The Exchanges, every show

Q life changing thing happens to you in the senior year of high school, and I think it happens, uh, during a visit to Penn. Um, tell me the story. You got, you're a senior in high school. It's 2009 and you are, you're in Philadelphia visiting Penn. Why are you there? Why are you visiting? You're visiting because you're presumably because you want to go there. Tell me the story.

A Yeah. So I was, uh, actually to step back, right? So a couple months before visiting Penn, um, I was starting to feel thirsty a lot. And so I got to the point where I couldn't go through a whole hour long class without having to leave, go get some water from the water fountain and come back. And then it got to the point that I couldn't drive from school back home without stopping at a convenience store to buy a gallon of water and drink it on the way back. And so I'm getting more and more thirsty. I don't know why I'm getting thirsty. And To me, drinking water is a good thing, so I wasn't too concerned about it, and then I got accepted into Penn, and so I went to Penn for this kind of new student day, um, and I got there with my family. We're staying in a hotel the night before this whole thing's scheduled to start. We walk around campus, um, and I, I pass out as we're, as we're walking around campus.

AI assessment note: “I pass out as we're, as we're walking around campus.”

Q All right, let's, let's switch topics a little bit and talk about how you run this thing and what tools to use. I know you're kind of a one man show here, built this app, did a lot of the marketing in the beginning. What are all the tools? What's your whole stack to run an app that makes 50,000 dollars a month like this?

A Uh, yeah, solo founder, I have help now. I think it's inevitable in terms of running, uh, everything I use, Firebase, uh, for the database authentication and cloud functions within the app, uh, super wall for paywall and subscription management. I use OpenAI for AI API. I use Mixpanel for in-app analytics, and of course, Cloud Code, the absolute best tool for actually building the app. And then for marketing, Kuroi for influencer tracking and payments. SideShift for UGC Creator, sourcing, tracking, and payments. Instantly for emails. This is great for sending out mass emails for influencer outreach. Cal for booking meetings. Agree for contracts. And then Google Sheets, I feel like I have to mention sort of run everything through that. And then just general business tools. These QuickBooks for accounting keeps everything super clean. And then one password for password manager on a personal note, absolute life changer. Could not recommend any more to set up a one password.

AI assessment note: “everything I use, Firebase, uh, for the database authentication and cloud functions”

Q Can you talk to me about what that is, how it was built, what it does? I'm just intrigued to see how companies change and how they operate.

A Yeah. It's one of the more significant developments in how we run the company of the last six or nine months. And we, we began by building what we call our MCP gateway. This is a single MCP server. That aggregates all of the main systems and services that we use to run the company. And so you can add this single gateway to your Claude instance, to your Codex instance, uh, and, uh, indeed to, to Pinecone, and basically via any one of those agents have full access with the permissions, of course, that you, as, as an individual at the company, you can't read someone else's documents, but you can read your own. You can read your own Slack messages. And it's kind of like having superpowers, right? You can, you can interrogate in essence, the entirety of the company, all information that is published, whether it's, um, slack messages or presentations or operating reviews, uh, and, and so on, and use access to all of that information to better reason, make decisions, get things done. Pinecone, uh, of course incorporates that MCP gateway, but then is a Purpose built harness for all of Sierra. So Pinecone knows how to build Pinecone. So it, there's a whole harness around the engineering of Pinecone and our engineers there are phenomenally productive. We have a whole harness around the core of our platform, our agent architecture, agent studio, where you build and deploy agents, speeding…

AI assessment note: “began by building what we call our MCP gateway. This is a single MCP server”

Q to the way that you run the company. It was so important in so many of my conversations before this. If we start with like the board meetings, I, I suppose as I said, many of the ambassadors, Every six weeks, not every quarter. Can you talk to me about your biggest lessons on how to really get the most out of your board and run the best board meetings?

