The Exchanges, every show

Q And what was your experience like on the street?

A Another buddy from Duke's dad was looking for an analyst at Smith Barney. My friend recommended me. I had a job in consulting at Anderson Consulting, which became Accenture, like a computer science programming job. Interviewed there, which was fortunate. Did that for a year as an analyst and became a sales trader at Smith Barney. With my math background, tried to get into equity derivatives. There was no seats. I got into interest rate derivatives at Smith Barney, which great first four years, then left there in 1995 to go with a senior trader who's an interest rate derivative trader to Citibank to become a credit derivative trader, which I actually didn't even know what that meant, but I trusted the guy and left with him.

AI assessment note: “Did that for a year as an analyst and became a sales trader at Smith Barney.”

Q Why don't you take me back to what led you into finance in the first place?

A I was a computer science major at Duke my junior year. A good friend of mine gave me a book. He said, I think you'll like this, and it was Liar's Poker. I read it in one night. Literally, it was a party that night. I stayed in my room, read the book in one sitting. I know Michael Lewis thought it was a way to dissuade people, but I took it the other way. I liked math. I liked puzzles. I liked risk, in a sense. Learned how to play poker with my grandparents when I was young. Hadn't really been much exposed to the markets, but after reading that book, I was like, this is something that fits me.

AI assessment note: “after reading that book, I was like, this is something that fits me.”

Q Just to pause on that. It was, you gave him a Twitter account. He started posting on there. People like me were following at one point. He said, I'm selling this PDF. Anyone want to buy? And again, people like me, I bought it. Click the link, went over and bought the PDF. That was the original part of the business.

A That was the first product. Yeah. So that was the, the first day he had his X account. I, that night I said, I want you to come up with a product that you can entirely make on your own and put up for sale. And when I wake up in the morning, I want, I want to review it and then you can launch it. And so that was when he came up, he built this whole site overnight, uh, Built this PDF. He did everything except finish setting up Stripe. He just needed me to give him the right API keys for that. So he got all this put together and I gave it the thumbs up and then he launched it on X. And I think it did like over a thousand dollars in sales that first day, which was pretty cool.

AI assessment note: “That was the first product. Yeah. So that was the, the first day”

Q Okay, so significant differences, as you said. Maybe can you help me break it down as I think about, um, the, all the different reactor types that are currently being pursued to, to come? Like, which ones present the easiest waste handling challenge? Which ones present the hardest?

A Right. So anything that is a high-temperature gas reactor that nominally uses Triso, and there's a lot of these that are in the mix there, right, that would not be a challenge. Any of your molten salt reactor designs that use a Triso fuel, that would not be a challenge. If you have a molten salt reactor with fuel dissolved in core, that would be potentially something that you need to address and condition and things of that nature. Any of the sodium fast reactor designs, so your TerraPower, your Oklo, things of this nature, this would be A conversation where you need to have, at a minimum, a waste conditioning component to this, and if you're a company like Oklo, that's very much thinking openly about recycling, right, you're, you're basically moving down that path anyways, so, um, and for any of the light water reactor designs that are being considered, like, uh, the GE Hitachi reactor, um, Westinghouse, right, that's not an advanced reactor design, and when the AP 1000, right, we have very established pathways with respect to managing those types of

AI assessment note: “anything that is a high-temperature gas reactor that nominally uses Triso... would not be a challenge”

Q Ok, well, now we have to, like, I gotta figure out where to jump around. I'm just curious about so many things. So, I guess, why did you feel like you needed to buy the IP versus just build it from scratch, or the million bucks just bought you so much time?

A Uh, it bought me a ton of time for one. For two, uh, a lot of my competition, again, the goal is to, to both build the moat and pull the drawbridge up from competition. So a lot of folks in my space were starting to, to look at it, see it, use it a little bit. And there were some big behemoth competitors like Memory Blue, abstract marketing and things of that nature on the like outsource sales development world that were starting to get their hands on it. And I was like, if I'm going to protect this and have a unique value prop, I got to own it. And I'm going to dictate who can use it.

