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Technology's daily show (formerly the Technology Brothers Podcast). Streaming live on X and YouTube from 11 - 2 PM PST Monday - Friday. Available on X, Apple, Spotify, and YouTube.
You're watching TVPN. Today is Thursday, 05/29/2025. We are live from the TBPN UltraDome.
Speaker 2:That's right.
Speaker 1:The temple of technology.
Speaker 2:The fortress of finance, the capital of capital.
Speaker 1:But now it's also the TBPN Ultra Dome.
Speaker 2:You heard it here first.
Speaker 1:It's really it's it's a rough year to be a super dome. Yeah. Because you thought you were at the top of the the food chain, you know, just as far as domes go.
Speaker 2:And then a new player comes in out of nowhere.
Speaker 1:Yeah. No one saw it coming.
Speaker 2:No one saw
Speaker 1:Kinda like, oh, like, Google Oh, we did. Tries to react to ChatGPT, you know, kinda blindsided by LMS.
Speaker 2:ChatGPT moment
Speaker 1:for SuperDomes. Yep. Exactly. Exactly. The UltraDome comes out, and you're like, well, now maybe we have to build an Ultra Dome.
Speaker 1:Maybe we gotta compete. How are we gonna do that? So, we'll see.
Speaker 2:Dome for dome with the best of them.
Speaker 1:We will. We will. Anyway, we got a bunch of news for you. We're gonna take you through about thirty minutes of recap of the news, then we got a stacked lineup. Ashley Vance is coming on.
Speaker 1:Chase Locke Miller from Crusoe Energy is coming on. We're we're we're talking to folks about, putting, data centers in space. We're talking about we're talking to Chase Crusoe about putting putting data centers in Texas. Yeah. And then the only thing bigger than Texas is space.
Speaker 2:Space data centers.
Speaker 1:We're going over to we're going over space. I'm more
Speaker 2:of an Abilene guy. Yeah. But I know you like space.
Speaker 1:I do like
Speaker 2:space data centers. I'm really excited to talk to Peter Hall
Speaker 1:from David Barking. He's making a fortune.
Speaker 2:He's back in the bar game.
Speaker 3:It's remarkable. And, I think the growth, I would say
Speaker 1:What was number? A hundred and forty five?
Speaker 2:Forty they're targeting a hundred and 40,000,000 in revenue in the first twelve months of That's insane.
Speaker 1:And Now now, to be clear, about 10 of that probably was from me when I was living on them exclusively for a couple weeks. Right. Because they they do cost a lot, but that's still very impressive.
Speaker 2:But yeah, it's one thing to get to a run rate like that. But even that is extremely difficult to do. But to actually do 140,000,000 in revenue that quickly
Speaker 1:Serious business.
Speaker 2:And seemingly has product market fit from coast to coast. They have you know, big presence, you know, in in the Yeah. Typical wellness y cities, but then also in, like, middle of nowhere.
Speaker 1:Yeah. Yeah. It's crazy. It's it's it's crazy. These second acts from these founders, they should work, but sometimes they don't.
Speaker 1:But he clearly hit knocked it out of the park with this. So we're gonna be talking to him today on the show. Anyway, speaking of space, man space manufacturing, space data centers, we have an update on Elon Musk. He's leaving Washington. Here's how that affects the Trump agenda.
Speaker 1:Interesting story with Elon. Obviously, he went into DC, built out the Doge team, had some wins, had some losses, some setbacks, some arguments, some debates, but he has now concluded his tenure as a special government employee departing the White House. It's back to work on space, baby. Yeah. He's going back in.
Speaker 1:There's a there's a lot at stake, at SpaceX. We're seeing the the ninth launch. Interestingly, the the latest star Starship launch broke up again. Another RUD, rapid unscheduled disassembly.
Speaker 3:Great.
Speaker 1:You hate to see it.
Speaker 2:Great name for when your spaceship blows up.
Speaker 1:But particularly bad for the people that were in the whole firmament camp Yeah. Because it broke up on reentry.
Speaker 3:Yeah.
Speaker 1:And so it had actually reached
Speaker 2:That group is in shambles.
Speaker 1:Velocity. So anyone that says, oh, it can't go through the atmosphere at all and space doesn't exist, bad day because the the latest the latest Starship actually does, in fact, reach its peak. It could, in theory, drop off, payload, and then it just breaks up on the way down, which means it's a very expensive nonreusable spaceship right now Yeah. Which is, of course, breaks the whole economic model. It doesn't actually work.
Speaker 1:So they gotta figure out how to get it back without it breaking up. But good news that they're at least getting it there, but they have some really important milestones on the horizon that they need to hit. And so Elon's probably going back in there. Also, saw the the the x outage and kind of, you know, things falling apart there. I'm sure he's like, I need to be sleeping in that conference room I need to be on the factory floor space, then go directly to the conference room.
Speaker 2:I heard a rumor that there were tents set up at the XHQ Okay. For people to sleep in in
Speaker 1:the office. Because it'd be o over the weekend when when the site
Speaker 2:was Recently. Recently. Because because it was, you know, it's embarrassing. Yeah. So to go down
Speaker 1:in such a big Yeah.
Speaker 4:Yeah. Yeah.
Speaker 2:And I'm sure that the culture there just doesn't tolerate No. You know No. Downtime.
Speaker 1:Although It held up really nicely for us yesterday. Was crypto day.
Speaker 2:Yes.
Speaker 1:Massive day on the Internet.
Speaker 2:Massive day. The interesting thing is that Tesla Tesla's stock price has been pricing in Elon
Speaker 1:Didn't even get to Tesla. Yeah.
Speaker 2:Elon leaving Washington for a while. It's up 22 percent over the past month alone.
Speaker 5:Because there
Speaker 1:was that there was that whole Wall Street Journal report report that said that Elon might be stepping down as Tesla CEO, and he said, absolutely not. No chance. But, yeah, I mean, he's getting back in back in the game. Can you pull up the poly market on Tesla new CEO? I wanna see that, and I'll take you through some of the news down
Speaker 2:out of horrendously.
Speaker 1:Probably. Now that he's not working for the government anymore.
Speaker 2:Yeah. He's had a 1% chance
Speaker 3:Yep.
Speaker 2:As of today. So
Speaker 1:So when is when is Elon Musk leaving the government exactly? As a special government employee, Musk's tenure was limited to a hundred and thirty days, which runs out at the May. A White House official said that Musk's off boarding started Wednesday, adding that he hadn't been he hadn't been a regular presence in the West Wing in recent weeks. As my scheduled time as special government employee comes to an end, I would like to thank president Donald Trump for the opportunity to reduce wasteful spending, Musk wrote on his social media platform x late Wednesday. And so
Speaker 2:They also cut this off Yeah. Because he specifically said the Doge mission will continue will only strengthen over time as it becomes a way of life throughout the government. And I just had to call out that the journal had to chose to not include the second part
Speaker 1:of that. Interesting.
Speaker 2:Yeah. Which I think is important. Yeah. He's not just saying, see you. Yep.
Speaker 2:He's saying, hey, I actually believe in this sort
Speaker 1:of I'm trying to reflect on like
Speaker 4:of the organization.
Speaker 1:How much of this was expected and what actually is happening? Because I feel like the prediction on the right was that, Elon and Trump are going to be a an unstoppable force forever, and they're they're they're gonna reign forever, and and and they're gonna clean up the government, do everything. And then and then the other side, on the left, it was like, they're gonna have the most massive blow up, and they're gonna be Yeah. At each other's throats, and they're gonna hate each other. Right?
Speaker 1:And it's like, what actually happened? Like, seems like they kinda did some work together. They like worked on a project together. They did a group School
Speaker 3:project.
Speaker 1:School project. And like, they it seems like maybe they're still like kinda friends and kinda chilling, but also Elon's like, I got other stuff to do.
Speaker 2:I think that Tesla Model y is still parked out outside of or or or it's a
Speaker 1:Yeah. S? Cybertruck or s?
Speaker 2:Model s. Okay. You know, Trump got the red Model s.
Speaker 1:He did. Parked
Speaker 2:out front. Yeah. Yeah. I'm I'm Yeah.
Speaker 1:It doesn't seem like a blow up, entirely, at least not from Trump and Elon. They seem to get along. Although Elon did say, like, it seems like Elon's more disillusioned with government overall because he said he's not going be donating again. That it seemed like there were so many special interests that once he got into the swamp, was like, it's just too swampy.
Speaker 2:I was like me with my HOA. Bought my house, went to the HOA meeting, being like, oh, this
Speaker 1:is a break. Getting excited.
Speaker 6:I'm going to
Speaker 2:revolutionize the HOA. There's so many obvious things that we could do to improve the operations of the neighborhood. Realized very quickly that there were some major I
Speaker 1:think you weren't chaotic enough. You should have launched like an HOA coin immediately.
Speaker 2:Yeah.
Speaker 1:Something really shook
Speaker 2:things Nobody's building at the intersection of HOAs and crypto.
Speaker 1:No. No. It is.
Speaker 2:White space.
Speaker 1:Long leg. Get on.
Speaker 2:White space.
Speaker 1:Anyway, the the the the Democrats are taking a little bit of a victory lap. Democrats took credit for Musk's decision to leave, Washington arguing that the extensive litigation and public pressure surrounding Doge forced his hand. He lost democracy one, wrote Norm Eisen, a co counsel for the House Judiciary Committee during Trump's first
Speaker 2:Which is interesting because as a special government employee, his tenure was limited to a hundred and thirty days, he's leaving at the end of that. Yeah. That's So taking a victory lap normal. But It's like saying you hired a contractor.
Speaker 1:But also the I I I know. I feel like that that hundred and thirty day thing, like, we're kinda hearing about that for the first time now. Like, that wasn't the way it was messaged beforehand. It was definitely messaged like, Elon's gonna be part of this team for four years. Like like, expect Doge to go on a generational run here and do a lot.
Speaker 1:It was not like, oh, Elon's hey. Just to set you just to set the record straight up front, he's gonna be in for a hundred and thirty days. So, like, let's see what we can do then. Yeah. Like, I I this is the first I've heard of the hundred and thirty day thing.
Speaker 1:So it feels
Speaker 2:like I had heard of that. Of that from another guest that we've had on the show who is a special government employee and and he had messaged it to me Oh, earlier? Like, oh, I'm it's just a sprint. It's sprint. Okay.
Speaker 3:Interesting. That's That
Speaker 2:was kind of core to the Doge strategy from the very beginning Mhmm. Special government employees that are on these sort of shortened stints.
Speaker 1:Yeah. Yeah. Yeah. That makes sense. So what did he actually do while he was in Washington?
Speaker 1:Asks the Wall Street Journal. Musk came into the government with bold plans to spat slash spending by as much as 2,000,000,000,000. In rapid fire succession, Doge and Musk dismantled agencies such as the US Agency for International Development and the Consumer Financial Protection Bureau, leaving thousands of federal employees out of work. Musk and his Doge team emerged after Trump's inauguration with a series of shock and awe moves that rattled the federal workforce. This the he the subject, he sent an email to a lot of government employees fork in the road, the same line Musk used in a 2022 email to Twitter employees shortly after he took over the company and renamed it x.
Speaker 1:Tens of thousands of federal workers took the offer, to quit.
Speaker 2:Send an email to the team tomorrow morning with the title fork in the road, and it's, do you guys want bacon or steak breakfast?
Speaker 1:You're gonna go somewhere. That's great. Must promise to slash the federal wedge by trillions of dollars, ran into the wall of nondiscretionary spending programs such as Social Security and Medicaid. Doge said it had saved the government a hundred and 75,000,000,000 from a combi combination of asset sales contracts, lease and grant cancellations, workforce reductions, and other moves made since January 20 inauguration. So it seems like maybe a decent outcome.
Speaker 1:I don't know. It's like the you know, better to not waste that money, but, it doesn't write it doesn't correct the budget. And so it's not
Speaker 2:Well, the new spending bill, you know, increases it dramatically.
Speaker 1:So it's not it it it's probably, like, better to have done some of that stuff than not, but the real question is Social Security and Medicaid. And so the answer to that, longevity drugs. Move the retirement age to 95. Solves all of this. Let's get Brian Johnson.
Speaker 2:Has his way, it'll be a 50.
Speaker 6:Yeah. Yeah. That Stay in the workforce.
Speaker 2:Keep grinding.
Speaker 1:It would completely change entitlements if, if people could work longer, but very unpopular to ask people to do that. Anyway, massive news out of Andoroll and Meta. They're teaming up. Meta fired Palmer Lucky. Now they're teaming up on a defense contract.
Speaker 1:The Facebook and Instagram parent is partnering with Andoroll Industries to develop combat VR headsets for the army. And, there's a beautiful picture of Palmer Luckey and Mark Zuckerberg there. Let's hear it for getting the band back together.
Speaker 2:We love it.
Speaker 1:Ashley Vance has an exclusive interview with Palmer Luckey. He's gonna be joining the show in just a few minutes, we'll break it all down. Very interesting because the narrative, I was hearing internally in the defense tech world, the some of the Andoril haters were kind of like, oh, they bought this IVAS contract for from Microsoft, but the contract's gonna get recompeted. And at a certain point, why wouldn't Meta compete against Andoril for this? They they have a great team.
Speaker 1:They have a lot of the hardware. They could actually deliver this. And, you know, the contract is up there. Like, let go get it. Well, problem solved.
Speaker 1:Now they're teaming up. And so if you think about it, it's like
Speaker 2:Zach started wakeboarding, doing jujitsu, and now he's a defense contractor.
Speaker 1:Let's go. Cyclist complete factory. Back to the back to the couch with the red solo cup drinking a beer. The the the first Zuck interview.
Speaker 2:It's great. Brother.
Speaker 1:It's fantastic. But, yeah, exciting exciting, interesting development. So, obviously, there was a messy split with Palmer Lucky years ago, but everything we've heard about that was that it it it like, Zuck was not super involved in that process, which is kind of crazy to think about. But Meta was such a big
Speaker 2:of a massive acquisition.
Speaker 1:It's such a big company that, yeah, maybe you bring someone in, you kinda let them work, and then at a certain point, like, this individual is layered, and you don't wanna just, like, immediately go over the top of, like, the the three people that are in between you and this new, employee that you've hired because I don't think Palmer was the CEO of Oculus when he went in. And so it's possible that he technically didn't he not like he technically reported to Zuck. And so, anyway, they've clearly rebuilt the relationship. Very exciting. And there's been there's been some progress even just in pub in public discussions on X between, Boz and, and Palmer Lucky going back and forth about what happened, what mistakes were made, some apologies.
Speaker 1:And it was just very cool that that the water was able to flow into the bridge. I I I really appreciated that.
Speaker 2:Yeah. I just like to see them doing business together again.
Speaker 1:Totally. So Lucky's defense company, Anderol Industries, Meta said Thursday, they will build a line of new rugged helmets, glasses, and other wearables that provide a virtual reality or augmented reality experience. The system called EagleEye will carry sensors that enhance soldiers' hearing and vision detecting drones flying miles away or sighting hidden targets, for instance. It will also let soldiers operate and and and interact with AI powered weapon systems. Andoril Andoril's autonomy software and Meta's AI models will underpin the devices.
Speaker 1:Very cool. Oh, that's interesting. Yeah. It makes sense. You you'd wanna have an LLM on board to actually interface with everything, but then you need to interface with all the different assets on the battlefield.
Speaker 1:And so Lattice comes in there. That's Anduril's autonomy software.
Speaker 2:Soldier on the battlefield. Llama. What do you got for me?
Speaker 1:Llama.
Speaker 2:Yeah. Was like
Speaker 1:It really is turning into college.
Speaker 4:GPT. But it's like Yeah.
Speaker 2:Yeah. Hey, llama. What what should I be paying attention to right now?
Speaker 1:I could get really dark. Llama, I'm bleeding out. Send the medevac. How do I how do I tourniquet myself? Rough.
Speaker 1:But, I mean, yeah, it it it like, like, you have to put you have to put these pieces together. But llama is a silly, silly name.
Speaker 7:Well
Speaker 1:But I love it.
Speaker 2:Wouldn't be surprised if they say eagle or
Speaker 1:Yeah. Yeah. Yeah. Yeah. They might need to they might need to hide that brand underneath the hood.
Speaker 8:Yeah.
Speaker 1:The collaboration brings together a social media giant that has long been the target of Washington scrutiny and a weapons maker that is a rising star inside of the Pentagon. The partnership author offers another example of Silicon Valley's ideological evolution and Big Tech's expanding embrace of defense work.
Speaker 2:Lucky says, I should look at this as I have succeeded. I've successfully persuaded not just Meta, but many others that working with the military is important.
Speaker 1:Yep. Yep.
Speaker 2:So cultural victory.
Speaker 1:Yeah. Yeah. I mean, in the previous era, the one company that was kind of pro working with the government was Microsoft. And of course, they had the HoloLens project, but they were also selling Outlook and Excel and all these different products. And I think that just came from the like, the Balmer era, the Gates era, where they saw their software as just enterprise software, they needed to get in the hands of everyone.
Speaker 1:And so it it became standard in on pretty much every military base that there's Windows machines. And so that that legacy there there was never it was kind of like even pre pre global war on terror. And so support was at an all time high. Those partnerships grew and grew and grew. And, of course, the, the HoloLens project was was very cool.
Speaker 1:And IVAS was was was just a cool project to work on. But Yeah. It didn't seem like they had the the maybe the defense contractor DNA to really take something into the hands of the warfighter, even if they were able to put Xcel in the hands of the warfighter. Yep. Yeah.
Speaker 2:And what what Pal sorry, what Andrew Roll has now is credibility Yep. With Washington Yep. And the existing relationships that say, we are going to be a distribution channel for the best technology in the world and work with the best partners Yep. To deliver the best end products. And
Speaker 6:That's great.
Speaker 2:It's a fantastic partnership.
Speaker 1:We gotta go get the demo soon. Meta, in recent months, has recruited former Pentagon staff to join its ranks in f an effort to navigate the labyrinth of the defense procurement process. In November, it opened up its AI models for military applications, a new line of business for the company whose profits have been powered by online advertising. In a statement, Zuck said the EagleEye technology will help US Soldiers protect interests at home and abroad. Meta and Andoril will have jointly have jointly bid on army contracts for VR hardware devices worth up to about a hundred million dollars.
Speaker 1:If awarded, it would be Meta's most significant tie up with the defense department. The contract is intended to vet headset prototypes that are part of a larger $22,000,000,000 army wearables product, project of which Anderol became the lead vendor in February after Microsoft failed to deliver a functional VR headset. Anderol said the collaboration on the headsets, which the companies have already mostly funded themselves, is going forward irrespective of winning the army contract, Anderol is betting. Other parts of the military will also be buyers. Yeah.
Speaker 1:That makes a lot of sense. Why would you not want this if you're in the air force or the navy or the or the marines? The Meta partnership delivers a victory lap to Lucky whose entrepreneurial roots and much of his fortune can be traced to VR. And and then they give a little bit of background on on Palmer's journey.
Speaker 6:So insane.
Speaker 2:Started Oculus when he was 15 years Yeah. It's amazing. Insane, insane story.
Speaker 1:The partner the new partnership gives Lucky access to all of his old VR designs, plus newer tech his team has built after he was fired. I finally got all my toys back, Lucky said. I love that.
Speaker 2:That's amazing.
Speaker 1:So good.
Speaker 2:It's still so wild that in 2017, the US has two real political parties. And in 2017, there was one that if you donated to, you would get fired.
Speaker 6:It it it made me very frustrated at the time.
Speaker 1:It was it was very, very rough. It was just like, that's not that's literally antidemocratic. The whole point of democracy is you can vote for whoever you want.
Speaker 3:Yeah.
Speaker 1:Like, it's like the one thing in democracy that is like, it's like, it's the definition of democracy. You have to be able to vote for whoever you want. Yeah. Like, it's like everything you've learned about how America's supposed to work is now just at at at stake is Yeah. Is bad, bad times.
Speaker 2:But Yeah. There's two parties, but but you can only donate to one of them. And if you donate to the other You
Speaker 5:get fired.
Speaker 2:But, hey, what matters ultimately
Speaker 1:Watering the groups
Speaker 2:have come around Yep. And, are moving forward. So Yeah. Good to see.
Speaker 1:In other news, we should go through I I this is a story that a lot of people have been asking me to kinda dig into. I was talking to Jordan Schneider over at China Talk about this. He said it'd be great to get people's view intact around this more political story, which we don't really which we don't really cover. But Trump's, I mean, currently waging a war on American universities, which does have some tech implications in the sense that the, you know, the Thiel fellowship has been in you know, renegotiating the relationship between entrepreneurship and higher ed for a long time, and then also a lot of tech companies hire from elite universities. And so, Trump has been threatening to withhold billions of dollars in federal research funds to punish campuses.
Speaker 1:And there's a story in the journal here about, the the punch that launched Trump's war on American universities. So I still don't know I still I still don't know exactly the angle of this. I think it's something we should dig into. I think the big question is, like, America has national interests, has different security interests, also has competitive dynamics around brain drain, and the and, you know, we want the top entrepreneurs. We want the top technologists, the top o ones.
Speaker 1:The administration has a view. Big tech companies have a view. It would be interesting to hear from tech leaders on where they think these these policies should go around, the top research universities. When you hear when you talk to biotech folks, they're like, we need research to be done in universities funded by the government in order for our business to make sense. You don't hear that much from tech companies.
Speaker 1:They're like, yeah. Like, the next big AI breakthrough probably just gonna come from Google. They won't be able to productize it fast enough, and we'll just roll it out into some B2B SaaS. And and so but at the same time, there are a ton of AI labs, Silicon Valley tech startups that want to hire tons and tons of talented individuals from all over the world, and there's big questions about how the Trump administration is is affecting that. There's that there's that famous quote from Trump on the all in podcast saying, we should we should staple a green card to every university diploma, and this feels like the opposite of that.
Speaker 1:And so there's a question about, like, was that a campaign promise?
Speaker 2:Big news. Next yesterday, secretary of state Marco Rubio Yep. Said that they will begin revoking the visas of some Chinese students, including those studying in critical fields. Sure. China is the second largest country of origin for international students in The US behind India.
Speaker 2:In the twenty twenty three to twenty twenty four school year, more than 270,000 international students were from China, making up roughly a quarter of all foreign students in The United States. That's actually down significantly. I saw Solana was posting something yesterday as well, just showing that that two seventy number is significantly lower than just a few years ago.
Speaker 1:Yeah. So there is there is a question about, like, trade balance a little bit where it's like, you know, if if if two what is it? 540,000? If 270,023 citizens are coming to American universities, should 270,000 Americans be at Chinese universities? Is there an imbalance there?
Speaker 1:That's an interesting question to dig into just in terms of, like, reciprocity of these relationships. Yeah. Just like if TikTok can operate here, well, can Instagram operate there? That would be an interesting trade. If if there's an imbalance, we probably need to have a discussion one way or another.
Speaker 1:There's also the question I
Speaker 2:mean, I Yeah. I I I would have to give a very rough estimate of how many Americans were at Fudan, which was the university that I studied abroad in China. And it can't, it had to have been less than fifty fifty. Total. Yeah.
Speaker 2:At least that I was aware of. Yeah. It was pretty easy to,
Speaker 1:you know. At the same time, I wonder how much of a how much of a shot across the bow, how painful is this to the Chinese government? Because if you if you remember the the whole story of
Speaker 2:Harvard.
Speaker 1:Is it No. No. No. No. No.
Speaker 1:Russia with the Magnitsky Act. It it banned, adoption from Russia to America, and that was, like, extremely painful for some reason. I don't exactly know why that was such a hot button issue, but that was, like, a key point of leverage against against Russia at some point. And so I wonder I I wanna dig into the reaction to this move by the Trump administration by the Chinese Communist Party. Are they upset about this?
Speaker 1:Like, does this go against their plan in, like, a very meaningful way? Is this something that will will be another another, poker chip on the trade negotiations around tariffs or around the the the the flow of fentanyl or something like that? Could this just be an an opening gambit that then gets negotiated down? And Trump's merely just putting this this piece on the chessboard to then negotiate against, or is this something that, maybe China doesn't really care about? And they're just like, yeah.