A We do a couple of things. You mentioned the, uh, six week cadence. We have kind of a tick tock, a three hour meeting and a one, a one and a half hour meeting. We've done this since the beginning of the company because we could just see if you're on the AI time clock, it moves a lot faster. Things are changing, and most recently we came back from winter break, and suddenly coding agents were amazing. You had, right, Claude, four, five, Codex, five, two. There is a fundamental step change in the capabilities of these models. It changed our approach to software development. It changed our approach to the core product, and so having a cadence where you can take in information, even from the last six weeks, kind of update your priors, and then change course, I think is quite important. As for running the board meetings themselves, we don't have board decks, we have board memos. So Brett and I write a usually six to 10 page memo. There's a saying, writing is just thinking on paper, and I think it's very hard to hide from writing. And so getting our thoughts clearly out onto paper, sending that in advance, giving each of our board members some kind of soak time to, to think through the issues and come prepared. Rather than be like presented to and managed, I think is a big part of it. And then the contents of the board letters themselves, I think is notable. We've done quite well in o…

AI assessment note: “we don't have board decks, we have board memos”

Q Did you consider training owned models and what was the thought process around not?

A That's a great question. We did briefly And discarded it. Uh, if you recall at the time, so late 22, early 23, as a startup in AI, you were kind of nobody if you weren't doing your own pre-training and building your own foundation models. Character, inflection, adapt, great people at these companies, but the capital expense, uh, the ongoing capital expense to create what is effectively a highly perishable bag of floating point numbers just doesn't work. Just doesn't work for any, but a small number of companies. And so our calculus was for areas that are deeply capital intensive. How do we slipstream behind the investments that the labs, that, uh, the hyperscalers are making and take as much as we can off the shelf while still being willing to, uh, engineer more deeply. So today we have a set of our own proprietary fine tune models. But these are fine tunes on top of open weights models. So we're not going, you know, all the way down to the, uh, you know, mega cluster training runs. Um, and I, I think it's important that you are in control of your own destiny enough and that you don't tell yourself a story that you need to go further than you actually need to do.

AI assessment note: “We did briefly And discarded it.”

Q to the way that you run the company. It was so important in so many of my conversations before this. If we start with like the board meetings, I, I suppose as I said, many of the ambassadors, Every six weeks, not every quarter. Can you talk to me about your biggest lessons on how to really get the most out of your board and run the best board meetings?

A We do a couple of things. You mentioned the, uh, six week cadence. We have kind of a tick tock, a three hour meeting and a one, a one and a half hour meeting. We've done this since the beginning of the company because we could just see if you're on the AI time clock, it moves a lot faster. Things are changing, and most recently we came back from winter break, and suddenly coding agents were amazing. You had, right, Claude, four, five, Codex, five, two. There is a fundamental step change in the capabilities of these models. It changed our approach to software development. It changed our approach to the core product, and so having a cadence where you can take in information, even from the last six weeks, kind of update your priors, and then change course, I think is quite important. As for running the board meetings themselves, we don't have board decks, we have board memos. So Brett and I write a usually six to 10 page memo. There's a saying, writing is just thinking on paper, and I think it's very hard to hide from writing. And so getting our thoughts clearly out onto paper, sending that in advance, giving each of our board members some kind of soak time to, to think through the issues and come prepared. Rather than be like presented to and managed, I think is a big part of it. And then the contents of the board letters themselves, I think is notable. We've done quite well in o…

AI assessment note: “we don't have board decks, we have board memos”

Q Did you consider training owned models and what was the thought process around not?

A That's a great question. We did briefly And discarded it. Uh, if you recall at the time, so late 22, early 23, as a startup in AI, you were kind of nobody if you weren't doing your own pre-training and building your own foundation models. Character, inflection, adapt, great people at these companies, but the capital expense, uh, the ongoing capital expense to create what is effectively a highly perishable bag of floating point numbers just doesn't work. Just doesn't work for any, but a small number of companies. And so our calculus was for areas that are deeply capital intensive. How do we slipstream behind the investments that the labs, that, uh, the hyperscalers are making and take as much as we can off the shelf while still being willing to, uh, engineer more deeply. So today we have a set of our own proprietary fine tune models. But these are fine tunes on top of open weights models. So we're not going, you know, all the way down to the, uh, you know, mega cluster training runs. Um, and I, I think it's important that you are in control of your own destiny enough and that you don't tell yourself a story that you need to go further than you actually need to do.