AI assessment note: “it bought me a ton of time for one. For two... build the moat”

Q What happened from there? Clearly, GMO has become a lot more than the two hundred and fifty million dollars compounded.

A We fairly quickly got into discomfort with each other having slightly different approach to investing. By accident, we hit on a clever formula, which was to have three divisions. So Van Otelo ran the international, which was brand new. No one did international. Dick Mayo inherited our joint portfolio and managed it for another 15 years. And I started a quantitative division, which started by developing an expert system By telling the computer what we thought were sensible, selective characteristics for a good stock, the computer would come back initially with some terrible ideas. How did that get in? How would we exclude it? So we redefined the formula until slowly but surely over a few months, we got it to kick out a portfolio, which was 90% the same as ours. Interestingly, the 10% that was different did just as well as our 10%. We called it the finished product, and we threw it into battle. Very slowly, we got a handful of big clients. After a while, as we got more confident, we used exactly the same model to test on different stocks. So we had a growth fund, as well as a value fund, and then we had a small cap growth and a small cap value, REIT fund, eventually an emerging market fund. All using quantitative approaches, but also willing to use the brain and look for exceptions on what was going wrong and change the model. Somewhere between a modern quant model and an expert …

AI assessment note: “By accident, we hit on a clever formula, which was to have three divisions.”

Q What aspect of that setup do you think influenced how you thought?

A I think frugality was the big thing in the war. I was up in the north in a coal mining town. Yorkshire, our county, is famous for its frugality anyway. That was two strikes. Then the man in the family, my grandfather, my father was overseas and then died fairly soon in World War II, had been brought up a Quaker. So that was strike three. By the time you had a Quaker In a Yorkshire coal mining town in World War II, you pretty well know that you're going to have frugality deep into your backbone. When I go into a restaurant, I still look at the menu and take price into account. I know there's quite a lot of us who do that. For some of us, it's not a question of whether you've got money in the piggyback. It's a question of what is right, and wasting money by paying more for some silly meal gets pretty high up on the agenda.

AI assessment note: “I think frugality was the big thing in the war.”

Q When you think about taking advantage of the other side of something that statistically is a valuation bubble, you run into the challenges of not knowing how long it's going to go and how far it'll go. How have you thought about those realities in investing capital on behalf of your LPs?

A Seeing the bubble is easy. Getting the timing right is apparently impossible. We have worked on many iterations of that. Keynes said that clients' patience is not as long as the market's ability to be irrational. And that's absolutely true. And in the great bubbles, they don't just reach two sigma, which is bubble territory. They go on to two and a half or three segment, but they go on for years. They're the only ones that really matter. It's the last year or two. That really counts. The clients have become impatient, and they can't stand it. They start to fire you. As we got older and wiser, we were perhaps more careful in how we phrased it. We made it very clear that US stocks are not the thing to own. For a long time now, you should be owning non-US stocks, developed value stocks, and emerging country value stocks. When I'm arguing with people, I don't get the time to say, It's not what you don't own, as in the US stock market, it's what you do own that counts. If you own stuff that does better, who cares that you don't own the US? The US is simply too high priced from top to toe. If you had to own US, and a lot of people do, then you have to own quality stocks, because they have survivability. What people found in 1929 Is the value stocks had a dangerous tendency to go bust in the Great Depression, and the Coca-Colas that were horrendously expensive did not go bust. In the …

AI assessment note: “As we got older and wiser, we were perhaps more careful in how we phrased it.”

Q But a hundred, but, but was a hundred grand in 2009? Was that critical? Was that like life changing money at the time at that point?

A You know, it really wasn't. I had sold my house. I had some capital from that. I had sold my boat. I had a little bit of money from that. Um, I really, you know, financed this thing a hundred percent of myself. I had gotten owner financing on the farm itself. I really got the loan because I knew that what, what came along with that was even more support from Whole Foods. And I wanted them as a partner. And we signed, we never missed a payment, never laid on a payment. We paid it off exactly in time. And at the time, you know, they had 11 regions of Whole Foods. There's almost no national purchasing. Each region made their own decision. So you had five separate billion dollar company, I'm sorry, 11 separate one billion dollar companies in essence with their own presidents and their own buyers. So you had to do one region at a time.

