier, you know, with cloud code and you know, like even stuff like lovable and replit and stuff. I don't know how much you've used with them, but, um, it's it's pretty simple, you know, to get something going. Not that there's not frustrations, there are, but um. You know, I just. Yeah, I think I uh, think we're going to go back. I really do. I m mean maybe, maybe it'll be prop. Like, you know, and it just be kind of like, you know, connect, connect this data set with that data set. I mean maybe that would be like the, the, the equivalent of the, the technology know how you're going to need to. Need to have. But maybe it's just like connect this data set with this data set, you know, and enrich this.

Speaker B: Um, but yeah, I mean that's, that's what REV OS does. So REV os, which is the uh, product we're, we're launching um, next Friday is uh, essentially it's a visual interface on the desktop computer and it has a bunch of these, these providers in a central uh, you know, command line API. And then you just say hey, give me this. And it immediately goes through all of them and just pulls them, deduplicates, merges them, pulls them into one tool into a table and then automatically uh, sends it off to places um, which we have to get you guys in there. That's part of the reason for this call. Um, but the thing is, for me the only reason I'm doing that, I mean again, if you told me this a year ago or two ago, I wouldn't have believed you. Um, but the reason that we had to go down that path is ReviOps, uh, started off as a simply a data warehouse. And the thing that we found was after onboarding 600 plus accounts was nobody could get data into the quality or the structure they needed to. And they would just dump crap into ReviOps into the database. And part of the database, what it does is it gives you that kind of ongoing ingestion layer from the standard data providers that typically would go through clay and then you'd clean it up and push it into our system and then we tie into all these different sequencers like you know, hey, reach and instantly and even the CRM like uh, HubSpot and we ingest it all and we marry all the data together so that you can do whatever you want with it. But the primary reason is you want to analyze the data, understand what's going on and then create amazing dashboards off of it and then also prevent re enrichment and that kind of stuff. But the problem was that people would just put crap in. They would, they would map it incorrectly or they would uh, it would just be half built. There are a lot of gaps in the data, not verified stuff. So we're like uh, so how do we get these guys to clean their data better? Because you can't really analyze data and use what we have, which is a person graph, uh, which is a unification of all the data streams across these platforms into one person. So Bob, how do you know Bob on Heyreach is the same Bob on LinkedIn, which is the same Bob and HubSpot. So we solve that problem. But if you don't have good data you can't analyze Bob. Uh, so we had to come up with a way to help people take these sources and then seamlessly engage with the back database. So that's why we're doing it. Um, but I agree with you. I mean our primary product is a person graph. It's a precursor to a aisdr. It's the, the backbone, the data backbone behind any agent that can look at the historical record and know what people did, when they did it, where they did it, across platforms, across statuses and across the lifecycle. But um, yeah, it's been frustrating how hard it has been to get people to understand the necessity of clean data. And it's kind of strange that the data is so crappy. Um, so you know, but we are kind of on the nexus of this.

Speaker A: But is it all just coming from their CRM? Because I mean that's like not, not all but like a lot of it. Is it coming from there or hay reach or whatever or where is it?

Speaker B: All of the places? Yeah, I mean it's, it's uh. So typically what would happen? I mean now it's like, let's say we take your data source and then they cross reference it with some um, Ocean IO data and they'll pull that into a staging database. Uh, with. It's just a table and it automatically looks into the data warehouse. Uh, so there's a staging database, there's a deeper data warehouse. It checks to see if you already have the data. And if you have the data you just won't go to the next step which is enrichment. Uh, so. And then also it previously it uh, will check on prior sends. So it automatically checks last contact date across the systems. So there's a last contact date for heyreach, a last contact date for instantly, a last contact date for HubSpot, and if it's an active deal cycle. So it pulls into the, let's say you and Ocean I.O. pull into a staging database, instantly looks up to the, the data Warehouse for last send date. Is it on the do not contact, Is it on the active deal cycle? Was it previously verified? And then there's potential things you can do with it, which is, okay, exclude all those people, only enrich the new guys. Load up anybody who is older than 90 days old because we want to retarget them and let's send that off to a sequencer so it does that automatically. Um, but the key idea is that it marries all that data together into one central record so you can do really useful reports. So I could say, you know, show me attribution, which records which deals are coming from Hayreach, or I could say show me all North American CTOs that had a subject line of Billy Bob and uh, over a million dollars in revenue and that also had at least three emails from instantly and one sequence from Hayridge. So it's different and better than a CRM, because a CRM is not a person graph. So it doesn't sync all activities very well or easily. Although you can like Jerry rig it to do it. It's just a pain in the ass. And one of the fundamental parts of our system is it automatically, uh, deduplicates, automatically normalizes. It's literally impossible to load a duplicate. So uh, we normalize against phone, email, LinkedIn, URL, there's two types of Twitter URL, I mean any URL. And um, anytime we see that in the future from any record to a connected system, all that activity ports back. Uh, so it's mcp, it's icp, I mean cli, it has everything that an AI can use to engage with it. Uh, and so that's why it's, people don't, I don't think they get that in our space. Is that the cdp, which is a client data platform, it's like look up segment from Twilio. It's an old school idea, uh, but in the outbound GTM space it's like a very new idea and uh, most people don't understand the value of it. But I think that moving forward with AI, you have to have a person graph. And I just think that's fundamental to AI taking over.