AI assessment note: “We did briefly And discarded it. Uh, if you recall at the time”

Q Can you talk to me about what that is, how it was built, what it does? I'm just intrigued to see how companies change and how they operate.

A Yeah. It's one of the more significant developments in how we run the company of the last six or nine months. And we, we began by building what we call our MCP gateway. This is a single MCP server. That aggregates all of the main systems and services that we use to run the company. And so you can add this single gateway to your Claude instance, to your Codex instance, uh, and, uh, indeed to, to Pinecone, and basically via any one of those agents have full access with the permissions, of course, that you, as, as an individual at the company, you can't read someone else's documents, but you can read your own. You can read your own Slack messages. And it's kind of like having superpowers, right? You can, you can interrogate in essence, the entirety of the company, all information that is published, whether it's, um, slack messages or presentations or operating reviews, uh, and, and so on, and use access to all of that information to better reason, make decisions, get things done. Pinecone, uh, of course incorporates that MCP gateway, but then is a Purpose built harness for all of Sierra. So Pinecone knows how to build Pinecone. So it, there's a whole harness around the engineering of Pinecone and our engineers there are phenomenally productive. We have a whole harness around the core of our platform, our agent architecture, agent studio, where you build and deploy agents, speeding…

AI assessment note: “we began by building what we call our MCP gateway. This is a single MCP server”

Q Is what you just described, uh, called latent MOE, or is that a different concept?

A Latent MOE is a specific, uh, innovation that we have in NemoTron three family. And, uh, what it does is actually, um, reduces the amount of communication that has to be sent through NVLink during MOE computations by basically down projecting it. So, you know, every token produces a vector and the idea is like, we're going to take that vector and learn a way to compress it. And then send that compressed thing through the network, and then we're gonna uncompress it at the other end. And as a result, we save on network bandwidth, and we also get four times the number of experts for the same inference cost. So you could think about it as like, you know, our library of books got four times bigger, um, and we get to, you know, read four times more books, uh, at the same inference cost because of, because of this particular innovation.

AI assessment note: “Latent MOE is a specific, uh, innovation that we have in NemoTron three family.”

Q And who, who were you thinking were your competitors? Did you think that, that Maybelline and Revlon and L'Oreal were your competitors, or not really, because they were, they were charging four or five times the amount that you guys would be charging?

A In terms of the space we were trying to get to, yes, very much. Maybelline, CoverGirl, There was some smaller brands, NYC and Wet n Wild. Those are more of our core, core competitors, because they were the value brands, even Rima London. But both Rima London and NYC were owned by Cody. Wet n Wild was a standalone. But that was the problem in the early days, that you had a few incumbents that owned a bunch of brands and controlled all the space at retail. So you had L'Oreal, which was L'Oreal and Maybelline. You had P and G, which owned cover girl at the time. So there was no room to you to like box your way in two feet of space because it was all controlled by four or five companies, even if it was 12 brands on shelf.

AI assessment note: “In terms of the space we were trying to get to, yes, very much.”

Q Okay. Seventy million dollars. So here's a question for you. At seventy million dollar valuation, why, why were you open to selling some or all of the business at that point?

A The first reason was we had an opportunity to do something for the family, which was perhaps not generational wealth, but wealth for definitely more money than I've made in my life. I was 29 years old. I was able to put money away, and if everything else fell apart, I would still have financial flexibility, and the second reason was I didn't know what I didn't know. Maybe L'Oreal and Revlon were working on the next thing to take me out in 30 seconds. I had no idea. Um, and then the third thing was really just, like, it's not what you know, it's who you know. So I think being connected with these guys who are in this private equity world that are selling companies every day, they know the steps, they know the game plan to make you sale ready for the future.

AI assessment note: “The first reason was we had an opportunity to do something for the family”

Q Yeah. Well, maybe tell me why you started Chewy. How'd you get that idea? And how'd you get into building this business from where you were coming from at that time?