AI assessment note: “You know, it really wasn't. I had sold my house.”

Q competition on YouTube. You could probably even record a Loom video pretty quick and rank on something niche if you have a niche SaaS. That's super cool. Let's dive into the actual playbook here. If you were starting over with a SaaS or with content in 2026 and you wanted to actually create content that ranked In YouTube search engines that actually drove revenue. What would be your step-by-step playbook?

A I'd repeat the exact same process every single time for any product that I build or any business that I work with. The first thing that I think people really get wrong in SaaS is they're so obsessed with getting customers. They don't take the time to think about who those customers are and the problems that they have. The first exercise that I would put you through would be a breakdown of the awareness ladder for your specific customer. So I don't worry about not aware of the need, which is Typically the bottom. I go right to aware of the need, then there's aware of some solutions, then there's aware of specific solutions, aware of benefits, and then convinced and ready to buy. I used to just do this in my head, but now you could talk back and forth with ChatGPT and think about the questions that someone who would want to buy your product would be asking at every step of the journey along the awareness ladder. You then want to figure out how are they asking it? Because a lot of people can say, I understand that someone would ask this question, but how they would ask it? Very different story. So use something like vidIQ or what I use Keywords everywhere to type into the YouTube search bar and see how many people are actually searching for that per month. I would then put my strategy together for which videos that I want to make prioritizing based on the intent of the traffic. So…

AI assessment note: “The first exercise that I would put you through would be a breakdown”

Q Would you be able to show me, like, Like a video or a search keyword that you rank for that drives meaningful revenue for your business?

A Totally. So the example that I'll show you is a video called best Instagram unfollower tracker apps, and it's got 25,000 views and it was made seven months ago. So anybody who's searching this in, they obviously have the immediate problem and they're looking for a solution. As I mentioned earlier, our biggest problem is that people have been scammed by the other options. And so this actually gave me an opportunity to also educate people on why and how we're different. Which made the conversion for this way higher. Here's the analytics. So you can see that I made this thing seven months ago. It took me 20 minutes to make. And look, it's getting this many views every 48 hours of people that are incredibly warm and ready to buy. And then let's see here in terms of the keywords we're ranking for. We are number one for unfollower app Instagram. Not bad. Uh, best Instagram unfollower tracker number one. As you can see, there's a lot of ones here.

AI assessment note: “the example that I'll show you is a video called best Instagram unfollower tracker apps”

Q You're gonna leave me with a cliffhanger gockle. You're like, there's some very durable characteristics. We can talk about them if we want. I would love it if we could talk about them. Can you please help me understand?

A It's basically a play on Hamilton Helmer's seven parts, but it's slightly different. I call it the eight motes. The first mote is data mote, which we all talk about, but it's truly has to be proprietary. It has to be data that nobody else has access to. I think Spotify is a good example. If you look at their Uh, the discover product, it uses a decade of listening behavior across hundreds of billions of people. You can't create that listening product, that, that discover product easily. Second is the workflow mode, which a lot of people argue it's a weak mode. I agree by itself. It's a weak mode, but the deeper you're embedded in the company, running their operations, moving their money, the, the deeper the workflow mode is just by itself. I don't think it's enough in perpetuity, but the deeper you embedding is, for example, NetSuite is an ERP that runs your business. They have a much, much deeper mode than say Zendesk. Which is a lighter workflow mode. So that is a mode. You can say it's one, maybe Zendex is a zero point, financially it is a one. Third one is regulatory mode. So licenses, uh, capital require multi-year procurement contracts. Coinbase, when I'm on the board, is a great example. They have MTLs, money transmission licenses, state by state. There is with the Fini, CN, all of those things. It makes it impossible for a company to use anybody else than Coinbase to cus…

AI assessment note: “I call it the eight motes. The first mote is data mote”

Q What's the breadth of the portfolio of what you're doing now?

