The Exchanges

Q Yeah. And is this all freemium right now, or do you have folks that pay and if they're paying, what do they pay for?

A So the people who pay in our products, if you think of our model, it's not a SAS model. It's what's, what's called the data network or community model. So if you're familiar with ways, you know, that as a consumer, I'm sorry, as a contributor of data, by using the product, you get the product for free. That's the individual contributor. When you're a consumer of the data in the B to C world, that's usually advertisers. So that would be like Dunkin Donuts or Starbucks paying for an ad. They're the, they're the person who pays in our world. It's B to B. So it's really leaders. And managers of a sales organization who want to get visibility into daily forecasting, know which deals are real, which ones aren't, to be able to do deal inspection, to understand what's going on. For those people, they're going to buy the product on a per seat basis.

AI assessment note: “leaders. And managers of a sales organization who want to get visibility into daily forecasting”

Q So who, who is buying collective AI today? What's your main customer segment?

A Yeah. So we sell predominantly to sales organizations. So when you're thinking about it, if you, if you run a sales org, you probably have no idea what they're working on. You have probably no idea what revenue they're going to bring in. You're trying to figure out what's changing in the market because it's always changing and you're trying to figure out how to manage more effectively. Well, that is essentially what we saw. We remove the need to log anything for those people who are suffering in CRM every day. Imagine a world where the AI did the work of logging all the people you spoke to The conversations you had, the communications that went back and forth. Imagine if it did that automatically. Then imagine if it told you which deals were real. And the reason it did that was because just like Waze, it's observing other sellers in our network and seeing how the buyer's behaving when they buy, when they don't buy, down to the individual. So it's telling you every day if this is a deal you should actually pursue. And you may not know this in sales, they had the saying, buyers lie, because you never really know if they're interested or not. Well, imagine if the AI could tell you, no, no, they are really interested.

AI assessment note: “we sell predominantly to sales organizations.”

Q Yep. I guess last set of questions here before we wrap up, you get going in 2008. Do you remember how you got your first paying customer? Tell that story.

A Yeah. Look, it's funny. In data networks like ours, you're actually not trying to get paying customers in the beginning. You're trying to figure out how to get data. And the challenge with neural nets in particular is they tend to be really bad until you get data scale. And so the first ways we went out and signed our customers up where people were trying to solve the hardest problem in sales that's out there, which is forecasting. And we went to one of the largest publicly traded companies in the world and said, let us be your first partner. Let us be your data science arm outsourced. Sign up for this new model that allows for sharing of data, but in a confidential way. And the thing about neural nets is their black boxes. People used to beat up on that a few years ago. Oh, it won't explain to you why it's working, even though it's really good today. That black box is actually the reason why people realize if I contribute my most proprietary data, it won't matter. It's totally safe. It's a black box because it can never tell me about what's going on. These guys realized that early on and said, if you're willing to bring that technology to bear, you're willing to fund that. We'll sign up. And that is to this day.

AI assessment note: “we went to one of the largest publicly traded companies in the world and said”

Q get an understanding of their top 10 deals. I think they're going to close in Q one, but oh crap, they just saw that one of those deals purchased by Basecamp, a competitor. They're now not a deal. How, how do, how do you give them the information that the ClickUp prospect just bought a competitor? Do you get receipt data from that person's inbox or how does that work?

A So the way we deal with it is we're actually, uh, it's a better way of thinking about it is by observing sellers across multiple buyers, I can spot when that particular buyer is behaving like they're going to buy or not, because for all the AI knows, he may buy both, right? I don't think they're going to, but let's say they make a mistake and they buy the wrong product, right? The odds are going to start changing as the buyer's behavior starts changing, as they don't bring in the right people, as they start communicating different things, or they say things like, Oh, that's really interesting. Can you send me this piece of information? But that always means they're not interested in buying. The thing is, you just don't know it. Because for you, it's your first experience with this buyer. But when an AI is observing multiple people selling that same person, it can spot it cold.

AI assessment note: “by observing sellers across multiple buyers, I can spot when that particular buyer is behaving”

Q Interesting. So I mean, everyone's going to want to know a four and twenty five million dollar acquisition price. You were CEO. I mean, do you get filthy rich on this or how did that work out?

A Well, filthy rich is always in anyone's eyes, right? Uh, it's what it depends what you want. Look, we were fortunate in the sense that we didn't come from a lot of means, uh, but we had worked really hard. We'd built the business that had become quite successful. It had grown really quickly. More importantly, it had enabled every other entrepreneur to make a living off their business in a way they couldn't have done today. So when you look at the creator economy, when you look at people who had blogs, uh, podcasts, well, all of them are making commissions on sales. From referring people to other websites. And that was really what link share enabled. And, and for us, it was just a joy to be there. So while I think we did very well and I'm very happy with how we performed, um, and the results we got, what I'm most happy is every month we were sending out millions of dollars worth of checks to people, um, and changing their lives. And I, and so I hope, uh, that business continues to change lives of all the great entrepreneurs that are out there probably listening to this amazing podcast.

AI assessment note: “while I think we did very well... what I'm most happy is”

Q All right. Let's talk about collective. I you launched, I think in 2008 walk us through who's, who's paying for this and what are you giving them?

A I mean, look, the benefit of having, uh, the, the exit that we had and a whole bunch of others is that it enabled us to fund something that probably people wouldn't have done. Um, you know, we are a neural net based technology. So for anyone who's used chat GPT recently or stable diffusion, you're starting it for the first time as an individual feel the power of this neural net AI that people like us have been talking about, but you had to be a real practitioner in this stuff. And Um, and we, we just had a great opportunity to be on that forefront of that group that today is, you know, open AI is not a young company. It's been around for almost a decade as well. We have all started at the same time. We're all getting to this inflection point where these technologies are just dominating the way we think about how the future of work will be.

AI assessment note: “we are a neural net based technology”