Speaker A: Right, yeah, that's great. That sounds cool.

Speaker B: Yeah, it's a pretty deep project. But it comes back to the discussion we're having which is um, how do you get good data? You know, how do you clean the data? Uh, and I'm curious for you, email verification obviously is a big step. Bounce band, a million verifier, the ones that people reference, I've been hearing. I don't know if it's you guys or if it was AI Arc or somebody, but somebody's claiming that they have Bounce Band verified emails. Is that true? What does that mean? Am I wrong?

Speaker A: No, no, they do. Yeah. They, I mean and we use them too right? So we, we verify with Bounce Band. So any of the one, any of the emails that are. Are catch all we send a Bounce band or um, shout out to Orbi Search which is a really cool new tool that also does it. Um, so uh, yeah, we send to one of those two and um, they're. They're pretty. I mean almost as good as like the SMTP verifications. Uh so you know like the normal, the old school smtp that's where basically where you know you, there's. It's kind of like a handshake. You know the one mail server talks to another mail server to see if that particular email address exists and then some of them say catch all. And then um, tools like Bounce Band and orbisearch, what they'll do is they can still. I don't know exactly what they're doing but they can still kind of figure it out. Figure out if that email does exist or not exist. Um so yeah, I mean that definitely that's Bounce Band verified for sure. People are, are using that and, and

Speaker B: they're a very solid provider and is that. So I'm curious how you guys do that from an economics perspective because I know that um, if I go to Bounce ban, I mean it's not inexpensive. It's. It can be expensive if you have a relatively large list. How do you, do you guys get a good deal from them? Or like how. What's the relationship like?

Speaker A: There, there are some deals that you can get it depending on how much kind of um, I think there is sometimes you can get a good deal if you go you know, really, really large. Right. So m. We've been able to get a decent deal with them. Um, there are um, I ah, will say this Orbi Search company, they don't have as much. They're not as strong as Bounce Band I don't think um, as far as um, they might be getting better though at this point. Um, but the pricing on that is significantly lower um than Bounce Band. Um, but the thing that you run into is that you can send emails to all, all the providers and I've done this multiple times. I was talking to some other people in GTM Cafe about this. You could send it in. Every, every provider is guilty of this. You, you can send like a list of like, um, a thousand emails and they'll come back. You know, let's say 925 are accurate and then resend that list like five minutes later. And it's not exactly the same. It's like 935 are accurate. So there, there. I don't know what happens. I don't know exactly. I don't know if there's like, maybe there's a timeout when they're hitting the mail server or something like that. Um, and then they just have a different value that comes back. So one of the bigger issues that I see is trying to, you know, we have to use multiple providers. So I'll run it, you know, once through one provider and then even the ones that say invalid, I'll double check to see that somebody else says that they're invalid. And then I'll know, okay, this is time to remove this, this email out of the system. Um, but yeah, I mean, you got to go, um, you know, big time bulk as much as you can possibly get and pay upfront if you can to get it as cheap as you possibly can. Because you want to, you want to test as many patterns as you possibly can to. Um, you know, there's obviously a way to figure out what most likely the pattern is for, you know, a particular email domain. But, um, you know, even then there's definitely differences. Um, so you want to just, you know, test them all if you can.

Speaker B: Yeah, I was. I, I know a lot of people do Million Verifier first because it's cheap, relatively cheaper, and then they'll do any of the ones that fail. Sorry. Or succeed, and then put them into Bounce Band. Yeah, I know some people that do the triple verification. Uh, I. It's interesting because I'm curious what Bounce bands, maybe we have to add them on the podcast. Their thinking is around the business model because it certainly cannibalizes some of their margin. I guess that's the nature of wholesale in general. But it just seems like they had such a strong and dominant position, at least within the GTM space. It would seem, because everybody seems to use Bounce Band and M Verifier, um, that they're saying, okay, we'll sell directly to, to, to data providers so they can sell an enriched list to kind of cut out that extra enrichment step. And I guess they're thinking, like, look, somebody's going to do it. We might as well be the guy kind of competing. I mean, what do you think? Like what, what do you think Their thinking is around that.