A We're working on sports stadiums with the airlines and tech with financial services. That's the scope of our creative studio. The creative studio is what I call the group that goes in and figures out how to bring these ideas to life and other industries. And we have our gatherings. We do the welcome conference, which is our big event in New York city, which is more of a Ted talk style thing, but centered around hospitality. It's become the biggest hospitality symposium in the world. It's at Lincoln Center. We have the Unreasonable Hospitality Summit, which is a two-day workshop, training, community gathering, and party, effectively, in Nashville. That's been fascinating to see the different industries represented in that small room. Then we have training workshops. I have a couple of people on my team that travel the country doing training workshops for car companies, travel associations. Then there's me doing some speaking, hanging out with people like you, writing some new books and everything in between.

AI assessment note: “We're working on sports stadiums with the airlines and tech with financial services.”

Q So you're, you're cleaning the dishes, right? You've already cleaned the furniture. I mean, I would Be thinking at this point, I should revisit this thing. This thing is actually, this is interesting. I mean, is that what you're thinking that night?

A I'm enjoying washing the dishes and seeing it rinse clean, and I look at it, and I'm like, oh my god, if it had a smile face on it, I could clean the silverware on both sides. And I grab a steak knife, and I, and I cut a smile in it, and I stick the spaghetti spoon in the mouth, and I squeeze it and pull. And both sides come clean at the same time. And when I tell you this, I honestly heard the angels start to sing. I like heard like, ah, I was like, oh my God, we missed it. This has nothing to do with you cleaning your dirty hands in a body shop. This is the greatest kitchen scrubbing tool in the world. I just need to market this differently. And that's when Scrub Day two point oh was born.

AI assessment note: “I was like, oh my God, we missed it. This has nothing to do with”

Q I, I'm curious. I mean, did you, I know that you sold your previous business to a part of three M that was not in household cleaning supplies or consumer products, but did three M at, at any point come and say, oof, We blew that one, or, or hey, should we, do you want to talk again, or anything like that?

A So in, uh, 2014, when I resigned, I said my goodbyes, and they said, Aaron, the, um, the home products division would like to talk to you. And I said, about what? And they said, oh, I'm sure you know about what. And I went and met with them, and they were all loving Scrub Daddy. And It looked like that was going to lead to another acquisition. And I was more than happy to entertain that. I felt like three M was my friends, like family. And unfortunately, one day the lead M and a person called me and said, we have to stop all of our discussions. The VP of the home products division who loves you and loves your product and sees the vision. They got promoted to another part of three M which happens all the time. And they brought in someone new who has no experience in the home products care. And their first directive was shut down every acquisition until I understand the business. And they stopped talking to us and gave back all our diligence information. And that was it.

AI assessment note: “the home products division would like to talk to you”

Q product engagement? And what I mean by that is, if you want simplicity, it wouldn't necessarily be depth of product engagement. Hey, make me a website that's like BBC News, but make it just for VCs and for funding rounds. Boof. That's not very deep engagement, but it could be a happy user. How do you think about that product engagement need versus actually just what's best for the user?

A So I think about it on multiple vectors. There's intensity of engagement. So how deep do I go? How much time do I spend? How complicated of the task that I'm trying to accomplish? That's one vector intensity. And you're right. Intensity is very powerful for social platforms because the more intense you are on them, the more The better user you are for them. For simple productivity tools, intensity is almost an anti-metric because intensity means like I'm getting stuck, like I'm doing too much where this actually is supposed to be easy. So intensity is often the anti-metric. However, there's also frequency. So how often do I come back and do it? Frequency is a big one. You always want to be in a habitual zone for our minds. Habitual zone for our minds is somewhere on daily or weekly basis. Anytime you move into being monthly, You're in the forgettable zone. I don't remember what I did last month. Like the world is moving too quickly. I don't even remember if I looked at the web, like some software last month, or even if I tried it last month. So trying to be in that daily or weekly habitual zone is super important. And then on top of it, you look at just what actions are meaningful. The worst thing is when you create frequency of engagement based on logins. That's a vanity metric. Everybody can log in. That doesn't mean they get value. So setting up that frequency engagement on …

AI assessment note: “For simple productivity tools, intensity is almost an anti-metric”

Q product engagement? And what I mean by that is, if you want simplicity, it wouldn't necessarily be depth of product engagement. Hey, make me a website that's like BBC News, but make it just for VCs and for funding rounds. Boof. That's not very deep engagement, but it could be a happy user. How do you think about that product engagement need versus actually just what's best for the user?