Speaker 1:We actually want our students here. And so, this is this is great for us. Please, please send send all the all all the student all the smart people back. Like, we we we don't wanna be brain drained. I don't know which way it's gonna cut.
Speaker 1:And so I wanna dig into it.
Speaker 2:Their foreign ministry spokesperson Mao Ning said, we urge The US to effectively safeguard the legitimate rights and interests of all international students, including the Chinese students overseas. So not really a real response Mhmm. But we'll see how it plays out.
Speaker 1:Yeah. Yeah. I wonder if there'll be, like, retaliation or some sort of, like, canceling of American students that are in China, how this will actually play out. But but we should start asking more of the guests about how they're reacting to this and especially if they have, you know, deep insight here. I don't just wanna hear random people yap about it.
Speaker 2:Anyway What? Guess how many American students studied in China in 2024.
Speaker 1:'5 thousand?
Speaker 2:Guess again.
Speaker 1:10,000?
Speaker 2:Guess again. 1,800.
Speaker 1:Eight hundred?
Speaker 2:So I was pretty close with being like, even in this was like 2016, I was like, maybe there's 50 other students
Speaker 1:That's really low. 800 is nothing. Yeah. I mean, have like millions of college students in America, right? You would think that you know, a couple percent of them would be over there on at least, like, one semester or something like that.
Speaker 2:So there was 11,000 prior to the pandemic.
Speaker 1:Okay. Wow. It really fell 90%. That's crazy. Wow.
Speaker 1:Anyway, in in other Asia news, Kim Jong Un launched a new North Korean warship using a risky side launch technique, and it completely crashed, I guess. Yeah. That's brutal.
Speaker 2:Yeah. After after that soundboard, you can never go
Speaker 1:to North Korea is not gonna be happy to see that. We're not gonna distribute the show in Pyongyang anytime soon, unfortunately. The unconventional technique for launching a big military vessel points to North Korea's haste in modernizing its outdated navy and lack of resources. Kim Jong un witnessed a warship topple over during its launch, deploying a risky side launch method. Naval experts cite inexperience, a rushed timetable, and top heavy warships as factors in the failed launch.
Speaker 1:North Korea chose a side launch to save costs while The US and South Korea use safer floating dock launches. Oof. Very, very rough. Imagine you're just like you so Kim Jong Un, he traveled to North Korea's City Of Iron. Okay.
Speaker 1:You gotta hand it to him. That's an amazing name. Let's hear it for the City Of Iron.
Speaker 2:City Of Iron.
Speaker 1:It's where they build the ships. Isn't that amazing?
Speaker 2:That's great.
Speaker 1:So they have a major industrial credit scene. Yeah. Yeah. Yeah. You gotta call a spade a spade.
Speaker 1:We're just calling balls and strikes here on this one. We're pro North Korea now. The city of Ireland is just too good. The it's home to a major industrial shipyard where a hulking warship awaited him. A VIP podium had been erected alongside the port for the vessel's launch.
Speaker 1:Officials awaited anticipation, but the big moment of celebration turned into calamity. The 5,000 ton destroyer lost its balance as it lurched into the water toppling over and embarrassing Kim who seeks to modernize his Soviet era naval fleet. And it's so funny because, like, they didn't send us video, obviously. Like, they didn't broadcast this because they would only broadcast it if it's, like, successful. So we just had, like, satellite photos of just it's just, like, getting destroyed slowly, slowly.
Speaker 1:Very rough.
Speaker 2:Brutal. What do you what do you do if you're the chief engineer that that tips the the warship over in
Speaker 1:Oh, I actually know what you do. You go to jail there.
Speaker 2:I bet.
Speaker 1:Or you So four North Korean officials have been detained over the mishap, which called it an unpardonable crime. Can you imagine the stakes of like, oh, yeah. You think it's hard being a hard tech founder in America? Oh, yeah? Try doing that in North Korea where you go to China
Speaker 2:successful you fail. Test.
Speaker 1:We've had nine Starships just blow up, and we're like, okay. Like Run it back. You're sleeping in the factory now, not you're going to jail. Yeah. Like, you're working harder.
Speaker 1:Our our answer is you work harder, not not you not not you go to jail. What has become clearer in the aftermath is how an unconventional choice of launch method, Kim's rush timetable, and a top heavy warship overladen with weapon systems was a recipe for disaster. They just keep being like, yeah. Just, like, throw one more missile battery on that. Yeah.
Speaker 1:Yeah. Yeah. Yeah. Put a Gatling gun on it too. Yeah.
Speaker 1:Yeah. Another cannon? No problem. Just keep doing it, and then it just rolls over. It was a recipe for disaster according to satellite imagery analysis.
Speaker 1:I haven't seen a failure like this one. I've never seen a failure. Said a retired marine colonel that they that Walter Turtle called up, and he's just like, this is insanely bad. Yeah. Rough.
Speaker 1:Who are you on the phone with, Jordy?
Speaker 2:No. Just calling up Mark Kansian.
Speaker 1:How about how much is this?
Speaker 2:I haven't seen one this bad.
Speaker 1:Seen one this bad. It was a 470 foot long warship. Kim's second, destroyer had been built in the in this in this Northeastern city and, did not go well. Anyway, our guests are gonna start
Speaker 2:rolling in. Stories here. We got Peter Rahal in the waiting room.
Speaker 1:Cool.
Speaker 2:Bring him in.
Speaker 1:Yeah.
Speaker 2:Let's do it. Peter.
Speaker 1:How are doing? Welcome, stream. Nice nice sweatshirt. Looking good.
Speaker 2:You. It's a sample
Speaker 7:it's a sample too.
Speaker 2:Oh, fair Congratulations on your second announcement of the week. Yep. This is the first real one, I guess. Sorry. Sorry that sorry that you got leaked, but very excited for you.
Speaker 2:Excited to have you on the show to break it all down.
Speaker 1:Yeah. What what what
Speaker 2:We are talking
Speaker 1:What is the most recent news? I only know the the incredible revenue number, but what else is what else is driving the news cycle right now for you guys?
Speaker 7:Well, so we acquired a supplier, an ingredient supplier called Fagi. Mhmm. And then, yeah, our valuation's $7.07 25.
Speaker 1:Wow.
Speaker 7:Okay. Forecast this year is $1.40. So I think those those numbers and, you know, I I think, you know, it's like in we've accomplished more in nine months than
Speaker 1:Yeah.
Speaker 7:We did in Rx over four years.
Speaker 1:Wow.
Speaker 7:So I think we're I think we're one of the
Speaker 2:fastest What other what other brands have achieved that level of revenue within a year of launch? It has to be like There's two.
Speaker 1:Feastables Feastables.
Speaker 7:And Prime.
Speaker 1:Prime. Yeah. Yeah. Because just massive distribution cannons and really, like, leveraging, like, essentially, like, you know, a hundred million dollars worth of free marketing on day one. Right?
Speaker 7:Yeah. Yeah.
Speaker 1:Yeah. So what's the secret for you? Because Here,
Speaker 2:it feels product led.
Speaker 9:Yeah.
Speaker 2:Is that is that is that the right assessment? Obviously, you have a bunch of lessons, you know, war stories you can pull on from RxBar, but, like, it feels like the product just sells itself in some way.
Speaker 7:Yeah. I would say it's definitely product led. We act you know, in CPG, it's pretty hard to differentiate Mhmm. Measurably. Right?
Speaker 7:It's it's and, you know, we are 75% of our calories are coming from protein. The market is at, like, 50. Mhmm. So so it's meaningful, and it's an example. Like, the market was on the surface.
Speaker 7:We don't apply rigor. It looks pretty competitive.
Speaker 1:Yep.
Speaker 7:But if you just, like, spend a little time, talk to some customers, you realize, like, NPS is pretty low. Like, no one's really happy. And a lot of people don't just aren't even in the category because of taste and texture and nutrition. So other big opportunity for us in in the bar business is, like, to really expand the category and make delicious, nutritious products that that people are like, oh, protein bars don't taste like shit. Like, the expectations are they're just they're just gonna be bad.
Speaker 1:Yeah. Yeah. Yeah. When you say people aren't in the category, are you talking about the difference between, protein shakes, which can typically be, like, extremely high protein from calorie ratio?
Speaker 7:More more specifically, people are like, people don't consume protein bars. Like, you'll you'll you'll you'll hear people say, like, oh, I'm just not a protein bar person.
Speaker 1:Sure. Sure. Sure. Sure. Yeah.
Speaker 1:That makes sense.
Speaker 7:So those those are the people, if you ask them, like, why not, it's fundamentally taste.
Speaker 1:Yeah. I I I wanna kinda push back on this idea that it's product led. I agree that there's product differentiation, but this feels like it's only possible from you because if you call up a major distributor or supplier or retail chain, they say, yes. I know you. You you you have a you have a win under your belt.
Speaker 1:I'm willing to go big, and we don't need to do some long extended test is this isn't to knock you. I mean, this is why you worked really hard and you're reaping the benefit of that. But is that is that critical, or do you think if you were nobody and you were cold calling
Speaker 2:My position is more so of, like, Peter would have the relationships to get the bars on the shelf. Yep. He's not making you know, holding every customer that walks into the store at gunpoint being, like, buy my product.
Speaker 1:You know? Mean, is very different than mister beast and prime where, you know, you have, like, major influencers driving this. Like, it is more product led in that sense.
Speaker 2:Yeah. And I would say,
Speaker 7:like, my credibility or track record is like a lubricant
Speaker 4:to
Speaker 7:facilitate things. Yeah. But but the repeats and it's it's all product driven.
Speaker 1:Yeah. Yeah. That makes sense.
Speaker 2:Talk about the decision to acquire one of your major suppliers. Was that something that you anticipated doing a long time ago? Or, you know, what was the point that that that made sense?
Speaker 7:Yeah. So when we started, you know, our business, you don't want any single source supplier. It's just too much risk. So it's like a it's it can become a nasty dependency. So we identified the technology, the ingredient.
Speaker 7:It was like, this is this is like magic, like technology should feel. Got close with them, became 90% of their volume. So we were basically, you know, 90% of the 90% of the revenue. And it just made sense to vertically integrate and derisk it, and so we can just control the supply. Yeah.
Speaker 7:And and and, you know, I can't imagine a company without without us being together, and and enable us. It just, like, totally widens the aperture of, like, where we can go and truly have a platform for across different different products, different different brands, different different consumer needs.
Speaker 2:And is this it's your as much, like, acquiring a supplier, but you're it's there's also internal IP that they have. Is that correct? Can you break that down? Yeah. Yeah.
Speaker 2:They they
Speaker 7:have the they have patents they have patents around the trademark and application of it. So so, you know, CPG sucks. It does.
Speaker 1:I know.
Speaker 7:It sucks because it sucks because of the competition. And so right? Like, there's, like we we do this, like, there's three modes. There's the brand mode
Speaker 6:Mhmm.
Speaker 7:Which is really abstract and takes a lot of time. So I don't put much merit to it, but but it is a real thing. And then there's, like, trade secret mode or distribution.
Speaker 1:Mhmm.
Speaker 7:And then there's a third mode, which is actual IP. And so we we actually have with our business now, and then part of reason we got a good valuation is because we have a really defensible business with three modes, which allows us to protect our cash flows. And so, you know, three years, we're not gonna have we're not we can't have 10 copycats. Like, it's just like they'll try on protein, but they won't get their own calories. So Yep.
Speaker 7:As like an entrepreneur in the business And
Speaker 2:and break down the notes. You have the key ingredient. You have are you counting brand at all in that
Speaker 7:Yeah. I count I count brand, but, like, if if I'm talking to an investor, I'm, like, not even bringing it up because it's so Yeah. Like Yeah. It doesn't mean anything. And it it
Speaker 2:can take it can take ten years to truly have a brand. Right? At least in the mind of the everyday consumer.
Speaker 1:How how do you dig into, like, the defensibility around that intellectual property? Because I feel like the gold standard for defensible IP in, you know, anything FDA regulated is probably, pharmaceuticals, like drugs. But as we've seen with, like, the Ozempic, Wagoovy transition, like, even GLP ones, they were able to kinda eat at the edges. Now Eli Lilly is taking share from Novo. And it feels like if it can't work in GLP ones, like, how can this possibly hold in a protein supply chain?
Speaker 1:Right? Like, you're gonna get someone who spins something up that's, like, one molecule different or something like that. Is that is that a risk, or did you dig into that at that level of depth?
Speaker 7:Yeah. I mean, it's there's one there's really one process to make to make the molecule.
Speaker 1:Sure.
Speaker 7:And, you know, it's like, sure, someone could go try it, but, like, we'll we'll we'll just litigate. And then Yeah. So they should calculate that.
Speaker 1:And I guess the I guess the the the bull case is that, like, yeah, you could go and fight on the IP stuff, but at the end of the road, you don't get a pharmaceutical product. You get a CPG brand. Yeah. Exactly. And so it's like ROI
Speaker 7:is not there.
Speaker 1:You're gonna have to fight real, real hard. And at the end, you then you also have to build a brand, also do the distribution, and also whereas, you know, Eli Lilly, like, if they spend a bunch of money to figure out how to, you know, alter the GLP one to get a drug that works, like, well, then it's just a matter of doctors prescribing it. Right? And it's, like and and it gets paid.
Speaker 7:And, like, in in CPG, you kinda have, like, two years runway before someone copies you.
Speaker 1:Yep.
Speaker 7:Now we have, like, structurally nine years.
Speaker 1:Yep.
Speaker 7:And I think by that time, it's kinda too late.
Speaker 1:Yep. Yep. Yeah. I mean, you already see that with the hundred and 40 revenue. Like like, as that gets bigger, it's just gonna be, ubiquitous and you own all the shelf space.
Speaker 1:So even if there is knock off copycat, it's like, yeah, you're already So
Speaker 2:you're less than a year in post launch. What does a good outcome look like? You've already had a multi hundred million dollar exit. I imagine you're aiming a lot a lot higher here, but but what is that in your mind?
Speaker 7:Yeah. I try not to think about the outcome too much, but I do.
Speaker 2:That's the most honest answer ever.
Speaker 1:That's the best. Yeah.
Speaker 2:That any any entrepreneur is like, oh, yeah. I've never thought about the outcome. It's just purely mission. You know?
Speaker 1:Yeah. It's amazing.
Speaker 7:Yeah. Like, I would be pissed if we don't get to a billion in revenue.
Speaker 2:Yeah. Yep.
Speaker 7:You know, I that that would be disappointing.
Speaker 1:What was RXBAR roughly? Like, what's your personal high watermark?
Speaker 9:Two, two forty. Okay.
Speaker 4:Yeah. Okay.
Speaker 1:And I think so.
Speaker 7:Maybe maybe a little bit more
Speaker 1:with Yeah. Yeah. Yeah. Yeah. So, like, x that.
Speaker 7:Yeah. So I but I I do think I I do think we can build a really diversified portfolio of brands
Speaker 1:Yeah.
Speaker 7:That really address a broad population and and create a ton of consumer surplus. So, you know, I think, like yeah. We just have so much work to do and yeah. I I I I do fantasize about being a public company.
Speaker 1:Let's go. I
Speaker 2:love it.
Speaker 10:Yeah. Think
Speaker 7:is what my dreams are.
Speaker 1:We'll have you back after your public company and and you're just be like, it's it's such a headache. It's so annoying. I can't say anything. I'm in a quiet period. I can't come on TVPN.
Speaker 1:It's the worst.
Speaker 7:Yeah. I I be irreverent
Speaker 1:Yep.
Speaker 7:For these calls. But but, yeah, I just think, like, there hasn't been a truly from scratch brand platform in the space. It's all been done through m and a.
Speaker 1:Yeah. I
Speaker 7:think it's just Trevonnie's doing it actually now.
Speaker 2:Yeah. Sure.
Speaker 7:And I think that
Speaker 2:And you're a believer in in these brands living under the existing, you know, c corp and or or, like, it doesn't sound like you wanna do, like, a studio.
Speaker 7:No. I I only studio analogy would be, like, we we we're not gonna do m and a. We're gonna we're gonna build them.
Speaker 4:Like Yeah.
Speaker 7:I I I me and my team, we've demonstrated we can, like, go from scratch, like
Speaker 1:Mhmm.
Speaker 7:Yeah. So so we would just build them. It would be a decentralized model, so, like, different orgs, you know. Yeah. So David's the master, David's Mars, and then David Protein is the operating business.
Speaker 7:Apogee will be its own subsidiary, and then we'll create other brands. And then they just have to be decentralized organ is, like, orgs because you you know, like, David and his life cycle is very different than a newborn baby. Yeah. And so, like, we need to design the org for up for agility. And and so so that's what we're designing for.
Speaker 2:And how much do you view yourself as a technology company? I I heard you bring that up earlier. As in you're wanting to pursue opportunities where you can have a unique sort of durable edge through some type of effectively chemistry. I don't know if that's the right Yeah.
Speaker 7:Chemistry. I you know, we're in the chemistry business now. I I wanna keep learning and and investing. We we you know, I don't know. We don't know.
Speaker 7:But, like, once you get in the pool, you'll figure things out. So, you know, like, technology is not really welcome in food. Mhmm. So we we we we restrain from the t word. But, yeah, we wanna keep making really valuable products.
Speaker 7:Like, that's just our focus. And and obviously, technology is really the best way to get there.
Speaker 2:Have you mapped out how to this? Technology Yeah. You have investors that are traditionally Sure. Technology investors. I mean, I I I don't think I've ever seen Green Oaks do a CPG round.
Speaker 1:Yeah. The last deal they did was, like, Windsurf. Right? They're, like, the last big outcome for them. A wildly different company.
Speaker 1:Have you have you taken a crack at kind of reverse engineering what it takes to really go after, like, a Nestle or a Unilever, like, one of the major, major conglomerates that has been built through M and A? But if you if you think really, really far into, like, what it takes to create a hundred and a hundred billion dollar outcome in consumer packaged goods Yeah. No one's even come close. Like, all cite, like, Red Bull is a great outcome. Sells but these are all, like, single things and very niche categories that are big categories, like energy drinks.
Speaker 1:So the companies get big. Red Bull's big. Monster's big. But no one's really been able to figure out the right corporate structure to to just compound and compound and compound and create this, like, hundred million dollar hundred billion dollar behemoth?
Speaker 7:Yeah. So the key way to address the really large TAM in consumer food is through different brands that have different DNA that address different sort of problems or consumer state like this. So, like, David I use analogy of, like, brands are just human beings. They have fathers. They have they have DNA.
Speaker 7:They have behaviors, beliefs. They, you know, have friends. You gotta let that brand or that asset be it be itself. Like, you can't jam it into places it doesn't belong. So, like, the David brand with our future portfolio and optimizing for calories coming from protein.
Speaker 7:So we look at David, you know, it's, like, the most protein, least amount of calories.
Speaker 5:Yeah.
Speaker 7:That that TAM is probably 1,500,000,000.0 in revenue Yeah. In The US. So so you really need multiple you need, like, a house of brands to go after very different parts of the population.
Speaker 2:Mhmm. Yeah.
Speaker 7:That's one in an international.
Speaker 1:Yeah. What's the
Speaker 7:They're mega scale, and they're global. So that's that's the key thing.
Speaker 1:What's the pushback been like from kinda like the trad community that wants to do everything all natural, everything like, you know, oh, just, you know, farm to table, the Michael Pollan crew, the RFK crew. Like, there's all these different Yeah. Different segments of kind of, like, anti modernity and food. What's the reaction been like?
Speaker 7:Yeah. So, you know, Peter Thiel talks about, like, anything with science at the end is not science.
Speaker 6:Sure.
Speaker 7:So nutrition science is is really to date not been science. Yeah. And so so it's really been it's a really emotional conversation and and and not a intellectual conversation. So that's the first place, and it is complicated. And and and our society is really confused around it.
Speaker 7:And so in under the confusion, in a way to simplify something on plaques, people find, like, just simple correlations. Like, if my ancestors didn't eat it, I shouldn't. And that that that's, like, a pretty good framework or, like, it's not bad advice, but it's clearly not sophisticated, and it's way more complicated than that.
Speaker 3:Mhmm. Yeah.
Speaker 7:So and it's, like, the interesting thing is, like, the ancestral movement. My friend told me this, and I thought it like, really good. It's, the ancestral food movements, like, it's like post traumatic stress response. Like, it's like a post trauma response where you're like like, you just stop and you, like, don't progress and you actually go backwards.
Speaker 8:Mhmm.
Speaker 7:Because you're scared to make it worse. And perhaps that's because food fucked up and food, you know Yeah. Got run by CFOs who just cut the bottom line and just cut cut cut. But I do believe, like, there's a way to advance food in a way that and it isn't that scary? And, yeah.
Speaker 7:So I think I I I a goal is to have, like, a more intellectual conversation on food, like, you know, not something like, oh, I can't pronounce it. Therefore, it's automatically bad.
Speaker 1:Yep. Yep. Makes some sense.
Speaker 9:Food food is what
Speaker 7:is what, like nutrition is about, like it's pretty simple. It's about the 80%. Like, don't overeat calories. Mhmm. Don't be fat.
Speaker 7:Turns out that's fucking terrible. Don't spike your blood sugar
Speaker 1:Mhmm.
Speaker 7:And don't get enough protein.
Speaker 1:Yeah. And hit the gym. I wish
Speaker 3:we had more I
Speaker 2:wish we had more time. There's a there's a bunch more questions I have. Yeah. But congratulations on the milestone. Yeah.
Speaker 2:We're just getting started. Cool. And appreciate you coming on.
Speaker 1:Yeah. We'll talk to later.
Speaker 2:Good night.
Speaker 1:Congrats. Bye. Next up, we have Ashley Vance.
Speaker 2:You know that you know that Green Oaks is writing underwriting that too. Small chance of a hundred billion dollar
Speaker 1:For sure.
Speaker 2:I think that's why they're they're doing the deal. Neil
Speaker 1:Mehta, undefeated. Next up, we have Ashley Vance coming in the studio from core memory. Bring him in. How you doing, Ashley?
Speaker 6:Oh, look. He's in
Speaker 1:the new studio.
Speaker 2:New studio reveal. Let's go. Exclusive. Let's go.
Speaker 1:Breaking news, core memory, interview Palmer Lucky. You're hearing about it here first, folks.
Speaker 2:You're cooking. You're cooking.
Speaker 6:It was a good good morning.
Speaker 1:It's Yeah.
Speaker 6:Yeah. Palmer came. Think he well, he came in yesterday. I think straight from there's this photo we posted of him and Zuck together. He's wearing the same clothes.
Speaker 6:I should have actually asked him, but, yeah, I think he came straight from there. They're peacemaking accord.
Speaker 1:Yeah. What's, what what was your read on it? How much of it was, how much were you focused on, like, the technical side of the deal between Andoril and Meta to work on VR for the military versus just the the the the the crazy full cycle narrative of the emotional journey?
Speaker 6:We kinda did both. I mean, he's he's sat here for a couple hours. From my memory, you know, I think the first forty five minutes was going into the deal, the backstory, all that. You'll the press release on this is super thin on the the technology and the the Wall Street Journal story didn't have that much either. And Palmer Yeah.
Speaker 6:He went into some some detail on our podcast about about the tech. Then, yeah, he got into the backstory. Mean, this is crazy. This if I've been following Palmer pretty close for the last couple years, I'd never would have imagined that this would have been possible given the, the intense the depth of this, like, nine year war that the this he and and Meta have had. So, I I did not fully see this Is
Speaker 2:it accurate to describe it as a war between Zuck and Palmer or a war between Palmer and Meta?
Speaker 6:I think it was like more Palmer and Meta. You know, if you go back, Blake Harris wrote this book, The History of the Future, and he he spends like 40, I don't know, 40 or 50 pages laying out the the saga. You know, Palmer put up this paid for this billboard and about Hillary Clinton and became a known Republican in public and was fired from Facebook. And in in, you know, in that book, you see a lot of operatives within Facebook,
Speaker 9:kind
Speaker 6:of pushing Palmer out. And and we talk about this on the podcast. I mean, if Palmer's being truthful in what he said, he didn't lay the blame directly at Zuck's feet. He he kinda felt like Zuck was doing what he had to do to protect his company and and what the employees wanted. So, so I think it was more more meta directed.