A We were, um, wanted to build something online and we were, uh, about to launch an online jewelry website. Did not know anything about jewelry. Went to a bunch of trade shows, bought hundreds of thousands of dollars worth of inventory. Built the website, had the distribution, and then I was shopping in a neighborhood pet store. Uh, I had a poodle, and I was going every few weeks, and It just hit me on one of my trips that I understood the product much better. It was a recurring revenue purchase. The market was still fragmented. The fact that there was still neighborhood pet stores at the time, and they had not been disrupted by Petco and PetSmart was fascinating to me. And then you had to eat, you had Amazon, which was established and had pet products since the nineties, but they hadn't really achieved real scale in the category. The vision was to replicate the same experience that I had at the neighborhood pet store, but do it online and do it at scale. And I looked at Amazon's best practices when it came to supply chain. So fast shipping, having a great selection, being competitively priced, and then the experience at the neighborhood pet store. Of knowing the products really well. And it was easy to be passionate about the pet category because I'm a pet owner and everyone we hired were pet owners. And the, it was all about market leadership as a low margin business. Hindsight…

AI assessment note: “It just hit me on one of my trips that I understood the product”

Q I'd love to double click on hedge funds. What do you think today about the role of hedge funds that you said at least once were 40% of what you're doing and how you're participating in that space?

A Hedge funds are no longer 40%, but they're still meaningful. Today, our target allocation across our long, short, and absolute return portfolio is 27% in aggregate. We're a believer. They have been a very important part of our portfolio, not only for meeting that return objective that I mentioned, they do so while maintaining our liquidity, also helping us to manage volatility. When you're spending five percent of the portfolio every year, All three of those mandates do matter. Something that gets lost when people talk about returns. If the goal of this endowment was to just put it in a drawer and not spend from it for 10 years, you might do something differently than if you knew you had to draw five percent from it every year. In that context, hedge funds make a lot of sense. They've obviously gone through many different cycles and periods where they've done better or worse. This is where Williams is fortunate in that we've had these longstanding relationships. We have the confidence to see through cycles. We have some amazing manager names that have done well for the portfolio.

AI assessment note: “Today, our target allocation across our long, short, and absolute return portfolio is 27%”

Q All right. So, on the strength of that, you, I guess, see the potential to really lean into restaurants, right? And You then open Budokan, also in Philadelphia, which, describe it for people, I've been there, but describe it for people who don't know what this restaurant is.

A Budokan, the original Budokan was a Pan-Asian restaurant, so it was sort of fusion. It's very theatrical in its design, and a centerpiece of it is a, like, you know, 12 foot golden Buddha. Again, I don't take credit for the culinary concept I did not create on my own. It started in, with Wolfgang Puck at a restaurant he had in California called Chinois on Main. Then, uh, another gentleman opened a place called China Grill in New York and in Miami, and I had gone to all these places, loved the energy, and I went and spoke with different designers to ask if they would design my restaurant, one of which was Philippe Stark, who turned me down, but said, there's ways of buying my furniture, call these people. So I ended up designing it myself with my friend Owen Kamahira,

AI assessment note: “the original Budokan was a Pan-Asian restaurant, so it was sort of fusion.”

Q Well, thank you for sharing that. Looks like a super cool app. What's your background? How do you get to the place where you're building super cool consumer app that's doing over 10,000 a month?

A My origin story starts as a gym bro. So I launched a fitness chain in China. We scaled it to fifty million ARR across seven locations, and then I moved to Europe. I worked for a German scale-up. I took them from a forty million series B to two hundred million series C, and throughout this process, I learned a crucial lesson. I understood that distribution has become the mode, and that should be the focus of every founder out there. I moved to New York, which is where I launched Flame. The reason behind Flame was because I saw there were a lot of apps out there to help you find a partner, almost none to help you make the relationship better afterwards. Started building Flame with my then French girlfriend. We started using the app to build a relationship. I also used the relationship to build the app based on what I learned, and we're married, so I guess it worked.

AI assessment note: “My origin story starts as a gym bro. So I launched a fitness chain”

Q sharing this. I mean, what I think is super cool is the amount of like scale and experimentation that you're doing here. And you kind of mentioned this earlier before, but you had like a whole spreadsheet where you track all the content. I think that's super cool. Could you show me that spreadsheet and kind of explain how you use it and how people could maybe create something similar?