Speaker A: Yeah, I mean, yeah, probably. Yeah, someone else is going to do it. I mean, um, you know, we've done deals with, with companies sometimes, you know, like where I'm like, well, you know, this could be somewhat competitive, um, to us, but you know what, it's worth it. Um, so that's probably what they're, they're thinking too. Um, yeah, I don't know. I don't know if. I'm not sure. I'm not for sure on that. But um, I'm sure a lot of different, you know, um, data providers like, like us are using, you know, multiple tools. Um, but I think the more you can do, the more you can, you know, in my business, you, the more you can triple verify the, the better, you know, so if you can do, do more of those, um, then it's just gonna, you know, people are gonna come back to you more.

Speaker B: Well, it's. And the thing that is surprising to me is how you guys worked out a good enough deal. The economics make sense where you're still able because part of your value prop is all inclusive, right? It's like come eat everything, you know, uh, and, but that, that also, that means that you're not going to make as much per unit, which means that your unit economics have to be pretty good. But it would seem like adding that additional verification would actually add cost. So I mean, I'm sure you went through with this a lot and said, you know, how, how can we make this financially viable from a business standpoint? I'm sure you're looking at that obviously. But you know, can you speak about that at all?

Speaker A: Yeah, for sure. I mean if you, if like if you look at a company, I mean it's, they're not um, since this is a, you know, zoom info is public, you can, you can see this but like, you can see their cost of goods sold, right? And then what their overall revenue is. So you can see like the margins are very large. You know, it's like the, like the gross margins are extraordinarily large. Um so you know, um, my feeling is like we're, we're not making nearly as much um, margin right now on, on this, uh, on um, you know, on this particular, um, on Quick and Rich. However, I do know that it will, it's going to be strong. There's just so much demand and there's so much coming in that I just, I, I know that we're going to get to a point where the margins are very, very high. Um, so. And I Also know that, you know, things have come down. I don't know how long is this, if this, you know, this is going to happen. Um, you know, but um, you know, these verifiers are. Some verifiers are cheaper now. You know, um, you know, there's definitely other stuff that's happening out there that's you can kind of do. I mean you can, you can build your own, you know, um, and at least use um, you know, that process yourself like the SMTP process, like that one would be pretty simple to build. That's why million verifiers so cheap because it's, you know, you're able to, to kind of build, build that out fairly easily on your own.

Speaker B: And it does beg the question of. In a world in which everyone wants to build everything and thinks they can. Whether they can or not I think is still debatable. But people truly believe that they. We are in this, this peak what I call Dunn, you know, not what I call what people call dunning. Krueger effect peak stupidity and confidence with least amount of experience. Um, so everyone's trying to rebuild HubSpot and you know, everything. I mean if they, if they can cut you out, they will cut you out. That is the game, especially in the GTM engineering space. I mean the GTM engineers are the. Which is highly ironic because the first people to be cut as GTM engineers in like the next three years when AI takes over all the data cleaning jobs. But right now they're trying to essentially build everything internally. Um, why not do this themselves? I mean what, what competitive advantage does a data cleaning company or a data selling company have that would not be disruptible by say a really great scraping agents and you know, some guy with the uh, will and a crazy mission to go do this himself.

Speaker A: Yeah, I mean, I think they're, they're that, I mean there is that there. I mean there is the ability to, to do that. I guess it just kind of comes down to you know, just like the economics of it. Like is it, is it. How much, how much are you going to be paying to build this, you know, and you know, create this on your own, um, and spending the time to kind of research it and all that kind of stuff versus just, you know, paying a certain amount. That, that's not that much, you know. Um, but you know, I do think that's there. I do think, I think we're in

Speaker B: just like a hype cycle right now.

Speaker A: I think that, yeah, I think it's getting a little too, too, too much at this point. There's just, because I've just done too many different things where you still need to know a fair amount of, um, of stuff to be able to use Claud code and, and these other tools. I mean, you need to be able to explain it and, and have a real background on it. Um, so I, I do think there it, it's a little bit, you know, especially when you look on LinkedIn and everything, you see all these, you know, like, I just did this in 15 minutes, you know, and so yeah, I, I, I, I agree with you, I agree with you. I, but I, I don't know how much it'll get better. I mean, you know, it might not.

