The Exchanges

Q So how'd you and Michael connect the side of buildings together? Like, he has a pretty unexpected background of his own, like firefighting and...

A Yeah, yeah, yeah. So, so Michael and I worked together at, at a previous company where I was the VP of product. He was the tech lead. He was the best engineer at the company. And, uh, interestingly, like, after we, you know, he, he left that company, Uh, and he had, he had done the fire academy when he was younger. He has really interesting background where he had gotten really passionate about that. And so after that company, he went off to be a professional firefighter. Um, yeah. And so he, he did that, uh, uh, uh, I think it was, it was during, I think COVID during the worst of the fires. So he was doing wildfires.

AI assessment note: “Michael and I worked together at, at a previous company where I was the VP”

Q like they'd want, like, the people to have the relationship where they're hearing from them, because they want a relationship with a person, because that's a really important, valuable relationship for us. But then in other cases, there's probably lots of people we're not getting that we could be getting. What parts should people do? What parts should a computer do? How do you, how do you think about this?

A Yeah, yeah, it's, um, what, what people say, people often ask me to, like, Judge my credibility. They're like, when do I not use AI moderated, right? It's, it's like, ah, they're testing me, like, when a buyer will ask that. The answer is, like, if you are, want to have a relationship with that person, then have a relationship with that person, right? Don't, like, put AI between you and somebody you want to have a relationship with. So, like, a hundred percent. But the question is, like, what is the, kind of, cadence at which you want to have a relationship, and what is the cadence at which you want to understand more? So, I would, like, people are all the time mixing. I'm gonna have a couple of interviews with people who are, They'll call it, like, uh, red carpet research, right, when you're really talking to somebody with deep relationship, and then the rest of the year, you're sending them AI interviews, right?

AI assessment note: “Don't, like, put AI between you and somebody you want to have a relationship with.”

Q How do you know how to meet their needs?

A How do you know what their needs are? How do you know how to position the thing you have? This, you know, this mug? How do you know how to position this to them and say, well, actually, it's really nice because of these reasons. Like, everything around you has been researched, right? You walk down a grocery store aisle, you'll actually see a thousand different, like, Packages. Every one of those had been tested, and, and those were the winner, by the way, of 20 other ideas that were tested. So basically, research, like, underlies everything that we interact with in the world. And so I think it's, like, a massive, massive opportunity. But, like, uh, unfortunately, the tools have been, like, pretty rudimentary. So, uh, the tools we've, we've had, like, what's been broken is, what we have is surveys, which are, you know, there are billions of surveys done a year, and they are the lowest quality data you could imagine.

AI assessment note: “So basically, research, like, underlies everything that we interact with in the world.”

Q So explain, so if AI is running the interviews, It might sound to some people like you're just replacing people. Like what, what's the remaining job for the human researcher? Are you even, are you creating more jobs in some cases for them to do? Like, like explain this.

A Yeah. So, so I think it's easy to think about this as a automation play, right? But it very much is not, like we have not seen that the case. So like, uh, uh, to draw an analogy or kind of a, an opposite analogy with customer support, you have a static amount of demand, right? You have the tickets you get and you're automating n percent of them. And you want that to be as high as possible, right? And that's cost. And that's, and that's just reducing cost and you make the money on the margin. I think the, uh, in the other case, like what, what we're in is there's not static demand. In fact, demand has been pushed down, uh, uh, because of the tools that we have. So if we bend the economic curve, we can do more.

AI assessment note: “In fact, demand has been pushed down... So if we bend the economic curve, we can do more.”

Q So let's start simple. Like what is that? So what was actually broken about how companies understood their customers before you came along with Outset?

A Yeah. Yeah. Okay. So, so, um, Outset, I'll start with that. Outset is What we call an AI moderated research tool. So what that means is AI is leading interviews, like actual one-on-one interviews with participants who are your customers, your users, people out in the market that you want to learn from, right? So you know, traditionally, you basically have to, well, actually, I'll take a bigger step back. Like, I think a lot of people in tech don't quite realize how big research is. So you think about, like, uh, I'll go all the way back to, um, uh, free market capitalism, Right? Is, like, the whole, the whole thing, you have to sell something, right? The whole thing is, you sell something to someone else. And that's how the whole, obviously, system operates. So the question is, how do you know what to sell? How do you know?

AI assessment note: “Outset is What we call an AI moderated research tool.”

Q How did you get, like, Microsoft, Nestle, Google, Uber? Like, how, these are huge companies. They've done this a lot. Like, why do they trust you to work with you?

A It's, yeah, so it's, it's a hard thing selling to the giant enterprises. Like, there's basically, there's both, like, headwinds and tailwinds into approaching them. So I think there's a, like, right now, obviously, the technology is moving extremely fast. Humans are moving reasonably fast, right? There is a huge amount of social pressure, and, uh, people want to keep their jobs, and there's a lot of that, and then enterprises move slowly, and so you're dealing with all three speeds at the same time. Um, it is, so, so ultimately, like, what we, what we do is, like, we try to sell very, um, it's very, like, consultative, where you're, we, we recognize that we're not just replacing something, right? We're not taking a thing you're doing and just replacing it.

AI assessment note: “what we do is, like, we try to sell very, um, it's very, like, consultative”