A So I think about it on multiple vectors. There's intensity of engagement. So how deep do I go? How much time do I spend? How complicated of the task that I'm trying to accomplish? That's one vector intensity. And you're right. Intensity is very powerful for social platforms because the more intense you are on them, the more The better user you are for them. For simple productivity tools, intensity is almost an anti-metric because intensity means like I'm getting stuck, like I'm doing too much where this actually is supposed to be easy. So intensity is often the anti-metric. However, there's also frequency. So how often do I come back and do it? Frequency is a big one. You always want to be in a habitual zone for our minds. Habitual zone for our minds is somewhere on daily or weekly basis. Anytime you move into being monthly, You're in the forgettable zone. I don't remember what I did last month. Like the world is moving too quickly. I don't even remember if I looked at the web, like some software last month, or even if I tried it last month. So trying to be in that daily or weekly habitual zone is super important. And then on top of it, you look at just what actions are meaningful. The worst thing is when you create frequency of engagement based on logins. That's a vanity metric. Everybody can log in. That doesn't mean they get value. So setting up that frequency engagement on …

AI assessment note: “For simple productivity tools, intensity is almost an anti-metric”

Q executives, and professionals who are actively looking for tools and services to help their business grow. If you want to put your brand in front of this highly dedicated audience that's difficult to reach, I'm currently looking for a few strategic partners for the channel. To learn more about sponsorship opportunities, click the link in the description. Let's grow together. What kind of structures are common in pre-IPO secondary deals?

A There's a wide variety. So one of the main differences to public investing is in public investing, you can go on your Fidelity app, type in a ticker for a company, and it spits out an offer for you essentially. In the private markets, you could have for one company five or even more different sellers, um, different prices and different structures. And there's also new structures being innovated all the time. So some of the most common are direct transfers of shares. That's where the investor holds the shares in their name and it's blessed by the company and goes through their review process. You also have SPV transactions being extremely common where essentially it's a dedicated fund that holds the shares and the buyer is subscribing to the fund as an LP.

AI assessment note: “some of the most common are direct transfers of shares”

Q I'm really, yeah, I mean, I think that's like a good thread to pull on because A-sixt Z is a marketing media machine. Mark has talked about this on plenty of podcasts. We see it with Eric and all the podcasts that are coming out. I can't keep up with them. Uh, but it's phenomenal. So how does this marketing machine that help with deal flow and helping these companies?

A Yeah. So, I mean, you alluded to how, how it started. Uh, I give a lot of credit to Margit, Grace, Maya, the marketing team of the early days who built the brand, uh, before we deserved to have one. We didn't have the returns. We didn't have the portfolio companies. And then as Mark has alluded to, that gave us, um, you know, the ability to, you know, win deals, um, support our portfolio. It gave us Power in the market. Strong venture capitalists have power that they can lend to their portfolio companies, uh, to enable them in times that they otherwise couldn't. So that's with hiring executives, uh, corporate partners, customers, the like. Fast forward to today. I mean, you talk about Eric and his ninjas on the new media team. We can go to an early stage founder and almost guarantee That their launch is going to go viral. Um, that is a differentiated offering. So, um, if you look across the operating platforms, we strive for that differentiation. It helps us upfront with deal flow because people hear about it. It helps us with winning because they reference it. And then once they're in the portfolio, we think it helps deliver outsized returns.

AI assessment note: “It helps us upfront with deal flow because people hear about it.”

Q How much money are they making? Do you guys have, is this like a five million dollar company? 50? 500? Like what's going on with this?