A So this is the beautiful spreadsheet. And what we do here on the leftmost side is we track all the accounts, what phones are on, and then the important work starts from here, where we are tracking what is happening with the account. Is it on warm-up phase one? Two? Is it paused? Is it healthy, unhealthy, stop, blog, etc. Then we switch to understanding what channel is it on. Does it have TikTok only? Does it have TikTok and Lemonade? Does it have TikTok and Instagram? So as you can see, Pat, some of them have reactions, some of them have slideshows, some of them have other kind of content. My team goes in every single day, and they track how many views are we getting on the videos, how many likes are there, and then they leave notes in the comments on what is working, what is not working. Why? Because this helps us understand what part of the video performed well, what should we try to replicate today, what should we not try to replicate today. Another cool spreadsheet we have is where we track the hooks and how they're performing individually. Based on this, if this is working, then we'll iterate on it the next day and the next day and the next day by changing small wordings or putting the placement differently. Um, one of the secrets we had is we were having a woman who was reacting on the camera. We discovered that if we put the text Right on her neckline. So not on her face…

AI assessment note: “So this is the beautiful spreadsheet. And what we do here on the leftmost side”

Q That's awesome. I just love how simple it is, you know, solve a small problem. Just helping couples connect in a small way can be an app that makes over 10,000 dollars a month. On a similar note, could we talk about the tech stack? What are the tools behind this 10,000 dollar per month app?

A For building, we have a simple set of tools, Claude Code plus Claude Design, OP. Then RevenueCat allows us to manage a monetization. Mixpanel, we're also using the free tier right now. Then TikTok Native Analytics, I think is the best. No other tool can compete with it. You can see the retention, you can see where people are dropping off, what's happening. Then we also use Hicks Field. And the last part is just having a portfolio of infrastructure, right? I mean, look at this. One, two, three, four, Five. Every phone has three to five TikTok accounts. You're looking at 15 to 20 TikTok accounts right now. This is the infrastructure we use to manage the distribution phase.

AI assessment note: “Claude Code plus Claude Design, OP. Then RevenueCat allows us to manage a monetization.”

Q Yeah. That's the highly optimized, uh, storage. What else? The networking part and what other pieces?

A So, so I, I, I was talking about this one cloud cluster product that We've got, and the, the way of just for everybody to think about this is like, okay, well, look, you've got a bunch of GPs. Let's say you've got a cluster of 10,000 GPs. Well, I want to partition that cluster up, and so what it is, is it's a bunch of GPUs, some CPU servers as well, because you need to have an orchestration, uh, fleet as well, and then you've got some storage, and, um, all of the CPU servers and the storage servers and the GPU servers are interconnected with the, the storage, so they can quickly read and write from it, and, um, so there's And that, that communication happens over what's called, you know, the in-band network. And then there's the compute fabric, which is where I was talking about where all of the sort of weights and, uh, feature activations are being shared, uh, throughout that compute fabric. And then there's an out-of-band monitoring network where you've got access to whether it's BMC or, uh, some of your DPUs. Um, and When you are trying to create a sub-partition of a 10,000 GPU cluster, you need to simultaneously partition the in-band, the out-of-band, and the compute fabric. Okay, so, like, that complex coordination between we've got a bunch of bare metal systems to, hey, we've got a virtualized system that has, you know, what's called RDMA, you know, RDMA, remote direct me…

AI assessment note: “simultaneously partition the in-band, the out-of-band, and the compute fabric.”

Q And blueberry. Ok, cool. Ok, a lot of questions for you. How did this idea come about? I mean, you can put protein in almost anything, but sprinkles? Who knew?

A Well, my co-founder and I are both full-time working moms. We are very busy, and we only get about two hours a day with our children. And we don't want to spend those two hours fighting with them and negotiating with them on what to eat. And we just wanted to be happier with them in the little time that we had. So Nicole came up with this idea around sprinkles because she's Dutch. And in the Netherlands, they eat thirty million pounds of sprinkles a year. And they eat it on toast for breakfast. So we thought, well, why don't we use that as the Trojan horse and sneak in some function and protein and fill them up if they love eating sprinkles so much?

AI assessment note: “Nicole came up with this idea around sprinkles because she's Dutch.”