Speaker B: I think that's the big debate, right? There's a fundamental question mark around how good can AI really get? Um, clearly it's amazing at coding. Um, clearly it's great at design and clearly it's great at a lot of things. Uh, but the question is how great for how long do we have before it becomes too great? And so I think that's the basic crux of the debate. I think the thing that is interesting, like you said, it still requires a bunch of these skill sets. What's been very counterintuitive and fascinating for me to watch is that there is a large swath of salespeople, not developers, uh, not technical people, salespeople that are deeply obsessed with becoming system architects, database, uh, architects. Uh, they're deeply now embedded in cloud code and GitHub. Uh, they are trying to learn every ETL system you can imagine. They're deep into Python. That is wild. I mean, thinking about that two years ago that there'd be a very large part of these individuals that are passionate about becoming a system architect. People go to degrees and masters in computer science to learn some of these principles. And people are like yolo. Three months in, I'm having conversations with my developer buddy. Look at me, I don't need a coder. And I think that, uh, for me personally, I run for SaaS companies, I've hired data engineers. I've seen the amount of attention into detail and difficulty with all the problems that can go wrong. Maybe I'm like the old guy who's kind of like these young idiots, but for me I'm just like, I just don't see how they're trying to take on the biggest and hardest data problems there are, uh, around sales and marketing and they're trying to do it in a weekend. My personal experience over the last decade is AI doesn't Solve most of the edge cases and problems. It's, it's, there's a lot of other things that go into it. So I don't know, I kind of, that's what makes me think, like, when people say these things, um, I just go, you're either bullshitting me or you are the most magical person on planet Earth. Because I, I'm as deep into cloud code as you can imagine. My whole team uses cloud code, uh, and Codex and all the fancy stuff. Um, and we still struggle. It's not like we're sitting here having a rosy, you know, tea party and everything is perfect. We have bugs, we have problems, we have issues, we have user experience problems. Um, you know, so I don't know, man. It's, it seems like that we're in a hype cycle for me.

Speaker A: Yeah, I, I agree with you. I, I think we probably are. I think we've kind of reached, um, the peak of, of of it, like, as far as being blown away with what we're, you know, seeing. And also I, I, I noticed, I mean, I get really frustrated now too, like when I'm trying to build something. You know, I have like, marginalized coding experience. Um, and, and I know what to, to tell it, but I just, um, it just, I, it always, there's always, um, seems to be a problem once things hit a certain level. Right. You know, it just, it just, and it, it's very, very, very frustrating. So, like, uh, because you know that they can do it. Yeah, you can do this, but they're just not understanding. Or maybe they're reading stuff that I said earlier or I don't, you know, I don't exactly know, um, what's happening. But yeah, it is frustrating. And I also find it, I, it's kind of ironic that we've, we've um, created like these tools to make like, where you don't have to code. However, just like you were saying, there's all these people that are like, trying to learn how to code and learn how to be technical. It's, it's kind of, it's kind of funny that like, you know, I don't, I don't know. I don't know.

Speaker B: It's counterintuitive because they, this is the thinking. Holy shit. Like, this is my macro level analysis of our weird human reality we live in. Anybody who thinks forward five years realizes that all of the technical requirements, if it's as good as promised, is no longer going to be a requirement. Right? We're going to be abstracted from the Layer of even having to look at these things. Right. It's the same thing with like, why don't we look at ones and zeros? There's always an abstraction layer at some point. Now I think the thinking is, well, I'm going to go learn this system. So I have a short term competitive moat m and advantage over the next, say two to three years while all the people who are lagging, you know, you can go sell stuff to them and look like a super genius. In the short term, I think that's the play. Uh, and I think it's just generally interesting to people. I think that, uh, Me too. I mean, I think that there's a, the thing that Clay I think really tapped into. There's a, you know, the book Outliers talks um, about this. It's like, what. Or is it a different one of his books, Malcolm Gladwell, where he talks about, you know, what, what causes something to go crazy, like a big spike in a trend or something usually. What's it called?

Speaker A: Was it Tipping Point?

Speaker B: Tipping Point, yeah, I think that's what it was. And he talks about how like, there's a certain set of things that have to be in place for it to go up. And one of the things that Clay I think accurately assessed in a brilliant way, I mean truly brilliant in uh, terms of their understanding of the cultural moment and what was happening with technology and with salespeople is I think there's just a large amount of, uh, pent up creative angst and desire within a whole group of salespeople that used to be like, they'd watch their product counterparts kind of feel a little dumber, you know. Oh, they get to like, do all the cool fun stuff and like make cool shit. And we're out here just like slamming our heads against the phones. And I think that there's a large part of them that are creative and they want to express that. And so Clay accurately named themselves Clay. You know, hey, look, here's this thing that you can do. Um, so I think it's partially that, uh, but it's a very interesting culture that the GTM community has created. It's a very particular type of people. Um, kind of the vagabond slash, like, uh, super, super salesperson that is also extremely creative and kind of a MacGyver type that is also a little bit arrogant but also, uh, deeply entrepreneurial. Like, it's like, it's like a cross section of all these characteristics and there's a lot of them and they're all the same Person. I did a meme on LinkedIn where it's like, we all look the same. We actually look the same. It's like, it's the weirdest thing that.

Speaker A: Oh, uh, that's hilarious.

Speaker B: Have you noticed that

Speaker A: kind of. Yeah, yeah. I don't know about the, I don't know about the, the looks. Well, yeah, I want to show you.