A I would put it in tens of millions, but this is the interesting like bull case is that right now they have like a very respected analysis group, but they're also launching a fund allegedly according to the information to invest in both semiconductor stocks and startups, and they're already doing angel investing. And then also they're going to do credit ratings. And so the real model to think about this, the comp is Moody's. So if you're familiar with Moody's, they do credit ratings and Moody's got started during the railroad build out because the, the, the railroad build that was so capitally capital intensive that there was a huge, there was a huge secondary market for like, okay, is this person that's going to build this railroad from, from Chicago to Atlanta? Like, are they good for it? Do they have the money? And so Moody's is now an eighty billion dollar business. And I, and if you look at the stock, like it, Moody's has not moved during the sell-off, like it's doing fine, and yes, this is very indexed to, to the AI boom, but the, the actual business can be very, very stable, very high margin, and very big, like much bigger than people think when they think of just like publishing analysis.

AI assessment note: “I would put it in tens of millions”

Q I'm always curious. I mean, you were mentioning second sight sort of, you know, flashes of light, and yet, you know, here, you know, how did you figure out the API? I mean, if I was, you know, trying to reverse engineer it, I guess I would, like, try to measure the signals. Is it similar with, you know, biology?

A It's just, it's difficult to measure the signals. So, brain-computer interface research and development is limited by your ability to record and stimulate these signals. The neuroscience, comparatively, is actually pretty simple. As soon as you can record the signals, we've very quickly figured out what, we talk about neural representations, what they are. Second site's instructive. So in the retina, there's three layers of cells that matter. There's a hundred fifty million rods and cones. This connects to a hundred million bipolar cells, bipolar because they've got two ends, and that connects the rods and cones to 1.5 million optic nerve cells. We call them retinal ganglion cells. Ganglion is like a fancy word for, like, reaches a far distance and connects to somewhere. We stimulate the hundred million bipolar cells. Second sight stimulated the 1.5 million ganglion cells. And so they were trying to get the signal into the brain past that 100 X compression. And the retina was doing a lot of computation there. The eyes of camera light shines in from the front. It hits the rods and cones like that. The representation in the rods and cones is a bit mapped image. It's just like you take the image, you tile it across the rods and cones. That's what it is now. And the The 1.5 million optic nerve cells. It's not like that. Like if you just project an image onto them, you get a bunch o…

AI assessment note: “As soon as you can record the signals, we've very quickly figured out”

Q So maybe to kind of clarify, like, we just had Shahar from Terra Security on here. They're kind of like AI pen testing sort of thing, right? At least that's how I understood it. Is that, would that be similar to what you do, or not so much?

A Uh, yeah, we do a lot of that. We, we view like automated penetration testing as like a use case of our platform. And generally what happens when you have companies that focus on that single use case, they're probably going to make something that's more broadly and widely applicable. Maybe it does like a better job at certain compliance things. You can offer it to like SMB and lower size customers. It's very PLG friendly. We are Building something more big and interconnected. So although there might be capability overlap, there's probably practically like no customer overlap. We're targeting Fortune 500 USG, which includes Department of War and like the federal government and state government. And they require something that's a little bit bigger, more intense, a little bit more trustworthy and something that is more cross-functional in nature. So on the surface, probably competitive, but like practically no. And we think that there's a big enough market to support both

AI assessment note: “So on the surface, probably competitive, but like practically no.”

Q How was the nut business? I mean, we've, was it successful? Were you doing very well? Like, how, how did, you know, two, three years in, Did you find it to be challenging, or did you start to really see some results?

A Well, it was, um, yeah, I think by, let's call it maybe, you know, I had a little, nice little factory in Salem, uh, with a warehouse. I had a, a broker. I was buying all my peanuts through, and, um, he tipped me off that there was, um, gonna be a big crop failure that year because of drought, and so I went very bullish. I speculated, which was, Really crazy. And I contracted way, way more nuts than I actually was currently producing. And, um, sure enough, I was sitting on all these valuable contracts and the market for, uh, peanuts essentially tripled. So I ended up selling truckloads of, you know, 50,000 pounds of peanuts. And I was making like 25,000 dollars a truckload. And that, that effort capitalized my little business, which prior to that was extremely thin on working capital.

AI assessment note: “And that, that effort capitalized my little business”

Q Is there a parallel to a prior time where you can remember this? Whoa, immense uncertainty. I do not know what happens. I'm better to sit out and watch because I don't want to see Monday drop another.

