01:07:13 · Mark Pincus, John Coogan, Jordi Hays, Luca Ferrari
You should be focused on your product, customer product team, not investor, IPO, media. [01:07:20] S1: Yeah. Makes a ton of sense. Well, the book is Life at the Speed of Play. It's available everywhere books are sold. [01:07:27] S0: Pick up the the launch and let's do this again soon. We we I have a Yeah. We're getting [01:07:32] S1: you all over the place. [01:07:33] S6: Yeah. There's so many more topics We'd love to get into. [01:07:36] S4: Would be great. [01:07:37] S6: Yeah. I'd love to talk about my broken internet strategy of public stock investing, which There [01:07:42] S0: we go. Yeah. We didn't even get to Snapchat. [01:07:45] S6: Yeah. Didn't get to Snapchat. That's a whole show. [01:07:47] S0: Yeah. That's a whole show. Let's do it soon. [01:07:49] S6: Come back at one together. We can debate like whether this whole journey into AR goggles is the best thing. [01:07:59] S0: Shareholders love the specs. Shareholders really love the specs. [01:08:04] S1: We'll get to the thanks [01:08:06] S0: for No, mean, wait until we see how many sell. Think, I you know, I'm I'm pricing in I'm pricing in 20 pairs. 20 pairs. But I think it could there's a chance that it surprises to It the [01:08:18] S1: could. It could. Anyway, thank you so much for taking the time to come out Have with a great [01:08:22] S0: rest of [01:08:22] S1: the Great [01:08:23] S0: to see you, Mark. [01:08:23] S1: We'll talk to soon. Cheers. Let me tell you about the New York Stock Exchange. Wanna change the world? Raise capital at the New York Stock Exchange.
01:08:23 · John Coogan, Jordi Hays, Shubh Sinha, Wayne Ting, Julian Hoenig
[01:08:23] S1: We'll talk to soon. Cheers. Let me tell you about the New York Stock Exchange. Wanna change the world? Raise capital at the New York Stock Exchange. [01:08:30] S0: Mark just gave the anti ad for going public. [01:08:33] S1: Hey, hey, if you need to raise capital, if you want to raise the most capital, you got to go to the New York Stock Exchange. That's the endorsement that we're proud to give. Our next guest is in the waiting room. Shubh Sinha from Integral is the co founder and CEO. Welcome to the show. How are you doing? [01:08:49] S8: I'm doing okay. Thanks. Thanks for [01:08:51] S9: having me. [01:08:51] S0: Just okay. Just okay. Okay. On this big day. [01:08:56] S8: I'm I'm I'm riding the highs and and the lows and getting getting back up right now. [01:09:01] S1: So excited. That's that's pretty normal for the entrepreneurial journey, but take us through your entrepreneurial journey. How'd you get here? What are you building? What's the news today? [01:09:10] S8: Yeah. Yeah. Definitely. Well, I'm excited today. They actually just announced an $18,000,000 series a for for my company, Integral. And so the Gong. [01:09:24] S1: It was a good warm up hit. Had to get the real hit in there. You like to warm up the gong on this show. [01:09:30] S0: Double hit. I don't think we've done [01:09:31] S3: that before. [01:09:31] S1: Warm up hit and then the real hit. [01:09:33] S0: Yeah. Yeah. That was special for Integral. [01:09:35] S1: So tell us about Integral.