Speaker 1:Yeah. Yeah. It is it is crazy to think about the post Oculus acquisition time period at Meta and just not having Palmer report directly to Zuck? Like, that seems like that's the real cardinal sin here more than the crazy stuff that happened politically. It's just like, how do you bring in someone who's, like, clearly the face of VR, generational founder, like, all these amazing things.
Speaker 1:And, like, it would just have been so much easier to manage that relationship if there was no one else in between, like, management layers in between.
Speaker 6:Yeah. And, like, almost definitely, I think Palmer's probably still at Meta making working on VR. Don't think
Speaker 1:A %. Mean
Speaker 6:Andrel. You know? Yeah.
Speaker 1:Didn't, Jan Koom from WhatsApp stick stick around for a long time? And, obviously, like, the Instagram guys had kind of a rough go, they're upset about it now. But, like, they I think they reported directly and, like, built that for a long time. It was just odd that, like, VR was so important in meta that, like, you know, Reality Labs, huge investment, and yet Palmer Lucky was not you know, he should have been on the board. Like, he should have been added really, really into the inner circle.
Speaker 6:It's been a while since I read
Speaker 2:Blake's twenties.
Speaker 1:Who cares? Right? Zuck was 19 when he started the company or whatever. You know? Break the rules.
Speaker 6:And it's it's been a while since I read Blake's book, but yeah, I mean, he kind of lays out all these layers of management that were put between Palmer and and and Mark. So but here they are taking photos as as friends again.
Speaker 1:Have you gotten a demo yet? Any of the Androle hardwares yet?
Speaker 6:I haven't gotten on on this headset. No. Mean, this has been in I I've been down at Androl doing some reporting and Palmer's always like, you because this this is like an extension of this IVAS deal that that Yep. That Androl took over from Microsoft. So I've been down there and Palmer's like, oh, my secret lab is over to the side, and that's that's where these guys are working.
Speaker 6:I mean, people should know it. One of the coolest parts about this, I think, is that I mean, yes, they're making this warfighter helmet of the future, like the sci fi thing we see in every military movie that doesn't exist. But beyond the, like, VR, AR, data feed coming in, I mean, and Palmer talks about this in the podcast. He's trying to, like, reinvent the helmet itself, right, to make it lighter weight, to make it, the ballistics upgrade, all of that stuff. I mean, so he wants to build it's not just, like, the, the the sort of tech end of this.
Speaker 6:He just wants to make a better helmet for soldiers too, and and I know he's been spending a lot of time on all the materials and and things like that.
Speaker 1:Yeah. Have you been tracking any other developments at Meta? There were some there were some setbacks with the LAMA team. It seemed like they kinda went a little bit too far into the pretraining paradigm and maybe not enough in RL. And so LAMA four has kinda fallen behind in some of the some of the benchmarks.
Speaker 1:At the same time, there's some really amazing, VR demos that are coming down the pipe, like the Orion augmented reality glasses. And then even just, like, the next Quest headset, I feel like is gonna be incredible because all that work that Apple did to get those incredible screens into the Vision Pro. Well, like, Zuck's probably gonna be able to buy that for the because it's two years later now. It seemed like that was something that was unique to Apple, but only for a small period of time, and Apple kind of whiffed on actually getting that into the hands of millions. If it's in the next quest, it could be really cool.
Speaker 1:So any takes on what on what Meta's been doing in VR or AI lately?
Speaker 6:I mean, I I do not follow them as closely as I do some other companies. The thing that's always stuck out to me was when they bought Control Labs, which was doing this weird take on a brain computer interface by reading the motor neurons in your in your wrist. And and, you know, we've seen Meta do some demos around this kind of like new interface using your body and to and your brain to navigate computers. I actually think that's like the under, you know, it's probably one of the hardest things they have to pull off, but it's it's kind of underreported in some ways because, I mean, if you look at what trying to read the tea leaves on whatever OpenAI and Johnny Ive are That's insane. You know, we're clearly we're lurching some companies are lurching toward a new kind of computer.
Speaker 6:We've been on the same basic computer for decades.
Speaker 2:Well, to be clear, the OpenAI thing, they were just they were pretty clear that they said it's a third device besides your phone or your computer that will work with those devices.
Speaker 6:With those. Yeah. Yeah. No. That is fair.
Speaker 6:That is fair. I just I still feel like we're
Speaker 2:Everybody wants a new toy.
Speaker 1:Yeah. Just give us a new toy.
Speaker 6:Somebody will figure this out. I am confident. I'm I'm old enough to remember, like, you know, when I first started reporting
Speaker 1:Yeah.
Speaker 6:It was, like, exciting. People had different operating systems. They had different takes on computers all the time, you know? And and I just feel like we've lost that. So I this is, like, the
Speaker 1:most important thing.
Speaker 6:Are you talking about? The new iPhone, it has a button on the side.
Speaker 1:There's an extra button now. We can only we can only refine
Speaker 6:the bezel so much. You know?
Speaker 1:You're looking a gift horse in the mouth, Ashley. Come on. This thing is awesome.
Speaker 6:I have I have a bone to pick with you guys Please. Which is, you know, you got this glowing profile in the information about your new media empire. I was quoted in the story, I didn't even bother to mention my new media empire. I mean I mean
Speaker 2:No. You're boom. Sorry. Sorry. I hit the wrong effect.
Speaker 2:Boom. I hit the wrong effect.
Speaker 6:Yeah. I mean, it
Speaker 1:was it was half like complete glaze gate, like, just so over the top, like, lavishing praise on us for genius and reinventing media as a whole from the ground. And then it had the other half was like a complete hit piece. It was brutal. And I guess you balance those out, and it's like, you know, kind of a kind of a nuanced take. But, yeah, they did they did some people dirty.
Speaker 1:They put our they put us in quotes. They put us in quotes.
Speaker 3:They They put
Speaker 2:us in quotes.
Speaker 1:But this will be the last time TVPN ever appears in quotes and the last time that Ashley Vance has ever quoted in the information without mentioning core memory. Dude. Corememory.com. Go subscribe right now.
Speaker 6:That hurt. That hurt. I Yeah. I like that everyone wants you guys to be the ringer or Grantland, and neither of you seem to know.
Speaker 1:I don't know what those things are. I've never heard of It's even worse than that because people are like, oh, it has, like, you know, SportsCenter. I'm like, haven't watched that. But Smockbox, also haven't watched that. Can you comp it to something
Speaker 2:like the wheel.
Speaker 1:We're reinventing the wheel
Speaker 2:for At TPBN, we're focused on reinventing the media's wheel. Yes.
Speaker 1:I love it. But, yeah, give me the broader update on core memory. I know you have a bunch of different products. You're filming movies. You're writing books.
Speaker 1:You're doing short documentaries. Like, give me the Yeah. Give me the overview. And I wanna know specifically about the latest update with Neuralink as well.
Speaker 6:Yes. Okay. Yeah. I mean, we've launched a bunch of stuff. I'm writing on Substack.
Speaker 6:We launched a podcast. Palmer's on it today. And then, you know, I think the thing that we're maybe most proud of or one of the things is, yeah, you know, I used to make a TV show for Bloomberg and and we did very well there and we kind of killed that, built our whole team from scratch. So on our YouTube channel and on our Substack, we've got I don't know, they go anywhere from like eight to twenty something minute episodes that are, know, dives into different inventors and scientists and startups. And then so anyway, please go
Speaker 1:to your kind of telling the story of these like various Brotopias that are existing all over Silicon Valley. You
Speaker 6:know, unlike some people that only Brotopia, we we do do many female scientists. But, you know, we're going all around the world, really. We'll we just went to so far, the early episodes were Silicon Valley based just because I had to get this up and running really quick. We just went and filmed in Switzerland for a couple And so part of that was, to your other question, you know, we're working on a movie about brain computer interfaces
Speaker 2:Mhmm.
Speaker 6:And and Neuralink is at the heart of that. So we're following this journey for for months, years maybe. And then and then we filmed a couple episodes with some Swiss companies.
Speaker 1:Yeah. How do you think
Speaker 2:about What? Oh, sorry. Judy. What's what's happening in Switzerland?
Speaker 6:It's kind you know, it's always like this mixed god. You're gonna get me on like a Europe rant. I'll try to contain myself.
Speaker 2:Switzerland is my second favorite country in the world.
Speaker 6:It's like, you know, if you're gonna go with the well made museum version of Europe, it's it's it's great. They do have some, I think, lot of well, okay. They have a lot of good biotech stuff coming out from pharma and and just like a incredible education system. And they have finance stuff, but we want robotics. You know, I went to a couple university robotics labs.
Speaker 6:I've the it's interesting. They're always doing pretty cool stuff, and then it's always hard for them, I think, to make the leap into forming a startup and and, like, really getting money behind that and sort of the same ambition that you would see out of a similar Silicon Valley company. But yeah, we met a bunch of young kids who were doing cool you will see it coming up in a future episode. We had a robotic swan. Robots robot swans dancing at a lake, shooting water and lights and we were up till two in the morning filming that.
Speaker 1:They actually have
Speaker 2:a fantastic gun on them yet?
Speaker 1:They actually have a fantastic gonna put a
Speaker 2:gun on the robot swan.
Speaker 1:I'd love that. The over in Switzerland, they they you gotta do a profile on this. They have a fantastic robotics industry all around telling the time. And so they have these amazing companies. You should do a whole profile on Okay.
Speaker 1:Philippe, Ademar Piguet, Vacheron Constantin. It's a it's like this machine.
Speaker 3:It's robots.
Speaker 1:It's basically robotics, but just to tell the time. And so I can imagine of, an Ashley Vance style deep dive on those companies being really, really good. It's hard tech.
Speaker 6:I would I would enjoy that, I think.
Speaker 1:That'd be great. Yeah. But, yeah, it
Speaker 6:was cool. It's cool. They have they got good energy. You know, they they the Swiss government really backs Mhmm. EPFL and ETH, these two universities kind of on a level that I don't know.
Speaker 6:You you I've been all over Europe filming the TV show and I'm always Yeah. I'm impressed when I go to Switzerland.
Speaker 2:Do you think American investors should be posted up in Geneva just slanging checks? Is the is are they ready for American industrial venture capital?
Speaker 1:Go from spending just the entire summer there to spending some time during the most of winter.
Speaker 2:And most
Speaker 1:winter, ski season. So ski season summer, but then they could also spend some fall and springtime there.
Speaker 6:It's I mean, I try and you know, I don't want to overgeneralize and crap on an entire continent. But, you know, I do think I do think there's opportunities there. But then sometimes you walk through the European startups and and the energy level is is not the same as you would you'd find in The US.
Speaker 1:They're not sleeping on the floor
Speaker 9:of the
Speaker 1:factory over there. They're going to see some
Speaker 2:companies where teams are sleeping in tents.
Speaker 1:Yeah. Yeah. We're about to talk to a billion dollar company where the founder's sleeping out in Abilene, Texas where you got Chase. Have
Speaker 2:you been to Abilene yet?
Speaker 6:I have. I went with Sam and oh, yeah. I'm gonna name drop. I went with Sam and Greg Cool. A couple weeks ago and got the That was like a big everyone in Europe was asking me.
Speaker 6:They're like, are we hosed on AI? And I was like, well, I just went Yes. To Abilene, and each one of those buildings cost about $50,000,000,000 and they have a lot of them and you guys have precisely none of them. So, I mean, was it was kind of like a harsh I gave a talk in Poland as part of this trip and people were not aware of the scale of, like, the investment.
Speaker 2:Even though 12 MGX setting up a data center in France.
Speaker 1:Yeah. Yeah. I mean, the the overall Stargate project is available to other countries, and and those deals are being negotiated right now. Because NVIDIA wants to sell chips and, you know, Crusoe will wanna sell energy and there's a lot of different companies in the Stargate supply chain that want to be a part of that even if it's in another country.
Speaker 6:From what I like from what I was gleaning from chatting with Sam on that trip, it sounds like France is the only country that's kind of ready to to pony up at the we would like to participate level.
Speaker 2:Interesting. Is it gonna be like luxury AI?
Speaker 1:LVMH GPT.
Speaker 2:These like branded tokens. Yeah. Chat LVMH artisanal tokens.
Speaker 1:That is what they do best. Anyway, this has been fantastic. Thanks so We'll talk
Speaker 8:to you
Speaker 2:it a regular thing.
Speaker 6:Thank you, Thank you. I would
Speaker 3:love that.
Speaker 1:Yeah. I'll I'll see you this weekend.
Speaker 6:Alright. Thanks, guys.
Speaker 1:Bye. Cheers. Up next, we have Chase Lochmiller from Crusoe Energy coming in the studio. Welcome to the stream. Chase, how are you doing?
Speaker 2:Welcome.
Speaker 1:Where are you in the world? Are you in Abilene or Texas? Abilene. It might be.
Speaker 6:I don't know.
Speaker 1:It might be in San Francisco. Oh, we don't have him. Oh, okay.
Speaker 2:He is not here yet.
Speaker 6:I thought we had him, but
Speaker 1:we can do some timeline. What do we got? Oh, this is interesting. There's a shakeup at a in in in terms of who's in charge of AI at Meta. And I wanna go deeper here because we're a a bit of an AI day is coming together in, on Thursday.
Speaker 1:We have OpenAI and Anthropic on board. We need someone from Meta. So this is a little call out. If you work in AI at Meta, let us know. We'd love to have you on the show and duke it out with the rest of the foundation model companies, CEOs, and and, research scientists.
Speaker 2:Lab on lab violence.
Speaker 1:Lab on lab violence. Anyway, speaking of the man who powers it all, we have Chase Locke Miller from Crusoe Energy in the studio. How are
Speaker 2:doing? Welcome.
Speaker 1:Welcome to the stream.
Speaker 2:We no sound.
Speaker 1:Are missing sound. Are are are you muted? Are we muted? What's going on? Let's check it out and get to the bottom of this.
Speaker 2:And he builds.
Speaker 1:We can hear you now.
Speaker 2:Makes David sound. He does he's not a a Zoom expert. Take a break.
Speaker 1:What is new with you? How are things going? Give us the latest on all the news that came out. I feel like the last two weeks have just been Crusoe wall to wall coverage, but how are things going in your world?
Speaker 10:Things are good. Things are busy. Yeah. Turns out, you know, AI needs a lot of power, and a lot all these chips need a lot of data center capacity. So Good.
Speaker 10:What we announced last week was, you know, the completion of funding for the expansion of our our facility in Abilene, Texas. Mhmm. That's gonna consume a total of 1.2 gigawatts of of total power capacity. And and that funding, we we did in partnership with Blue Owl Capital. So the total funding is is about 15,000,000,000 in in total capacity, to build out.
Speaker 6:That's good. I'm sorry. It's amazing. We love to see it.
Speaker 1:What how how is it different working with, an investor, a financial institution like Blue Owl versus some of the investors that you've worked with on the venture side? Is it more Excel and less Vibes and Dex? Is it is it a wildly different underwriting scenario? Like, you you know, we're we're more familiar with the venture style where it's usually just a a handshake and a term sheet on the back
Speaker 2:of a napkin. Prayer.
Speaker 10:Yeah. I feel like I left the the Vibes investors, you know Behind. You know, a long time ago. That that was more like kind of the earlier stage stuff. But Yeah.
Speaker 10:So, you know, certainly certainly different. And I think with BlueOwl, they've been a great partner, you know, one of the leading real estate, you know, private equity practices in in the world. So very sophisticated. I mean, they've been super supportive and helpful across getting the entire deal structured and
Speaker 6:Yeah.
Speaker 10:And and financed. So and then, obviously, very, very deep deep pocketed capital providers to really help us, you know, make these projects happen at really significant scale. Mhmm. Now what's different is, like, this is not an investment in Crusoe,
Speaker 8:you
Speaker 6:know Yeah. Equity.
Speaker 10:Right? This is a this is a partnership with BlueOwl for this specific project. Got it. And just you know, Crusoe has a business model that is, you know, not asset light. It's very CapEx intensive
Speaker 6:Yeah.
Speaker 10:Which requires, you know, being able to tap into those very large pools of capital
Speaker 6:Yep.
Speaker 10:To basically make these large scale AI factories happen.
Speaker 1:So what so so, really break down the the anatomy of, like, how these deals work. Is it like there's a new LLC or a new c corp that's that's created? And then is there something that looks like a mortgage with, like, a thirty year payback period, interest only period? And then I imagine that this facility is gonna make no money for a few years while you're building it out, but then it'll start making a bunch of money. And so is there some sort of repayment schedule that's responsive to that dynamic?
Speaker 10:Yeah. So the breakdown of the structuring is so this also came out. There was a you know, the exact number
Speaker 1:is Yeah. I'm sure you
Speaker 5:can't share.
Speaker 10:But, the, there is, construction financing that's being provided by JPMorgan. Mhmm. So on the expansion, it's a little over 7,000,000,000. Mhmm. That's provided by JPMorgan and then a number of other banks and and and capital providers in the syndicate, including Bank of America and a handful of others.
Speaker 10:Cool. But the the way it works, I mean, it is it is a prop co. Right? So it's a property company that basically you know, it's an LLC that ends up owning the the individual, you know, campus or the buildings. All of those buildings have an affiliated lease with them
Speaker 6:Yeah.
Speaker 10:With with with our customer, which is a long term lease agreement. You know, being able to partner with a large scale investment grade customer is really what helps unlock a lot of the capital here. Yep. You know, when when people are talking about these very, very large quantums of capital, credit is like the magic unlocked. Right?
Speaker 10:So so so when you have big long term off take agreements with high credit quality customers, that's where you can really unlock these larger pools of capital, and, you know, we can put $15,000,000,000 to work, in a, in a positive capacity.
Speaker 1:And then so, so that's happening over there. How do you make money then?
Speaker 10:Sure. We we make up money as both a project developer Sure. As well as we we are a partner with Blue All in the ownership of the entity.
Speaker 1:Makes
Speaker 10:sense. Our customer you know, it's it's it's basically like we're the landlord. Right? We have a customer that is paying us a a a monthly rent for, fifteen years, and, you know, we make money, that's in excess of, you know, sort of our debt service obligation.
Speaker 1:Yeah. Then talk to me about the energy side. That's kind of the bread and butter, like, the history of Crusoe as I understand it. How important is it to find, unique kind of combinations of resources to provide energy to these large scale data center projects? What's happening in Abilene?
Speaker 1:What's unique about Abilene? And then, and then I wanna dive into a little bit more about the life cycle of that energy production plan.
Speaker 10:Yeah. So so much of the bottleneck of scaling AI has boiled down to just lack of energy or lack of access to energy. And, you know, Crusoe from its founding seven years ago has always taken this energy first approach to building computing infrastructure. Mhmm. Instead of thinking about, you know, how do I build the next data center in Northern Virginia, we've always kinda thought about, like, where can we access low cost, you know, clean as much as we can and abundant energy to to power computing infrastructure.
Speaker 10:And sort of the revolution you see unfolding with with AI and sort of this this complete transformation of the digital infrastructure landscape is is is pretty mind boggling when you when you really think about it because Northern Virginia is sort of, like, the center of the world for data centers. Everybody's like, okay. The Northern Virginia corridor, that's sort of where Internet is happening. That's probably where, like, you know, this Zoom conference is being, like, hosted. Yeah.
Speaker 10:You know, just so much of the Internet happens in Northern Virginia.
Speaker 1:AWS US East. We know and love it.
Speaker 10:Yeah. Exactly. Everybody uses
Speaker 2:backbone of of our industry.
Speaker 10:Yeah. Totally. Exactly. So all of the data center capacity we've ever built in Northern Virginia
Speaker 1:Yeah.
Speaker 10:Is about four and a half gigawatts.
Speaker 1:Wow.
Speaker 10:What we're doing in Abilene, Texas is 1.2 gigawatts.
Speaker 1:Wow.
Speaker 10:And, you know, we're looking at trying to do more. Yeah. We're one company. This is for one customer. We're looking at other sites that are five gigawatts.
Speaker 10:Right? Yeah. So you're talking about building a whole Northern Virginia that's been built over the last three decades. Yep. It's one facility for one customer.
Speaker 10:Right?
Speaker 1:You
Speaker 10:know, there's just fundamentally not enough power there in Northern Virginia to make that happen. And so, you know, what's happening with AI is, like, you're seeing everybody start to take an energy first approach to developing this infrastructure. And that's really what led us to Abilene. Right? Abilene is a market where, you know, there's an abundance of energy, a lot of wind particularly, and solar had been built on the back of production tax credit incentives.
Speaker 1:Mhmm.
Speaker 10:And their problem was actually they didn't have enough demand for energy. Mhmm. Right? They would frequently get curtailed, which means, like, they would have to sell power at a negative price. Mhmm.
Speaker 10:So they're shutting down their wind farm, or pricing would go negative, and they would sell at a negative price. So their issue was actually just not enough demand for power. So it a it was a good natural fit between AI factories and, you know, low cost, clean, abundant energy. You know, it's a it's a pretty awesome, you know, setup. We're gonna account for about I I think the number is about 30% of the total tax revenue for Abilene, just like we're, projects.
Speaker 1:That's amazing. Can you give me, like, kind of an energy one zero one on on energy production in Northern Virginia between, I'm sure natural gas is in there. There's solar. There's wind. I don't know if there's any nuclear.
Speaker 1:Are we still using coal at all? Like, I really have no idea.
Speaker 6:Yeah.
Speaker 1:Okay. A lot of What about, like, crude oil or fuel or, like, just gasoline? Does that power AWS at all? Like, I'm just curious about that mix.
Speaker 10:Yeah. No. There is actually, you know, especially during moments of peak demand. If you if you have peak demand at night
Speaker 1:Diesel generators, probably.
Speaker 10:Extremely cold night. There's obviously no solar.
Speaker 1:Sure.
Speaker 10:And, you know, people are, you know, oftentimes having to use oil Okay. You know, oil to produce power, which is, like, not a good, you know, not a good
Speaker 1:Yeah. It's just it's, like, the dirtiest possible option here. Like, we could be a lot cleaner.
Speaker 2:It's expensive.
Speaker 1:And it's expensive. Yeah. Then inexpensive. Yeah. And then contrast that with Abilene.
Speaker 1:What's the energy mix look like there? Is it similar, or is there something different? You mentioned wind. Is there more wind in Abilene than Yeah.
Speaker 10:I mean, Abilene is one of the windiest places in The United States. It's sort of this corridor that just gets a tremendous amount of wind, and that's why a lot of renewable energy developers built there. Sure. The you know, I I think it's actually important to understand this production tax credit. So the way this works is that the independent power producers that build these renewable energy facilities, they get paid production tax credit for producing and selling a kilowatt hour of clean power.
Speaker 10:Mhmm. Now they get paid that regardless of who they sell it to and at what price.
Speaker 1:Yeah.
Speaker 10:And so that has led to these consequences where, you know, you'll often see power prices go negative because their actual realized price is, you know, after you factor in the production tax credit subsidy they're getting, is positive. But it's, like, kinda they're having to pay someone to take that kilowatt hour, and then they go collect that that that subsidy through the production tax credits. Mhmm. So now the issue becomes those production tax credits only exist for ten years. And so at the end of the ten years, you still have this working wind farm, and you're like, okay.
Speaker 10:Power prices go negative. Now I have to curtail, and I have to shut off.
Speaker 1:Mhmm.
Speaker 10:Which means I could be producing power, but I'm not because there's literally no marginal demand for the power.
Speaker 6:Mhmm.
Speaker 10:And, you know, I think this is where, you know, having this alignment of, you know, markets where you can produce power in a very cost effective capacity, and you can actually build an AI data center there, to soak up that energy, is a very good alignment that, you know, Crusoe has tried to facilitate.
Speaker 1:Can you talk a little bit about the history of the company? I know that at one point, there was, some some crypto mining with gas flaring stuff going on, and then the transition from that into the new current AI boom. Was that a major emotional roller coaster, or did it kind of overlap in a perfect way where it was just, like, all growth?
Speaker 10:Yeah. Totally. So, you know, we started the company. One of the first applications of energy was was was, as you as you talked about, was basically capturing waste methane from oil production that would otherwise be flared
Speaker 1:Okay.
Speaker 10:And then utilizing that to power initially Bitcoin mining data centers, but Yeah. You know, we also powered, our early versions of our AI data centers.