A Yeah. But so March of 2000, uh, almost 26 years ago, tech stocks dropped between 30 and 40% across the board. And then if you missed a quarter, you were down over 50 or 60%. And we lived in that malaise Uh, March through the summer. And by the way, we were still able to get a couple of IPOs out at, but at lower, um, multiples during that time. And we just sat in misery, um, until nine, 11, which what I call was the coup de gras to finish us off. And, uh, the market got destroyed and we were thinking, Hey, you know, we don't do.com investments and insight. We're going to be fine. Cause we knew Then in 99, that there was a bubble and we knew that dot coms were going to blow up. We said, look, it just can't be sustainable. There's not enough commerce. There's not enough people on dial up. You can't do commerce on dial up and there's not enough fiber in the ground. So sure enough, there was a burst. However, the tsunami came in, took out the dot coms and then took out all software. We all went down and we were in misery for years.

AI assessment note: “March of 2000, uh, almost 26 years ago, tech stocks dropped between 30 and 40%”

Q an ideas video that did 260,000 views, and so we have, we have a bunch in the six figures, but most of them, you know, they get to five or 10 K, and then they're like, bam, even though we have a 115,000. So, do you, do you agree with that assessment of, like, email and podcasts is more the owned media, or how, how do you think about it?

A I split the, uh, platforms into two buckets. I call them discovery platforms and relationship platforms. Discovery platforms are anything with an algorithm where there's like inherent mechanism for connecting new consumers to content. And in relationship platforms, there is no mechanism. It's decentralized. So on the relationship side, there's only a few, and I agree with you. It's email, podcasting, SMS, and private communities. So to me, the game is Using discovery platforms to reduce your dependency on discovery platforms is basically like, I'm going to go over here. I'm going to perform for the algorithm so that I can get people to my email list or so that I can get people to my podcast or private community. And some people do SMS really successfully, especially in the e-com space. But, um, that is kind of the game today is to say, I don't want to depend on discovery platforms because that can be, it's certainly going to be changed if not completely eliminated from being useful to you. But the longer that you can leverage it to not need it, the stronger your actual leverage becomes.

AI assessment note: “So on the relationship side, there's only a few, and I agree with you.”

Q Wow. So, uh, how long ago did you start the company and, and what's the, what's the evolution look like since inception?

A Yeah, we started in 2019, um, January of 2019, um, we grew about 50% a month, every single month from January of 2019 all the way up to March of 2020. Yeah, I don't have to tell you what happened in March of 2020, um, but essentially we dropped like 90, 80, 90% almost overnight. Um, and then pulled ourself out, uh, took us about a year and a half, two years, pulled ourself out, uh, and, you know, now we've got, you know, thousands of businesses on our platform, you know, uh, you know, millions of consumers on our platform, and it's, um, you know, been, been a, a fun run, but, uh, uh, but definitely has its ups and downs, and, and we, you know, rolled those downs, learned from them to get better at the ups.

AI assessment note: “we started in 2019, um, January of 2019, um, we grew about 50% a month”

Q Ed was on the show last year, so just for perspective, before we dive into what's going on in venture capital, why don't you share a little bit about Gresham and how venture fits into what you're doing?

A So Gresham, we're a thirteen billion dollar multifamily office. What that means in practice is we manage money for about a 130 different folks. Think of these as people who generally made the money themselves. A lot of them are GPs. The commonality across that subset is they all are worried about taxes. It's a very important component of our investment mandate. There's a lot of things we can do on estate planning, carry planning, things that are very valuable. They're also very astute investors. They're looking for a high class portfolio. What that means on my side, Is we're trying to build essentially an endowment portfolio that's tax-systemized. The tax side, on the public side, we have lots of strategies. And private's generally a tax-efficient part of the portfolio. If I think about venture specifically, we want venture to be the highest performing part of our portfolio. We lean into the risk. Like most LPs, we certainly have our multi-stage funds. We have two folks we think are fantastic. They give us exposure. They give us alpha. Most importantly, they give us the confidence to think risk forward. What that means for us is the rest of the venture book looks early. It looks small. Most recent deal we did was a fifteen million dollar solo GP. We want to essentially be the first check into a company's life cycle. The real goal there is the last 20 years, it's been the best p…

AI assessment note: “we're a thirteen billion dollar multifamily office... If I think about venture specifically”

Q Oh man, that's crazy. So we'll get into the story here at LifeKid, which, which started about five years ago, I guess in twenty-twenty-one, but maybe a little bit of background, because I know you had some other startups before that. So maybe tell us a bit about kind of Your history and maybe a little, you know, even more about that. Exactly the previous startup, right? EV Labs.

