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TBPN is a live tech talk show hosted by John Coogan and Jordi Hays, streaming weekdays from 11โ2 PT on X and YouTube, with full episodes posted to Spotify immediately after airing.
Described by The New York Times as โSilicon Valleyโs newest obsession,โ TBPN has interviewed Mark Zuckerberg, Sam Altman, Mark Cuban, and Satya Nadella. Diet TBPN delivers the best moments from each episode in under 30 minutes.
Watching the TBPN. Today is Tuesday, March 17. It's Saint Patrick's Day. We are live from the TBPN UltraDome, the temple of technology.
Speaker 2:The fortress of finance.
Speaker 1:Let me tell you about ramp.com. Time is money. Save both. Easy to use corporate cards, bill pay, accounting, and a whole lot more all in One place. Place.
Speaker 1:Let me also tell you about Shopify because that's why I have this green suit. Shopify is the commerce platform that grows with your business. Lets you sell in seconds online, in store, on mobile, on social, on marketplaces agents.
Speaker 2:Saint Patrick's Day. We are not drinking Guinness currently. You are.
Speaker 1:Yeah. Enjoy it. Very exciting. I haven't seen a lot of a lot of Saint Patrick's Day posts on the timeline. What's going on?
Speaker 1:We gotta step it up. I feel like I feel like we should have planned planned, you know, much much more. But it seems like Tyler is in the Saint Patrick's Day mood and is wearing a fantastic Saint Patrick's Day hat.
Speaker 2:Good to think
Speaker 1:of my hat. How did you wind up here? That's a nice hat. Where did that come from? Did we just have that?
Speaker 3:No. I I had this.
Speaker 1:Oh, you just Oh, you had it.
Speaker 2:Yeah. You had it handy.
Speaker 4:You just daily
Speaker 1:You you just daily drive that. Yeah. Okay. Yeah. It was in your car?
Speaker 4:Or Yeah.
Speaker 2:You wore it to the gym this morning.
Speaker 1:Okay. Oh, yeah. Cool. Yeah. Yeah.
Speaker 1:Apparently, you gotta wear green. Doris going with a little bit of a darker green today Yeah. But we will still be having fun. Let's let's pull up the linear lineup, show you who we have on the show today. Carry no interest.
Speaker 1:The anonymous poster is coming on
Speaker 5:the show at 11:45. Then Shyam Sankar from Palantir Technologies is coming for a massive book launch. We're very excited about that. And then we
Speaker 1:have a lightning round going through cybersecurity, ceramic AI. We have Gecko Robotics on the show. I don't know how familiar you are with Gecko Robotics, but it's a it's a very, very interesting company where they crawl up oil and gas infrastructure, like literally like geckos and will, like, inspect everything. There's a lot more to the business.
Speaker 2:So like humans in gecko outfits crawling around? No. Robots. Robots.
Speaker 5:Yeah.
Speaker 1:Okay. They've doing robots for a
Speaker 2:long time.
Speaker 1:Okay. Well, Linear, of course, is the system for modern software development. 70% of enterprise workspaces on Linear are using agents. So in the news, The Wall Street Journal has an article about this is a scoop from Berber Jin. We didn't have to break down the paywall because we get a copy of The Wall Street Journal every day.
Speaker 1:Although, I don't know if this particular piece made it into the print edition today. It might be in the print edition tomorrow. But has a scoop. He says, OpenAI's Fiji Simo told staff last week that the company could not afford to be distracted by side quests, main quest only. Main quest only.
Speaker 1:What is the main quest? Probably just scaling compute, but we'll get into that. So the the and and and there's a whole bunch of takes back and forth. So the company execs are are actively looking at areas to deprioritize. Of course, throughout last year, OpenAI launched a ton of different initiatives across consumer hardware.
Speaker 1:And I think a lot of people were starting to say, like, okay, like, do you wanna be fighting a battle on all these different fronts, or do you wanna just really nail consumer, nail enterprise? And now that is what they are signaling internally. So from the Wall Street Journal article, OpenAI's top executives are finalizing plans for a major strategy shift to refocus the company around coding and business users, recognizing that a do everything all at once strategy has put them on the defensive. Fiji Simo, OpenAI CEO of applications, previewed the changes to employees at an all hands meeting, telling them that top leaders, including Sam Altman and Mark Chen, were actively looking at where which areas to deprioritize. They expect to notify staff about the changes in the coming weeks.
Speaker 1:We cannot miss this moment because we are distracted by side quests. Side quests is the key term there. We really have to nail productivity in general and particularly productivity on the business front. I was listening to a podcast with the head of ChatGPT last night, and there were a
Speaker 2:bunch of things.
Speaker 1:Yeah. Nick Turley. Just talking about, like, it's such a weird product surface area because people come to it for all frontier image model for a while.
Speaker 2:Sora Yeah. We had talked about this a while back, and it seems to be confirmed now that that Sora's functionality will just end up in the in the main ChatGPT app.
Speaker 1:Yeah. Yeah. So last year, OpenAI announced a new an array of new products, including the video generator Sora, a web browser called Atlas, a new hardware device, and ecommerce features for ChatGPT. Some of those are, like, wildly different timelines for these things.
Speaker 2:Yeah. And remember, during this time Mhmm. That's when I started talking about viewing OpenAI's activities like they were a hyperscaler. Yeah. When Google launches a new product
Speaker 1:Yeah.
Speaker 2:Not you don't have to assume that, hey, it's gonna work every single time. Mhmm. And in fact, it's the exact opposite. Yep. A lot of these experiments don't end up going anywhere.
Speaker 2:It's fine. And OpenAI, like, again, you have this, like, massively scaling, you know, core business. Yep. You start to say, like, hey, let's experiment in a bunch of these different areas. Yeah.
Speaker 2:Some of them are gonna work. Some aren't. It's okay. But then, ultimately, that that creates a scenario where you're sort of like opening up maybe too many fronts, right, to the competition. Right?
Speaker 2:You're competing with Meta and Social, with Sora. You're competing with with Google in some ways. You're competing with even like the Microsofts of the in other ways. And ultimately, this just feels like, hey, let's like narrow the fronts. And I think a lot of people expected something like this
Speaker 1:Yeah.
Speaker 2:For the last few months.
Speaker 1:OTP in the chat is surfacing a very interesting old take from Ben Thompson about OpenAI should not be in the API business. And now, based on the way things look, it it it Originally, that was like, oh, wait. Well, maybe Microsoft be able to handle and scale the API demand. But API demand has been so huge, and it's allowed Anthropic to develop such a solid business there that it's undeniable that OpenAI should also be in the API business, especially Yeah.
Speaker 2:The the other side of that is, like, they clearly need to be in the harness business too Yeah. Which is why Codex is
Speaker 1:accelerating. Yeah. Yeah. And so the the the hyperscaler, you're like like, your take of, like, think about OpenAI as the new hyperscaler, I think the takes have aged extremely well, especially with recent interviews with Dylan Patel and Dor Kesh. Jensen just went on Strutecari.
Speaker 1:There's a whole bunch of new data points that show how compute constrained we are, how tricky it will be, how chips will be the key bottleneck. There's that interesting story that Tyler was recounting from Dylan Patel about how the TPU was developed by the TPU team. DeepMind didn't realize how important it was gonna be, how compute constrained they were gonna be. So the TPU went and sold it to Anthropic, and then DeepMind went back to them and was like, wait. Wait.
Speaker 1:Wait. Wait. We want those chips. Like, we need the chips. And so there's there's this, like, the hyperscaler phrase is is really, really important in that it designates not just a big consumer company or a big business.
Speaker 1:It's particularly about the ability to marshal compute at hyperscale. Right? And so I I was interested to look at, like, the history of side quests among mag seven companies, among trillion dollar tech companies, because it's all over the place. And so it's very hard to paint with a broad brush. I think right now, there's a huge narrative around, like, OpenAI was doing way too many side quests.
Speaker 1:They need to do zero side quests. And the reality is probably, like you know, we've seen this with Riley Walls joining the labs team. Like, there will be small projects. There will be acquisitions. There will be a continuum of bets.
Speaker 1:There's just a level of refocusing that's going on right now Yep. And and reorganization. And this has happened at many, many times.