Q things like that. I also think about obviously someone that might've built the, you know, inventory, you know, robotic system to manage Amazon warehouses, to get delivery time down by a millisecond, because then we spend more. I think about someone like you work with DARPA, right? You know, great university going to, I mean, why is your background and your team sort of best suited to win this space?

A Two things. The three co-founders, Daniel Gitesh and myself, like you mentioned, worked on the world's first full autonomous helicopter, won national awards for the work we did with aerial autonomy. So we had deep expertise in physical AI for aerial autonomy, and my thesis focused on how to make robots curious, and they're curious here in warehouses about boxes, barcodes, inventory, and workflows. So that, that makes the technology alignment quite strong. And the second thing is over the last six or seven years, how our stack has evolved. Now our stack is the only stack in the world that you can put on a moving camera that you buy out of Best Buy and turns it into an autonomous data gatherer. So that's the technical side of it where we have a data moat and a tech moat of, of given our technical backgrounds. But also over the years, We have assembled a bench of people from Amazon, from Uber, from, uh, Secret with deep logistics background. So our product has evolved to support logistics players in how they want to be supported. So I think those two combinations give us, uh, an unfair advantage to, to serve effectively in this space.

AI assessment note: “I think those two combinations give us, uh, an unfair advantage”

Q What do you think Elon Musk would say about mistakes?

A They're an absolutely vital part of long-term success. If you stack up a few of the kind of aphorisms that you hear him say a lot of times, he's actually encouraging these small failures. He's mandating small failures because that's how you find the limits of what works. Um, you don't know if you have something really an elegant design until you've taken away more things than you need to. You see what breaks, then you add back the essential, right? He says, if you're not adding back 10% of what you removed, you're not removing enough. So you have this clutter, you have mess, you have over engineering, you have redundancy, and it's only when you push things with something to the point of breaking it that you learn what's really vital. Um, he also says in hiring, he's asking for people like, what problem did you solve and what mistakes did you make along the way to solving it? What walls did you have to bounce off? He wants to work with people who understand how to make a mistake and iterate because reality is the ultimate teacher. And so the organizations are designed to Iterate quickly, move fast, uh, fail almost intentionally, and then learn from that failure as quickly as humanly possible.

AI assessment note: “They're an absolutely vital part of long-term success.”

Q What's one thing most people don't know about you that you find interesting?

A I said I love basketball. I was so fortunate in high school. I played with two future NBA players and actually played on the fourth ranked team in the country where we got to play against all sorts of people that played in the NBA over the last decade or two. It was this amazing experience because it was one of these times where not only was it so fun and my big passion, but I learned about teamwork and leading and Then also how to take a back seat, and the experience of being a part of something great. I still look back on that, and I'm so lucky and fortunate to be a part of that experience. There's one more I should probably say. It was fun talking about my professional accomplishments, but actually I'm far from the most successful person in my house. My wife is an amazing author, Christina Alger. She's written four best-selling books, and they are phenomenal. If you're going to start with one, I would start with The Banker's Wife, but What really makes all of this possible is her and her support. She's really the most talented in the family.

AI assessment note: “I played with two future NBA players and actually played on the fourth ranked team”

Q and, and those who import into the U.S., and There's all the standard versions of differentiation. The product is different. Cars are different from each other. Um, what do you see as, like, the biggest differences amongst the major players, at least in China? Are they approaching the market with different strategies or different types of products, or are they all pretty similar and they're just cutthroat with each other?

A Oh, there, there's, I think of them as, you don't ever see that picture of, like, a massive school of Schools of fish all mixed together, shaped sizes, colors, going in different directions. That's China's auto industry. So you have niche players, you have mass scale players, you have electric only specialists, you have companies that will produce every powertrain. But if I were going to think, try to distill it down, you have really, uh, Old generation or your legacy automakers who are dependent on scale and low cost. That's your BYDs and GLEs and SAICs. Then you have this new generation. That's all about software and software defined vehicles and over there updates and digital interfaces. So they're focused on making the car gadget, the urban, the ultimate urban device. And so you have. Kind of, if we put it in American terms, you have Ford, GM, and Chrysler on the one hand, and you have Tesla, Rivian, Lucid on the other. That's the biggest distinction, uh, relevant to us in our discussion today.