01:09:35 · John Coogan, Shubh Sinha, Jordi Hays
[01:09:35] S1: So tell us about Integral. [01:09:38] S8: Yeah. So what Integral does is we sanitize proprietary real world data sets Yeah. Such that AI builders can get very bespoke, very sensitive data sets. [01:09:47] S1: Okay. [01:09:47] S8: But that data holders can also make sure that privacy and compliance is adhered to. So And if those things this looks like medical records, financial transactions, etcetera. Mhmm. You know, a lot of this contains the real world human behavior patterns that people like you and me have. Yeah. And there's a ton of individuals and enterprises who are now monetizing it since the AI companies and the AI builders want it. Mhmm. That being said, they're the the builders want the signal. They don't want the secret values. They don't want the the the proprietary secrets, so to speak. And so Integral sits in the middle where we ensure that through privacy engineering, both with our privacy teams and our engineering teams, we can retain the utility of the signal while also ensuring that privacy and regulatory and contractual [01:10:29] S0: Yeah. So how how about this for an example? There's there's been, you know, there's been examples of of, you know, a company buying a company just for the data around how that company was operating, like just for the Slack. But then you can imagine a Slack, there's so many things that so much information in Slack that, again, you talked about as signal, but not necessarily information that should be available for even people, let's say, working at the labs to see, right? That's like PI, etcetera.
01:10:29 · Jordi Hays, Shubh Sinha
[01:10:29] S0: Yeah. So how how about this for an example? There's there's been, you know, there's been examples of of, you know, a company buying a company just for the data around how that company was operating, like just for the Slack. But then you can imagine a Slack, there's so many things that so much information in Slack that, again, you talked about as signal, but not necessarily information that should be available for even people, let's say, working at the labs to see, right? That's like PI, etcetera. [01:11:02] S8: That's right. And and folks at the labs don't even necessarily want to see that. Right? They want the context of it all. And so it's a real win win for data holders who want privacy and trust and also revenue. Mhmm. And then the lab, who are or the AI builder who wants to ensure that they're not stepping into anything they shouldn't be stepping into, but they get that value. [01:11:20] S0: What are some types of companies that have valuable data that that don't know that it's valuable and it would be something that they could monetize if they were, let's say, working with you?
01:11:20 · Jordi Hays, Shubh Sinha, John Coogan
[01:11:20] S0: What are some types of companies that have valuable data that that don't know that it's valuable and it would be something that they could monetize if they were, let's say, working with you? [01:11:30] S8: For sure. And and that's that's one of the bets that we're making, that there's this new proprietary data economy of individuals and enterprises. And so we've seen kind of all across the board, just given the the kind of newness of the economy, the types of datasets that are coming in, and the bespoke demand. So we've seen, for example, like, mid market hospitals that see compressing revenues Mhmm. But have a very, very specific treasure trove of data, because they focus on a rare disease or a specific procedure, and and that's not available anywhere else. Fitness apps. Some people, you know, they log their entire lives into fitness apps, and these apps have free or paid users. They're only making money off of paid users, but they can then those apps can then monetize what is that entire real world signal and really continue to make that app free and and introduce a ton of value. And it all becomes circular because as AI gets better, a lot of these same companies will actually use it. [01:12:20] S1: Sure. [01:12:21] S8: So it's a real win win there.
01:12:21 · Shubh Sinha, John Coogan, Jordi Hays
[01:12:21] S8: So it's a real win win there. [01:12:22] S1: What's the state of the art in, sanitization and maintaining privacy? We talked Ed and Ty from the National Design Studio on Monday. They shipped something like less than 15 meg. It's like small language model for sanitizing documents in the browser. It runs very efficiently. Are you just throwing open source models of this? Do you need frontier models for data sanitization? And then is there some worry where it flows back if you're using some closed source model and the frontier lab takes the data that you sent them and and stores that improperly? [01:12:57] S8: For sure. For sure. And and there's a variety of solutions out there mixed mixed with, call it, human services or BPOs and entire teams of PhDs looking at it. All of might just do [01:13:07] S1: reg x's. Right? Like, if it's a phone number That's right. Identify it, just turn it into x or number number number sign or something like that. Right? [01:13:14] S8: That's right. That's right. So there's a ton of solutions that kind of do that first layer. I think where Integral really specializes is, you know, we got our start in health care. [01:13:22] S1: Yeah. [01:13:22] S8: And health care has all proprietary data. Yeah. You know, your doctor should not be putting your Internet on or your data on the Internet. Yeah. And to and as far as I can tell, they are not [01:13:30] S0: I told my doctor, go ahead.