Speaker 1:Sure. Like smaller training runs. Right?
Speaker 10:Yep. And this was, like, pre chat GPT. This was, you know, we were working with, like, you know, the the MIT department of physics doing, you know, early simulations of the big bang.
Speaker 1:I know what you're
Speaker 10:saying. Use and
Speaker 1:That's good.
Speaker 10:You know, CCL department at MIT, like, you know, early early work in our AI cloud development. But, ultimately, you know, when I started the company, I really wanted to build an AI cloud platform. That was like so a lot of people are like, wow. This was a great pivot, and I always tell them, like, no. This was, the plan from day one, believe it or not.
Speaker 1:Sure.
Speaker 10:And, you know but I always felt like energy was the thing that tied together all computing infrastructure. And any computing application when really scaled out, energy does become the bottleneck. Mhmm. And I had seen that and experienced that in these proof of work blockchains like like Bitcoin and Ethereum. But and I felt like if if AO is gonna scale, you know, energy would be a a massive component in the overall operating cost of operating intelligence systems at scale.
Speaker 10:And so we did a lot of early investment in in terms of, like, making the platform, you know, work with with Crusoe Cloud and and trying to, you know, figure out what what we wanted to build and for who. Mhmm. And then, you know, we kind of you know, with the with the launch of ChatGPT, it really sort of catalyzed just massive investment and attention to, you know, purpose built GPU infrastructure, and we had done that from the ground up all the way from energy, data centers, as well as managed infrastructure as
Speaker 6:a service, at the software layer.
Speaker 1:Did you lose any sleep over the deep seek, news, or were you Jevan's paradox pilled from day one and you knew that it was just gonna keep going?
Speaker 10:Yeah. I I I think just, like, the way that got spun up in the media was, like, so misdirected.
Speaker 2:Pro China media spin. Yeah.
Speaker 10:It was crazy. Very early on.
Speaker 9:Absolutely. I think
Speaker 10:anybody everybody was like, wait. Like, there's no chance this was, like, a couple of hobbyists that had, a couple of GPUs in their garage and they trained this model off of. Like, that's just, like, not what happened.
Speaker 2:Did this training run with scraps.
Speaker 1:With scraps and a cage.
Speaker 2:Powered with a bicycle. They packed Yeah.
Speaker 10:Yeah. Yeah. Totally. Yeah. It was a couple couple guys with pens and paper.
Speaker 10:Just
Speaker 1:But I guess I I I I guess, like, the bigger question is, as it does feel like we're somewhat shifting from a pretraining to an RL environment, the scaling laws are holding in the macro, but it seems like there's a series of s curves in terms of the different training and improvement paradigms that lead to just better products. And so, yes, we can we can do another 10 x increase in pretraining, but maybe we hit a data wall or there's some problems there. What are you seeing in terms of trade offs for demand on the data center side as we go through these paradigm shifts in terms of what's important to create a really, really performant AI product. Is it just we're shifting from training to inference, and that doesn't even affect you? Does it affect you?
Speaker 1:Do you need a different different build out for a large training run versus just mass inference of complex models consistently forever all the time because demand is so high, but it's smaller models all over the place? Like, does any of that affect the way we build data centers?
Speaker 10:I think it does affect some of the ways that we build data centers. But to the question of slowing demand, I mean, the conversations that I'm involved in, there's like, if anything, we're seeing demand accelerate. And for, you know, bigger, you know, larger scale clusters and and and just the overall demand is is increasing quite a bit for for for inference as well. Mhmm. And I think I think you see kind of this transition where, you know, folks will use a very large AI factory for a training run that, you know, gets some state of the art model.
Speaker 10:You know, that infrastructure is still useful for, you know, a long period of time to serve, you know, both inference workloads as well as, you know, any of these, like, post training, test time compute scaling, you know, chain of thought reasoning models that, you know, are are really basically, you know, taking inference queries and then thinking about them and sort of plotting out a whole bunch of different scenarios and then coming up with better, smarter, more intelligent answers. But you know? And and I think that's, like, the crux of, you know, the infrastructure. It's like Mhmm. What we're building are these AI factories.
Speaker 10:Right? So they are factories that manufacture intelligence.
Speaker 1:Mhmm.
Speaker 10:They're factories that manufacture intelligent outcomes that, you know, are are prompted by, you know, inputs from users. And I don't see any near term shortage of demand for, you know, more intelligence.
Speaker 2:What are are you bullish or bearish on sort of upstart or or SMB players that that wanna build AI factories or data centers that, you know, see the broader opportunity and then are maybe, you know, putting together sometimes sophisticated teams, sometimes less sophisticated teams, able to pool together capital and, you know, want to bring data centers online and just assume there's gonna be demand waiting there, or just assume that they can actually, you know, build something that's state of the art?
Speaker 10:Pretty bearish. We've seen a massive influx of, we call them like two guys in a pickup truck where
Speaker 2:Yeah.
Speaker 10:I got my cousin Lenny who has this plot of land out by his ranch. And know, there's a power line that goes through it, and he knows someone that works at the powers. You know, just
Speaker 3:like Just put up
Speaker 2:a barn and throw some racks in there.
Speaker 7:We're in business.
Speaker 1:Got some
Speaker 2:Hit up a hype. Hey. Hey. I'm a Google shareholder. Right?
Speaker 2:I'm gonna just call up
Speaker 1:Sundar. Sundar. Yeah. Yeah. Yeah.
Speaker 2:I got a contract. No problem.
Speaker 1:Yeah. I got I got some '30 nineties in here. Got Orly Yeah. I got a I got a couple ten eighty Ti's right over there.
Speaker 2:Yeah. Couple propane tanks.
Speaker 1:Yeah. Propane. Barbecuing Yeah. GPUs. Yeah.
Speaker 10:I I think the I the thing is that these projects, you know, they're so big Mhmm. That like the capital investment to, you know, make them happen is so massive. And you're seeing people speculatively build smaller scale stuff.
Speaker 1:Mhmm.
Speaker 10:But the, you know, the really big stuff that's, you know, whatever, a couple hundred megawatts or, you know, gigawatt plus, you really need credit to make it happen. Like, you know, I said it before, but credit is the unlock to all of this infrastructure getting built. And we have companies with the greatest balance sheets in the history of business that are going all in on this, you know, technological paradigm shift underway.
Speaker 1:Mhmm.
Speaker 10:And with that, you can unlock a lot of, you know, infrastructure capital to make all of this happen. But, you know, if you're gonna speculatively, you know, spend, you know, $20,000,000 kinda trying to build an AI factory. You know, it's like shooting a BB gun at a grizzly bear, you know.
Speaker 1:It's like,
Speaker 10:nah. You know, you're not you're not you're not
Speaker 2:Well, yeah. And at $20,000,000 scale, you can get a a group of smart people that can make a deck. And then investors are gonna see that and be like, I wanna make money on this AI thing and be like, well, yeah. We'll throw twenty, thirty, 40
Speaker 1:Chase has the best animal based metaphors. Because I remember we were at that nuclear conference and you said that the demand for energy is so high that companies would burn whale oil if they could. And for some reason, you keep coming back to these, but the BB gun at the grizzly bear is great.
Speaker 2:Where, so I'm sure you work with hundreds of different vendors for different components, parts, etcetera. Where do you think there is major supply chain risk or shortages? Like, where would you like to
Speaker 1:see I was about ask this. We heard something that a large portion of the transformer supply chain, not the algorithm to the transformer, the physical infrastructure comes from China, and maybe there's a risk to the supply chain with the trade war there. Would love to know what the key inputs are outside of we all know power. We all know NVIDIA GPUs. But what else could we be constrained on, whether it's cement or transformers or copper?
Speaker 1:I don't even know. Lay it out for us.
Speaker 10:Yeah. I mean, you know, the bottlenecks move around. Mhmm. You know, the bottlenecks for AI infrastructure builds kind of move around. You know, you sort of had this moment of, infinite demand for h one hundreds when they first launched.
Speaker 2:And Yeah.
Speaker 10:You know, I think Elon famously said that, you know, it was way easier to acquire illegal drugs than get an h 100. And so so so, you know, it's it's rapidly shifted into energy and data center capacity. And and what does it mean to actually build that stuff out? High voltage transformers are definitely like a big bottleneck. A lot of that capacity does get built in China.
Speaker 10:It's a diverse enough supply chain that I'm not that worried about you know, trade war kinda impacting Yeah. High voltage transformers. But, you know, they are kinda long lead time assets. Mhmm. You know, outside of you know, there's a whole stack in the in the in transformer side too.
Speaker 10:So, like, you have the medium voltage transformers.
Speaker 6:Mhmm.
Speaker 10:Switch gear can be, like, a a a major long lead time item that Crusoe actually started manufacturing in house. So we have factories in Tulsa, Oklahoma Mhmm. In in right outside of Denver, Colorado.
Speaker 1:When you say switchgear, is that like network switches, like Ethernet routing or something No.
Speaker 10:Sorry. It's it's electrical switchgear. Okay. This is basically like your, you know, electrical room that has all of the, you know, breakers that that feed into the the actual data halls that are powering the
Speaker 1:Yeah. It's like a power strip. You plug it in the wall, get six outlets out of the back, kinda like that. But, like, the big version of that, is that right?
Speaker 10:It's kinda like, you know, it's kinda like your, you you you know, it's it's kinda like your your breaker box in your house. Okay. Got it. You you trip a breaker.
Speaker 5:Yep.
Speaker 10:You gotta go down and you gotta go flip the switch. It's like that at, you know, a a gigawatt scale data center
Speaker 1:or something. Yeah. That makes sense.
Speaker 10:But switchgear is definitely a bottleneck.
Speaker 1:Sure.
Speaker 10:Chillers is another big thing. Oh, yeah. You know, I think an interesting trend in in data centers right now is with the with the introduction of the g v 200, the new NVIDIA chip, basically, you're seeing this massive transition to liquid cooled computing at significant scale. Mhmm. So, you know, a lot of the, you know, government labs and high performance computing communities have been experimenting with things like immersion cooling, you know, single phase and two phase, as well as, you know, water cooling DLC for decades, but no one's ever done it at the scale that's unfolding right now.
Speaker 10:And the reason it's happening is because you just have so much energy density, so much heat being produced by these new NVIDIA chips as we move on to, you know, more advanced architectures that there's, you know, simply just not enough heat capacity, basically move that heat off the chip from a traditional aluminum heat sink or even something that's, like, so big that, you know, it would be
Speaker 2:just Can you talk about the life cycle of water in some of these AI factories? We had somebody on the show, I don't remember their name. But I do remember that they said, we don't have enough water in Abilene to to run, you know, these data centers. And that didn't quite feel correct. So I'm not gonna call them out.
Speaker 1:Is that a bottleneck?
Speaker 10:No. It's not. Mhmm. It depends on how you design it. So we've tried to you know, I I think water can be a very sensitive topic depending on the communities that, you know, you're engaged in, and Crusoe's always trying to be a, you know, phenomenal partner to, you know, the local communities that that that we're working with.
Speaker 10:So, you know, the way we've designed our AI factories is what's called a closed loop architecture.
Speaker 2:Mhmm. Yeah.
Speaker 10:So that means, basically, you have cold water that flows into the rack, and it flows over the the chips over over this you know, through this through this copper pipe, and you have this heat exchange between the silicon that goes through through the copper and then to the water, and then hot water sort of exhausted from from the rack. That hot water then goes out to a heat exchanger that's a that's a chiller that's outside. And it's basically just
Speaker 7:you can kinda think of
Speaker 10:it as, like, a a massive maze of of copper pipe, and then you, like, blow air over it. You try to blow cold air over it, and then the heat basically gets exhausted out of the water. And then that and then you basically have cold water from that that then feeds right back into the system. Mhmm. So And I think people 1,000,000 gallons of water per building, we only fill it one time.
Speaker 10:Right? It's not
Speaker 1:like we're using a million gallons of
Speaker 2:Yeah. This this is what this is what the media has implied is that, like, every time you make a cute Studio Ghibli image, you're, like, dumping a gallon of water.
Speaker 1:It's like, yeah, a gallon of water might flow over the chip while you're doing that, but then it flows over the next one and the next one. This is recycling, which is not Yeah.
Speaker 2:And at that kind of scale,
Speaker 8:even a million
Speaker 10:that are open loop where, basically, you have a fresh water supply. And that is, you are consuming a little water in that scenario.
Speaker 1:Yeah.
Speaker 10:Yeah. But in our case, we've designed it with a closed loop architecture that is like a, you fill it one time, then you're done.
Speaker 5:What's there for closed loops?
Speaker 2:I love closed loops. Last question I have. How do you evaluate your pipeline? I'm sure you're a very popular guy, getting phone calls and emails from all over the world. But you're building physical infrastructure.
Speaker 2:It's not like you can just copy and paste what you're doing in Abilene or or some other areas a million times. I'm sure you have to be pretty
Speaker 10:Yeah. I mean, we're trying. We are we have a couple other projects that are underway that you know, hopefully, we'll be able to talk about, you know, more soon, but, you know, similar scale or bigger is kinda like, you know, what we're seeing. So, you know, a lot of demand, you know, unfolding, you know, within the ecosystem. And, you know, I think we we try to be thoughtful about our partners.
Speaker 10:I mean, we we really ultimately want the space to be successful. You know, I I view AI as a generational opportunity to transform, you know, human prosperity around the world, And we just wanna help make that happen. And, you know, we we don't think any one company is gonna do it alone. We wanna help support the entire industry in terms of making this technology successful, scaled, and and and really rolled out to the masses.
Speaker 2:Makes total sense. Fantastic. I have a
Speaker 1:ton more questions, but we'll have to have you back on because this was a fantastic conversation. We'll talk to you soon, Chase. Thanks so much for stopping by.
Speaker 2:Cheers. Appreciate it. You.
Speaker 10:Take care.
Speaker 1:Before our next guest, let me tell you about Ramp. Ramp is ramp.com. Time is money. Say both. Easy to use corporate cards.
Speaker 2:Tears earlier tears of joy Yes. Using Ramp Travel.
Speaker 1:It is the most amazing product. I know this is such a shill that No.
Speaker 2:It's not a shill. Was off air.
Speaker 1:You were an incredible John was saying it's
Speaker 2:a beautiful thing that I can get IMO gold medalist to make me my travel business travel app.
Speaker 1:Yes. Any I mean, when we talk about software that is lacking, you just the most obvious example is like the airline app. Right? The airline app is notoriously buggy, and you're not logged in. Ramp travel saves your all of your information immediately.
Speaker 1:It shows you all the flights across everything. You just click one button. It just books it. And then it's just boom. You're you're just booked.
Speaker 2:No chasing receipts.
Speaker 6:No chasing receipts because
Speaker 1:it happens inside a ramp, which is amazing. But even aside from the expensing, even if I had to do something else on the expensing side, just the experience of actually booking on is so much easier. It's like I mean, like,
Speaker 2:like wish we had it on camera because I know.
Speaker 8:I know. Yeah.
Speaker 1:It was a really special model, John. Leaked. Leaked.
Speaker 2:Yeah. I'll be right back.
Speaker 1:Anyway, our next guest is coming in the studio. But first, let me also tell you about Figma. Think bigger. Build faster. Figma helps design and development teams build great products together.
Speaker 1:Go to Figma.com to get started. We have our next guest coming into the studio, Factory AI. Matan, welcome to the stream. How are you?
Speaker 11:Thank you so much for having me. I'm good. How are you?
Speaker 1:I'm great. Thanks so much for hopping by. Can you kick us off with a little introduction on yourself and also, the company and the news? I know that there's big news coming up.
Speaker 11:Yeah. Absolutely. So, I'm Matan, CEO at Factory, here to share a bit about our latest launch. We released droids. Droids are not just your regular everyday coding agent.
Speaker 11:They are full end to end software development life cycle agents Mhmm. Built for the enterprise. Yep. Big enterprise, not just not just startups. Although, you know, we've, in the last twenty four hours seen a pretty big explosion of usage with, startups, which has been really cool.
Speaker 11:Yeah. That's, it's been it's been it's been fun.
Speaker 1:Talk to me about the evolution of AI agents and coding agents. I remember there was a company a couple years ago that was training their own foundation model. Then the regime seemed to to seem to shift to, okay. We're not gonna do any pretraining on code specifically. The foundation model companies got that covered, but we will do a bunch of post training.
Speaker 1:Then it became more maybe there's some RL fine tuning. Maybe there's just some some reasoning that we're doing now. It feels like a lot of the coding agent companies are just kind of like, we're we're rappers, but we're still printing money and rappers and unnecessary pejorative. It's actually the best way to build this business, and it's helping us. So so where do you fall, and and and what is your take on the different paradigm?
Speaker 2:I I just got here. I gotta compliment Metano.
Speaker 1:I know. I Fantastic. Fantastic. He looks great.
Speaker 3:You know?
Speaker 2:Thank you
Speaker 11:for When you when you come to the temple when you come to the temple, you must be We appreciate it. Appropriately. Yes.
Speaker 1:Yes. Thank you.
Speaker 11:Anyways But yeah. Yeah. No. Great question. I think it's been interesting how in the last two years there have been so many trends within the kind of, like, subsector of AI for software development.
Speaker 11:Yep. You're spot on. There was a really big trend, especially if you measure it by the capital that was put into it in terms of companies coming out there saying that they're gonna train models in particular for code Yep. Or fine tune models for code. Yep.
Speaker 11:There's been I mean, there's been quite a few who raised up to the tune of, like, half a billion dollars to train their own models. And I think something that a lot of people were saying at the time or maybe whispering because they don't wanna offend those who put half a billion dollars in was, code is a core competency for the foundation models. They will not allow a startup to, you know, fine tune their way to having the best models. As you see it in in general with code, it's like alternating between OpenAI, Anthropic, Google, xAI in terms of the number one spot in code. And so I think it I guess it took a lot more money than it should have, but I think people come to the realization that fine tuning an RL for code specific models will be won by the foundation models.
Speaker 11:Yeah.
Speaker 1:Is that actually just a function of, like, of, like, code is the Internet. The Internet is code. Code is on the Internet. And so if you train an LLM on the Internet, like, you're gonna learn like like, out of the box, if you do a robust large scale training run on web text, you're gonna get Reddit answers, and you're also gonna get a pretty good model that can code. And so unless OpenAI says, let's not train it on code, it's just you're going to lose to them because they're putting the most, you know, energy, the the most cycles into training the big model, and so you just get that as a byproduct.
Speaker 11:Yeah. That's that's spot on. It's actually I would go even further to say that, and I know, there were papers on this in around, like, 2022. I'm sure there have been since.
Speaker 1:But Yeah.
Speaker 11:There is a direct correlation between how good a model is at code and how good it is at other general purpose tasks.
Speaker 1:That makes sense.
Speaker 11:So it is just it is like it is, you know, table stakes for the foundation models to be the best at, like, raw coding. Yep. So exactly right there.
Speaker 1:So how do you how do you build a business without getting rolled? Yeah.
Speaker 3:How do how you how do
Speaker 2:you guys navigated just partnerships with the labs Yeah. Yeah. In general where it's your kind of frenemies? You know? It's like, hey.
Speaker 2:We we can work together.
Speaker 1:But Yeah. This is a good question.
Speaker 2:Point, somebody's gonna try to kill the other one.
Speaker 11:Yeah. No. This this is this is a really good question, and it's very top of mind. I mean, I think the reality is the closer you are to the zero to one or, like, the vibe coding or the, like, less technical your audience and the smaller the company, that's really where, the foundation models are, like, about to sweep it up or or already have. So it's like Mhmm.
Speaker 11:You know, building apps from scratch, building websites from scratch. That's something where the model providers will basically get it for free, like, plus or minus some deployment details in terms
Speaker 3:of Yeah. You remember with
Speaker 2:with, pre ChatGPT, there were a bunch of players that would generate copy for you based on the OpenAI API. And they had just rocked their revenue rocketed.
Speaker 1:Yep.
Speaker 2:And I remember it was the same sort of like people like the same behavior of like posting screenshots of like the revenue dashboards. Yeah. And then those screenshot shots stopped being shared post ChatGPT because all that revenue, you know, went away.
Speaker 11:Yeah. As Shakespeare said, these violent delights have violent ends. There was a really there was a really great great tweet I saw the other day. I can't remember who posted it, but it was, you know, a lot of people are talking about these, like, record breaking, like, runs to, like, you know, large ARRs.
Speaker 1:Yep.
Speaker 11:And what's gonna be coming soon is some record breaking churn for a lot of this, like, monthly these monthly subscriptions. So that'll certainly be interesting to see. But yeah. So that's the part of the spectrum where, the foundation models are best poised to tackle is that, like, zero to one, less technical use case. For us, we've been focused on the enterprise from day one because, one, it's really not sexy, and so it's not, like, conducive to, like, viral demos and all that hype.
Speaker 11:But that's where a majority of developers are getting a majority of their pain. Like, dealing with, like, COBOL, dealing with these, like, twenty year old code bases, migrations, refactors. And in order to handle that well, you can't just, like, one shot with, like, a ChatGPT or a Codex or a Claude. You need to have deep integrations with their code base. Oftentimes, it'll be, like multi repo integrations.
Speaker 11:You'll need to understand not just where the code is now. Next, love the little thumbs up guy. Not just, not just where the code is now, but how it got there, what the best practices of that org are, integrating with tools like Jira, Google Drive, Sentry, Datadog. The kind of first principles thinking there is that in order to produce the quality that an engineer who's been at that company for, like, ten years would produce, you need to have access to the same information. And that information sometimes requires some really ugly integrations.
Speaker 11:You need to, kind of get the the workflows that are a little bit more specific to these large archaic orgs, which is a little bit further afield from what the foundation models are are are working on right now.
Speaker 1:Talk to me about synchronicity, asynchronous coding agents versus synchronous, copilots versus autonomous agents. Where do you think it's going? Do these lines blur? Are they are they distinct product? It feels like OpenAI has three coding products now.
Speaker 1:I can go to o three, and I don't even ask it to write code, and it just does. I imagine that the amount of code that's being written for nontechnical people who don't even know that code would be useful in giving them an answer to a question is just skyrocketing right now, in that product. Then there's Codecs. They also have Windsurf now. And so you can imagine a few different bites of the apple there.
Speaker 1:Are is that, are they are they indexed in the market, or are these distinct different markets? Will there be one power law outcome in the coding space in terms of paradigm?
Speaker 11:Yeah. Great question. I think the the first order, like, bifurcation that'll happen is between the nontechnical audience and technical audience. Yeah. So, like, for nontechnical, being able to spin up apps on the fly is, like, cool and fun, and that'll continue to be something that's useful.
Speaker 1:They're the vibe
Speaker 11:They're never gonna yeah, exactly. Right. And they're never gonna really be that concerned with, like, going too deep into the code itself. They'll just like, I want a to do list app. Make it for me.
Speaker 11:Okay. Great.
Speaker 1:Yep.
Speaker 11:For the technical user, I think that's where it gets a lot more interesting. Basically and maybe maybe this is just a quick, like, kind of philosophy that we have at Factory. Every transformation shift has a very clear behavior change associated with it.
Speaker 6:Mhmm.
Speaker 11:Right? So, like, Internet had people, getting most of their information from, like, TV, newspapers, books to then, like, going on this console on their desk and, like, you know, getting all their information there. Mobile had people, you know, walking around, like, heads up to now, like, you know, walking around on TikTok, subway servers all the time. Right? These are very visceral obvious behavior changes.
Speaker 11:Yeah. And yet everyone talks about AI as it's, you know, the the the largest one to come. It's gonna put these other ones to shame. And yet if you look at the most used product, the most used AI products right now, there is no new behavior. Like, ChatGPT perplexity, that's just Google with better results.
Speaker 11:Mhmm. The behave like, the silhouette of that behavior doesn't actually look that different. Similarly with a tool like Copilot or Cursor, the way that software development is is looking, the behavior is still the same. You're still in this IDE, which was built for the world where humans wrote a % of their code. We're quickly going to a world where humans will write 0% of their code, and our take is that their behavior will fundamentally have to shift then.