Speaker B: You're going to crack up when I show you this. Um, it is, it's uncanny that there's like this whole swath of these, um, these guys that all have the same kind of. Here, I got it. Uh, I'm going to share my screen. Let me see if I can pull it up. This was, I probably posted this, I don't know, like a couple months ago. Let me let this load. Of course. It's, uh. Oh, here it is. Okay, this is going to crack you up. I pulled all the GTM agency owners I know, um, that are heavy on LinkedIn. Can you see this? I mean, I mean, that looks like a trend to me, man. You know, it's like, it's like a bunch of brunette guys that all have similar facial structures, you know? You know, it's. So anyways, like, I just thought it was funny because I was like, every time I'm like, people are jumping on calls with me, like, you look familiar. I'm like, I don't look familiar. I just. Every single one in our industry looks exactly the same. Um, so, yeah, so, uh, but getting back to reality. So you think that people can, if they want to go build their own database, they certainly can. Um, but it's always a question of time. It's like the age old question of buy versus build. And I think if you look at vertical integration, which is this idea of, um, buying versus building, every industry goes through this. So I think it was in the 70s, the oil and gas industry decided they were going to vertically integrate, which means buy all their suppliers, buy the trucking company and buy the whatever company, buy the real estate, buy the building that they're living in. And at some point they decided, hey, we're not very good at running janitorial services and having a commercial real estate building. Maybe we should sell this asset, uh, bring in guys who specialize in this. And they just did the analysis. It didn't make sense. I think that what's causing the build versus buy dynamic right now is just we've never had this level of access to this level of intelligence ever. And so it's causing this belief that things are easy, but they don't understand product management and system management is not fundamentally about code. Um, it's about a lot of other trade offs and decision making and edge cases and user experience and yada yada.

Speaker A: So yeah, I mean I think it also just goes back to like, like we were talking about. If you want to build something simple, that's fine. It works great. If you want just a really simple kind of thing that just has a couple different things, great. Um, I mean maybe that'll change in you know, three years, five years, um, and it'll get a lot easier, um, and it'll understand better. But right now I feel like yeah, just simple app. Yeah, we, I can do that but building like multiple complex things, it's, it's hard.

Speaker B: Well that's what they ironically are trying to do. They're trying to build these very deeply integrated. I mean essentially they're trying to build what I have, which is reviops, but they're trying to do it with a mixture of sepabase and vector databases and Obsidian and you know, GitHub and it's, they're, they're choosing the hardest project. Um, I will say like, for me I'm um, like lovable. I have ah, this application, uh, let's see if I can show you. I um, have an application that I used to uh, host my course for the Cloud Code Challenge. So I'll show you. Um, so it's called agency.reviops.com. uh, there's several courses on it but one is called the Cloud Code Challenge. Uh, and we meet every Tuesday 12 o' clock and we record the sessions and then we slice them up into these different lessons. So I wanted to create like a course portal. Uh, again that's why it's like I'm not hating, I do this too but I also know the limits of it. So we created this thing and I wanted one where I could create my own banner ads that led to a slippery slope of like demonstrations for ReviOps but also had a course functionality and so you can go in and I created a whole layout structure, links to my other stuff. It works, it has a whole payment gateway, uh, it upsells to, it connects to Stripe and you can uh, upgrade to $25 a month. There's a whole uh, like you know, account management, uh, element of it. But I'll tell you what, you know, it's, you're now running a product and I cannot tell you how many times the stupid thing has disconnected. Um, the email verification sending system. So it's like somebody's like messaging me, like, hey, we didn't get the login credentials. And I'm like, so now I have to stop and I have to go handle something. Uh, or then like the other day somebody messaged me and was like, hey, Ben, uh, in this particular lesson, the, uh, one that I'm not sharing anymore, but one of the lessons, lesson four, is actually lesson five, and it's missing lesson three. And it's like, what the fuck happened? It's because the tool degraded. There was no continuous integration. Um, I've since changed that. But it just goes to show that, like, that's not a complicated product, but it's flipping out and it takes maintenance and it takes upgrades. And so that's when people do these very advanced things where one of our competitors just announced they Vibe coded a competitor in four weeks. They said they spent twelve hundred dollars a week on Claude code. And they are, uh, they did 400,000 lines of code. And I'm like, look, yeah, you can get a prototype out. That probably looks reasonable, but does it scale? Like, that's the fundamental question. Does it actually do its thing? So I just. That's where I feel like I'm living kind of in this weird reality. I'm like, maybe I just don't know how to use the tools without making them buggy. But it just. I don't think so, you know, I

Speaker A: don't think so either. I mean, most of the people that I. That. The coders that I know, you know, but I'm. Maybe they're against it though, you know, just because they don't want to be for it. But, um. But they have real issues with it. I mean, they'll use it for, I think, simple building, um, blocks of particular codes, um, or, you know, little code bases or whatever. But they won't, you know, they just won't let everything go. There's a lot of control they have over it.

