00:51:30 · Sagar Kadakia, Brett Caughran
They ran those same companies uh across all the other networks, and and guess what happened? Uh we obviously got that information uh uh faster, higher ends, higher quality, right? And so this is uh where the world is going. Is if you want alpha, you have to have the systems and the infrastructure to launch primary research very quickly, get that data back very quickly, which qualitate does, and then understand what that information means very quickly, and then make an action on it. That's where the alpha is going to be. And in six to 12 months, if the majority of your checks are being done through humans, you know, uh there will absolutely be no alpha there, right? Uh, because by the time you finish those checks, the market will have already moved. [00:52:17] S1: And so I wonder, you know, it's like in with with Yipit, for example, there was a sort of first order chapter, and now it's a second order game where it's like Yipit says the data's good, top line's good, but it's because they're discounting, so they're gonna beat on top line and miss on gross margins, and everyone's positioned long, so it's actually a short, right? Yeah, like I wonder if there will be a counter, that will be a good problem for a qualitate because that means you sort of reach market adoption. But I wonder if there'll be a counterpunch sort of dynamic where hedge funds are thinking about qualitate more as setting the bar and then having to find some differential view of why that bar has some fallacy embedded.
00:52:17 · Brett Caughran, Sagar Kadakia
[00:52:17] S1: And so I wonder, you know, it's like in with with Yipit, for example, there was a sort of first order chapter, and now it's a second order game where it's like Yipit says the data's good, top line's good, but it's because they're discounting, so they're gonna beat on top line and miss on gross margins, and everyone's positioned long, so it's actually a short, right? Yeah, like I wonder if there will be a counter, that will be a good problem for a qualitate because that means you sort of reach market adoption. But I wonder if there'll be a counterpunch sort of dynamic where hedge funds are thinking about qualitate more as setting the bar and then having to find some differential view of why that bar has some fallacy embedded. [00:52:58] S0: I I think uh, and Yipit's a great business. I think uh the difference with Yipit is Yipit is presenting the same information to everyone. [00:53:05] S1: Yeah, right.
00:53:05 · Brett Caughran, Sagar Kadakia
[00:53:05] S1: Yeah, right. [00:53:06] S0: The difference on qualitate is every fund will have a slightly nuanced set of even on the same company. Let's let's just use Microsoft or Nike as an example. Uh a hundred funds on uh the same day and the same time. So they all have the same infrastructure now. So we're 12, 24 months into the future. They all launch at the same exact time. They will have to have slightly different questions, right? It's unlikely that all of the audio questions are gonna be identical, right, to each other, uh, or all the questions they want. And so therefore, uh uh all the responses are gonna be slightly different, right? Uh and the responses won't just be slightly different because the AI interviewer is dynamic, it could be vastly different uh uh on how the interview works. And so I think um part of it is you will always have an edge to the extent that you can ask the best questions and to the extent you have the right infrastructure to capture primary intelligence the fastest. Those two things to me are if you can solve those problems, and I already see with funds trying to solve those problems, how do we ask the best questions and how do we generate primary research the fastest? That will be your alpha.
00:54:13 · Brett Caughran
[00:54:13] S1: Yeah. Have um have you thought about uh corporate access at all? The sort of the, you know, people's sort of logic through all these things, see AI doing more and more. It's like, well, we still have the relationship with uh, you know, corporate IR, corporate CFO, but it's not that big of a leap to start um sort of, you know, um calibrating these voice agents to go have a conversation with an IR, which probably means the IR has their own voice agent. They're uploading the context that they want to disclose, which by the way, sort of brings it back to the compliance consideration. Like that's a pretty good, that's a pretty compliant situation where investor agent speaks to IR agent and all the little things that IR is spending time hand-holding investors through. There's that's probably a more from first principle is a much more effective way to handle that customer relationship. Now there's body language and relationship and all the sort of things beyond that. But uh curious your thoughts, thoughts there.
00:55:12 · Sagar Kadakia
[00:55:12] S0: Yeah, I I first of all, I I completely agree with you. Um uh I think um you something happened like six to 12 months ago. It's it's like it's like a very like uh it's a very interesting world we live in today, which is I think pretty much everything everyone says at this point is being transcribed. Uh and that was not the case uh uh like 12 or 24 months ago, right? Whether you're using tools like granola or whatever you're using, right? Zoom, everything is being recorded at every moment in time. Um, and so uh what this is doing is uh the context window uh or the context that you can upload into a system is vast. And now that we have found is like it's such a powerful way to ultimately um uh uh have transcription of what you did that day, who you spoke to, and then how do you use that information later on? And so I'll kind of even tackle it from that perspective. So all the use cases you mentioned totally make sense because if you have the recordings and the context, then you can use that information later on. And it doesn't have to be just AI interviewing experts. It could be, it could be anything, right? Uh, meeting notes, whatever it may be. And so uh I do think that we're just getting started uh on this. Um, people are accepting that this is now the, at least in my opinion, the the dominant form factor of of where things are going. Um uh uh and I think some of the recent acquisitions in the news probably prove that on just how powerful uh AI moderation uh and AI interviewing is going to become uh as the dominant uh um form factor for collecting this information because the speed, scale, and cost at which you can do it at, and the fact that that's where all of the value is, right? There's no more value in publicly available data. So uh yeah, I Brett, I think that's it, those are all natural extensions to where this can go pretty quickly. It's already being done, right? It's already being done. How many conversations are recorded on granola every day?
00:57:16 · Brett Caughran
[00:57:16] S1: I don't know, thousands, millions, and so side calls, talk to the sell side analyst, just took this company around Europe on a non-real roadshow. That's right. Have my voice agent talk to their voice agent. Now there's sort of disclosure compliance situation about selective disclosure there, et cetera. But yeah, there's a lot of different um fascinating, a little terrifying. Um what's it what's what's what's it like building a high growth organization these days is checking on LinkedIn, 163% uh employee growth since since September um of last year. Um, versus like the expert network industry, I you know, I sort of always sort of view as like more stodgy and mature. It's uh sort of a different, different, different animal. What have been the surprises, the sort of most fun parts, and what have been the challenges about scaling the organization?