Speaker 2:Yeah. I think the bet with the OpenAI Labs team is you have a a really small group of people that are focused on
Speaker 6:Mhmm.
Speaker 2:Creating or being quick to new product
Speaker 1:Yeah.
Speaker 2:Like approaches. Yeah. And but that can be, again, like a two pizza team. Yep. And that type of experimentation is gonna make more sense if the rest of the company needs to refocus on Yeah.
Speaker 2:On enterprise and the core business.
Speaker 1:Yeah. Tyler, is that is that data point you shared earlier, like, public about the size of that particular team? I didn't wanna mention it if it was The Sword team?
Speaker 3:Yeah. I think it's people know it. Okay. People know it. At one point, was very small.
Speaker 1:It was like six people
Speaker 3:Also, was gonna say, I think now that you have so many Neo Labs doing these kind of like weird, like kind of moonshot research projects, I think it also, you know, adds to this thing where like, maybe you don't actually need people internally doing those same things. Right? You can just acquire them if they work.
Speaker 1:Yep. Yeah. Did you we we gotta get into that conspiracy theory later, but there's this idea of, like, all the NeoLabs teaming up to marshal Yes. Together. We we we gotta dig into that.
Speaker 1:Anyway, history of side quests and tech. Can we paint with a broad broad brush here? You know, spoiler alert. I think the answer is no. But there are some fascinating stories.
Speaker 1:So Google has a balloon Internet play that's a serious company called Project Loon, where they they inflate balloons that go into the stratosphere, I believe. They go high up and then they deliver Internet, sort of competitor to Starlink. Some promising stuff there. They also have
Speaker 2:fiber they have the fiber play too.
Speaker 1:Fiber play. Oh, yeah. They just sold that. Yeah. People are so upset about that because, like, dealing with your ISP is one of the roughest things ever.
Speaker 1:I've I've had such a bad time with, various ISPs. Oftentimes, I would running a small business, running a start up, I would put it on my personal account, and then I would have, six lines for different people, and then one of them wouldn't get paid, and I'd get sent to collections. Yeah. It happened that happened to
Speaker 2:The only time I've ever had Yeah. Like, some some Yeah.
Speaker 1:Yeah. Bad credit.
Speaker 2:Issue Yeah. Was I I returned the Yep. The router
Speaker 1:Yeah. Yeah.
Speaker 2:And and then Yeah. They they even though I still had an active account Yeah. Just another another line Yeah. With them. They were like, you didn't return it.
Speaker 2:Yeah. We billed you. Yeah. And somehow Wait. It did.
Speaker 1:Nick, are we still paying Internet at the Jonathan Club or did we fix that? I sent you this.
Speaker 7:Canceled the plan.
Speaker 1:You did?
Speaker 8:But we haven't returned the equipment.
Speaker 1:Returned the equipment.
Speaker 2:Okay. We're definitely we're we're
Speaker 1:We're definitely getting we're definitely getting fleeced for sure. But but but that was the that that is the status quo and it was awesome when Google was just handling it with Google level But
Speaker 2:hard to sell ads against.
Speaker 1:Hard to sell ads against, so they divested, I guess. But that's not even close to the weirdest thing. The weirdest Google side project that I found is they make a contact lens that will tell you how drunk you are. I'm not kidding about this. Google, they have a project.
Speaker 1:It does more than that. It's supposed to do biometrics, and it can do a lot of different things. But one of the things that it can measure, it can measure glucose in your tears. It can measure any sort of, like, biomarker that's in your tears, which apparently is like a rich source of data. But one of them is blood alcohol level.
Speaker 1:So you can put in this contact lens from Google and it will tell you how drunk Jarvis. Am I drunk? Am I good? Am I good? Am I good to drive?
Speaker 2:You're absolutely not.
Speaker 1:He's like, you sir, you should take a Waymo tonight. Would you like me to connect you? See, this is all part of the plan. They're gonna stop people from drunk driving.
Speaker 2:Alright.
Speaker 1:Give them Waymos. It So that was a weird one. They also briefly owned Boston Dynamics. But most importantly, they bought DeepMind, was completely seen as like this wild card side project. How does that fit in?
Speaker 1:You know, it's bunch of researchers, and then it became like the the most critical thing in
Speaker 2:Amazon has taken tons of shots at journal related delivery Yep. And home security stuff. They also own Twitch, but they never really linked the site to live shopping and closed that loop.
Speaker 1:Have you thought that that's
Speaker 2:And Jassy doesn't do earnings calls
Speaker 1:on Twitch. She's good. And and a lot of the Looks Maxxing live streamers have decamped to a different platform. Let
Speaker 2:me get some what are you w's? Let me get some w's in the chat for this For
Speaker 1:this CapEx guide. Let me get some w's in the chat for the capex guide.
Speaker 2:For sure. Of course, Apple.
Speaker 1:Tried to build a car, then pulled out. Who knows where the Apple Vision Pro goes? Although, of course, I hope it continues. I'm hoping for another another Apple Vision Pro. Apple Vision Air.
Speaker 1:Just make it lighter, same screen. That's the trick. Make it like a thousand bucks, I think it'll sell. And they have a bunch of health moonshots. They've been working on non invasive glucose monitoring, which is there's some book about it called like the white whale of biotech or something.
Speaker 1:It's something that people have been working on for so long and they've never been able to crack it. They've worked on this. The best thing I think they have is that you can get a continuous glucose monitor. Thank you for the Ws in the chat, by the way. There's a lot of Ws in the chat.
Speaker 1:You can get a continuous glucose monitor that is invasive, meaning it is pricking you, it is measuring your blood and then calculating the amount of glucose in that. That's helpful for continuous glucose monitoring. There's a number of companies that do that in sort of a D to C realm levels. There's a number of biotech companies that offer that as a health care product. And you wirelessly connect that now to your Apple Watch, but they can't do it in the Apple Watch.
Speaker 1:There's always been the question about, could you just shine a light through your skin and detect the blood, the glucose in the blood? Very, very difficult to do. Potentially, always possible. Everyone thought, oh, it doesn't break the laws of physics. Lots of money spent with little to show for it, at least so far.
Speaker 1:Meta is probably the most, you know, egregious
Speaker 2:They do side quests. Side quests. They do full quests.
Speaker 1:They they create side quests in in VR. There's actually one of the popular games. It's all about doing side quests.
Speaker 6:Okay.
Speaker 1:And, yeah. I mean, they bought Oculus. They bought a bunch of VR studios. They rolled everything up. They renamed the company, spent tens of billions of dollars on what is starting to feel like less of a side quest, but still is is so early.
Speaker 1:You know, the meta Ray bans are like a success, but to the tune of millions of units, not, you know, billions of units or anything like that or hundreds of millions of units. I think the iPhone has an installed base over a billion now, and the ramp from here to there on Meta Ray Bans is gonna be long. Tesla launched a premium tequila in a lightning bolt shaped bottle. So, you know, it comes for all different next seven companies. That one's more of a of a stunt.
Speaker 1:But truly, like, side quests come in all shapes and sizes. Some are just good for morale. Like, it's just fun. We do we we do side quests here all day long. TBPN simulator, that's a side quest.
Speaker 1:We what was the other simulator? Jeremy Gaffan simulator, that was a side quest. You know, they're just fun. Some of them are good for marketing, good for attention, good for fun. Some are complete dumpster fires where you just pour money in and it just it just sucks all resources and you get nothing out.
Speaker 1:And then some reshape businesses entirely. DeepMind's a great example, and and there's certainly others in in in in the Mag seven that have really, really changed the business. So Eric
Speaker 2:Gabes said, seems like the entire Boring Company is a side quest.
Speaker 1:Yeah. It's technically a separate company, but Elon's, yeah, king of king of side quests. Although, I feels
Speaker 2:like it feels like the company that gets the least amount of attention that's not, like, on the critical path for Yeah. Many of the other Totally. Projects. Totally.
Speaker 1:Yeah. Still still a very cool idea. I mean, every time you're sitting in in traffic, it's so, like, tangible to know. Oh, like, if we just had more
Speaker 2:Yearning for the minds, basically.
Speaker 1:Yes. Yes. Yes. But very, very difficult. And so I mean, it's been, what, over a decade?