Speaker 11:And not, like, in some iterative approach where you, like, iterate your way from an IDE to whatever this new thing is, but our take is instead you need to build that from the ground up. And this is kind of a long winded way of answering your question about the, like, async versus sync. The reality is in this future, developers are going to be natively working with agents. And so they'll need to, like, dynamically adjust between if they're collaborating with an agent to, like, look through their code base and understand how they should plan some new feature and then having a good plan for it and now, like, firing it off, delegating it to these to these agents. And at the same time, it's gonna put more emphasis on the testing.
Speaker 11:Because if you just shoot from the hip a ton of agents or, in our case, droids, then you're gonna have a ton of code to review before you release it to production. Because, ideally, you're gonna review it. Right? But that's kind of a depressing world where, okay, we don't write any of our code, but now you just need to read, like, thousands and thousands of lines before you can ship anything. So if you as the developer have better tests and you know, hey.
Speaker 11:If it passed these tests, I don't even need to review it because I, like, expressed all of the constraints that I had through these. So if it passes, great. Let's ship it. There's kind of this new emphasis on testing, to kind of enable that more delegative workflow.
Speaker 1:What's the secret to avoiding churn in the enterprise? Are you trying to ink multiyear contracts up front? Are you just playing the same game as everyone else and getting a bunch of experimental budgets because money is money? And, you know, that seems to be the meta right now. But I imagine that you're you're you're thinking about this or at least, like,
Speaker 2:messaging this to the When a competitor gets to a customer first, are you kind of hovering with the understanding that like, hey, maybe if we can really show that we're significantly better, we could win.
Speaker 1:Is there like a little bit of a price war here? Because you imagine that you come into an enterprise and you say, hey, this would cost you so much to do with Accenture. We're going to do this replatforming of your COBOL application to dot net or something or Python. Yeah. And and you would spend 10,000,000 on that.
Speaker 1:We'll do it for 8,000,000. And that's all of a sudden all of that's all of a sudden, that's, like, 90% margin for you instead of, like, 50% margin. But then your competitor comes in and says, well, we'll do it for 7,000,000. We'll we'll do it for 6,000,000. We'll do it for 5,000,000.
Speaker 1:And so there's there
Speaker 6:it seems like there's some sort
Speaker 1:of price war dynamic that might happen. Walk me through all of that of, like, winning in the enterprise financially.
Speaker 11:Yeah. Yeah. That's that's a great question. So first of all, yes, we do year long contracts just because it's important to have that mutual commitment, and in particular, because adopting the tools is not enough. Like, there I cannot tell you how many, like, CIOs and CTOs I've spoken to who have adopted, like, the hot new AI IDE.
Speaker 11:And then you ask them the question that they don't wanna answer, which is how many people are actually using it. Mhmm. And it ends up being, like, 20%. And of those 10 to 20%, a lot of them are just using it like the IDEs of old. Totally.
Speaker 11:So they're kind of, like, adopting these new tools, patting themselves on the back being like, look, CEO, we did it. We adopted AI. Job's done. Give me the big bonus. But the reality is is, like, they're actually not getting any productivity improvements.
Speaker 11:And so part of why we do these longer term, commitments is because one of our core competencies is not just having the best agents in the game, but also
Speaker 2:Droids.
Speaker 11:Helping them Droids
Speaker 6:and me.
Speaker 2:Love love it.
Speaker 11:I love it. There we go. We're also helping them adopt their behavior patterns. Because that's the thing. It's like, could have a tool that's 50% as good, but if you have twice the adoption, then you're now at parity.
Speaker 11:And so I think it's just a lot less sexy because all the, like, you know, great engineers who are coming out of Stanford wanna work on spinning GPUs and all that stuff. Talking about, like, behavior change in the enterprise, that's like, you know, like, you know, they don't wanna think about that. But that is where the ROI is gonna come. And, also, John, to your question, like, the way we actually get in and do these deals is focusing on those deliverables about this was scoped out to be four months, and we did it in two months or one month or two weeks. That's ROI that actually matters to, like, the c suite.
Speaker 11:When you talk about, like, we shipped tests 10% faster, it's just so, like or we 20% more lines of code. It's just so amorphous and so not tied to real business outcomes that if we can come in and actually, tie things to, like, things that are shipped per quarter or, you know, pulling in dates for certain deliverables, that's where it's just like it's so frictionless because it's not really existent elsewhere in the market.
Speaker 2:It's good. I was texting with Adam and Ben, from Genius, one of your investors, and they said to ask you about a fateful walk that you had with Sean Maguire. Does that ring a bell?
Speaker 11:Yes. It does. So this is, this is in, like, the founding history of factory. Basically, two years ago or two and a half years ago, I was doing a PhD in theoretical physics at Berkeley, which is what I was doing prior to factory. And
Speaker 5:You just
Speaker 2:refroning it in? You wanna kinda take the easy path in school?
Speaker 1:Or Yeah.
Speaker 11:Yeah. Exactly. Yeah. You know, just, some fun string theory, which, to be fair, it is really fun, and beautiful. But, decided it wasn't a path for me in particular because, you know, to to be a good physicist, you kinda need to thrive in isolation just, like, in your room alone, like, reading papers.
Speaker 11:Did that for, like, ten years. Was really stubborn. It's kind of a long story, but ended up, reaching out to this Sequoia partner, Sean Maguire, because he also used to be a string theorist. And, he ended up, you know, going into entrepreneurship, sold the company for a billion dollars, joined Sequoia, saw, like, a random podcast with him, and I was just like, I've never seen, like, another physicist who has somewhat social skills. Like, let me hit him up and get some get some life advice from him.
Speaker 11:Ended up going on a walk together. On this walk, he said, Matane, you need to drop out of your PhD, and you should either join one of my portfolio companies, and just, like, work on Glu. You know, the thumbs up. Let's go.
Speaker 3:Or Let's go.
Speaker 11:You should join you should join x because Elon just took over, and you'd have to be a badass to voluntarily go there. That's good. Or you should start a company. That's awesome. And so, that was, eight days later, dropped out of the PhD and started Factory.
Speaker 1:So Well, congratulations.
Speaker 3:Very cool.
Speaker 1:And, yeah, thanks for stopping by. This is fantastic.
Speaker 2:And, thank you for giving agents a cooler name. I love droids. You gotta get yourself some droids.
Speaker 11:Yeah. Yeah. We gotta get you guys some droids. Thank you, guys.
Speaker 2:Awesome. Thanks, Have a good one, Matan.
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Speaker 1:Vanta's trust management platform takes the manual work out of your security and compliance process and replaces it with continuous automation whether you're pursuing your first framework or managing a complex program. And next up, have Johnny from Muon Space. We're talking about data centers with Chase Locke Miller in Abilene, Texas. Now we're going to space, and we're putting the data centers in space. Very excited to talk to Johnny.
Speaker 1:So welcome to the studio. Johnny, break it down for us. And I'm gonna let Jordy do the intro on this. And, well, if you're on your phone, would you mind rotating it 90 degrees so it's widescreen? Thank you.
Speaker 2:There we go. So do mind kicking us off? What's going on? Great to have you.
Speaker 1:Are you calling in from?
Speaker 8:Guys. Randomly, I'm actually down in y'all's neck of the woods in Long Beach. I'm hanging out with a friend at Bass today.
Speaker 2:Nice. Nice. So, yeah, why
Speaker 3:don't why don't you give
Speaker 2:a quick intro, background on the company, yourself, all that good stuff.
Speaker 8:Yeah. Sure. So, Eon Space, we're a 50 person startup in the Bay Area. We're building a a platform to deploy large numbers of satellites in constellation format, for a lot of different mission types. We were founded in 2021.
Speaker 8:It's been a pretty pretty big rocket ship ride so far. It's been really fun. My background going back a ways, I mean, I I've I've been in space for, you know, well, longer than I care to admit, including I was an intern back at SpaceX in the very early days, like 02/1934. I was part of a I was the chief engineer at Skybox Imaging, which was the kind of first venture backed satellite company, kind of space before space got cool.
Speaker 3:Very cool.
Speaker 8:And so, you know, taking a lot of the lessons learned and kind of things I've seen from those experiences to the new company.
Speaker 2:Awesome. And then breakdown breakdown the new company.
Speaker 8:Yeah. So, I mean, I think the way to think about this is, you know, a lot's changed in the last decade of space. Traditionally, if you wanted to go do something in space, it required a lot of sophistication. You had to have it literally a team of rocket scientists. You often had to have a lot of deep expertise in everything from, you know, ground stations to to launch to avionics software.
Speaker 8:You know, barriers to entry have come down in a lot of ways, but a lot of that complexity remains. And our kind of goal is to really abstract a lot of that away from a customer that wants to do something in space and doesn't wanna have to go build that full vertical technology stack for every new use case that comes up. And I think that was a a big lesson we took out of Skybox is we kinda built two companies under one roof, a satellite company and a data company. And that the future should really about be about, you know, a data company being able to go build a data business without having to deal with all the complexity on the the space, the ground, the operations, the hardware, the integration side, and that's really what we're trying to solve.
Speaker 1:I wanna know about the potential of data centers in space. It sounds like a crazy idea. I know some folks are working on it. Just kind of like, what's your high level take of the progress we've been tracking, you know, dollar per kilo to Orbit. It's been falling, but recently, there's been setbacks in different programs, and there's more competition, and there's a lot of different dynamics going on.
Speaker 1:What do you think, the key milestones are to get us to a future where we're really doing, you know, mass manufacturing, big, you know, mega scale projects in space?
Speaker 8:Yeah. Well, let me let me start off. I mean, I think you guys are hitting on the right metric. Right? It's like the the hardest part of this is what does it cost to get a kilogram in space?
Speaker 8:Because everything else kind of derives from that. Ultimately, if you want a certain amount of power, if you wanna be able to put an aperture in space to do communications, whatever it is, like, that's really driven by what it costs to get it there in the first place.
Speaker 9:Mhmm.
Speaker 8:And I think it's important context to kinda see how far we've come down that path. I mean, largely driven by SpaceX over the last decade. And and, you know, I I like to tell the story of, you know, about fifteen years ago at Skybox when we were trying to launch these small satellites. We were literally going to Southeast Russia and launching satellites on converted Russian ICBMs.
Speaker 10:I mean,
Speaker 5:they were they were popping
Speaker 8:out of the ground and putting satellites into space. That's And that was the only way for, like, a Silicon Valley venture funded startup to go put something in space.
Speaker 1:Wait. Wait. Hold on. Did I hear you correctly? Like, the the the ICBM actually launches from one of those missile silos in the ground and makes it to orbit?
Speaker 8:Pops out of the ground and goes to orbit. I mean, you see videos on YouTube. The the network if you search the network on YouTube, you can see this. And it's it's crazy. I mean, it you know, and that that's that's what it took before SpaceX kind of, like, revolutionized the launch business.
Speaker 1:Wow. Yeah.
Speaker 8:And even at that, we were spending something like 10 times as much per satellite or per kilo
Speaker 1:Mhmm.
Speaker 8:Is what we can go buy on a transporter launch today. So there's been at least one and arguably two orders of magnitude improvement in the last, call it, decade on kind of what it means to launch things to orbit. I think if you imagine that happening again, another order of magnitude, it you know, again, it's gonna dramatically change the way you think about feasibility of some of these things, like putting very, very large power hungry things in orbit. We know how to do the solar. We know how to do the structures.
Speaker 8:We know how to get, you know, we know how to make electronics work in the radiation environment, which is always a concern and do it reliably. And so really, ultimately, I think it's gonna come down to unit cost, and and and what does it actually take to do that. The great thing about space is you have virtually limitless power, the sun. You know, you can you can go into orbits where you're in the sun all the time. So it's not like solar on Earth where you're going in and out of the cliffs or in and out of night.
Speaker 8:Like, you can be on all the time. And then you have this cosmic background of three Kelvin cold sink that you can go dissipate all the thermal energy you need from running your electronics and stuff. So it's actually in a lot of ways, like, sort of an ideal environment to do this if you can actually get there. Yeah. So I yeah.
Speaker 8:That's the kind of way I'd think about it.
Speaker 1:It's interesting. What it feels like SpaceX has commoditized launch and is kind of the power law winner there. Obviously, there's other companies that are competing, but, they've they've standardized, launch. They've also standardized Starlink, and then we talked to the Endurosat founder about standardizing a satellite bus platform and kind of getting to the mass manufacturing less bespoke, less customized, less hand built pieces. What else are you seeing as kind of critical pieces of the space supply chain that need to be standardized or you might be trying to standardize?
Speaker 1:Where where are the pieces in the supply chain to do the things that you wanna do, where it's still like, oh, that has a long lead time or that's hand built or that's way too customized. I get what I want, but it's exquisite system. It's too expensive. Talk to me about the supply chain.
Speaker 8:Yeah. And I'm I'm actually gonna give you the answer you asked for and then an answer I wanna answer, so I'll I'll do both.
Speaker 6:Yeah.
Speaker 2:On the supply chain side, you
Speaker 8:know, I think there's a lot of progress been made. Like, our satellites look very much like, you know especially things like the electronics look very much like what goes in into an EV right now. The batteries, a lot of the electronics, a lot of the individual kind of semiconductor parts have heavily leveraging other commercial industries. So it doesn't look like a traditional space supply chain. I think that problem more and more is is is solved.
Speaker 8:And I think EnduroSat obviously is is doing very similar things in that way.
Speaker 2:Some of the places where we really see that that that has not yet happened, one is in more on the
Speaker 8:payload side. So things where, like, you know, you think about the spacecraft bus and EnduroSat's building buses. Mhmm. You know, I do think there's a path to that becoming very standardized. But every mission you put in space has a bus and it has a moneymaker.
Speaker 8:It has something that's actually doing what you need it to do, whether it's communications or remote sensing or beaming power. If you're trying to be in power, if you're putting a data center in space, it's got a payload of of compute or whatever. And what we see in most of the kind of traditional space missions is that that now has become kind of the hardware bottleneck. It's like, how do you actually get the payload you need to solve your ultimate business problem built in in quantities large enough to deploy in a constellation? It costs low enough that you can do it.
Speaker 8:And I think there's a lot of movement in the right direction in that way, but it's we're not there yet. And and the the the other question that I kind of I'll I'll answer that you didn't ask is, I think the other big bottleneck outside of hardware is still software. So, like, you know, we we spent there's a lot of talk right now on hardware supply chains across a lot of industries, including space. I think there's paths to address that. What is still very true in space is that these are very complex autonomous robots with global networks that are having to communicate in in inside communication, outside communication.
Speaker 8:And software is really the glue that holds all that together. And in a lot of ways, the integrate integration of all these things and the software integration, the hardware integration is still the hardest part. It's really making these large complex systems work as a whole. And I really think that's a place that there's not being enough emphasis put in the industry right now. It's something that we're really trying very hard to solve with kind of the the kind of core hardware software stack that we're building.
Speaker 8:So that instead of, you know, for every new use case you're trying to solve in space, you're going in from the beginning having to do this crappy new integration of a bunch of parts of hardware and software that don't actually aren't actually designed to talk together. You have a platform that much like a data center rack today, everything from the lowest levels of hardware to the high level application software is designed to interoperate. It's designed to be pluggable. Mhmm. And so, you know, when Google's putting data centers in the data center, they can go take a bunch of stuff off the shelf.
Speaker 8:It does every rack doesn't look the same, but they have a bunch of parts that are interchangeable. They can go throw that in, plug them in, the software works, the hardware works, etcetera. We've we've gotta get to that with space too.
Speaker 1:Yeah. I mean, we've done that a ton in data center, like cat like cat five cables, cat six cables. Like, are all standardized. Even Facebook open open source their their blade design for their rack mounts, and there's a lot of other, you know, ecosystem tools. Last question from me.
Speaker 1:Are you tracking the SpaceX Starship progress? There was news today that Elon Musk is winding down his relationship with the government as a special government employee. That was a hundred and thirty day mission. Probably spending a little, fewer nights at Mar A Lago, more nights than the SpaceX factories. Starbase.
Speaker 1:Are you are you tracking that? Are you excited about that? And I guess the big meta question is, Elon has always seemed interested in getting to Mars, but he's willing to go to Mar A Lago if that's what gets him to Mars. He's willing to go to Starbase, Texas if that's what get him to Mars. So it feels like this might be a moment where he's shifting his focus.
Speaker 1:The mission's the same, but he's shifting his focus from from regulation to engineering challenges.
Speaker 2:Mars is back on the menu, boys.
Speaker 1:Mars is back on the menu. But what what what has your take been on the on the recent star Starship or SpaceX news?
Speaker 8:Yeah. I mean, I I guess I would start by saying, look. It it you know, building rockets is really fucking hard.
Speaker 11:Like, I
Speaker 8:mean, I I think anybody that says it's not is crazy. It is rocket science. I think we we've almost been lulled in complacency by how successful SpaceX has made it and, like, how how easy they can make it look. But, I mean, Starship is a crazy, complex complex rocket unlike any that's ever been built. So, like, at some level, I feel like these setbacks should be expected and people shouldn't act as surprised.
Speaker 8:I I think there's I don't have enough inside information to know if, like, forward progress is being made or sideward sideward progress if there's more focus, whatever. But, like, this stuff's really hard, so I don't think it's that surprising that there's setbacks.
Speaker 2:Mhmm.
Speaker 8:You know, I think that the the key thing for the launch unit economics, which I think ultimately, you know, start a ship, we're hope we're all we're all hoping it's gonna get us another order of magnitude on unit economics. So much of it of it is not even about scale or size or technical performance or capability. It's about launch cadence. It's about rate. It's about flying.
Speaker 8:It's like airlines. Right? If you bought a seven thirty seven and flew it five times a year, nobody would be able to afford to fly. But because it flies 10 times a day, all of a sudden that the amortization of that fixed asset over those flights makes a ton of sense. So I think for Starship to really go, what we need is we need applications that require Starship, like Starlink has for Falcon nine, that drive us to launch it three, four, or five times a day like you fly seven thirty sevens.
Speaker 8:And I think if that happens, like, it it will we will get another order of magnitude in the unit economics. And, I mean, maybe tying the loop back to the original question, you know, you start thinking about applications like deploying data centers in space where you're putting thousands of things that are each many, many tons each into space, and there's, like, a really core first order economic driver requiring that, needing that to scale AI training and these type of workflows. That's the kind of thing that I think could drive the demand that you need for something like Starship to really get us another another sort of order of magnitude unit economics. I don't know if I really answered your question, but that's
Speaker 1:the way I about it. No, man. No. Makes sense. Well, thank you so much for stopping by.
Speaker 1:This is great.
Speaker 2:Come back on again soon.
Speaker 8:Yeah. Yeah. You bet. It great talking to you guys.
Speaker 1:We'll talk
Speaker 6:to soon. Cheers.
Speaker 1:And while we're waiting for our next guest, let me tell you about Linear. Linear is a purpose built tool for planning and building products. Meet the system for modern software development, streamline issues, projects, and product road maps. And Linear has been
Speaker 2:just lucky to use for over a decade now.
Speaker 1:Over a decade on Linear? Must be. That's crazy. I'm surprised the company's even a decade old.
Speaker 2:Seems No, no, no. Sorry, half a decade.
Speaker 6:Half a decade. Half
Speaker 1:a Okay. Yeah. Because a decade ago, you were
Speaker 2:in middle I was about to say the better part of a decade.
Speaker 6:Yeah. Yeah.
Speaker 1:Okay. Well, we have our next guest. David from Retool is coming in the studio. I'm very excited to finally talk to you. I was at your YC demo day, alumni demo day.
Speaker 1:And no one's gonna believe this, but I it's true, so I just have to say And and you were the one company that stuck out, and I was like, that company is gonna be amazing. And I wasn't I didn't even think of myself as an angel investor. Should have just been like, let me please let me invest. Anyway, it's been fantastic to watch the arc of retool and everything that you've built. So congratulations, and, thanks for joining the show.
Speaker 9:Thank you. Great to be here. Huge fan of TBPN. So Amazing. Excited to be here.
Speaker 2:It's great to have you.
Speaker 1:Can you give us a little update on the company, of, define the different eras, what the product is, and where it's going? I know, obviously, we're in a period of transformation with artificial intelligence. I'm sure we'll go into all of that. But what has the bread and butter been for the last couple years?
Speaker 9:Yeah. So for the first few years of retool retools like Legos for Code. Yeah. Which is we allow you to build a sort of higher level building blocks, and you can code faster. You could see you could piece them together, and you can build software.
Speaker 9:Yeah. And the weird thing about retool was that we always focused on this category that we called internal software, which is extremely not sexy because no one ever thinks about internal tools. But surprisingly, it's something like 50 to 60% of all the software in the world is actually internal facing. Yep. And no one thinks about it, actually.
Speaker 9:And so that's where Rutal started. The idea behind that
Speaker 2:And and engineers are not like, oh, I wanna work on the internal tools. It's typically like, you know, you put the engineer that's not cracked, you know, and Yeah. Yeah. Be like, yeah. Just focus on this
Speaker 1:this dashboard. Just, like, grinding away. But yeah. I mean, this was the big we were talking to Joe Weisenthal about this with the the question of, like, meta training llama. And there's a bunch of places where LLMs will will instantiate themselves in consumer facing products across meta, but, also, they have a massive amount of internal tooling that can benefit from from AI.
Speaker 1:And so, you know, not having to fork over endless boatloads of cash to, you know, to just check, is this does this have profanity in it? 1,000,000,000 times a second. Right? It's like that could be a very big, OpenAI bill if they don't have train that internally. And so, yeah, I think the the the the the the dark clouds over the internal tooling world is something that most people might not be aware of, but you've obviously been living in that.
Speaker 1:So talk to us about the growth of the company. What's the mix? Is this all enterprise driven? Is there small and medium businesses that benefit from retool? What's the bread and butter on the customer?
Speaker 2:If you look at the home page, it's like Every company. The logos, it's like, oh, so you guys work with every company?
Speaker 1:Every company.
Speaker 9:Yeah. It's pretty cool. I mean, we work from companies ranging from the US Army to the US Navy Wow. The state of Utah, all the way to small startups, 2% companies, 5% companies.
Speaker 2:So You don't get put on the American dynamism market maps. I'm gonna start to Yeah.
Speaker 1:Yeah. Yeah. We gotta put them on the American Some
Speaker 2:respect on retool
Speaker 1:his name. Yes.
Speaker 2:That is the backbone of the US military.
Speaker 1:No. Seriously. I mean, there's there's internal software everywhere. But, yeah, may maybe it'd be good to shift into kind of, like, the AI moment. It feels like retool was kind of vibe coding without the vibe coding meme or without the it allowed you to vibe code without the actual instantiation of you know, you're not in an IDE, but you're effectively vibe coding or building something that's, very quick.
Speaker 1:How have you been processing the the AI boom? When did you first think about implementing LLMs and other AI technology into Retool, and and how has it been going so far?
Speaker 9:Yeah. So the really cool thing about Retool is that once you have these Lego blocks built
Speaker 1:Yeah.
Speaker 9:You could actually give them to AIs to actually use, actually, which is really interesting.
Speaker 6:Yep. What
Speaker 9:you believe is missing now, which is today, if you look at how many dollars have been invested in The US in AI
Speaker 1:Mhmm.
Speaker 10:I think it's around
Speaker 9:a trillion, maybe a trillion and a half or so. But if you actually look at how much revenue there is from all AI products across all companies
Speaker 1:Mhmm.
Speaker 9:I think it's, like, $2,030,000,000,000. Mhmm. That's pretty crappy ROI. That's 3% ROI.
Speaker 1:That's terrible.
Speaker 9:And the question is, is it all a bubble? Yeah. We figure out some use case for this LLM beyond just chat?
Speaker 1:Yep.
Speaker 10:And if you look
Speaker 9:at where we are today in the consumer market and the enterprise market, it pretty much is all just chat. Mhmm. I think if you look at that $2,030,000,000,000 dollars of revenue, I wanna say something like 80% of it is chat GBT revenue plus cloud revenue. Yep. Which is awesome.
Speaker 6:Yep.
Speaker 9:But chat is pretty limited. I mean, it's tracking to grow 30 x from where we are now. It's hard to say. I think probably no is the answer. And so what we think is really missing is a way to actually leverage and use LLMs to actually go actually go automate labor.
Speaker 1:Mhmm.