Speaker B: So I, I just posted this on two days ago, um, because Claude just came out with this, uh, article. I'll share my screen again. Um, and what it, uh, what it's about is kind of the evolution of this discussion. It's this one called When AI Builds Itself.

Speaker A: Right? Yeah.

Speaker B: Did you see this article? It's going to load it.

Speaker A: I saw it. I didn't read it. I saw it all over the news, though, because they're basically saying, AGI is there. Is that what they're saying?

Speaker B: Well, they're saying. So this infographic is a good example where in 2021-2023, it's like, okay, we built the first CLAUDE code. It was actually us writing the code. And now that output of that effort led to a chatbot, uh, in 2020, uh, three to 2025, which was, you know, chatgpt and all the stuff we were used to. And then in the last, say, year and a half, you know, it went into coding agents really, uh, year, uh, coding agents. Um, and that's where the chatbot then, you know, can basically talk to agents that autonomously go do some tasks and come back and report back. And now there's this concept of autonomous agents where agents can now run code themselves and delegate hours of work to other agents. And so the thing they're saying is like, okay, in the future, agents could become capable enough to build and train models themselves. If this happens, future versions of CLAUDE could be continuously approved by CLAUDE itself. So it's not just like the agents doing the task from the LLM, it's the LLM writing code to improve the LLM. Um, and so there's a. And what? The end of the call to action here. I'll go to the end of it. Um, it's a bunch of these slides basically having these people reflect from within Claude about how, like, I started learning hard. Uh, I'm reading this for people who are listening on Spotify. I started leaning hard into cloudifying about a year ago. That's been a crazy adventure. And it's now been five months since I last wrote any code myself. And, uh, it goes on to just show all these graphs of the capability of code contributed by model and how it's getting better. This quote says, Claude written code was somewhat worse than human written code at anthropic in late 2025 is roughly at parity today, and we expect it to be strictly better within a year. Um, it finally goes on. The conclusion of the whole article. Where is it? Um, goes on to say, what should we do? And these are actual screenshots from the article. And it said, if we're. If it were possible to effectively slow the development of this technology to give ourselves more time to deal with its immense implications, we think that would likely be a good thing. But if a slowdown simply lets the least cautious actors catch up technologically, it could leave everyone less safe. Without a global coordinated mechanism, companies and governments will have to make difficult decisions about safety while under competitive and geopolitical pressures. So they basically were saying, like, look, let's, you know, let's slow down AI. But it has to be concerted and, and careful. So I certainly think Claude is notorious for fear mongering, but maybe they're not, you know, maybe they're just being honest, but it happens to be very good press, you know?

Speaker A: Yeah. Um, yeah, when is there. When? I. I do kind of wonder why. Like, so when is there ipo?

Speaker B: I don't know. I don't know.

Speaker A: Yeah, because I just was wondering if it had some, like, I don't know, just beefing it up even more. I don't know. You know, just getting. Pumping up the hype machine even more. But, yeah, I don't. I. Yeah, maybe. Maybe they have. They're saying that they will have that. It's not here, but, like, very soon they will have that, I guess.

Speaker B: Yeah. I mean, that's what they're. They're hinting at. Uh, but then they made a big deal about Mythos too. I think it was Mythos where they were like, look, this model is not safe for release into the public because it has fundamentally, it has fundamental capabilities that allow people to do nefarious things and break into systems and, you know, uncover bugs in the fabric of the Internet reality. Um, and so they delayed Mythos because they felt like they had to put safeguards on top of it. Um, so I don't know what they were seeing internally, but that's what I understood to be. At least that's the podcast I was listening to in a couple different articles suggested that, um, and from what everyone's reporting, this is over the last two days that Mythos is outperforming significantly in terms of its functionality and output. Uh, but again, this is the crux of the issue. Does that go on forever? How scalable is it? Uh, the basic idea that I understand is with the. What do they call it, the fundamental base technology of these LLM systems. The transformer, I think is what it's called, which came out like 2018, 2019, um, is like, okay, you just add more data, scale up the compute, and voila. Uh, you get a faster, better, bigger brain. And from what I'm understanding and listening to all the podcasts, is that they think we're not really near the edge of that yet. I think that's debatable. And I don't think that people truly know until they know, but that's certainly what they're selling, you know, and so there's a question of, like, that's what's so hard about being human is that we know there's incentives for these guys to bullshit. We know that they obviously are incentivized to maximize valuation because they have to attract more capital to keep on funding the fuel. So it's really hard to discern what's real and what's not, what's hype and what's not. But because the technology is just so powerful that people are hook, line and sinker buying in. Right, but it might be real, I don't know, you know,

Speaker A: Right. That might lead more, even more people to buy in because they're like you were saying, there might be a little bit scared, you know, like you're talking about like salespeople all of a sudden becoming, you know, vibe coders and stuff. So maybe it's a similar kind of thing. All right, maybe more people need to get on the bandwagon. You know, we gotta go in deep on Claude. You know, I don't know.