A Yeah. Um, so this is my fifth company. You know, the first one that's really kind of started to do well, but, uh, I, I grew up always wanting to start a company. My dad was in tech startups in the eighties and nineties during semiconductors and GPUs and DSL and all of these kind of foundational technology shifts, mostly on the hardware side. And, you know, I got out of school, and I'm like, I'm gonna start a company. So I, I went and I joined, um, Y Combinator, the 2007 class of Y Combinator. So it's the fifth batch. Did a company then, and, you know, I was a kid right out of school, and actually most of the founders at that time were just kids out of school.

AI assessment note: “this is my fifth company. You know, the first one that's really kind of”

Q go back to the grid, then, just to wrap up. Over time, and obviously this will take a long time, but if over time, if we go and start to, one by one, go throughout the transmission distribution system and replace all of these traditional oil-filled transformers that are on the grid right now, ultimately with solid-state transformers, like, big picture, what does that enable from a grid management perspective?

A Well, utilities and grid operators right now are facing, um, a lot of pressure, right? They've got aging infrastructure, growing demand, um, and they, they're in the market for new solutions, and luckily, SSTs can provide a ton of value propositions beyond just voltage transformation. Um, an SST can have a cost similar to a traditional oil-filled transformer, um, but at the same time provide functions that would be That would be provided by popcorn components around the transformer. Functions like overcurrent protection, fault isolation, what an automatic tap changer does for voltage correction, uh, what three phase balancers do to enable higher utilization on the different phases in the distribution grid. They can provide the spinning inertia type functionality that synchronous condensers do for frequency regulation. Um, and they can also take the place of cap banks for power factor correction. So With the choice to go SST the next time they need to place a, uh, a distribution substation down or replace an aging, fifty-year-old, you know, 34 KV to two away transformer. They're at the same time getting all of those other value added functions kind of for free. And what those other value added functions do is enable more utilization of the existing poles and wires. And utilization is the key to affordability. Um, if you look at the rate cases, uh, uh, for public utilities, uh, a…

AI assessment note: “SSTs can provide a ton of value propositions beyond just voltage transformation.”

Q the reply to that mentioned, uh, that he had contacted a lawyer. Um, so there's my apology. I still don't love the puzzle, but, um, back to you, Steven, this is, this is a little bit, uh, what would you, what actually, let me just ask you the question without leading the witness. Is it narcissism or is there actually something, something, you know, potentially disconcerting happening behind the scenes?

A I think it's very brave in that by and large, these are people sacrificing very large amounts of money to give the warnings they are. I do wish that they would be more direct. Um, but to put it in context, you know, back in. It seems that OpenAI and Anthropic had secret non-disparagement agreements, which in OpenAI's case, at least, you know, plausibly not permitted by law, the way that they operated this, where To keep your already vested equity, the compensation you had been told was yours. You had to sign away your right to say anything negative about open AI and in fact, sign away your right to tell anyone that you had signed this contract. Um, and this was secret and kept under wraps for years until Daniel Cocotelo, um, who people might know from leading AI, 2027, I think very, very courageously forwent this agreement and forfeited something like 80% of his family's net worth and said, Sorry, I'm, I'm just not waiving my right to criticize open AI. Um, and in the wake of that, you know, there was a bunch of outpouring open AI and anthropic changed the nature of these contracts. And still it's pretty intimidating to speak out against these massively resourced legal operations. Um, you know, not afraid of subpoenaing different people and getting into legal conflict. You want to be really, really careful about what you say. And so in Renox case, I noticed in the footnotes, ri…

AI assessment note: “I think it's very brave in that by and large, these are people sacrificing”