AI assessment note: “Old generation or your legacy automakers who are dependent on scale and low cost.”

Q to the U.S. then, in the rest of the world, you know, these Chinese cars are flooding the market. Are we seeing automakers outside of the U.S. start to feel the pain meaningfully? Like, are we going to see a wave of failures of automakers of basically everybody who's not Chinese and not U.S.-based, or at least not importing enough into the U.S. for it to make up the difference?

A We're already seeing it in Europe for sure. Uh, Volkswagen's come out and said between now and 2030, they'll lay off 50,000 people. This is unprecedented. They're closing plants for the first time since World War II. And the Germans are not alone. Honda, also for the first time since the 19 fifties, uh, announced a loss last year. Why? They're getting beaten down in traditional markets like Thailand. Indonesia, Australia, the UK, across Europe, uh, virtually every legacy automaker is taking it on the chin, and the most vulnerable right now, there's two of them. One is Nissan, and the other is Stellantis, the group that owns, you know, Fiat, and Maserati, and Jeep, and Chrysler brands. Those two are the most vulnerable, so what will happen is either they'll joint venture with the Chinese And eventually be taken over by them, or the Chinese would just buy their brands outright. I mean, we're not talking about something that might happen three to five years from now. It's happening right, right now in Europe. Enormous pressure on the existing automakers just to stay alive.

AI assessment note: “We're already seeing it in Europe for sure. Uh, Volkswagen's come out”

Q that you mentioned, they're selling at 10,000 dollars. Even with a hundred percent import tariff, they're still going to be cheaper in the US. Like, if you're trying to buy an EV in the US, it's hard to find a 20,000 dollar EV as well. So I'm sort of surprised that even despite that tariff, we haven't started to see a lot of imports. Why do you think that is?

A I really appreciate it at the outset when you said we might go deeper in the weeds because it gets a little bit hairy here. So a couple of caveats that, that under 10,000 dollar vehicle that's sold in China would not qualify for registration United States on safety. And probably on overall emissions, depending on if it's PHEV or EV, but mainly safety, homologation is not there. So, for example, that same seagull that sold in China at 10,000 dollars, let's call it, sells in Mexico for 20,000 dollars. Mexico's standards are not as high as the United States, so we're talking about 25, 30,000 dollars. Now you have a small car with small range, and guess what? The American consumer goes not really into that. I know it's 25, but it's small and short range, ah, not that great looking. Uh, no, I want a bigger vehicle. So let's be care, I think we need to be careful about what kinds of cars would actually, the Chinese would actually aim to sell here. Second thing to note, the Chinese are starved for profits, and when I talk to the Chinese manufacturers, they're far less interested in solving America's affordability program And far more interested in saying, how can we attack the larger SUV and pickup truck segments? BYD, for example, recently launched this midsize pickup truck called the Shark. It's aimed directly at the Tacoma and the Ranger, and I know other Chinese automakers have th…

AI assessment note: “under 10,000 dollar vehicle that's sold in China would not qualify for registration”

Q autonomy for a second. Where, where does autonomy fit into the mix in China? Um, you know, obviously in the U S we have, we have Waymo, we have the Tesla robotaxi coming. There are others like Zoox who are starting to arrive in some cities. Is China seem ahead behind? Is it, uh, is it part of the export strategy or is it not? Like where does autonomy fit?

A Great, great, uh, arena here. Autonomy. So it is a two horse race. Between the United States and China, the edge to China comes on the regulatory front. Here's a comparison. When I talk to regulators in China, their job, as they see it, their mission is to smooth the way to commercial ramp of autonomous in China and other markets worldwide. Make it as easy as possible. Facilitate. Make it go faster. What do you need to make it happen? Whereas here in the United States, the regulators are much more cautious. How do we make sure things don't go wrong? How do we make sure it's safe? So in that respect, The U.S. leads in technological innovation, but China, once again, is quicker when it comes to commercialization. Not only inside China, but we're seeing the Chinese autonomous vehicle makers move into the Middle East, and in the U.K. and Germany now, moving quicker to market than their U.S. counterparts, and this is where we're at risk. We might be the inventors, but China commercializes more quickly, and that's a problem for us.