Speaker B: I, I have a quote from. This is like one of the, the developers that I've worked a lot with over the years. And uh, and I said to him, I sent him this, there's a, like a, let's see if I can pull up on LinkedIn from the head of Anthropic. Uh, he said something like, I don't prompt Claude anymore. I have loops running that prompt Claude and figure out what to do. My job is to write loops. And then I asked this developer, um, who I respect, I said, do you agree with this? I said, like, do you think full systems can be developed end to end without any coder involved? And he said to me, look, I haven't been writing code by hand for the last two years, starting with GPT4O. What I do now is define the requirements for the module I need to implement, describe how it should be validated. More precisely how the AI should validate it, and then run it in a loop. The AI stays in the loop until the acceptance criteria are met. And those acceptance criteria in practice represent functional correctness. The module either works as expected or it doesn't. So, and he's, I would say he's more on the fear mongery side of like, you know, he believes that his job is just gone. He just like, he's like, I spent the last 25 years of my life perfecting this skill set. And he's like, it's now a commodity, so he's a little bit depressive about it. Um, so I don't know, man. I think that's what makes this such a weird time, is that it's, it. There's a certain amount of debate on both sides, but it's, it's, it's hard to know. And, and, and I've heard people. One of the GTM agencies that I coach said to me, um, look, I think we have two years left before, like, we just. GTM agencies aren't a thing. Um, and so we need to our agency as big as possible and sell out to private equity as quickly as possible because, uh, they think that they're cooked. So that's another angle too, right?

Speaker A: Don't know.

Speaker B: Yeah. All right, well, I know, um, we've been on for over an hour. Um, so do you have, um. What I like to end these podcasts with is, you know, um, one. Where can people find you? Uh, online.

Speaker A: You know, I'm usually on LinkedIn all, all the time. Um, you know, um, and I'm not really on X, um, or any of those other kind of, um, I'm not really on X, not on Instagram. I'm just basically LinkedIn.

Speaker B: Okay.

Speaker A: And, uh, if you're talking social profiles

Speaker B: and it's just Tom Blue.

Speaker A: Yep.

Speaker B: Man, you couldn't make that easier. It's kind of like Ben Reed. Yeah, yeah, totally.

Speaker A: Yeah. Seven letters.

Speaker B: Yeah, exactly. Um, and then, uh. Okay, and then, um, can you tell us the website again? What's the website everyone can go to?

Speaker A: Quick, Quick Enrich. So quick. And then enrich IO.

Speaker B: Okay. Quickenrich IO. It'll be in the show notes somewhere, everyone. So definitely go check them out, uh, and show them some love. Uh, obviously they're. They're giving a great deal, it sounds like. So certainly worth asking for a sample. Right? That's. That's kind of the next step. Um, so last thing here that I always ask every guest is, and this is kind of more philosophical and deep. Um, you know, if you had to leave the world, anybody, whether you're, you know, you're transported to another dimension, you're not here. I don't like to say die, but you are leaving a message for. For the world. And they will only listen to this message. It, uh, doesn't have to be about what we just talked about. It could be about anything that you've learned in life. What is the number one message you would want to leave behind?

Speaker A: Just keep going. Like there's going to be problems and, you know, happens and sometimes stuff sucks, but just keep going. You know, my dad. My, uh, dad just recently, um, passed away. Um, and, uh, so he was. He, uh, had Parkinson's and he had it for like 18 years. But he and I recently, you know, spoke at the funeral and everything. And he, um. And that was like my, my biggest message and what I learned from him most was just like, you know what, just keep going. Sometimes it sucks and you're just going to have to like grind through some, some crap. Um, but just keep going and get to the other side and you're gonna find joy on the other side. So it's something that um, I appreciate that he left me as an, you know, an example of that. Um, you know, he wasn't like, it wasn't per, you know, obviously wasn't a perfect man or anything like that. But you know, he just, you know, was able to just get back on and you know, go through the bad times and then you'll get to the good side.

Speaker B: Mhm. Is, is that a, was that a, an attitude that he naturally came to or was that something that you think he learned or where do you think he got that?

Speaker A: I think, um, I don't know. I think his sister is kind of similar. So it might be something that he kind of learned as a kid or maybe it's a genetic thing. Um, but he had a lot of stuff that happened to him, um, through the years. Just like everybody does. Everybody has stuff that happens. Um, but he was able to um, just kind of say, I'm going to deal with it, you know, and it's, you know, this might suck part, um, of it's going to suck, you know, but I'm still going to live my life and, and be content, you know.

Speaker B: Hm.