Speaker 9:And the weird thing about this is that LLMs are actually plenty smart already. If you know, I use chat a lot. I'm sure you you two both use chat quite a bit as well. It's basically AGI at this point. I mean
Speaker 1:Yeah.
Speaker 9:It was the classic Turing test thing of, you know, Oh, yeah.
Speaker 1:We blew past that.
Speaker 9:Way past that.
Speaker 1:Yeah.
Speaker 9:And yet, AI is not doing anything yet. Yeah. It feels like this big disconnect.
Speaker 1:Totally.
Speaker 9:What we're here to really solve is can we actually allow AIs or LMs to actually do things in your business? Yeah. So right now, it's just chatting back and forth. Can we actually allow it? Can we allow the US Navy, for example, to say, hey.
Speaker 9:It's not just using ChatGPT to answer some questions, but actually ChatGPT actually, or LLMs, actually do things in the US Navy, whether it's approving orders, whether it's approving plans, whatever it might be. That I think is the next frontier for AI. It's AI that actually does things. I think we're almost there,
Speaker 1:which is
Speaker 9:pretty cool. That's what we're working on.
Speaker 5:You Super exciting.
Speaker 1:Help me work through this question of there's, like, this AGI, ASI narrative. And then, like, how does that not destroy every software company? Because I I I'm seeing MCP servers spun up left and right. And my question keeps being, like, if the AI is so smart, why does it need a server? Why can't it just use HTML and UI like any other human?
Speaker 1:Right? At a certain point, is it gonna is is there is there a world where I say, I wanna sell a T shirt online, and instead of spinning up a Shopify store, it just writes payment interop code. It doesn't even use Stripe. It just builds Stripe from scratch at a moment's notice if it's so smart, and it just works for a billion human hours added up at 160 IQ. And it just builds me Stripe for this one t shirt that I want to sell.
Speaker 1:That feels like it aligns
Speaker 2:with
Speaker 1:That's
Speaker 2:also maybe not the best example of the regulatory component. But we also had Steven on, co founder and CEO of Lambda Labs. His point of view was broadly that you're just going to generate the software that you And you might generate 500. And I'm sure this is stuff that Retool's already doing to some degree. Generate a bunch of different versions of what a tool could look like Yep.
Speaker 2:Rank them, allow you to sort of try
Speaker 1:Yeah. Yeah.
Speaker 2:Different versions of it before landing on
Speaker 1:So there's like this one there's this one world where, like, having retool primitives that LLMs can interact with is amazing in terms of, like, making sure that there's robust performance and everything gets up to speed really quickly. At the same time, you know, in the really long term, do we even need these primitives, and can we just do everything from scratch? What's kind of your long term view of how this plays out?
Speaker 9:So this is, I think, a secret that we've actually discovered is in what cases do you want determinism versus what cases do you not want determinism?
Speaker 1:That's interesting.
Speaker 9:And we actually just announced yesterday that, we have automated a 30,000,000 of work Mhmm. For our customers over the past twelve months. If you divide out the math, that's around 70,000.
Speaker 6:Sorry. It's a lot of hours.
Speaker 9:Yeah. I think the secret is exactly what both of you just pointed out there, which is in what cases do you want AI and what cases do you not want AI? Mhmm. And to give an example, OpenAI has a product called operator.
Speaker 1:Yep.
Speaker 9:It's agent like thing that does things on your computer.
Speaker 1:Yep.
Speaker 9:And actually for consumer use cases, Operator is really good. But what Opera does is basically LM with one tool, and that one tool is use the computer. Yep. And if you want to go buy a, shirt, if you want to go buy a pair of socks, that agent is really good. It you know, what it'll do is you say, hey.
Speaker 9:I want, you know, socks, size medium, in navy blue color. It will open up the browser. It'll Google. I'll find the sock. I'll buy it.
Speaker 9:I'll use a credit card. It's done. And that's fully nondeterministic. You know? It's kinda making it up on the spot, and that's pretty good for a consumer kit use case.
Speaker 9:Mhmm. Whereas for a enterprise use case or a federal use case, for example, you actually don't wanna do that. And so I'll give you an example. One of our customers, a large company, actually uses Retul for employee onboarding. And so what they say is, hey.
Speaker 9:Every time a new employee starts, you gotta go do all these tasks. Maybe you have to go send them a laptop. You gotta send them a key fob. You wanna do this. You wanna do that.
Speaker 9:Whatever. And if you ask operator to go do that, operator says, I got one tool, and it's web search.
Speaker 6:Mhmm.
Speaker 9:So how do I onboard an employee? I'm gonna open my browser. I'm gonna Google. How do I onboard employees? You can find a WikiHow article.
Speaker 9:It reads the WikiHow article. It's like That's not at all what you want. Business has very specific ways of onboarding an employee. Yeah. And the AI actually calling those specific things because you actually don't want it reinventing the wheel all
Speaker 1:the time. And a lot of those might be gated and private and maybe not even on the open web and also very high risk from a regulatory perspective even.
Speaker 9:So you don't actually want AI reinventing it all.
Speaker 2:Sure.
Speaker 9:So that's kind of what we mean by the building blocks. This you almost give AI these building blocks, these tools, these MCP servers
Speaker 2:Mhmm.
Speaker 9:And then have AIs call them as opposed to AI reinventing it all the time. Yeah. Because, yeah, you don't actually want AI rewriting your security policy, or how do I send laptops to employees every day? Instead, you wanted to say, hey. Oh, let me think.
Speaker 9:There's a big storm happening in the Southeast. So for me to get the laptop in on time, have to ship via express, which is ground. That's something you want the AI reinventing and reasoning about, but you don't actually want the AI reinventing. You know? Do I use FedEx today, or do I use UPS?
Speaker 9:What do I feel?
Speaker 5:Yep. You
Speaker 9:know? You know, you have a contract with FedEx. You wanna use the FedEx one.
Speaker 1:So Sure. Sure. Sure. Yeah. And that's something that, like, a good office manager, good person in HR who's onboarding would actually think about and reason about, and you could inject that with a reasoning LLM.
Speaker 1:That makes a ton of sense. Talk to me about the evolution of the business model, consumption versus seat based pricing. And then going into the future, are we gonna be looking at outcome based pricing like what we're seeing Mark Benioff talk about in terms of Salesforce, where it's more like resolution based? You want a job done. Our agents, our tools can get that done, and you're gonna pay for results.
Speaker 1:Yeah. So this is a fun this is,
Speaker 9:I think, a dirty secret too is that I think outcome based pricing is basically designed to rip customers off.
Speaker 1:Okay.
Speaker 9:The reason why I believe that is when Mark Benioff tries to charge you per outcome, he's like, well, what's the value of what I deliver? And great. Pay me that. Whereas in reality, it costs a lot less for him to actually go deliver that value.
Speaker 1:I mean, that's why Software, we want 90% margins. Come on. What do you mean against high margins? Well, for us Yeah. Hear you.
Speaker 2:Yeah. No. It it creates a but isn't couldn't you argue with SAS? The SAS vendors basically trying to charge as much as possible without the person saying, oh, we're gonna build this ourselves or, oh, that's Good. That's interesting.
Speaker 2:You know, we can just hire two more people to do this. You know? It's always this dance between you wanna be you know companies should be trying to capture all the value that they create. Right?
Speaker 9:Yeah. Because no one would buy a product.
Speaker 3:Yeah.
Speaker 9:So the way that we're pricing is pretty interesting, which is that we price based on inputs, which is we just say our agents work for $3 an hour, and that's it. You want a smarter agent, you can hire smarter agents. So, actually, that $3 agent is a deep seek agent, so it's a you it's like a Chinese agent for $3 an hour. You could go buy o three, which is a, quite smart, probably the smartest right now, reasoning agent. I think that's something like maybe a hundred $20 an hour or something.
Speaker 9:So it basically depends on what kind of task do
Speaker 6:you wanna do. If you wanna do
Speaker 9:a simple task, actually, DeepSeek is quite good at that. At that $3 an hour, you're getting a really good deal compared to hiring human labor. Whereas if you want something, you know, really knowledge worky or something really nuanced done, maybe o three is better, actually. But I think the innovation here is that we are charging on a per runtime hour basis. What we've discovered is that an hour of runtime for o three, for example, is actually worth something like forty, fifty hours of human labor.
Speaker 9:Wow. You've tried
Speaker 3:to at
Speaker 9:least do it in an hour, I mean, I probably could have been doing it, honestly.
Speaker 1:So
Speaker 9:that I think is really cool. And then what happens is you actually look at how much the customer pays per task actually completed. If you compare, you put our pricing to Salesforce's, for example, per ticket resolved pricing, pricing ends up something like 95% cheaper, which is really, really cool. I mean, it'd like if AWS charged you not on compute or storage. If they charged you on how much money does your app make?
Speaker 9:Let's charge 90% of that. That's a total rip off. Right? So that's kinda how
Speaker 10:we think. We're almost, you
Speaker 9:know, we're trying to be almost like an a AWS of labor, if you will.
Speaker 2:So Yeah. How do you evaluate as the businesses have evolved? I'm I'm sure people early on, it was pretty easy to communicate even to investors. Hey, people spend a lot of time and energy and engineering resources on internal tools. We can be a big company just doing this.
Speaker 2:Now I imagine as you look at the business evolving, you're like threatening a lot of different categories of software because you're saying, we're gonna enable teams to more easily make the decision, do we buy this or build it ourselves? And like maybe retool is like some middle ground. I don't know if that's the right way to think of it. But how do you evaluate kind of the scale of the opportunity now?
Speaker 9:So the internal goal that we have, and it's pretty early, but our internal goal is to go automate the equivalent of, if not actually, 10% of the labor in The US by 2030 is our goal. The idea is Modest. Hell.
Speaker 1:Let's go. What an ambitious goal.
Speaker 2:I love it. I love it.
Speaker 1:It's amazing.
Speaker 2:So here's the TAM.
Speaker 1:Yeah.
Speaker 2:US labor.
Speaker 1:1010%.
Speaker 9:Yeah. Actually, the cool thing is we're on track, actually.
Speaker 1:That's amazing.
Speaker 9:Over the last few months, if you draw that now Yeah. Twenty, thirty is a long time away, we draw the line out. Comes to, I think, set, actually.
Speaker 1:That's incredible.
Speaker 9:It's pretty cool. But a 30,000,000 hours automated is no joke.
Speaker 1:So Yeah. That's serious.
Speaker 2:Yeah. That is absolutely wild. Well, fifteen minutes was not enough time.
Speaker 1:We have three more minutes.
Speaker 3:We have
Speaker 2:three more minutes.
Speaker 1:Next guest at 01:20. Have one more question. Talk to me about the knockout drag out fight happening in the foundation model space. You mentioned Deepsea get $3 an hour, versus o three at a hundred and $20 an hour. How do the other Foundation Labs compare?
Speaker 1:What do you do to benchmark them internally? Are the benchmarks cooked? What's the take on Meta and LAMA? There seem to be a ton of energy there. In terms of your use case, having an open source model that you can inference for cheap or at cost seems incredible, and yet they've it seems like they're they're a little bit behind on the reasoning models.
Speaker 1:Are you optimistic there? Are you kind of model agnostic? What's your overall take on the foundation model space?
Speaker 9:It's pretty cool to see this play out, especially with our customers because yeah. So for us, we're theoretically agnostic. You can use whatever model you want. Sure. But it's really cool seeing what customers prefer, actually.
Speaker 9:Yeah. And, so for example, with a, you know, large federal, customer, you might think, oh, you know, actually, on prem is really important, and so they actually prefer to use LAMA, you know, or something like that.
Speaker 1:Sure.
Speaker 9:That's not the case. Actually, you know, OpenAI has been selling a lot of OpenAI, it turns out. Actually, there's a lot of traction even in sort of big federal agencies for something like OpenAI, and they actually have contracts already. And so they plug in the retool and just works.
Speaker 1:That makes sense. I was
Speaker 9:you know, I think I I would have thought if you asked me three years ago, is the federal government happy giving, all their data to, you know, LM? No. Hell no. It seems very unlikely, but it's happening, which is really cool. But you also see this obviously happening in other countries too, where unlike whether it's Saudi Arabia or Europe with this trial, for example, people are getting kinda nervous about sending data to other countries' LLNs.
Speaker 9:And so it's gonna be cool to see how the world plays out. It's surprise it's very much not a meritocracy, is maybe one way of putting it, which is we thought that when we build our agents product with an evals framework, people would just say, let's see who does best, who does it for cheapest, and let's go. And actually, that's not the case, which is I don't know how I feel about that, honestly.
Speaker 1:Is that because, like, there are SDRs that are buying steak dinners for people and swinging them over, or is it more like there are qualitative metrics that matter more like, the nature of the contract matters matters more than just benchmark, price, etcetera, like the the quantitative metrics.
Speaker 9:There's that, but maybe another way of putting it is LLMs have been stickier than I thought, which is maybe the branding is so important or something like that.
Speaker 1:Yeah. Everyone just says, oh, it's just one line of code to swap out. But if you're used to the certain, like, what what what is undercover, they call it, like spiky intelligence. There's, like, certain models that spike in certain ways and you have expected behaviors and they yeah. They might all hallucinate at the same 3% rate, but if they hallucinate in a specific way, build around that, that type of thing.
Speaker 1:Okay.
Speaker 9:Exactly. Yes.
Speaker 1:Very cool.
Speaker 9:Cool. But excited to see how the space develops.
Speaker 1:So Well, thanks for coming on. This is fantastic. Yes. We'd love to have you back and and just chat about AI and and automating 10% of labor. Good luck.
Speaker 2:Give us access to the internal tool that's just the labor automation tracker. I'd love to just follow along.
Speaker 3:We can put it up as
Speaker 2:a ticker on the It's just like progress. No, I love the ambition and excited to follow and come back on again soon.
Speaker 1:Yeah. Thanks so much for stopping by. We'll talk to
Speaker 8:you soon.
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Speaker 1:Anyway, our next guest is from Pallet. We have Shashant in the studio. Let's bring him in and hear the latest update. How are you doing?
Speaker 4:Doing well. How are both of you?
Speaker 1:Fantastic. Would you mind kicking us off with a little introduction on yourself and the company? How are you doing?
Speaker 4:Yeah. Sure. So I'm Sushant. I'm the founder and CEO of Palette. So what we do at Palette is we're an augmented AI platform built for the logistics Mhmm.
Speaker 4:So we automate away all the back office tasks you do in running a logistics business. Right? Like data entry, quoting, appointment scheduling, tracking where your ocean containers are. So there's about, like, $11,000,000,000,000 spent on logistics, but what's surprising is about a trillion dollars of that just goes on these back office tasks that you have to do. Mhmm.
Speaker 4:So what we saw was here is, like, a real prime application of using AI agents to go and automate away these tasks. But we took an augmented AI approach in that we always have humans QA the work that agents are doing. That way, the completion of the task is near guaranteed to very high degrees of accuracy. So that's why a lot of the leading logistics providers really trust us to automate away what basically eats up about 10%
Speaker 9:of their OpEx.
Speaker 1:What's the wedge products that most companies start with? Or did you have do you think of it that way, or are you trying to sell, like, a whole suite of products on day one?
Speaker 4:So what's interesting is that, like, we built like, the technology we built is pretty generalizable, and you can customize it per use case. Yeah. So customers have started with a wide range of use cases. Like, one of the largest cold chain storage companies started off doing data entry. Another very well known shipper has started off automating how they do how they basically bid for freight with different transportation providers.
Speaker 4:Other folks have looked at using us for automating spot market quoting. Other folks have started off with appointment scheduling. So we always try to start off with one use case, but that use case could be quite distinct, and the agent we deploy could be quite distinct client by client.
Speaker 1:When you say data entry, are you migrating people from other systems primarily or Excel, Or are there still paper based workflows out there? Like, what are you actually seeing in the modern American economy? Because I feel like a lot of companies will give the pitch of, like, get off of paper. But, realistically, most companies are on Excel.
Speaker 2:Have a humanoid robot that that goes to a printer, sends something out, and then faxes
Speaker 1:Fax machines. Still still a joke. Generally,
Speaker 2:of course.
Speaker 4:Humanoid robots will come in soon. But, the the way it works actually is that, like, you go to one of these like, even the largest logistics providers in the world, right, like your DHL or your FedExes, they still get paper documents sent over email. Basically, the way it works is you have your email inbox. Mhmm. You have a bunch of emails coming in with documents attached to it.
Speaker 4:You look at it. First, you look at, is this customer someone I should be working with? Is this someone I work with or not? Then I look at the document. Does it have all the fields I want?
Speaker 4:Then I go type in literally all the fields off the document into my system, and I'm doing this, like, a hundred times every single day. Right? Like, I think the whole reason I started this company was I went to one of our customer's offices. It was, a Sunday afternoon, and, he was, like, typing in data. Like, he was typing in a hundred orders into a system on a Sunday afternoon, and he's like, I can't watch the NFL playoffs.
Speaker 4:I have to type data manually in from my inbox.
Speaker 2:That is your
Speaker 4:Sunday transportation management system, and this is how I spend my Sunday evenings. And I was like, that's crazy. Like, there has to be something better we could do What
Speaker 1:the Super Bowl of the industries that you sell into? Like, are there specific conferences where everyone gets together in terms of, like, logistics that are super important for you to sponsor? Or like what does the top of funnel look like for you? Or is it very much just like cold outreach, sales reps, dinner, steak dinners, that type of thing? Or what's the actual go to market motion?
Speaker 4:So the go to market motion, I think we we rely on a wide range of channels. We rely on conferences.
Speaker 1:Mhmm.
Speaker 4:Cold dialing, believe it or not, is still really popular.
Speaker 1:Like, when
Speaker 4:I got our first customers, like, I would actually do something even more extreme. Like, I would drive around the East Bay and Stockton, literally knock on warehouse doors, get kicked out until I could get our first customer in. So I went a bit extreme, but a lot of her SDRs still cold dial. And then there's quite a bit that comes in inbound. But I think the key the key, though, it's not just about the method of the outreach.
Speaker 4:Like, all these companies are a bit skeptical of Silicon Valley technology companies naturally. Right?
Speaker 3:Yeah. Yeah.
Speaker 8:So the
Speaker 4:thing they tell like, the thing that we've done is about 30% of pallet has come from the industry. They've worked at places like Siva. They've worked at places like Uber Freight. They've worked at places like Flexport, and they understand all the use cases in the company. So I always tell everyone on our sales team, you have to earn the right to talk about the technology.
Speaker 4:First, you need to show the customer on that cold call that you understand their business really well. That, like, if I'm talking to a freight forwarder, I understand terminology, like, how they deal with containers, what are Incoterms, and all this industry specific jargon. And if I can show that, the person on the other end is more receptive to hearing me out about how I can utilize technology. Because if you understand their business processes, you understand their workflows, they're like, this is not just another company that's in Soma that's just, like, looking at logistics terms on ChatGPT. These are people that actually just get me.
Speaker 1:How much has AI been just an accelerant of the product that you're already selling versus something that you can upsell and bolt on as like a new product?
Speaker 4:I think AI has been a huge game changer where it's like been like the primary focus of the company. Right? Like, if these foundation models didn't exist, our value prop of our product wouldn't be as strong as
Speaker 5:it is today. Mhmm.
Speaker 4:So I'd say it's very critical. And I think, in fact, one thing that's made it even more critical is what's happening with the recent tariffs. Right? Like, the volatile like, what tariffs have really created is, like, this extreme sense of, like, volatility where, like, April container volumes in Long Beach are spiking. Now they're down, and this is the courts reversed the ruling, and it gets reversed back in the last hour.
Speaker 4:So people have no idea, like, my my volumes keep going up and down, and I just can't scale up the human capacity to support it. And now AI really just allows me to kind of come in. Like, if I'm getting more emails into my inbox, I can deploy these agents, and then they can go and handle this excess volume that I would otherwise have to hire contractors really quickly to go do. So it's a huge game changer, especially with what's happening right now.
Speaker 2:What was it like selling, maybe call it a month ago when when the trade the trade war chaos was sort of peaking in some ways? And I imagine people at that point, you know, feel like they probably need better tools, but at the same time, they're like, hey, like, I can't really handle, like, onboarding. But I'm curious what what your experience was actually like.
Speaker 4:What I tell them is like, look. You're you're kind of like, right now, the way we price this is on a per success outcome. Your cost of doing this, let's say, is a dollar a house. It's gonna be a dollar 30 on pallet. So if we do what we're gonna say, you're gonna have guaranteed ROI, and you're gonna have immediate time value.
Speaker 4:The second thing is that we built a very the way our product works is we can ingest the customer's SOP and immediately spin up an agent that replicates their workflows. So we also really emphasize our time to value where we're like, look. You're not changing your tech stack. You're not changing anything about your operation. You're just sharing you just literally give us a video recording of what you do.
Speaker 4:We'll capture that, and we can then create the agent to go and automate it, and it requires minimal touch. You're not changing your system.
Speaker 2:Have to call something out. So you were at retool before Pal. Just had David on, and he said that, he it's just so funny. He's he was, like, charging on a per outcome basis Yeah. Is BS.
Speaker 2:That was his take. I don't I don't fully, you know, necessarily agree with it, but but it it's it's amazing that that you were just there. Yeah.
Speaker 4:Yeah. I love David. He used to be my former boss, but I think that's one area we'd probably disagree on. I think the outcome based pricing is is a game changer? Yeah.
Speaker 1:Interesting. Yeah. Yeah. Talk about the round. It's only been seven months since you raised your $21,000,000 series a.
Speaker 1:Now there's $27,000,000 series b. I'm sure there's a valuation markup. I don't know if you're sharing that, but it's not a huge step up in terms of total capital raise. So what does that tell us about the structure of the business and the burn rate and how you're thinking about capital consumption? You mentioned, that AI has kind of infiltrated everything.
Speaker 1:Is that a cost center now? Are you actually spending significant amounts of capital on inferencing AI models? Or is this more just we have something that's working, so we're gonna hire more, write more code, more sales reps, more dinners, more steak dinners, more champagne, more hitting the size gong when you get a big client.
Speaker 4:So, actually, we had we most of the capital from the last round, we hadn't even spent. Like, we barely touched tapped into that. Mhmm. So this round was entirely opportunistic. And the the the of the reason it came together with the folks at General Catalyst Mhmm.
Speaker 4:I knew them really well. We got to know each other really well, and they helped build Sensara.
Speaker 1:Mhmm.
Speaker 4:Sensara was probably the most iconic logistics software company. So a lot of it was Hemant, Mark, and the team there, like, really Sure. Knew how to build a big business. Mhmm. The second part was I kind of felt like the industry was going for a big change, and I just really wanted to accelerate our momentum on our product and our enterprise sales motion where what I saw was, like, I was talking to the CIO and the CEOs of companies like DHL, Kuna Nagel, Estee Lauder, and all these corporations, and they were telling me, like, we're trying to figure out how to use AI.
Speaker 4:We have no idea which model to pick for which use case. It's like these models keep evolving at a rapid pace. We need someone to be that translation layer to kind of make that happen. So that that was very obvious from the conferences that I was going to. And the second thing was when these tariffs kicked in, I was like, there's so much volatility that the the value prop of this stuff is like a no brainer, and we just really need to accelerate at this time.
Speaker 4:So that's why, like, it was completely opportunistic where we had the right partner. The market felt like it was just getting ready for the technology, and we were like, we know exactly what we wanna do. So, like, let's go and accelerate.
Speaker 1:Yeah. You mentioned some of those really big companies and kind of them not being able to decide between different models. Have you bumped into any of the large consulting firms, the McKinsey's of the world? Because we heard this narrative early in the AI boom that they're making more money than the startups and the tech companies because they're they're selling pitch decks at extremely high margin, talking about AI transformation, and then just saying, hey. If you're a big company, you can go build it.
Speaker 1:Here's here's our take on AI, or are they more of a partner that could recommend you? Is do you have any insight into how, the the big management consulting firms are are confronting the AI issue and plugging into, like, the Fortune 500?
Speaker 4:So what we've generally seen is that the consulting firms are, like, very much open to partnering with companies. I think the way that you think about it is, like, the executives of these businesses, they're like, there's a lot that's happening. There's a lot of noise. I'm not even sure who the right partner is. Do I go with a horizontal solution like Microsoft Copilot?