Speaker 1:And there's really, like, a very limited rollout of that technology. So but it's still cooking. I think the business is still is still going, they're working on it. So some of OpenAI's teams come from very small teams with relatively tiny compute budgets, but they get a lot of attention sometimes because of the particular product category. So Osor is a great example where small team, not huge resource investment, probably a lot of inference and training cost.
Speaker 1:But relative to Codec's 5.4, Spar, I I I don't know. I don't really actually know, like, how we're looking on the order of magnitude there.
Speaker 2:Yeah. I think I think a big yeah. A big part
Speaker 1:Superviral. Of it is like
Speaker 2:Right? And what what percentage of the team Yeah. The overall team's energy is going towards a specific product. Yeah.
Speaker 1:Yeah. Then But So the idea of experimenting quickly and then consolidating efforts even faster makes a ton of sense. Nano Banana was a big deal for Gemini, bringing image, video, and audio generation together in a single ChatGPT flow is clearly the next step. And so some of this is not it's probably not going take the form of, like, stop doing the side quests. It's like, let's fold that into the main quest because clearly, the interaction pattern of ChatGPT is is where people want to go.
Speaker 1:And so once we've done the experiment, it works. Put it all together. Dylan Patel and Dorcache said the TAM for GPT 5.4 was north of a $100,000,000,000, which is crazy for a bunch of reasons. I mean, it's a huge market that was created in just a few years. The enterprise opportunity in general is crystal clear for anyone involved.
Speaker 1:At the same time, you still do need to experiment to make sure you are early to create the next breakout product experience. And so with that backdrop, OpenAI Labs and being more efficient with the shots on goal makes a lot of sense. I still think that the overall narrative around AI, just in broadly this year, will be about the main quest, which I see as, like, compute scaling.
Speaker 8:Yep.
Speaker 1:And so raise the money, do the deals, grow the capacity. I was thinking back to Travis on the show saying, if you're doing something and it's easy, it's not valuable. The key is, if money matters, which I think we say, we would say it does, especially in certain categories, he was probably thinking of ride sharing where there was a capital war and AI compute where there's also a capital war. You need to be the best
Speaker 2:in the world at it. Yeah. Yeah. And I think I think overall, there's real competition now. Yeah.
Speaker 2:It's intense. Right? You have you have OpenAI and Thropic
Speaker 1:Yeah.
Speaker 2:At the frontier pushing very, very hard. But the the the other side of this for OpenAI is like all the Yes. There was a ton of stuff that was announced last year. There was there was hardware, Sora, etcetera. But Sam was running around doing all these different mega deals for the compute side Yeah.
Speaker 2:That now serve the main quest. And so the positioning is actually great.
Speaker 1:Yeah. Yeah. Let me tell you about Labelbox. RL environments, voice, robotics, evals, and expert human data. Labelbox is the data factory behind the world's leading AI teams.
Speaker 1:And let me also tell you about Gemini 3.1 Pro. With a more capable baseline, it's great for super complex tasks like visualizing difficult concepts, synthesizing data into a single view, or bringing creative projects to life. And speaking of Google, Google Capital bloke, the longtime Google bull, says, so Anthropic and OpenAI are going to just give this consumer market to Google. And I don't know how how I mean, yes. They're like like, OpenAI reportedly planning to shift the strategy to refocus around business users and vibe coders.
Speaker 1:There's a lot of stuff going on there. Listening to some of the data around the retention curve of the various products, like, I don't think that they're giving up on consumer at all. That seems like sort of an odd read. It will be interesting to see the next iteration of Google's consumer surface area because they have AI search overviews, AI mode, the Gemini app. It's in Gmail.
Speaker 1:But there's clearly some, like, UI fighting going on. I I just it's so funny when you're in Chrome and you have the ability to open one Ask Gemini panel in Chrome, and then you can open a second Ask Gemini panel in Gmail, the app. And so both at the web layer and the browser layer, you have two you have two chat boxes that look exactly the same and do the same thing. This feels like a very much like a v one of what they will do here. So there's certainly an opportunity for Google to to to reintroduce the AI features, make them more tightly aligned with what people are actually doing.
Speaker 1:And you have to imagine that a lot of the progress they're making on agent decoding can then come to the Gemini surface area in consumer overall.
Speaker 2:Yeah. And and we can pull up this chart that Sam showed Yeah. Of codex usage. This is like the the again, this is like what I think is informing, like, the battle with Anthropic as well as, like, this chart
Speaker 1:Mhmm.
Speaker 2:Is gonna inform the strategy shift, which is like, hey, we can run we can we can take revenue into the hundreds of billions of dollars with the current products that we have. Let's focus on them and make the best possible products.
Speaker 1:Dean Ball is using Codex? Is that what he's saying? Just hit a personal record for single coding agent session of a little under ten hours. GPT 5.4x high in the Codex app unsurprisingly. No flashy app, just really complicated economic research prompt.
Speaker 1:At this point, most of my prompts do not stress these agents all that much. To be clear, this is ten hours of continuous work. I have meaningfully exceeded ten hours if we include periods when the agent was waiting for jobs to complete. So what does this mean? Is he talking about like firing off one prompt and coming back ten hours later or just going back and forth?
Speaker 1:Because he says agent session.
Speaker 2:I I think he's saying think he's talking about firing off one ten hour.
Speaker 1:Yeah. What what do you
Speaker 3:I thought he meant he's in Codex Codex. Like the, you know, terminal. Yeah. And he's in there for ten hours.
Speaker 1:Locked in. That's amazing. Oh, wow. Yeah. Yeah.
Speaker 1:It it is very interesting to see I mean, Doug O'Laughlin was talking about how how he moved a lot of his research. He wasn't building soft
Speaker 4:No.
Speaker 2:He's saying I it's not a flashy app that he's building just a really complicated economic research prompt. To be clear, this is ten hours of continuous work. I've meaningfully exceeded ten
Speaker 1:So so so I think what he's doing is he's is he's asking, like, okay. Pull together all the census data about jobs. Now go and pull together all the economic indicators. Pull out pull together all the inflation.
Speaker 2:Somebody somebody asked how much time was back how much was back and forth was going on versus the agent spending time on its own? He said zero back and forth. So this is ten hours of the agent just like cooking.
Speaker 1:Wow. That's really crazy. What is this prompt? It must have been pretty long, I imagine. That's crazy.
Speaker 1:It fit within his current rate limits on the $200 OpenAI tier in the Codex app where rate limits are currently higher than usual. They outperform expectations. The prompt was primarily about the political economy of central government transfers to Indian states slash union territories. I think I think, honestly, this matched my expectations of what five point four can do when it's properly prompted. What does it get stuck on?
Speaker 1:It encountered a bunch of problems along the way but doesn't appear to have gotten stuck for too long on any one thing. It was just a step y process, so it just kept working. That is absolutely crazy.
Speaker 2:In other news, OpenAI is forming a joint venture with TPG, Advent International, Bain Capital, and Brookfield Asset Management.
Speaker 1:This is great news.
Speaker 2:To form a joint venture with enterprise products across the firm's portfolio companies and beyond the proposed deals
Speaker 1:Fogo Chao. Bain Capital owns Fogo de Chao, the Brazilian steakhouse.
Speaker 2:We're gonna could take Apple Pay.
Speaker 1:Oh, yeah. We got cooked on that. That would be a good I don't know. Maybe they deliberately don't. I wonder I wonder if Apple Pay is expensive for them.
Speaker 1:And then this is actually a cost consideration. Fiji Simo said, this news came out a little bit earlier than we planned. We're excited to be building a deployment arm and we'll share more details soon. Companies have a ton of urgency to deploy AI in their organizations, and we're sprinting to meet that demand. More than 1,000,000 businesses run on OpenAI products.
Speaker 1:Codex is now at 2,000,000 weekly active users, up nearly four x since the start of the year. API usage jumped 20% during the week after GPT 5.4 Watch launched in Frontier, which launched last month to help enterprises build, deploy and manage AI coworkers that can do real work, has way more demand than we can handle. That's why we launched Frontier Alliances so we leverage our ecosystem of partners and scale and, to scale. And that is also why we're launching a dedicated deployment arm tasked with embedding forward deployed engineers deeply inside enterprises. This project has been in the works with our investors and alliance partners since last December, and we are grateful for them and their partnership.