Speaker A: So I think it's uh, I don't know if it was something he, he learned or it was genetic, but it was um. I, I'm very happy that he, he gave that to me.

Speaker B: Mhm. Yeah. I think just anybody who does goes after big dreams. I don't think it's, I don't think it's possible not to encounter some block. Whether it's luck, whether it's just the nature of hard things being hard. Um, I just think it's inevitable that you deal with, and you deal with stress, uh, and you deal with, you know, friction whether it's self induced or not. I just think it's like there's unavoidable. Um, and I was saying to my like my business partner today, I was like, you know, the number one skill is just patience. You know, it's just like, because there's just so. And that comes back to the whole vibe coding thing which is like, look like building these applications, it's easy to get a first version. Um, can you handle the 50th? That's the question. I have for you. And that's where, uh, I think that's the same thing of life. It's like we have different versions of ourselves. We have different versions of how we update our own little models and fix our own little bugs. And the question is, is the code deprecating faster than you grow? You know, if we're using that analogy. So, yeah, I think persistence and patience. And I'm reading this book called Letting Go. Over the last year, I've done a lot of thinking about, like, you know, more spiritual. How do you let go of all the emotions and all the pain and all the process? Because, man, holding on to it is. You just drive a person nuts, you know?

Speaker A: Yeah.

Speaker B: So.

Speaker A: Oh yeah. I'm so guilty of that. And it. You can just get sucked up into, um, is good, you know, just like the, in end of the day to at least have some period of time of like, just, just nothing, you know, like, you know, no matter how hard you're working. And, you know, I know a lot of people that are listening to this work really hard, you know, and are trying to strive and, and build something, um, of real quality. So I know that they're trying so hard. But yeah, I mean, if you can just get that little bit of a break, I think it just, you know, resets you just a little bit and then the next day makes it a lot easier.

Speaker B: Yeah, I, I, you know, what's funny about my, my personality or, uh, I don't know where that this has to be genetic because I, it certainly is not something I learned. I'm, I tend to be more high strung, a little neurotic, you know, around, like, very attention to detail, kind of super driven, but stressed, you know, I wouldn't say I'm like, I think any entrepreneur would probably characterize themselves in that way.

Speaker A: Yes.

Speaker B: You know, um, so the thing that I have found though, is that when I fail, like, I really fail. Not just like one failure, but like, it's like, okay, game over, you know, because I've, I've failed at a lot of businesses, probably like 10 at this point. Um, when that, like during it, if all those micro failures really hurt. But when the final bell tolls and it's like, okay, we're fucked, like, yeah, we're shutting it down, there's, uh, a real piece of it, you know, it's like. And I won't, I don't give up easily, but when it goes down, I walk away and it hurts for about a month. And then like, you know what? That was an Interesting experience. Let's go on to the next one. You know, I think there's. There's something about me that allows me to do that, which is very interesting, because in the short term, I suffer, but when I look back, I go, whatever. You know, lost a couple hundred thousand dollars. There it goes, right?

Speaker A: Yeah. No, that's awesome. I love that. I love that you've started so many companies, too. It's so great. I did the entrepreneurship program at usc, and so there we would have people coming in all the time. And the best thing about that, um, that program wasn't just, like, all the business aspects of it. I think it was just the. More the philosophy that you would get from the people that would come in and speak to you. And they all. And some of them did have different philosophies. There were some people that weren't as gung ho as. As others. Um, but you. You just kind of get to see, you know, some people that have had roller coasters, you know, a lot of roller coaster rides, you know, um, and. But they're still, you know, happy and they're having fun. And, um, and they just keep trying, you know, M. They didn't show. It wasn't all highlighting the. The good stuff. You know, I mean, they did definitely had good people come in and speak to us, but it was not highlighting all, like, the. Just the best, you know, which was great.

Speaker B: Yeah. I feel like I'm on the part of the roller coaster where they just give you a couple bumps at the front. You know, I've never been up to that experience. Mine's more like, you know, how you go down first. You know, you just go down. Um, they built a little tunnel into the ground. That's, uh, that's where I've been living for the last five years.

Speaker A: Well, it's just hard. I mean, it's all really hard. You want to do something, you know, and it's fun, you know, because it's exciting. You're doing it. And, um, and it's, you know, you're putting it. Putting it all out there, which is great. But, uh. But yeah, I mean, it's hard and fun at the same time.

Speaker B: Yeah, it is, it is. Well, great. Well, thank you so much for, uh, coming on. Uh, yeah, appreciate hearing your story. Uh, obviously. Again, everyone go ahead and try them out quick. Uh, and Rich, and, uh, I'm sure we will have you in Rev os, um, sooner rather than later, uh, because people are asking, uh, but, uh, again, signing out from the Next Gen Sales Leader podcast. See you guys. And we will, uh, we'll talk to you guys on the next episode. See ya.