AI assessment note: “China, once again, is quicker when it comes to commercialization.”

Q Okay, let's get a little closer to home, um, and start with the, our neighbors to the north and to the south. So you mentioned Canada got rid of its hundred percent tariffs. You mentioned Mexico still has a 50% tariff. So in both of those cases, how quickly and how much have Chinese vehicles started to penetrate those markets?

A Okay, so let's take Mexico first, because there's a little bit of history there. Mid-COVID, the government decided, oh, Our consumers need affordable vehicles, so we'll drop import duties to zero, and that was the beginning of this torrent of Chinese cars going into Mexico, and for the last two years, Mexico's has been the single largest destination of Chinese exports. So much so, the United States said, hey, what the hell's is going on down there, guys? You're letting all these Chinese cars, and I was recently in Mexico City, you see dozens of brands with, there's Chinese cars all over the place, dealerships going up. And so the U.S. put pressure on Mexico finally in January of this year to raise that import tariff from basically nothing to 20% last September to now 50%. So we've seen a deluge or an outpouring of cars into Mexico. There's several 100,000 on the road now. Many brands. It's slowed a little bit by the tariffs, but guess what? The Chinese are now Eyeing plants inside Mexico. They'll do assembly and sell to Mexican customers. So Mexico is a strategic launch pad for the, for the Chinese in North America. And if we go north of the border, just as you said, Canada held the line on imports at a hundred percent with the United States until earlier. Gosh, time flies so fast. Earlier this year, when Carney went and said, we're going to allow our first 49,000 cars from Chi…

AI assessment note: “There's several 100,000 on the road now. Many brands.”

Q Do you think we're going to end up with like one model that just sort of like dominates, or do you think we're going to end up with niche models and they're effectively going to be commodities in some way?

A I think it's highly dependent on how things play out. There are certain examples in the verticals, especially in the coding one, which is probably the largest vertical right now where people have swapped out models. You know, cursor even lets the, The user picked the model that they're using, and as we move towards optimization and price optimization, which isn't really the objective function right now, but it will be in a few years, um, you may see more people try and do those swaps. I think the thing, you know, that could cut against that, if the regulation gets extremely difficult and mundane and expensive, that could actually lead to more oligopoly. And I think some of the players know that and are begging for regulation.

AI assessment note: “I think it's highly dependent on how things play out.”

Q Guy gets pitched 20 times a day. What, what broke through? How, how did you get Chris to pay attention?

A I actually think the product and package did the work for me. In a big way. Cause he saw it. And then I painted the vision and the vision a little bit was to get 50 in the room and how fifth could be the face of the brand. And he before had never done that. He'd been like with sports and Reebok and this and that music, but actually the face of a beverage brand was first time. Here's a problem though. I couldn't afford 50. I mean, he was getting a million dollars to go perform in Libya for a night. You know, this is in 2004 now who knows, but I'm like, I said to Fifa, I can't afford you, but I have an idea. I said, we can do an equity game. I'll give you skin in the game if you wanted to do this. And he's like, I'm in.

AI assessment note: “I actually think the product and package did the work for me.”

Q For those who don't know your origin story for Impulse and why you decided to go after this after SpaceX, could you share more on that?

A Uh, sure. Yeah, I, I was, um, a founding employee at SpaceX. I developed, I led the development of the, the propulsion systems for Falcon and Dragon, and, and also started the origins of what became Starship. Um, my, probably my proudest development was the Merlin engine, which is, which is currently flying on Falcon nine, the most reliable rocket engine ever developed, and also the highest thrust of weight of any rocket engine ever developed. But I, I worked on Starship for the last six years that, uh, that, uh, SpaceX. So when I left, I had the plan of, you know, launch mostly being solved or is being solved. It's like the next big opportunity is to move all that, all that payload, all that cargo around in space. So I started impulse space to do in space transportation as opposed to from earth to space transportation, which was SpaceX. So we feel like The next step is in space transportation, including to, to the moon and, and landers on the moon, which we're also bidding on.

AI assessment note: “So I started impulse space to do in space transportation”