Speaker 1:We're still early, but the speed of adoption is a clear signal of where this is headed. We're excited to not just be building these technologies, but also building many ways for companies to deploy them and get impact. Interesting. Because there there was a big there was a big talk for a long time about, like, the the next gen private equity firm will will buy businesses and, like, deploy AI inside them. And this feels like, well, maybe that works at the mid market private equity level, but you don't necessarily need a new AI native private equity firm because Bain Capital
Speaker 2:Yeah. That's what I've always said. Like, traditional private equity is not gonna just be like, oh, we'll we'll figure out AI in like Mhmm. 2030. Mhmm.
Speaker 2:It's not really on a road map right They're working as hard as they can to implement it. Question is like, will will traditional private equity be able to basically, like, roll out and unlock the value Mhmm. Of AI better than some of these, like, AI native, more, like, venture oriented roll ups?
Speaker 1:Okay. So 5.4 Mini announced today. So different sizes of models don't count as side quests. These are main quest. Right?
Speaker 1:These are main quest aligned. But what is the pitch for a mini or a nano model?
Speaker 3:Yeah. I mean, so this is just like I I think someone ran some evals and mini models are like equivalent to g p d five when that first came out.
Speaker 1:Okay. But it's
Speaker 3:just way cheaper. Right?
Speaker 1:So if
Speaker 3:you're like, chance sensitive, if you're running a ton a ton of queries Yeah. But you they don't need to be like the max, you know, intelligence, then you just use this.
Speaker 1:So cheaper, but also faster?
Speaker 3:Yes. Probably. Yes.
Speaker 1:And potentially runs on older hardware?
Speaker 3:I think that's unclear.
Speaker 1:This is bullish for our vintage Neo Cloud. Yeah. The oldest GPUs
Speaker 2:Yeah. This was a great idea yesterday.
Speaker 1:Yeah. Tis for my gaming
Speaker 2:love people love classic cars Yeah. Why not classic GPUs?
Speaker 3:Yeah. Mean, it's like doesn't I like, how small the model is doesn't actually, like, matter on
Speaker 2:imagine running your vintage Neo Cloud with with wood fired hearth Yeah. Powering it all.
Speaker 1:Yeah. But I mean you have to imagine that what what what's after Blackwell? What's the like well, the one that announced today? Vera Rubin? Rubin.
Speaker 1:Rubin. Rubin. Like, you have to imagine that that there will be models that only run on Rubin and need to be sort of re architected to run on Hopper, I would imagine. Do you
Speaker 3:Sure. I mean, I I think there there's like small performance boost that you can get.
Speaker 1:Or it might just be slower.
Speaker 3:But but you can always you can run any model on, like, any hardware. It's just like it could be, like, way, way slower because
Speaker 1:you have Way, way slower. The memory is on a fleet of a one hundreds when you could just be on, like, like, a smaller rack of Rubens. Interesting. Well, that's exciting.
Speaker 2:Cal sheet came out with a $1,000,000,000
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Speaker 1:You can win $1,000,000,000 for entering this competition?
Speaker 2:I haven't read the fine print Okay. But it seem it is positioned that way. Vinny says, it would be hilarious if Citadel built out a team to attempt to financially impair their competitors' SIG through this. So SIG Parametrics Okay. From Susquehanna Yeah.
Speaker 2:Yeah. Is is basically like the financial backer for this promotion. So if Citadel can figure this out, they could leave their competitor with a $1,000,000,000 bill. Yes. Obviously, trying to trying to nail the perfect bracket
Speaker 4:Yes.
Speaker 2:Is functionally impossible. But So
Speaker 1:it never been this past. So I 10 grip. So there are nine quintillion possible brackets. So the odds of a perfect bracket are one in nine quintillion. And I believe that they are capping
Speaker 2:I like my
Speaker 1:odds. I like my odds. And I believe that they are capping entries at 10,000,000 entries. So if you math that out and then you take some insurance out, like, you should be able to, you know, hedge hedge hedge out any of the risk. If you are good and you don't suffer from any skill issues, you can basically with strong basketball knowledge, if you have ball knowledge, apparently, you can get the odds down to, like, one in 10,000,000,000.
Speaker 1:And at that, you know, not from nine quintillion to 10,000,000,000
Speaker 3:if you have nine quintillion, you can sort them by how likely they are. Take the top 10,000,000. That's, you know maybe there's some kind of power law thing here where we actually
Speaker 1:Yes. You're getting your odds are getting close. You're getting close. But I think I think if there's 10,000,000 if there's 10,000,000 if it like, assume that there's 10,000,000,000 reasonable brackets, and and out of the nine quintillion. So you're you're discarding, like, all the craziest upsets to get to that 10,000,000,000.
Speaker 1:You already ranked it and narrowed it down by, like, what, six orders of magnitude or something. And then you're submitting the top 10,000,000 of those 10,000,000,000, like, your odds of winning are still one in a thousand.
Speaker 3:You can make a thousand accounts.
Speaker 1:And that's what no. No. No. No. I think that they are capping the entire campaign to 10 the first 10,000,000 entries.
Speaker 1:So you have to be on Kalshi and you have
Speaker 2:My open claw already submitted
Speaker 1:Nine quintillion. The
Speaker 2:SEC prepares proposal to eliminate quarterly reporting. If you liked earnings
Speaker 1:I was thinking like,
Speaker 2:is We this bad for
Speaker 1:we like earnings days, but it's not like our whole shtick. So I think we'll be okay. We will continue soldiering on.
Speaker 2:Good Alexander says, finally, less transparency around financial results. Yeah. I don't I don't know I don't know if this is gonna be enough to to make it easier to be a public company. It's sort certainly a burden. But the kind of shareholder lawsuit risk and all that other stuff feels like a much bigger burden.
Speaker 2:What this is just gonna do is create more volatility. Right? If you're only getting
Speaker 1:Oh, sure.
Speaker 2:Updates, you know, twice a year. Yep. Can just see these like massive swings because it's like, hey, we haven't heard from this management team in in a in a formal capacity for six months. The the business could have, you know, changed wildly, you know, growth rates fluctuating, etcetera. And so, again, if you if you if you love volatility, you're probably gonna love this.
Speaker 1:So it's good for the VIX. That's bullish for VIX investors out there. It there is a world where I mean, the the typical, like, public versus private debate is that when you're private, can think in multiple years, depending on who your investors are, maybe think in decades even. Whereas, when you're in the public markets, you need to think in quarters. And so advancing that from quarterly to six months and you start putting management teams in, okay, what can we deliver that will show up in six months as opposed to what can we deliver that shows up in three months?
Speaker 1:That's twice as long. It feels like you could wind up with better run companies doing more ambitious things. I don't know. There is a bull
Speaker 2:Yeah. The the no. I mean, the other side of it is you just get more complacency. There's There's less of that. You're you're not like on the daily the daily March.
Speaker 2:You know, you finish earnings and you're like, okay. Ninety days. We gotta do this again. Like, there's no days off Yeah. Kind of thing.
Speaker 1:So are you are you an advocate for monthly report reporting?
Speaker 2:Daily. Daily earnings calls. Potentially hourly.
Speaker 1:Just put I mean, this is the this is the crypto folks. They're like, it on the blockchain. I wanna be able to see in real time the revenues of this asset stream and how things are moving around. I want full transparency at all times as an investor. I don't know.
Speaker 1:I I think that there is a there's a potential for a good outcome here, but we will have to keep an eye on it and see what actually happens. This is just a proposal. It's just the proposal isn't even we are preparing for a proposal. So the proposal hasn't happened. We're preparing for the proposal.
Speaker 1:It's an advanced
Speaker 2:idea. No.
Speaker 1:It's preliminary talk.
Speaker 2:It's an advanced idea Yeah. For a talk for early talks
Speaker 1:For
Speaker 2:early a proposal.
Speaker 1:Exactly.
Speaker 2:Around a potential proposal.
Speaker 1:You know the FDA actually works that way? ANPRMs. It's like advanced notice of proposed rule making. So they tell the industry, okay. We are thinking about making a rule.
Speaker 1:Good luck. Good luck. And then everyone's like, we're suing. Don't change the rules because I have set up my entire business for this particular set of rules. And if you change it, it's, like, completely over for me because every business is, like, set up like this.
