He Built the AI Network Big Tech Wants Access To (Followers Don't Matter) with Daniel Faggella

Brad Weimert (0:00)

Forbes called you the poster boy of million dollar one person companies.

Daniel Faggella (0:04)

That was for Science of Skill. That business was explicitly grown and sold to get into artificial intelligence.

Brad Weimert (0:10)

You didn't have any full time employees.

Daniel Faggella (0:11)

We crossed a million, maybe a million three, with zero full time employees. The whole goal, the entire way, was like, this thing will be sold and I will be working on AI full time for the rest of my life.

Brad Weimert (0:20)

Most people that look at a business model around information and education go broad. And they look at how you monetize an audience. You seem to have moved in the direction of not monetizing an audience, but monetizing powerful people.

Daniel Faggella (0:34)

Back in those days, these heads of AI, VPs of AI, nobody wanted to talk to them because they didn't control any budget. But I thought, well, I think that's going to change. And so the bet was, if I have heads of AI at all these companies, before anybody cares about them and nobody else is building an audience, try starting an AI podcast now. Nvidia started four or five years after us with their own podcast. We're still twice their listenership. We're not monetizing the bulk of how many people download. We've just got the direct connections with those heads of AI in that area.

Brad Weimert (0:59)

And so how do you get the credibility in the first place?

Daniel Faggella (1:02)

It was a...

Brad Weimert (1:04)

BJJ national champion, bootstrap and sold science of skill. You are now the host of the AI in Business podcast and the founder and head of research for Emerge, Dan Faggella. Welcome to the show.

Daniel Faggella (1:16)

Absolutely. Brad, glad to be here.

Brad Weimert (1:18)

Forbes called you the poster boy of million dollar one person companies. Is the size of the company or your lifestyle more important to you?

Daniel Faggella (1:27)

Man, well, I mean, that was for science of skill, which was an e-commerce business that I was running up until about eight or nine years ago.

Yeah, I mean, frankly, that business was explicitly grown and sold to get into artificial intelligence. So I'd sort of decided by the time I got out of graduate school at 22 and a half, 23, whatever it was, that sort of the big implications of things that are smarter than people, more capable than people would be the only thing worth focusing on. But I was running a martial arts gym in a 4,000 person town and didn't have a computer science background.

I went to UPenn for cognitive science. So by the time I got out, I was like, oh, I maybe should have studied AI. So I needed to become location dependent to move to the Bay Area, which is where I started the market research company and did the in-person interviews at LinkedIn and the big venture firms and all that when AI was new. And the econ business was to sell it. So why was it so lean? In part, it was like, expenses low enough to have profit big enough where if I get three turns on it, I could have a second comma, and I could just focus on AI for three, four years without having to worry about making money.

Brad Weimert (2:25)

Wow. Okay. Well, we'll back into that story. But since you hit on AI, it's 2026 It's August of 26 right now.

You have spent a ton of time focusing on AI in Enterprise, in particular, which is wild. How are enterprise companies approaching AI and how can small businesses learn from that?

Daniel Faggella (2:43)

Man, well, this is so different now than it would have been two and a half years ago. So for a very long time, AI was still just pilot programs and tiddlywinks. And even then it was like in the Fortune 50, Fortune 100, right? It was like Goldman and like these other guys were like doing some stuff and like everybody else was like doing LinkedIn posts but wasn't doing anything. They weren't even doing LinkedIn posts a decade ago, but I've been writing about, it wasn't a business, but writing about this stuff for 14, 15 years. So that's a bigger picture consequence of AI. But today, for enterprises, it's a lot easier to adopt. So that the process used to, it's gone granularly from a really, really hard challenging get your data house in order stuff. Think about the inventory prediction, all the systems related to inventory for some $5 billion, $10 billion manufacturing company somewhere. A lot of it's in goofy spreadsheets and wherever. Getting all that stuff in order, which was often like 18 months of hoopla. People would hire a head of AI out of Carnegie Mellon 8 nine years ago and the guy would just sit there with nothing to do because we don't have any data to work with. This was like literally what was happening at AIG and these other big companies who were interviewing all these people. So it was like tons and tons of work and then like we think this use case might work and the companies had no ability to stomach the experimentation that AI requires. AI is a, it's sort of probabilistic, not deterministic. And so it's more like R&D than it is like IT and culturally nobody was ready for that. So impossible data lift, really kind of not super proven use cases and a culture change that nobody was ready for. We needed three or four years in the enterprise of everybody being like, this isn't like IT and then canceling projects or having things go nowhere or whatever. That happened for a long time. Now, a lot of those lumps have been sort of turned into learning in some of the more mature companies, look at Global 2000, Fortune 500 And they're now more nimbly able to say, okay, to connect the big thing for enterprise that they're able to do now, which I think small businesses could have done years ago, but the tech wasn't there. Is sort of look at where do we want to be in two, three years? Nobody's thinking five, ten years anymore with the speed of things. Where are we going to be in two, three years? In terms of our operations and how we deal with customers. So outward-facing, inward-facing, what are we going to be that will fundamentally win in the market, market share-wise, and hitting our goals? And then from there, what capabilities do we have to build, potentially with AI, but it doesn't have to be with AI, that are going to get us there? And then AI fits into those business strategies and fits into that future vision. That executive AI fluency took, no joke, about a decade to build.