Speaker 4:Do I go with one of these newer players? Like, where where do I allocate my capital, and who do I take as my partner? Yep. And think of the consulting firms as a way to kind of help these companies filter out all that noise on who is the right partner for me to work with. So we very much think of them as critical partners that help us, like, earn the trust of these Fortune 500 corporations, and that's how that's how we view it.
Speaker 2:Actually, one of the as the CEO of one
Speaker 4:of those firms is actually someone who I've gotten to know well, and, he actually has helped us actually open a couple of doors.
Speaker 1:Very cool.
Speaker 2:Just gotta say you're exceptional at sales. I'm ready to sign up for Palette right now. And I have nothing to do with your
Speaker 1:industry. Logistics? Hey, we're going be shipping a lot of hats,
Speaker 9:a lot
Speaker 1:of jackets.
Speaker 2:You're like, he's like, well, a lot people think that our solution might not be right for them. But Yeah.
Speaker 1:I'm like, I want actually try. Yeah. Yeah. Yeah. Yeah.
Speaker 1:Yeah. Yeah. The the the the anti sell sells sells better than anything else for sure. Yeah. Anything else, Jordy?
Speaker 2:No. Congratulations on the new round.
Speaker 1:Yeah. This is fantastic. Progress.
Speaker 4:Yeah. I mean, grateful to be here. Yeah. One last thing, by the way, is that one thing we've we've really kind of pushed for a pallet is this concept called hybrid work. Sure.
Speaker 4:Where what we believe in is actually that, like, there's only two places you could work a pallet, either in the office or at the customer site.
Speaker 5:Oh. So it's four
Speaker 2:point engineers.
Speaker 4:Every month, we make our team spend a week at the customer site. So last month, our entire team was out in Medellin at a customer's BPO, learning their operations. Wow. And learning how they can, like like, every single aspect of their process. And I think this is really important because in a world where software costs are trending to zero.
Speaker 4:Like Mhmm. If you don't understand the customer's workflows, like, in excruciating detail, you might not be, like, building the right thing, and they don't build good documentation. You can't just go chat to BT the right answer on how does container track and trace work. You actually have to sit there, observe, learn, and then
Speaker 2:that's Yeah. I I I love that that the hiring
Speaker 6:hybrid work.
Speaker 2:The hiring process candidates like, so do you guys support a hybrid work? And you're like, of course we do, you know. Yeah. About a week out of the month, you'll be, you know, in South America. You know?
Speaker 2:And you and and you built out that function at at retool. Right? The sort of concept of this deployed engineer. Is that correct?
Speaker 4:That's correct. Yeah. Yeah. I was probably the first forward deployed engineer there.
Speaker 2:Very cool. That's cool too.
Speaker 1:I love So you're pretty bullish on the forward deployed engineering meme kind of just growing and growing in the future broadly for, like, every company, or is it specific to b to b software or something about AI automation and, something that needs to be customized? I imagine that there there have to be some organizations that they they don't need it because the product is so standardized. Right?
Speaker 4:Yeah. I think I think it's not necessary for every type of business. I think in our case, we're dealing with a company that has a lot of internal systems and processes that you
Speaker 3:have to hook on to.
Speaker 1:Yep.
Speaker 4:And we're trying to make change management. To your point, what you said earlier was like, hey. I don't wanna change my operations too much. I wanna stick with what I do. So if we wanna make change management simple, we need to hook them with your existing infrastructure.
Speaker 4:So at least for Palette, we think the forward deployed engineering function is critical to make change management easier. But there's obviously tons of companies that could succeed without that function.
Speaker 1:Yeah. Yeah. That makes a ton of sense. Well, thank you so much for stopping by.
Speaker 2:Yeah. Congrats on all the progress. You're super bullish.
Speaker 1:Yeah. We'll talk to soon. Cheers.
Speaker 4:See you all soon.
Speaker 1:Bye. How'd you sleep last night, Jordy? Put up historic numbers I actually
Speaker 2:had kind of a brutal We on so much caffeine yesterday, John. Did you track how much caffeine you were on? I was on a lot of
Speaker 1:A fair amount of caffeine. A couple of Yerba Monsieur. I only
Speaker 2:got six and a half hours of sleep, which
Speaker 6:is Almost bit more
Speaker 2:for me.
Speaker 1:And 82. What'd you put up? What was your final number? Did I beat you?
Speaker 2:No. You didn't.
Speaker 6:No. You went on my worst night,
Speaker 1:John. My
Speaker 2:worst night this month. You rocked. Could still beat you. Got an 84.
Speaker 1:Anyway, go get yourself an eight sleep, five year warranty, thirty night risk free trial, free returns, and free shipping.
Speaker 2:It's what Charles Leclerc
Speaker 1:sleeps on.
Speaker 2:That is crazy. Himself to sleep.
Speaker 1:Yeah. There's a timeline post I wanted to highlight here from Andy Nexuist because it ties into our next advertisement. Stripe put up a, a digital billboard in Grand Central, and it just says Stripe Billing, usage based billing to scale your business faster. And, and the meme is is everyone there having the same thought bubble. What a silly niche ad.
Speaker 1:Nobody at Grand Central has even heard of Stripe, let alone implemented it in prod like I have. And it's so funny, but it's so true. Like, I have actually implemented Stripe, and I'm sure so many people that go through Grand Central have. I it's So interesting takeaway from this sorry is that there is a desire for we talk about out of home, obviously, and we're going to read you an ad quick ad. Don't worry.
Speaker 1:It's coming. But I think that there are companies that feel like they need to abstract their message into even broader terms when, in fact, I think just being clear about what the product is and just knowing that, yes, implementing Stripe is a common thing at this point. And those people walk through Grand Central.
Speaker 2:Yeah. So A lot of do start yelling about their company without being clear about the product marketing
Speaker 1:effectively.
Speaker 2:Yeah. And then that can just make your life so hard because people are aware of your brand or your company. But when the moment comes that they would convert, they don't actually think of you in that context.
Speaker 1:Yeah. Like, there is a different version of this Stripe ad that is like a photo of Patrick Collison talking about increasing the GDP of the internet and stuff. And it's very high fidelity I would like to see him And I'd love that.
Speaker 2:Going like one of the classic Arnold
Speaker 1:Hitting the ziz, the ZYZZ guy. Yes, absolutely. But aside from that, it's it's Okay to just send the message up front. Just be straight up with it. Like Adquick, out of home advertising, made easy and measurable.
Speaker 1:Say goodbye to the headaches of out of home advertising. Only Adquick combines technology, out of home expertise, and data to enable efficient, seamless ad buying across the globe. And, yeah, I'm still super long on out of home advertising. You can see Stripe is doing it in Grand Central. It And it's
Speaker 2:the most fun. It's the most real. Yep. Seeing your ad on Meta X, etcetera, is cool. Seeing it in the real world?
Speaker 6:Nothing like it. Nothing like it.
Speaker 2:It's different.
Speaker 1:Did you see Nathan Fielder piloted a full Boeing seven thirty seven plane?
Speaker 10:Yes. So I
Speaker 1:didn't finish this show yet.
Speaker 2:I finished it. It was kind of a challenge because I kept falling asleep
Speaker 1:Yep.
Speaker 2:Like two thirds of the way through
Speaker 1:every episode.
Speaker 2:So I feel I've seen the entire season, but like, I actually should probably watch it back
Speaker 1:again. Awkward.
Speaker 2:But but yeah, it was it was absolutely wild and especially the context Yeah. Like the timing of when of when the show actually premiered and and the episode started rolling out. Yeah. It really is insane. Absolutely wild.
Speaker 2:But he's one of the best ever to do it and he's he's a he's a real icon. Yeah. And we have to I would like to be in the next his next season.
Speaker 1:Well, speaking of icons, there's nothing more iconic than a than a Rolex. And you can get one on Bezel. Go to getbezel.com. Your bezel concierge is available to source you any watch on the planet. Seriously, any watch.
Speaker 1:They have 26,000 luxury watches available, and so go check it out at getbezel.com. And next up, we have, Rob Toews, a good friend of mine. I grew up with him. I went to middle school and high school with him. Now he's a venture capitalist at Radical Ventures, and, he's a great columnist, and writes about artificial intelligence and has been writing about artificial intelligence since before it was cool, which is fantastic.
Speaker 1:So we'll bring in Rob and ask him a bunch of questions about artificial intelligence. How are you doing?
Speaker 2:What's going on?
Speaker 5:I'm doing great. Thanks for
Speaker 1:having me, guys. Welcome to the show. What is top of mind for you right now in AI? What's the biggest thing that you've gotten right over the last few years? I know you have that scorecard, 10 predictions, then you rank yourself.
Speaker 1:As you look back on all of your predictions, what's the big one you got right? What's the big one maybe you missed on?
Speaker 5:It's a great question. Yeah. Every year I write a column of 10 predictions for the year to come in the world of AI. And to keep myself intellectually honest, I go back at the end of the year and grade which ones were right, which ones were wrong. Let's see.
Speaker 5:So far this year, I one of the predictions I made at the end of last year was that president Trump and Elon would have a messy falling out, which would have various implications for the world of AI, which looks like it may be playing out. We'll we'll see how that all comes together.
Speaker 1:Yeah. We were reading that today because Elon is no longer a special government employee, but at the same time, messy seems like the keyword. There might be a split, but if it's clean That's true. You're you're getting a yellow yellow light.
Speaker 2:Clean split. We need satellite imagery of whether the the red Tesla is parked at the White House.
Speaker 1:If there's a Tesla still at the White House by the end of the year, I think you gotta you gotta fail that one.
Speaker 8:Yeah.
Speaker 1:But Yeah.
Speaker 5:Not messy yet, for sure.
Speaker 1:Yeah. It's interesting because it is kind of a narrative violation because half of the half of the world was like, they're gonna be together forever. They're in love. They're gonna be partner for four years, maybe for ten years. And then the other half was like, this cannot last.
Speaker 1:It's gonna blow up, and it's gonna be super messy. And it feels like right now, it might just be, hey. Back to business, sleeping in the factory. Who knows?
Speaker 5:Yeah. Well, the the the book is not yet over. So
Speaker 1:we'll Yeah. Yeah. Yeah. Anything could happen.
Speaker 6:Playing out.
Speaker 1:Anything could happen. What what about things that you feel like have played out as expected? Have you been have you been kind of reassured or shocked by the data wall, the pretraining wall, the shift of the focus to reinforcement learning, the importance of tool use going forward, the importance of prioritization versus this, like, this narrative around, oh, yeah. Just scale up the big transformer model. GPT seven will be ASI.
Speaker 1:Done.
Speaker 5:Yeah. Yeah. No. I think I think those trends are all very much playing out and it's been interesting to watch. I think it was it became increasingly clear over the course of last year that the pre training scaling laws really were plateauing.
Speaker 5:And this narrative emerged around towards the end of last year as OpenAI was increasingly teasing its reasoning models and its O series of models that there is this kind of next frontier, next vista for scaling, which was inference time compute and scaling reasoning models and so forth. And the reasoning models o one, o three, o four, you know, DeepSeq's r one, etcetera, they have been very powerful and have unlocked a lot of new capabilities and use cases and so forth. I think the jury is still out as to whether they really represent like this next massive runway for scaling that will take us many years into the future. I think it's still maybe the case that the fundamental underlying capabilities of the models are not growing as quickly as they did in the like GPT-two to GPT-three to GPT-four era. But importantly, that may not matter that much to your point, Jon, because I think the value and the activity is increasingly moving up the stack.
Speaker 5:And even if all model capabilities basically were frozen in time today and there were no further advances, there's literally trillions of dollars of economic value to be created by just figuring out how to productize these models, Yeah. Build particular solutions for particular end markets. And even the big frontier labs, OpenAI, Anthropic, etcetera, are increasingly focused further up the stack. So I think that's where more and more the action will be.
Speaker 1:Yeah. What what is your take on how the shift from pretraining to, to test time compute affects, like, data center build out, the need for these, like, hyperscale, Stargate like massive data centers? That stuff made a ton of sense in the Leopold Aschenbrenner where just gonna scale it up and get this massive data center going for, like, the biggest pretraining run ever. In in a test time inference regime, it feels like maybe it's more on demand. Maybe you don't need as many big super clusters.
Speaker 1:But am I just thinking about that incorrectly because maybe we still need the massive clusters because the rate of token production is just going, going completely parabolic?
Speaker 5:No. I think that I think that narrative conceptually holds true, and I think a key insight, as you've touched on, is inference can be done in a much more decentralized way. You don't need to have like hundreds of thousands of GPUs that are all really tightly interconnected, like Yeah. As close together physically as possible. So, in a world where more and more of the compute is inference, either like productionization of models or even inference time compute, that can be done on GPUs that are more spread out.
Speaker 5:And you don't need to have these like five gigawatt clusters. The counter narrative is the like, and I'm sure you guys got sick of like hearing Jevan's Paradox get referenced like a million times over the past
Speaker 1:few months,
Speaker 5:but I never
Speaker 1:We love Jevan's Paradox. We think it should be top middle I
Speaker 2:almost got it tattooed right here. Never forget.
Speaker 1:Forget. Never forget.
Speaker 5:Yeah. That guy has really been immortalized. Yeah. But but I I like, I think there is a narrative that even as inference becomes more important, you as compute gets cheaper and cheaper, there will still be bigger and bigger and bigger pre training runs, and so that will justify the the massive CapEx build out that we're seeing.
Speaker 1:Yeah. I've always been interested to see if there's going to be, like, the the the the, you know, the big transformer training runs, like the GPT four five train type training run-in image diffusion or in robotics or even in I was thinking about, like, you know, we we we've kind of solved chess engines, but, like, what happens if you scale that up seven more orders of magnitude? Like, just do you get some weird outlier scenario? The the question I always have is, like, when can I see it on semi analysis? When can I see it from a satellite image?
Speaker 1:Then I know that the big training run's happening. I'm wondering, are there any other areas or or or regimes of of training that aren't just predict next word that we could see a huge data center run happen? Or is it really just text is the only option for that scale? Or will we see, like, a v o four training run, and we'll be hearing about, like, oh, wow. It's at five gigawatts scale because v o four just needs that much pre training.
Speaker 5:Yeah. Yeah. So this was one of my predictions for 2025 is that we would start to see more and more scaling laws in data modalities other than text, other than And I do think that we're starting to see that play out in robotics, for instance, in biology. I mean there's like the whole premise of the whole justification for building such massive compute clusters is this notion of scaling and as you increase compute and increase data, the model gets reliably better. And we are starting to see like nascent signs of that in some of these other data modalities.
Speaker 5:I think one important challenge or constraint though is there are very few other modalities that where there is as much raw data available as there is for text. Yeah. Like there is not an Internet for robotics training data Yep. Or an Internet for biology even, and so the training data sizes can limit, like if like it doesn't make sense to have massively over parameterized model if there's just not enough training data. And so Yeah.
Speaker 5:For the foreseeable future at least, like I think the amount of training data available will cap, like, how big the biggest robotic foundation model can be, for instance, relative to the biggest general purpose language model.
Speaker 1:Do you buy the whole, like, robotics thesis of, like but but transfer learning's really effective and, like, we can totally simulate this because we have Unreal Engine. It feels like a little bit of potential cope being like, well, we know that you don't have the trove of the robotics data. And so you're coming up with something that hasn't worked in other modalities. But at the same time, Unreal Engine's pretty real, and you can imagine walking a robot around and learning a bunch of stuff if you train. So what is your take on robotics data and transfer learning, simulated learning, that type of stuff?
Speaker 5:Yeah. I mean, think there's no question that robotics foundation models are getting increasingly generalized. And like there's more and more signal that you can in fact build a general purpose robotics foundation model and you can expose it to some previously unseen scenario and it knows how to navigate the real world in a way that generalizes the same way that language models started doing a few years ago. I think this question around training data is a really, really important one and a really key one. And there are very strong opinions on both sides.
Speaker 5:Robotics experts who swear you can use simulation and synthetic data to massively scale up the training data set and use that to kind of erupt these scaling laws. And there are other folks who believe that, you know, there's really no substitute for real world data. Yeah. And you can supplement it a little bit with simulation, but like the sim to real gap is still a very real, like there's a meaningful gap there, and so you can't get that much juice out of just simulation. I think today I fall more on the side of the importance of real world data.
Speaker 5:Like I think there's just so much nuance about the real world that that isn't adequately fully captured in Unreal Engine or whatever, like, sophisticated simulation engine
Speaker 2:TMP three.
Speaker 5:You can build. But I do think as time goes on, like, that Sim De Real gap will probably close. And I would imagine it ends up looking not dissimilar from the way the autonomous vehicle industry is
Speaker 1:I was about to ask.
Speaker 5:Where, like, real world data is essential, right? And everyone remembers seeing Google's cars driving around for, you know, over a decade collecting data. But no major autonomous vehicle program today is not deeply based on simulation as well. And so I think some mix ends up being necessary. Think in robotics today, it's still you still have to be heavily weighted toward real world data.
Speaker 5:But hopefully, for the sake of leverage and technology advancements, I think simulation will end up getting better and better.
Speaker 1:Is there an analogy between the pre training to test time inference regimes in LLM training and chat models to what's happening in autonomous vehicles and the difference between pretraining on all the data and on policy training that you can do and then also add simulation on top of that. Is that is there is there an evolution of the of the kind of, like, mix of training paradigms that you would use in in AV? And I guess, like, bigger question is just, like, Waymo versus Tesla, Is there a meaningful data gap between those two companies? Because Tesla's obviously been tracking tons and tons of data, but Waymo seems to be working pretty well when you get in the back of one. And so it seems like both companies might just have enough data, and it and we might wind up in a situation where, you know, like, no one's talking about a data gap between Gemini, ChatGPT, Anthropic, Llama.
Speaker 1:They all just have all the data.
Speaker 5:Yep. Yeah. Totally. Yeah. It's it's interesting.
Speaker 5:The the way that autonomous vehicle AI stacks work today is like they're because they were kind of crafted and developed in the pre generative AI era Mhmm. They're not like these massive pre trained foundation models where they just fed all of the data in the world. Like they are much more handcrafted.
Speaker 2:Mhmm.
Speaker 5:But it is an interesting thought experiment. And there are like more younger next generation autonomous vehicle companies that are taking this approach like, let's build a foundation model for driving. And, you know, and can we do something, you know, similar to what companies like physical intelligence are trying to do for general purpose robotics. Can we just apply a model like that to autonomous vehicles?
Speaker 1:How would a startup like that get data? That feels like the hardest thing because you can't just crawl the web, right?
Speaker 5:Exactly. Yeah, yeah, yeah. It's much harder to get training data for To question on Google versus Tesla, Waymo versus Tesla, I
Speaker 1:think this
Speaker 5:is like a fascinating age old question. And you're right that Tesla has way more training data in the sense that it has this fleet of personally owned vehicles driving around. The quality of the data is lower than Waymo's though for the specific concrete reason that they don't have LiDAR as a sensor modality. So this is like the big debate that what before I got into BC I worked in autonomous vehicles and like even back then this was a debate like can you get to level four autonomy without LiDAR? And Tesla needs to believe that the answer is yes because their business model is selling cars to consumers and LiDAR is so expensive that like it would completely wreck the unit economics of selling a car to an individual.
Speaker 5:Waymo can stomach the cost of a LiDAR because their model is a robotaxi model. Honestly, think the jury is still out. Like it's Tesla folks will tell you that like we're very close to that and LiDAR was never necessary, but it's I think it's not it's not completely clear yet. Mhmm. And so in that sense, like the the dataset that Waymo has is more robust just because it's much more multimodal.
Speaker 1:Do you have do you have a sense for why LiDAR is so expensive? Like, I mean, Elon Musk was able to make rockets cheap. Like, the iPhone is cheap. Like, how can we not get LiDAR down? Like, even just to, like, a couple thousand dollars, like, that wouldn't break the Tesla paradigm.
Speaker 1:Right? But I assume that when we're talking about a LiDAR package on a Waymo, we're talking, like, 5 or 6 figures, and that breaks the model. But we historically, humans have been really good at, like, mass manufacturing expensive stuff and make it cheap. So I've always wondered about you know, Elon for a decade has been, LiDAR is doomed. We don't need it.
Speaker 1:We don't need it. But he also changes his mind. And so I wouldn't be surprised if one day he's just like, yeah. We figured out how to do it. We do we have LiDAR on the cars and, like, you know, too bad.
Speaker 1:Yeah. Yeah. Yeah.
Speaker 5:Yeah. And there there have been rumors of Tesla, like, experimenting with LiDAR here and obviously under wraps. Like, I should say that it I wouldn't it wouldn't shock me. I think it's a great question. I think the short answer is just like, it's a complicated sensor, there's a bunch of lasers, they're moving around so But to your point, the cost curve has been coming down on LiDAR.
Speaker 5:We'll continue to kind of so like the original like kind of like bucket shaped LiDAR that Bell and I made were like $64,000 a pop on like the really old school Google self driving vehicles. Now they're probably like a few thousand dollars each. You need several of them on a car, so it doesn't add But I think you're totally right. And like there are like research prototypes of LiDAR that are solid state, meaning they don't have
Speaker 1:pieces of move and that makes
Speaker 5:them a lot cheaper. And people talk about like $250 per unit LiDARs, which again are not like production ready yet. But I think you're certainly right that like the way this debate could be resolved is like it may just all converge because event in a few years lighter gets cheap enough that like Tesla can use it and everyone can use it.
Speaker 2:Yeah. I I had a buddy, a friend of the show, share recently, I'll give you some context. He said, Waymo will change the world. Walk down the street. $1,000,000 in metal sitting unused twenty hours a day on every street in America.
Speaker 2:Garages will be useless turning into storage or ADUs. Parking lots, the same thing. Street parking will be more lanes. Driving offense revenue, DMV revenue, parking tickets all go to zero. Gas stations will be worthless.
Speaker 2:Sounds insane, right? But Uber went from nothing to everywhere in a decade. Waymo might take fifteen years, but the disruption will be nuts. How do you think about the downstream, you know, assuming that you believe some of that is is real and and I think Sean makes some great points. How do you think about the investment opportunities that are downstream from ubiquitous autonomous driving?
Speaker 2:And like, is it even are there going be as many venture opportunities?
Speaker 1:AI car washing startup. Pull the autonomous vehicle in. It washes itself.
Speaker 2:It's great. That would be different than a regular
Speaker 1:drive through. No. No. Roll up. We're doing a roll up for
Speaker 2:Humanoid. Humanoid. Yeah. Let me me Yeah. So I just to me, this seems like a lot of opportunities on the real estate side is like, hey, can we turn this into more housing?
Speaker 2:Yeah.
Speaker 9:Or, you
Speaker 2:know, whatever. But
Speaker 1:Parking lots aren't cheap,
Speaker 2:though. So this is this is the context here is that Waymo monthly rides were sort of ticking up gradually
Speaker 1:Even spike a lot.
Speaker 2:Shot up to 708. And I guess they're now doing more rides than Lyft in the Bay Area as well.
Speaker 1:It's crazy.
Speaker 5:Yeah. It is it's been amazing to see how quickly Waymo has gone from like a novelty to just a a piece of the fabric of life for people in San Francisco. And like most people that I know in in the Bay Area in in San Francisco use Waymo more often than they use Uber and Lyft. And it's like quickly spreading to beyond just being a Bay Area phenomenon. Like they're now live in LA as you guys know and in Phoenix and in Austin and so forth.
Speaker 5:And, yeah, so I think it is it's remarkable to see it after all these years finally become like a real business that's scaling quickly. And I really like the excerpt from your friend. I mean, I think I very much agree with that line of thinking. And one of the things that initially attracted me to the world of autonomous vehicles back like a decade ago was this fact that it's really fascinating technology, you know, it's difficult technology being able to get a car to drive itself. But it also, once you solve it and you start scaling it, it has so many broader implications, second and third order impacts on so much of society and the economy.
Speaker 5:Like, so much of modern life is built around roads and cars Mhmm. And the way cities are designed and so forth. So I do think it will have dramatic implications on civilization, bigger picture. It will play out over a longer period of time because we're talking about the built world and it takes time to adjust. But, yeah, I think especially in cities, at least to start, especially in urban areas, It's some crazy stat, like a third of real estate in the average American city is devoted to parking.