Speaker 1:So they're, like, narrowly thread whatever regulation is there. Really quickly, let me tell you about vibe.co. We're do you see brands, b to b startups, and AI companies advertise on streaming TV, pick channels, target audiences, measure sales just like on Meta. And let me tell you about Cisco. Critical infrastructure for the AI era.
Speaker 1:Unlock seamless real time experiences, a new value with Cisco.
Speaker 2:So NVIDIA. NVIDIA. Announcing NVIDIA DLSS five and AI powered breakthrough in visual fidelity for games coming this fall.
Speaker 1:Video games are gonna get more realistic. Prepare. Over the next couple decades, this is gonna be this is it's entirely possible this happens. Continue. Let's pull up
Speaker 2:the video. Funny because when I when I see I
Speaker 1:a video wanna see a big
Speaker 2:I see the video Yeah. I my my thought was weren't video games already this realistic? But I haven't played video games besides Tetris on the nomadic
Speaker 1:They oh, yeah. Long time. So let's play GeForce RTX DLSS five. DLSS, of course, stands for deep learning super shading, something like that. DLSS.
Speaker 1:It uses deep learning. It's used AI for a long time. The whole pitch for DLSS sampling. Super sampling has has always been go from a seven twenty p render to four k. Upres.
Speaker 1:Just interpolate the pixels. This is different. You can see that the it's not just becoming higher resolution. It is becoming Gen AI. Like, there is a world where you render that at higher res.
Speaker 1:The lighting is changing. The shading is changing. The makeup, the structure of that person's face is changing, but it's still driven by the underlie why are you posting the bubbles? Because I'm ranting. I wanna see
Speaker 2:You're not the LSS five do this.
Speaker 1:Oh, yeah? But but this is what we were talking about yesterday. We were like, what will it take to get TBPN simulator photo real? I think we should try and pipe it through DLS s five. Although, who knows what this is gonna run on because NVIDIA's not shipping new graphics cards to gamers or something like that.
Speaker 1:They're delaying the next the next gaming graphics card. But I think that looks better. I don't know. People are upset. There's a community note that has yet to be accepted, but the community notes are not happy.
Speaker 1:Like, one of the community notes is just AI slop. And it's like, yeah. Like, that's in the name.
Speaker 2:It's deep
Speaker 1:learning. Like, what do think DL stands for? And also, why are we on DLSS five? Oh, because this is like a decade old technology. I don't know.
Speaker 1:People are people are very upset. One of the community knows Yeah. I think I the issue is on the environment. Is The majority of gamers
Speaker 2:Is did not ask for this. Is that the Are those the most photorealistic video games that we have today? And then they're showing how they're even getting more realistic? No. No.
Speaker 2:Yeah. They don't look they don't look They look like games that are Yeah. Like kind of like animated characters that aren't trying to be photoreal.
Speaker 1:So there's a range of games there that some of them have pretty outdated engines, pretty outdate outdated rendering pipelines. Some of them are on the latest and greatest and look very high fidelity. There's also a whole bunch of trade offs in game development around the scale of what you're building and how how many, you know, poker chips you wanna put into great graphics. If you're making some moody first person shooter that's gonna be dark and and there's gonna be lots of, you know, like, blood splattering and aliens and stuff, like, you could potentially make it ultra photoreal. But if you're trying to build, like, something like Minecraft, it's like this massive world that's interactive with a ton of different people.
Speaker 1:Like, by narrowing down the scope, like, the beauty of Minecraft was that the whole thing's voxel based, which is like these little blocks. And so Right. The so the computer can store, like, an entire world very efficiently because all it needs to say is just there's a cube there, and it's green. There's a cube there, and it's black. There's a cube there, and it's gray.
Speaker 1:What do you think?
Speaker 3:I was gonna say Minecraft is a good example of of what this can improve. Right? Yes. Go to the next post.
Speaker 1:Oh, yeah. Can pull that one up. Yes. This is what
Speaker 3:Minecraft could look
Speaker 1:like. This is what Minecraft could look like. There are there are drawbacks, obviously, because you're driving the the style transfer from the particular images from the shape. You're not reconstructing the entire image. You're just applying this, like, you know, rerendering on top of the underlying structure.
Speaker 1:And so you can get really weird outcomes like this. Of course, you can you can rerender the full scene. But if you're doing that on a per frame
Speaker 4:basis That's powerful.
Speaker 1:So right now That's
Speaker 2:powerful stuff.
Speaker 1:Basically, what we're seeing with with DLSS is is the best AI can do in ten milliseconds, I think, or something like that because it's it needs to do this. Basically, if you're running a game at 60 FPS, it needs to be able to do it 60 times per second. So you can't be waiting around for images in Chatty PT or Nano Banana to cook for a minute on each frame. It has to be real time.
Speaker 3:But also on consumer hardware. Right? Yeah. On consumer hardware. Not gonna be, like, hosting the cloud.
Speaker 3:Yeah. The whole point is that it can run-in your gaming PC.
Speaker 1:And and by that token, this feels this feels like it's not just four years behind. This feels like it's, like, one or two years behind in terms of the actual quality of the output. I'm I'm I'm actually pretty impressed. If this was if this was going back to, like, Dolly two quality, I would have expected that because Dolly two was running on a fleet of a one hundreds, h one hundreds, something like that.
Speaker 3:Sure. But I mean, underlying tech is very different. Right? Because you have, like, a very clear ground truth.
Speaker 1:Ground truth.
Speaker 3:Yeah. Right? Where you it's like, you know what's supposed to be there. You it there's this kind of upsampling.
Speaker 1:Well, so the I mean, the the the data pipeline here, the actual ground truth thing is a little bit harder for something like this. They have they probably have to use ImageGen to generate the high res versions to actually generate all that. Because previously, DLSS was really easy to train because you would just go and run the game at four k on great hardware and then go and run the exact same you would render it twice, like from the same exact game footage. So you'd have someone play the game, and on one monitor, it'd be 720p. On one, it would be four k.
Speaker 1:And then they would record both, and they would say, these are exactly the same thing. Every frame is the same. Learn how to take the 720p image and create the four k image. Uh-huh. Now they have to say, okay.
Speaker 1:Well, we have a four k image. Let's run that through a Gen AI pipeline to make it like photoreal. Do that a ton, probably on a fleet of GPUs that are not consumer hardware. Then you have your training data. Then you go run the DLSS pipeline and get this, like, style transfer algorithm that can run 60 FPS.
Speaker 3:Anyway But I mean, there have been, like, open source image models that have done, like, really good upsampling for a while now.
Speaker 1:But at this level? Or wait. Wait. Are are you talking about upresing? Or are you talking about, like I would consider this, like, re rendering using, like, something that feels like diffusion.
Speaker 1:Like, this feels like because if you look at if you look at any of these examples, it's very clear that they're not just increasing the pixel count. Like, the hair in that woman in in in that woman's image, like, like, the hair has a different shape after you turn DLSS on. It's not just higher resolution.
Speaker 3:Yeah. That's true.
Speaker 1:So, like, there has to be something, like like, that they're training on. But and I think that there probably will be very small hallucinations still if you were, like, to zoom in. Whereas that wasn't really true with, like, the the seven twenty p to four k pipeline. Like, you would get, like like, whatever the cutout of that image was that you were uprending would would remain the same, whereas this is actually, like,
Speaker 2:rerendering it, basically. Well, none of this will matter when we have a billion GPUs in space. Head over to space.
Speaker 1:Let me tell you about Phantom Cash. Fund your wallet without exchanging your middlemen and spend with the Phantom card. And let me also tell you about Okta. Okta helps you assign every AI agent a trust agency. You get the power of AI without the risk.
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Speaker 3:Okta.
Speaker 2:Take us to space, Jensen.
Speaker 1:Let's listen to Jensen talking about data centers in space.
Speaker 2:Of Nick.
Speaker 9:I'll spend very little time on this this this time. However, we're going to space. We've already been out in space. Thor is radiation approved, and we're in satellites. You do imaging from the from satellites in the future.
Speaker 9:We'll also build data centers in this in in this in space. Obviously, very complicated to do so. We have we're working with our partners on a new computer called Vera Rubin Space one, and it's gonna go out to space and start data centers out out in space. Now, of course, in space, there's no conduction. There's no convection.
Speaker 9:There's just radiation. And so we have to figure out how to cool these systems out in space. But we've got lots of great I'll spend very little time
Speaker 1:on this. Great engineers working on it. It was very funny listening to Dylan Patel talk about space data centers and saying that, like, yeah, even if you solve everything, like, it's still hard to make chips. It's just like it still comes back down to the chip bottleneck above above all else.
Speaker 3:Yeah. I mean, this is why you see Elon doing the TeraFab thing. Right?
Speaker 1:Yeah. Yeah. I I really wanna know what his plan is for that because has he put in an order for a tool, or is he gonna do Terra ASML? Like like because there's, like, the supply chain is with 10,000 companies, 10,000
Speaker 3:I mean, historically, they've they've done they've gone super, super early in the supply chain. Right? They've been, like, you know, mining stuff and
Speaker 1:Yeah. Yeah. Yeah. Yeah. I don't know.
Speaker 1:Tool making is a special special industry. Well, speaking of Dylan Patel, semi analysis was featured at NVIDIA GTC. The inference king has been crowned. NVIDIA won a massive belt. And it looks like Jensen's holding it up.
Speaker 1:He is in fact standing in front of a LED wall or projector. But a beautiful thing to see the semi analysis logo.
Speaker 4:It's all
Speaker 2:WWE. The entire world is WWE.
Speaker 1:NVIDIA Extreme co design revolutionized token cost. The GB NBL 72 is the inference king with 50 x higher performance per watt on Inference X by semi analysis. 35x lower cost. Very, very exciting. And congrats to Jensen for becoming the Inference King and winning the Inference Max Award or Inference X, as it's now known.
Speaker 1:Jensen is also confirming what we see in our GPU availability data. There is an epic scramble for compute B200, basically unavailable. Availability for GH, Grace Hopper, 200, H200 and A100 also collapsing. Low availability means high demand. And people were people were kinda going back joking about, like, he he said, like, 1,000,000,000,000 of demand or something over some period of time.
Speaker 1:And and and people were like, oh, so he's guiding down. And it's like, I guess that's where we're
Speaker 2:at. Yeah. It's hard when you're doing when you're doing cumulative Well revenue.
Speaker 1:Let's move over to a much smaller deal for Netflix and Warner Brothers and the final deal, which went to Paramount, of course. And David Zaslav is in The Wall Street Journal. First, I will tell you about Vanta. Automate compliance and security, Vanta is the leading AI trust management platform. So David Zaslav deal could deal pay could top $800,000,000 after last minute tax benefit.
Speaker 1:So Warner Brothers chief executive David Zaslav could collect more than $800,000,000 in severance and other payments after rival Paramount acquires the company. The sum of cash includes the sum includes cash and payments for options and restricted stock holdings as well as a newly adopted tax reimbursement for Zaslav in a securities disclosure filed late Monday. The total doesn't include the more than 20,000,000 he's likely to get for shares he owns outright. So he's been he's been investing as well. About 504,000,000 is due to Zaslav if the deal closes, the company said, while 47,000,000 would be triggered if he is fired or leaves under certain circumstances within a year of the close.
Speaker 1:A 116,000,000 in equity has already vested. Some 335,000,000 would only kick in if other payments trigger a 20% federal excise tax on golden parachute severance payments he receives. The ultimate value of the payout is based on tax code rules that are expected to cause it to significantly decline with the passage of time, the company said in a filing. Without the tax payment, the total would be about 667,000,000. Companies typically try to keep severance at or below three times salary and target annual bonus to avoid or minimize the tax, and investors often criticize companies for reimbursing or grossing up executives for the tax.
Speaker 1:The company said Zaslav wouldn't have to pay the excise tax if the deal closes in 2027, which would save the company costs of the tax gross up. So there's there's been a lot of back
Speaker 2:and forth. Yeah. Lot of people are are absolutely earn that? Yeah. Absolutely.
Speaker 2:Value. And the reason that I think it is warranted from a business standpoint is Mhmm. When we were sitting here last year Mhmm. Talking with someone who won't be named in the in the in the media space
Speaker 1:Yeah.
Speaker 2:Who has done deals not at this scale but in the billions of dollars. He was sitting here telling us at this very table that he didn't think there would be any bidding process Yeah. At all. Yeah. And it was just gonna land with the Ellisons Yeah.
Speaker 2:For a pretty predictable price. Yeah. And Zaslav did his job Mhmm. As CEO Mhmm. To get the best possible price for shareholders.
Speaker 2:And it's remarkable. And that is why, you know, he was able to have outsized impact. Yep. He's getting he's getting outsized compensation. Yep.
Speaker 2:He increased the enterprise value through his deal making by tens of billions of dollars. And he's getting a significant, but I think warranted payment. And if you if you don't like this, then you probably have don't like the game of business very much either.
Speaker 1:Or you think Warner Brothers Discovery should have just used like Open Claw on a Mac Mini or something to negotiate the sale. Put the whole company on eBay and let everyone bid. Just do an auction. It's actually, eBay's fees are pretty high. I think I think eBay takes, like, multiple percentage.
Speaker 2:They would take a higher fee.
Speaker 1:I think they would take a higher fee, actually. It's like a $100,000,000,000 deal. Don't they take it, like, percent? So by that, I mean, maybe we're saving money using a human in this case. But, of course, some of what he did, you know, between the dinners and the photos and all of these different negotiations and somehow continuing to get Paramount to make offers, but then say that it wasn't their best and final.
Speaker 1:I don't know how you do that. He's clearly
Speaker 2:Multiple offers back to back that were not the best and final.
Speaker 1:But Quickly. Let me tell you everyone about Lambda. Lambda is the super intelligence cloud building AI supercomputer for training and inference that scale from one GPU to hundreds of thousands. And without further ado, we have carried no interest, the anonymous x user with There he is. The most fantastic profile picture.
Speaker 1:How are you?
Speaker 2:As always. No interest.
Speaker 4:No. I'm doing pretty well.
Speaker 1:I'm going to repeat carried no interest as many times as possible so that I don't say your real name, Carrie.
Speaker 2:I'm gonna say But your real name.
Speaker 1:Good to have you here, Carried. Thanks for taking the time to join the show. Why don't you tell us what's been on your mind lately?
Speaker 4:Well, it it feels like the chickens are coming home to roost in old private credit software land, gonna take a little cheeky victory lap here
Speaker 2:and see
Speaker 1:if can predicted this. Who could
Speaker 4:have No one could
Speaker 2:have predicted this except you on 12/12/2024, you wrote a post called the Bubble in Software Private Equity Private Credit Edition, and you did a deep dive on a ton of lending data and discovered, you said, some hilarious stuff. You said for
Speaker 4:a while,
Speaker 2:there have been rumors forming that a bubble in private credit related to private equity. I'm somewhat certain a mini bubble forming in software, private equity, private credit, and then you get into it. So what were you what were you seeing back then that, prompted the post?
Speaker 4:So what I was seeing, right, and and it's wild that kind of a a nameless private markets investor with a PitchBook subscription, no one in touch will advertise it for them just there, was able to pull this data and knew it themselves, but anybody with a PitchBook could have done it. What what prompted this is that I was simply being shown deals by investment bankers where the financing structure, I think, would make the average American kinda scratch their head. Right? Like, if you had told the average person on the street that you could take a business with a $100,000,000 in revenue, and no profit and lever it up to buy it with no profit at all and have to repay a good chunk of it in four years, your average American would go no way. That's not a thing that you're allowed to do.
Speaker 4:Right? But depending on how good your relationship was with a certain subset of of private credit lenders, that is certainly a thing that was allowed to do. And so what prompted the post was that I kept being shown deals with with financing structures I I could not believe. Right, from very good sponsors historically who were excellent. And then I thought to myself, you know what?
Speaker 4:I'm just gonna pull all this data from Pittsburgh, do this thing myself. And what I found was this giant bar in the years 2028 and 2029 where a lot of my wonderful colleagues in software private equity would have to pay a good chunk of that debt back. And it kind of terrified me because something else was happening at the same time that was somewhat scary, which was you could see on a there are a bunch of different investment banks who have published this. Mhmm. Carl Square, a bunch of the other ones that are at software boutiques, you could see on a graph that the multiple on ARR was coming down in private market transactions.