Speaker 5:So much of that can go And it's like such an inefficient use of space. Yeah. So you can imagine cities being totally redesigned around humans rather than around cars, more pedestrian areas and so forth. You can also imagine there's a lot of people talking about the rise of exurbs, like being able to live further and further outside of urban setting because when you're commuting, don't have to be driving. Like, can imagine the entire form factor of a car changes and, you know, maybe you have a desk.
Speaker 2:Yeah. Want it to be a desk and a couch and I just wanna yeah. I mean, I've spent a bunch of time thinking about this because I kind of have a gnarly commute right now. And it will just become so much better, you know, within when I can get
Speaker 1:The majority's in Malibu. I'm in Pasadena. You've spent time in both. We'll get you back here eventually.
Speaker 5:Yep. Yep. No. Hey. Yeah.
Speaker 5:I'm excited for LA for Waymo to expand the geofence in in LA more and more.
Speaker 1:Yeah. Yeah. Well, this is fan this is fantastic. We'd love to have you back. This is a lot of fun.
Speaker 1:We could talk about 25 other topics in AI since this is the most fascinating in industry right now. We didn't even get a chance to talk about deals and stuff, but I'm sure we'll I'm sure we'll go into all that in the next time you're on.
Speaker 5:Yeah. That sounds great. Thanks for having me, guys.
Speaker 1:Thanks so much. We'll talk soon. Bye. Next up, we gotta sing. We gotta hit gongs.
Speaker 1:We got lots to do. We're gonna say are we is he gonna sing with us? It's always hard to sing with a remote guest, but maybe we should sing beforehand.
Speaker 2:Find your happy place. Your happy place.
Speaker 1:Book a wander with inspiring views, hotel granted amenities, dreamy beds, top tier cleaning, twenty four seven concierge service. It's a vacation home, but better folks. And we have the founder of Wander in the studio coming in to announce a massive round of funding. Welcome to the show. How are you doing?
Speaker 1:Get on that wide. Hit that gong. Hit that gong.
Speaker 2:Breaking the gong.
Speaker 1:Hit that gong. Welcome to the studio.
Speaker 2:John, it's great to have you here.
Speaker 3:It's it's great to be here. I was hoping that I'd be able to sing with you guys, so I'm a little I'm a little disappointed that
Speaker 1:I It's really hard with the delay. We gotta figure this out somehow, some latency mitigation or something.
Speaker 2:We also need to make it a full song Yeah. Because oftentimes, I get to place
Speaker 1:There needs be choruses and verses. Yeah. Yeah. We need to integrate the whole pitch into one big song. But congratulations.
Speaker 1:Break it down for Ideally
Speaker 2:ideally, when somebody walks into a wander for the first time, they're sort of serenaded by us.
Speaker 1:Yes. Yes.
Speaker 2:And we can sort of, you know, two, three minutes Yeah. On the whole home speaker system. So we'll work on We
Speaker 3:can make that happen for sure.
Speaker 1:That'd be great. That'd be But, yeah, give us the business update, break down the fundraising round. What are you actually spending it on? Because I know it's an asset light model, but explain how the round came together, the progress of the business.
Speaker 3:Totally. So it's a $50,000,000 series b led by QED, Fifth Wall alongside Redpoint. Logan
Speaker 1:Bartles in. Okay. Redpoint. Cooking.
Speaker 3:Yeah. Redpoint, Starwood, bunch of bunch of really incredible folks. Fantastic. And yeah, in terms of capital, it's really I mean, I'd love to get on here and act like we're gonna do some, like, crazy stuff, but it's really scale. Like, we have three core priorities, which is quality stays, quality customer support, and then quality homes.
Speaker 3:And that's really where we're where we're laser focused.
Speaker 1:Very cool. What's the key to onboarding more, wanders? I I I've seen the numbers ticking up every week as we cover you guys. What is the what's the funnel look like?
Speaker 2:Yeah. It feels like you guys have a relentless pace Yeah. And, like, a culture that really celebrates that. And, yeah, break it down.
Speaker 3:Yeah. Wander Wander definitely has a very high output culture as a as a start up. I I tell the the team that, like, culture is not, you know, like, happy hours and, you know, like, that kind of stuff. It's it's about winning. And so, you know, as as a company, we we obviously very much focus on on that idea.
Speaker 3:From from a growth perspective, there's about 300,000 Wanderworthy locations across North America and Europe, and so that's really our our target. You know, from a systems perspective, we actually built out a a fleet of AI agents that went and found each one of these homes and then enriched it with owner contact information. So we we know exactly who we're who we're going after. And so that's sort of the the focus right now is really a sales driven model, onboarding these homes onto the platform, and then automating their operations with Wander OS and delivering that great experience to customers.
Speaker 2:Talk about kind of looking back a little bit. I mostly wanna spend time looking forward, but, like, navigating through the the ZERP era, lessons learned, the, like, that era The COVID for a very different type of business model that you guys have have evolved, but I would love to hear kind of the the backstory.
Speaker 3:Yeah. I mean, when Wander started, interest rates were were pretty much zero. And so that allowed for us to have a very asset heavy model where we actually went out and bought those first few locations on balance sheet, really with the idea of solving the cold start problem of the marketplace, do things that don't scale. And so as as we grew, obviously, that had to transition. You had two crazy events happen.
Speaker 3:Once, you obviously had sort of the massive, you know, rise of interest rates. But Wander actually, at the time, had a hundred million dollar credit facility with Credit Suisse as a six month old start up, which was incredibly hard to put together. And then to have, you know, this systemically important bank, you know, explode as a as a CEO trying to scale the company was pretty pretty wild.
Speaker 2:Traumatic.
Speaker 3:That was, yeah, a pretty quick transition.
Speaker 1:Talk about, I I I feel like one of the one of the craziest things you can potentially do as an entrepreneur is go into a a category where there is a there's an active start up, even if they're scaled, that's still founder led. That always seems dangerous because they're still somewhat agile, but, obviously, you've counterpositioned the company against Airbnb. But but how, what decisions are you making? How are you thinking about maintaining differentiation and and really competing as, you know, Chesky goes on a on a on a run building out different products and different strategies, and it seems like there's more opportunity than ever to differentiate. But how do you think about it?
Speaker 3:Yeah. I mean, first of all, like, think Airbnb is a great a great company, and Brian's, like, a a incredible founder. So I have nothing nothing negative to say there. Yeah. Yeah.
Speaker 3:I think I think for Wander, you know, we we do deliver a little bit of a different experience. So our our net promoter score for q one is, you know, 85, which is, you know, phenomenal phenomenal. Thank you. Yeah. Like, true true customer love.
Speaker 1:Yeah.
Speaker 3:And that doesn't happen by accident. I mean, literally, if anyone has a negative sentiment in the concierge chat when they're, like, talking with our support
Speaker 1:Mhmm.
Speaker 3:That literally gets flagged across the entire company.
Speaker 1:Mhmm.
Speaker 3:If there's a stay that's below an eight out of 10, then they're gonna get a call from our COO. And, like, I see that feedback, and we're gonna be hyper aggressive on fixing it. Yeah. And that's across, you know, every single stay, every single home, every single customer. And so I think that that, like, relentless customer focus is just a very different model than, you know, air Airbnb.
Speaker 3:Airbnb is sort of that unmanaged marketplace Yep. Versus, you know, Wander. Like, we truly do care about the quality of our inventory, the quality of your customer experience, you know, to the point where literally, like, I will hop on the phone and, like, deal with whatever the issue is to ensure that it gets there.
Speaker 1:And Mhmm.
Speaker 3:And I know that sounds obviously, like, very unscalable, but I think that just purely from a a cultural perspective, it forces, you know, systems and automations to be built in in a time where I think that, you know, given everything that's happened in AI, you actually do have this opportunity to deliver hospitality and, like, perfection at scale. And so that's that's really where our focus is.
Speaker 1:Can you talk about advertising in this category? I was I I was we were talking to Keith Raboi about whether or not Airbnb should have an advertising product because we've seen with Uber and and is it Instacart where where Fiji Simo was where she spun up advertising? There's it's it's not quite a marketplace business, but there's an element of that. But the take rate was low, and so the advertising product was very meaningful at those companies. Keith Ruboy's take was that in the housing vacation stays market, it's less relevant because the take rate's a little higher.
Speaker 1:But do you agree with that, or do you think that there is a future where where just broadly the category is driven by advertising dollars in a meaningful way?
Speaker 3:Yeah. Mean, when you go on to, like, Booking.com as an example or even VRBO, like, will see
Speaker 1:ads. Sure. Yeah. Promoted to get to the because if I have a house and I want it rented, I'm willing to sacrifice a little bit of my margin to try and rise to the top of the rankings. Right?
Speaker 3:You'll actually even see off platform ads. So interesting. Like, very typical, like, you know, go buy this product type ad, where they're actually taking people off platform, which is pretty interesting. Like, I'm I'm sure that it's a a revenue source. I mean, these these platforms spend a ton of money on getting traffic.
Speaker 1:Yeah.
Speaker 3:From a performance marketing perspective, from just
Speaker 6:your own
Speaker 3:internal marketing, SEO, etcetera. And so it certainly makes sense to, like, try and monetize a percentage of that traffic that's never gonna end up converting Yeah. To actually, like, booking a home. But that being said, like, Keith is obviously, you know, correct and and very smart that the the take rate on vacation rentals is really high.
Speaker 1:Sure.
Speaker 3:For for Wander, our average order value is about $5,400, and you're looking at, like, an average take of about 30%.
Speaker 6:Mhmm.
Speaker 3:And so, like, for us, that would have to be a a lot of, like, random ads to, you know, like, make that up. And and, of course, the customer experience is is not not great. We're really trying to market users on on that that that booking. But, yeah, I mean, I think for a company like Airbnb, it would totally make sense, especially to the you know, on the avenue you mentioned where hosts are just paying a little bit more to promote their house to the top of the feed. You know, they have a lot of inventory and Yep.
Speaker 3:You know, sort of pulling pulling yourself out of that is, you know, difficult for your mom and pop Airbnb host.
Speaker 1:Yeah. Talk about disintermediation in the context of marketplaces and platforms. We saw the canonical example of the dog walker, which is basically just lead gen company. Because as soon as you find a good dog walker, you immediately disintermediate. And there's some things that those companies can do to prevent that.
Speaker 1:With
Speaker 9:with some
Speaker 1:rentals Wander. Much like, I'm gonna be in this town for just this week. I need this place. I'm not going to bother disintermediating. But if you fall in love with a place and you're going there every year, we've heard about people kind of trying to Google the place and find a different
Speaker 2:way to get Airbnb has an issue where property management companies list a home, and then the person that's booking the home Just Google that. Stay there and then just reach out to the property manager and do a deal off platform whereas Wander, you guys are just full stack. Right? So it's like you could even get you could look up the title or whatever and get to the owner and they'd be like, okay, if you wanna book the house,
Speaker 1:like Could still go on through Wander. But yeah. Yeah. How do you think about that? What what does that tech stack look like to prevent that or help that?
Speaker 3:Yeah. I mean, so I I do think that it is, like, the core the core risk.
Speaker 1:Mhmm. And I
Speaker 3:think that's even something that, like, has been talked about on their earnings calls is, like, direct booking websites. Mhmm. And there's ways for them to mitigate it. And you also have a ton of supply that isn't professionally managed
Speaker 9:Sure.
Speaker 3:Where the operator isn't gonna have their own direct booking website. They're just gonna list on Airbnb. And so I think they're actually, like, positioned relatively fine. You know, I think it's like a a a travel hack that not many people use. You know, for for Wander, like, I am a huge fan of, like, having complete and total control over the business and the platform that I'm building.
Speaker 3:Like, when I was a kid, my first little company, I was, like, 13, 14. We were, like, hosting Minecraft servers and whatever else. And when Minecraft got purchased by Microsoft
Speaker 1:Yeah.
Speaker 3:They rolled out a EULA, totally killed my little business, had to fire, like, four or five people. So I learned about, like, platform risk at, like, pretty, you know, pretty young age.
Speaker 2:Never again.
Speaker 1:Wow. Never again. That's a great story.
Speaker 3:And and so, you know, for Wander, I knew I wanted it to be verticalized. I knew I wanted to have, like, my own booking engine. I knew I wanted to have my own property management software. I felt like that was, like, the most durable piece. And then you also have to ask yourself, like, you know, as a space and a and a brand matures, like, that brand that brand promise, you know, actually matters.
Speaker 3:And so if people look at Wander and associate it as a a brand that says, hey. This is a quality stay. You know, you don't get replaced, quote, unquote. And so I think that's also, like, a really important piece is the underlying brand and sort of that guarantee to the customer they're gonna have a good trip. Yeah.
Speaker 2:How do you think about taste in the context of of what you're doing? Wander's always felt like a just a very it's felt like a hospitality brand as much as it's felt like a technology company. Where did that come from? From anywhere? I mean, were you were building coding coding tools historically, which, you know, I guess it's, you know, important to have good design in that category.
Speaker 2:But maybe when you started Coder, it wasn't even the case. So I'm curious where it came from besides, you know, people like Kyle crushing it.
Speaker 3:Yeah. You know, my my my journey as a founder has been, like, pretty, you know, pretty fascinating. Obviously, like, my first venture backed company, started when I was 17, 18, you know, coder, enterprise developer tools. So radically different space than, you know, travel. You know, however, like, I've always had a deep passion for, you know, design.
Speaker 3:And as a kid, I, you know, did high school online, traveled, you know, two hundred plus days a year all over the world.
Speaker 2:And you were you were you were a race car. You were driving
Speaker 3:race I was. Yeah. I really talk about it. Yeah, I used to race Formula Four.
Speaker 1:No way.
Speaker 3:Then, yeah, Formula Mazda.
Speaker 2:It was funny. John and I got lunch in Malibu like a couple years ago at this point. And I was like, oh, you want to like I knew he drove cars at a high level. Was like, do want to go drive my Ferrari or whatever? And John gets behind it, and he like you know, normally when people, like, are, like, trying out a car, you
Speaker 1:know, like, in everybody's
Speaker 2:car, they're, like, taking it to him. And he's, you know, really experiencing the nat naturally aspirated v 12.
Speaker 1:That's amazing.
Speaker 2:But I was confident. I was I was okay. I was confident that that he was gonna take care of it. So
Speaker 1:You got the experience for it. That's awesome.
Speaker 3:Yeah. It was a it was a great it was a great time. Great meal. And so so, yeah, I I think with Wander I'm candidly speaking, like, Wander is, like, my soul, but, like, as a company. Like, everything needs to be high quality.
Speaker 3:The software needs to be on point. I also don't think people realize, like, how much is truly automated with Wander. Like, at the top, you have the booking platform, but underneath is literally this property management software that's running all of the vendor communication, coordination, payouts, preventative maintenance, task tracking. Like, Wonder doesn't actually employ any local property managers. That's all just through software, the underlying coordination of vendors, which I don't think that, like, anyone fully, you know, realizes because, of course, we don't we don't market it that way.
Speaker 3:Like, you you want it to feel like a like a magic trick. And so I think, like, I think that's probably where you're seeing the design come from is that, like, if there's anything on the site that that that bothers us, like, the entire team is just obsessed over this this principle of quality.
Speaker 1:Last question for me, and we'll let you go. Are experiences or local other services a true complement to vacation bookings and and housing, or or is that kind of a round peg in a square hole?
Speaker 3:Yeah. The the way that I look at it is that I I think it's a feature of a platform. I don't think it's a platform in and of itself. And I think that you have this rare moment where you can effectively abstract the way that you connect to these service providers. So, you know, if if you were to go back in time, let's look at, like, OpenTable as an example, they had to build out integrations with the restaurants.
Speaker 3:The the restaurants had to use OpenTable to measure you know, manage their reservations or whatever else so that you could provide this online platform. You know, now with, you know, what exists from a technology perspective, you can have an AI agent call a restaurant and make the reservation for you. And so you no longer need to force these types of integrations. And so the way that I look at it from a services perspective is you just end up with this, like, agentic concierge that exists inside of your travel app that goes ahead and calls the, you know, local chef or the restaurant or whatever else. And so you don't really end up building necessarily direct integrations.
Speaker 3:You more build, an abstraction layer and a curation layer on the experiences.
Speaker 1:Makes a lot of sense. I I mean, I have one more question about SEO is really big in the early Airbnb story. Are you looking at any of these AI SEO tools? Do you think that it's important to show up in ChatGPT, for example? And are there any services or kind of best practices that you think relate to AI SEO?
Speaker 1:Or g isn't it GEO according to Andres Norwoods?
Speaker 3:Yeah. I don't know what the the acronym is, but that that sounds like a good acronym.
Speaker 1:I think called that.
Speaker 3:It's actually something that we we have started focusing on pretty intensely. Sure. So the the real key is sort of where these agents are referencing, you know, the materials and the facts that they're that they're getting. And so what you end up with is, like, you basically need a source of truth strategy
Speaker 1:Mhmm.
Speaker 3:Which is is a really interesting phenomenon. Basically, it's like, how do you how do you sort of, like, become the system of record from fact perspective or get the data onto, you know, a platform that that is is viewed as having the facts? Like, for example, ChatGPT references Wikipedia a lot.
Speaker 1:Sure.
Speaker 3:And so, like, that, candidly speaking, it's a little bit harder to, like, quote, unquote hack versus, like, traditional SEO, and there's definitely gonna be, like, an entire push. And I actually think you're gonna see a lot of value creation from platforms like Wikipedia come from the fact that they are viewed as, like, a source of truth.
Speaker 1:Very cool. Anything else, Jordy?
Speaker 2:You said there's 300,000 homes that you guys have sort of soft circled as targets. I'm assuming you're gonna get all of them in the fullness of time. What do you what do you wanna do in the next what do you wanna do by 02/1930? Do you have a do you have a target?
Speaker 3:Yeah. I mean, hopefully by 2030, we've accomplished that for sure. That's the that's the pointing to the yeah. Point pointing to the the
Speaker 2:I like I I I can't wait for there to be like five homes left and you're just following your last you're following around owners,
Speaker 1:like, in
Speaker 2:a in a helicopter being like, I gotcha. I gotcha. Give us the keys. Listen. We'll we'll we'll we'll get
Speaker 3:it done. And before I jump, one thing I wanna note is as a a sponsor of of this show, how incredible you guys are and how happy we are. And so to anyone who's watching this thinking about where to spend their ad dollars or what podcast to sponsor, I cannot encourage you more enough to work with these boys. They are incredible, and the ROI is is through the charts.
Speaker 1:Thank you.
Speaker 2:You're the man.
Speaker 1:That that really means a lot. You guys It's been really fun.
Speaker 2:Bet you guys bet early. You're one of our very first ones, and we will never forget it.
Speaker 1:Yeah. I think my favorite thing is that people might think that we ran the song by you before doing it live. We did
Speaker 2:not live.
Speaker 1:We didn't get any pushback, and I think it's all for the better if there's less oversight. So we've been having fun.
Speaker 2:It's funny. Did we create did did we
Speaker 1:create We created singing. We created jingles.
Speaker 2:We created the jingle.
Speaker 1:We created the
Speaker 9:jingle. Jingle from First
Speaker 2:Principles. We the jingle. No. We literally
Speaker 1:they didn't send us any copy. We didn't ask for any copy. We didn't ask, like, how do you do an ad read for your company? We just went to your website and just sang the first tagline on there. If it had said anything else,
Speaker 2:we wouldn't have sang that. So drilled into my brain now that I just assumed that you guys
Speaker 1:were singing Like, we just we just went to wander.com and started singing Find Your Happy Place. Well, we should take you out.
Speaker 2:I wanna make I wanna make like a a We
Speaker 6:should
Speaker 2:we should make like a radio.
Speaker 1:Oh, absolutely. Yeah.
Speaker 2:Hey, this is John and Jordy VPN.
Speaker 1:Thank you so much for coming on the show. This is fantastic. And congratulations on the
Speaker 2:series. Super excited for you and the team. And John and I are, like, basing our summer plans off of Wander Vacation Wander the World. For
Speaker 1:sure.
Speaker 2:Thank you. Congratulations.
Speaker 1:We'll talk to you soon.
Speaker 2:Bye, cheers.
Speaker 3:Thank you guys so much. Later, John.
Speaker 1:One last news item I want to hit before we get out of here. We we were talking about the poly market on Elon Musk out as Tesla CEO in 2025. It is lower than ever. It was around 20% in March, crashed down to 15%, was recently sitting around 13%, and now is down at 9%. And so, the news about Elon leaving the US government has been, you know, a bull case for him staying as Tesla CEO, which makes sense because he has a lot of work to do, a lot of opportunity between, self driving and humanoid robots robots.
Speaker 1:Who bettered around that company than Elon?
Speaker 2:And another one I've been tracking Circle IPO in 2025 is up to 94% chance. It drops to 47% chance on May 21, so about a week ago. And it has just rocketed up to 95. And I expect we'll see some news on that front.
Speaker 1:Yeah. I mean, the other markets. Since since we're in poly market mode, these are fun. I love doing these. So there's, of course, our market on how much the iPhone 17 will cost.
Speaker 1:Over a thousand dollars is a 13% chance. Over $1,500, 2 percent chance. Over $2,000 is only a 2% chance. I think Tim Cook's going to get it done. Keep iPhones cheap.
Speaker 1:Cooking. The other interesting thing is which company has the best AI model at the May? Google's running away with it at 96.5%.
Speaker 2:Yeah. That's May.
Speaker 1:You go out farther 60. Yeah. If you go out farther to the end of the year, Google's at 40%. Open a OpenAI is at 22%. XAI is at 23%.
Speaker 1:Anthropics at 7%. And so a little bit closer of a horse race towards the end of the year. What I want is I want benchmarks and evals for video models now because Yeah. V o three seems to be just completely running away with the game. It but Yeah.
Speaker 1:You know, we haven't seen what the next iteration of Sora looks like. OpenAI clearly cares about that. They've been building a product in ImageVio. In fact, the first the first consumer product from OpenAI was not a chat model. It was Dolly.
Speaker 1:Dolly two was the first was the first product they released, and then ChatGPT came out. And so it's obviously in their DNA. They're not just gonna let that they're not gonna just gonna let Google run away with it. There's so much more that you can do in video. We see this with Runway.
Speaker 1:We see this with a bunch of other products in the space. It'll be interesting to track that. But we don't really have a great benchmark or eval for that. So we'll have to get one, then we'll have to put it on Polymarket.
Speaker 3:Got it.
Speaker 1:Anyway, we will see you tomorrow. Wait. It's gonna be a light show.
Speaker 2:Yeah. Aurora.
Speaker 1:Oh, yeah.
Speaker 2:We launched a new product today. A filtered showerhead.
Speaker 6:Oh, okay.
Speaker 2:There you go. Let's Big news for Aurora.
Speaker 1:Filtering your showerhead, cleaning up
Speaker 6:the water you're bathing in.
Speaker 2:Yeah. So we What's is a product that was in the works Yeah. For a couple of years at this point.
Speaker 9:Very cool.
Speaker 2:We we were pretty strategic about when the right time would be to roll it out and made the best shower filter in the game on a bunch of different metrics, flow rate, filtration
Speaker 1:Very cool.
Speaker 2:Ergonomics. And, go check it out. I made a code or I had Brian, CEO, make a code, TBPN.
Speaker 6:Google Mormon. And
Speaker 1:Love it.
Speaker 2:Yeah, excited to see how this goes.
Speaker 1:So we will be back tomorrow. Little bit of an earlier show. I'm doing some traveling. And it'll be no guests, just me and Jordy chopping up the timeline. Take It's to be Throwback episode.
Speaker 1:Think these are some of our best.
Speaker 2:It's going be fantastic. I can't wait.
Speaker 1:So it'll probably be an hour, hour and a half, around 10AM. If you're looking to tune in, we will, of course, let everyone know. And it'll be in your RSS feeds. And if you're listening to the RSS feed, please go leave us five stars on Apple Podcasts or Spotify.
Speaker 2:Do it. Ben Ben's pointing a a gel blaster at us right now.
Speaker 1:You for watching.
Speaker 2:Take care, guys.
Speaker 1:We'll see you tomorrow. Bye.