Speaker 4:Let's not even talk about Publix, which now become more relevant than ever. And I kinda looked at that graph and the graph of all the debt coming due, and, you know, my stomach churned a little bit. And then I wrote it And so
Speaker 2:and so the the sponsors, their idea was, hey, the debt's gonna come due in 2028 or 2029, but we're gonna roll it over, and we'll just kick kinda kick the can down the road. But then now with AI, disruption and a lot of questions, people aren't gonna wanna lend, in the same way to these businesses.
Speaker 4:I mean, the irony is it all kicked off with interest rates back in, like, 2021 and 2022. And you could see in the post, right, from CarlsZquare in 2021, right, the the a unprofitable growth software company in Carlsquare's own investment banking data traded at 9.1 times ARR. In 2022 I am I'm so excited to hear what sound you use next. In 2022, it was 3.4. So you would nine times.
Speaker 4:Yeah. There it is. That was very good. Very, very mature. You went from 9.1 times ARR down to 3.4 times ARR, and you can even
Speaker 8:Like, how
Speaker 4:much software M and A took place in '20 and 2021 that was sponsored back? It was a lot. Who is in control of these things?
Speaker 2:I am. Sorry. I'll stop.
Speaker 1:Oh, wow.
Speaker 4:You're really good at that, man. You you got a lot of talent. And so it's kinda scary. Now we have, you know, 30% of sponsored bank transactions with software at one point, and everybody's been kind of kicking the can for a while, and now everyone is noticing.
Speaker 1:Okay. Take us take us back and explain, like, our when when I when I think about these, like, software deals, take privates or buyouts of private companies and there's debt involved, I go back to, like, the LBO boom, the milking era, barbarians of the gate. Obviously, those were big deals. Is that the correct structure that I'm thinking of? It's just LBO, but instead of, a cigarette company or a food company or some other industrial company, it's a software company now.
Speaker 1:And and then after some of that history, wanna know, like, what is fundamentally different about these software deals? Because that LBO boom seemed to last for decades.
Speaker 4:Yes. Yeah. And so I think that it all comes down to the fact that they were borrowing against ARR and not EBITDA. Right? There's there's very little profit to borrow against with a high growth, you know, enterprise software company.
Speaker 4:And so what do you do? You need to juice your IRR somehow, so you need to borrow. So you'll go to a bunch of private credit funds and you say, look. I've never lost money. I'm talking about a very specific firm right now.
Speaker 4:I've never lost money on a deal. You can't lose with me. Just let me borrow against the ARR. And before it comes due, you know me, I'm gonna sell it. And we're all gonna buy, you know, another Gulfstream, and we're gonna laugh about this
Speaker 2:next,
Speaker 4:you know, in four years. The problem was is that the liquidity just left. Right? Both in terms of the most important person who could buy it, which was a strategic acquirer. Right?
Speaker 4:When stock prices fell and interest rates went up, you're just not as inquisitive. But now you lump in the fact that AI is creating all these existential questions for enterprise software companies
Speaker 1:Yeah.
Speaker 4:And you have the depressed public valuations Yeah. You know, and you have no profit. So your recovery rate with your lender might even be low.
Speaker 1:Sure.
Speaker 4:Right? Sure. You can't even you can't even take the keys of this business and milk it for dividends.
Speaker 1:Yeah. Yeah. You have to
Speaker 4:do massive restructuring. That's a really scary proposition.
Speaker 1:Did did you did you did you see the the the AI disruption disruption of enterprise software coming, or did that hit you like a flashbang?
Speaker 4:I mean, I did, but I was also an AI or data scientist, as you know, for for many years. So I thought it was somewhat inevitable. Was a
Speaker 1:true flashlight. Okay.
Speaker 2:Yeah. No. To to almost said your name, Carried's credit. This this time last year, you were talking about a specific product category and you were saying, you know, ten years ago to build a product in this space Mhmm. I would have needed to raise $30,000,000 to build something competitive.
Speaker 2:Yep. Now, I can do it with $300. Yep. And I'm gonna go out I'm gonna go after it. You were working on some deal Yeah.
Speaker 2:At the time. So I think you did, you know, I don't know about on on 12/12/2024 but certainly at the very beginning of 2025, you were seeing that like, hey, a lot of these companies that that previously would have taken, you know, tens to hundreds of millions of dollars to, like, rebuild and actually be competitive with Yeah. You can now do with with much less capital.
Speaker 4:Oh, a 100%. And, I mean, I think that that is kind of I think that and here's maybe even a spicier take that I swear I'm gonna write a longer post on. Mhmm. I think a lot of the stuff around, you know, a really good enterprise software company is cooked because somebody's gonna vibe code it. I don't think that's gonna happen.
Speaker 4:Right? I think that the scarier thing that's occurring now for private equity is actually is actually that we're hearing rumors of customers that aren't signing for your deals. Right? The entire basis of software PE being some of, like, one of the best private market categories was that you had enterprise customers with three year contracts.
Speaker 1:Sure.
Speaker 4:If every other enterprise customer says, actually, AI is just so amazing now, and we don't know where we're gonna be and what it's gonna look like, we're only doing one year deals.
Speaker 1:Okay.
Speaker 4:That has, like, much bigger implications than, you know, somebody vibe coded a Notion clone
Speaker 5:on
Speaker 4:a Sunday. Sure. Right? That's a big deal. And I think that the reason that that they're doing that, and this is the bigger threat, I think, to a lot of these companies, is the adjacency within the market map.
Speaker 4:Like, if you look like Rippling Mhmm. And you have a very talented, like, group of people like Rippling's Rippling's employees, They can attack so many parts of the HR platform space now that they simply couldn't three years ago.
Speaker 1:Sure.
Speaker 4:And I think that's a lot scarier than the vibe coding to my colleagues in software private equity than, you know, somebody just whipping one of these up and going out with a bunch of cold emails. It's the adjacency threats Yeah. In the market map. It's scary. Because now who's Rippling gonna go after?
Speaker 1:Yeah. Yeah. Yeah. Yeah. Yeah.
Speaker 1:And and and so you have, like previously, there was, like, it's not an a like, a HR company. It's, like, HR for hospital or HR for golf courses or some sort of narrow niche in a niche, and that all of a sudden has been pretty easily added to Rippling's TAM, for example.
Speaker 4:Yep. Okay. And, you know, and another thing that I'm seeing just when I talk to people in the industry, I don't know that there's a scarier seat right now Mhmm. Than at TOMA or Vista because the liquidity is just evaporating. Evaporating.
Speaker 4:The the appetite in the public market's gone. The ability for these mega these mega technology companies to do splashy 15 times ARR acquisitions for $15,000,000,000, gone. That's a really scary thing.
Speaker 2:Yeah. All your you know, the the hyperscalers wanna put money into CapEx or like various like, you know, core AI bets. The public markets are gonna put a massive discount because they don't know what you're gonna be doing in in ten years or even three years. There's so much uncertainty. And who wants to buy these companies at at at least the the marks that that they've had over the last few years because of that same uncertainty?
Speaker 2:So it's like where where do these companies go? They they they you know, the funds can continue to do continuation vehicles, but it's just like everyone is just kind of kicking the can down the road forever.
Speaker 4:You know, it's a it's a scary time in the asset class. I don't think that there's, you know I I I think that there's gonna be a massive kind of resurgence of super ops focused software funds that might even be far more AI focused than than we could imagine. Right? Where you have PE funds where 50% of management fees is going to AI professionals. I could see that very near term.
Speaker 1:Yeah. What's your mood on partnerships between the labs and the private equity firms? We've seen a bunch of those happening versus, like, starting a new private equity firm that is, like, quote, unquote, AI native.
Speaker 4:Oh, I mean, I think that it's yeah. Not to use the cliche, but, like, it has to be, like, founder mode from the top. Like, you can't be trusting OpenAI to make your forecast more money. I I think that it needs to be very much from the top of the firm really understanding what AI is capable of. Because I don't know how many times in history that it used to take five years to build a factory, and now you can build it in two weeks.
Speaker 4:Mhmm. Right? That's the scenario. You probably want an expert in factory building at the top of the fund, right, or near the top. And the rumors are that there's been a very slow adoption, at some of these funds that were just used to ZERK, and they were they were rolling in it, right, for fifteen years.
Speaker 4:And I don't think
Speaker 1:place it in the and and when you say slow adoption, slow adoption for AI, like, pushing AI first initiatives across the portfolio companies?
Speaker 4:A 100%.
Speaker 1:Like, the deal teams, but the the actual operations teams. Yeah.
Speaker 8:Let me start.
Speaker 4:So, you know, you own, you know, six different businesses that are doing 30,000,000 low end of ARR.
Speaker 1:Yeah.
Speaker 4:How involved is your deal team in I'm not even talking about AI adoption in the ops level. I'm saying full blown AI adoption across both product and ops. Mhmm. And I think that if you're not doing that, you're gonna be in big trouble in, like, three or four years. Journey.
Speaker 2:It's inevitable. What's the what's the takeaway from Mark Leonard's, you know, kind of half retirement last year? It felt like in many ways, he he like perfect I don't know if he he didn't he didn't like his exit didn't top ticket, but he bailed right as there was gonna be kind of like infinite questions around the sustainability of the the model that that got them to that point. Obviously, built a, you know, fantastic collection of of software companies.
Speaker 4:Well, here's the trick. You know, Constellation never overpays. Mhmm. So, like, they didn't do what, in my opinion, firms like Vista and some others did, which was borrow and pay large ARR multiples for unprofitable businesses. Like, Constellation does not pay frequently more than six times EBITDA for a software company.
Speaker 1:Yeah.
Speaker 4:And price is, like, such a good insulation. Mhmm. So if you just assume assume, like, a certain gross retention and you pay a certain price, Constellation is is is likely safe. Yeah. I think that I think that they sit they face the same threat that others do in the adjacency of the market now.
Speaker 4:Right? But their risk is much lower simply because they never overpay. Right? So
Speaker 1:How how are you seeing, like, diffusion amongst these, like, sort of deeper in the economy software companies where the small business that they sell to might not be tracking the latest frontier model development. Oh, 5.4 nano launched today. I can cut my cost. They're just like, yeah. My business is is golf courses, I need to pay my employees, so I'm just on this particular platform.
Speaker 1:And I don't even take calls from competitors because it's just so far down the stack. It's only 1% of my cost here. I don't think about it. Is
Speaker 2:there
Speaker 1:a world where because AI takes time to diffuse, there's a whole bunch of humans in the loop that some of these software companies are actually just stickier than we expect them to be, even though on I on principle, they should be disrupted faster?
Speaker 4:I a 100% think that we are overestimating the the rate of churn. Right? Yeah. I think that that is pure timeline nonsense. And somebody who sees I was just gonna write this, like Yeah.
Speaker 4:Because I was, like, actually getting frustrated. Yeah. I literally see these company financials. You know? I've seen them many times.
Speaker 4:I'm not seeing this alleged vibe code churn yet. Right? It's just not showing up.
Speaker 2:Horse horse horse carriage manufacturer says order order flow looks great. We have a big backlog. There's no issues.
Speaker 4:Sure. Like and maybe at some point, you're gonna see, you know, every other, you know, middle market electronics distributor rolling their own ERP, but it's not happening right now. And so I try to keep, like, a check on my colleagues in other industries, right, that are nontech. And I literally, like, once a month, I just say, are you gonna roll your own ERP this month? And the answer is always no.
Speaker 4:Right? And at some point, that answer will start to shift. And then one or two will do it. It will go horribly. And then I will
Speaker 2:do that. But the but but people people have you know, VCs are still funding AI native ERPs. Like, they're funding Exactly. More But I so so I don't think the question has ever been I I haven't seen anyone saying all you you know, you know, these, like, core system of record businesses are totally cooked. It's every other it's the next, you know, 50 pieces of software that might integrate into said ERP that there's a big question mark around.
Speaker 4:So this actually circles back to this actually circles back to, what I was saying before. Right? Mhmm. It there's a reason I said, like, half of your half of your, like, deal team, if you're in PE, should have some level of AI expertise because of that exact situation. That's actually what I'm talking about.
Speaker 1:And and so sorry. For to to clarify there, deal team, you mean evaluating the transaction, setting the price versus the operations team that's actually gonna go and work with management, jump on the board?
Speaker 4:Great question. Depending on the size of the fund, those could be the same group where they could be separate. Okay. But no matter what, there should be a good amount of either AI first developers Yeah. Or just, like, AI first product people Yeah.
Speaker 4:That are helping your portfolio companies compete Yeah. As the VCs subsidize the CAC of the next wave of, quote, unquote, AI first replacement. Sure. Right?
Speaker 1:Yeah. Because my my my my my, like, default assumption would be, okay. Yes. You need your deal team to be able to understand what moats are are AI resistant, what aren't, like reevaluate the the prices in the AI era. But then the real AI expertise of selecting products, advancing software development workflows, figuring out the right structure of the team mix.
Speaker 1:Like, that's gonna be much more on the operational side of the of the folks that you deploy into these companies, the management teams that you hire.
Speaker 4:Yes. Like an investment banker, no offense to my pals in investment banking, is not gonna help your portcodes be like, you know, AI first competitor proof. Yeah. That's not gonna happen. That's your biggest existential threat.
Speaker 4:Right? So, you know, I think you're going to see a re reshuffling for the best firms that can see what is so clearly the future of ops around that to deal with all of these AI first, you know, YC and and and venture backed competitors. I I think that's inevitable, or you're you're gonna be in trouble. I think another side of it is is I think that you're gonna see a lot of point solutions not trading hands at good multiples. Mhmm.
Speaker 4:Right? I think that you will see a big flight to pure mission critical software and PE, whereas I saw plenty of point solutions changing hands Sure. In in '23, 2024. Yep. Now I I don't I don't see them trading, and and I don't I think you can't underwrite that risk for your LPs.
Speaker 4:You you simply can't. If I could swap it out in twelve hours, I I don't think you can use your LP money on that in private equity.
Speaker 2:How how existential do you think everything that we're talking about is for, like, the big platform software PE funds? Like, do you think do you think they can, you know, eventually figure out how to exit some of these businesses? They have some funds that that, you know, underperform or lose money, but then but then they're able to kind of reposition? Or do you think it's it's just it's over?
Speaker 4:I I don't know, man. It it's not looking good. Like, I mean, you get the public markets are are saying are telling us what the appetite is for these late stage software companies without massive AI tailwinds, and it's a very scary proposition. I I I don't see a shift barring interest rates going down and everybody jumping back into the casino. I don't I don't see it, but I've been around before.
Speaker 2:So Well, the good news is interest rates are going up for the last couple
Speaker 4:of months. Perfect. This is gonna be great for everybody. This is gonna be wonderful. But, yeah, I think it's existential.
Speaker 4:I think there's no doubt about it. There's there's no seat I would want less than at one of the bigger software PE funds right now. Scary stuff. And the rumor is that a lot of people are are looking looking for the doors. So
Speaker 1:What about the what what about other stuff that's like software adjacent, AI adjacent? Like, we've seen a lot of the the private credit funds do deals for, you know, large AI data centers, and that feels like a complete balance against, like, you know, people are people are worried about software PE debt, but then if that firm has a, you know, some contract with Meta for some big data center, like, Meta seems like they're gonna be able to pay that bill.
Speaker 4:Well, it's funny you say that. I think it all comes down to recovery rate. Right? Like, you know, if if the recovery rates are poor enough, there will be nothing that that daddy's up can do to save them. Right?
Speaker 4:There's nothing he can do. Right? If Well,
Speaker 1:he can rate his is his, his, data center bill on time.
Speaker 4:Well, yes. The but but there there will be very little to hide from. The recovery rates are really real low. I I don't know what those are gonna look like. Right?
Speaker 4:And and there's a whole new asset class coming, in my opinion
Speaker 1:What's that?
Speaker 4:Of of of software special situations that's eminent. Right? And you could think about a deal structure that that would look like. And it this is actually a really fun thought exercise. Yeah.
Speaker 4:Right? So you have a software PE fund that was over levered.
Speaker 1:K.
Speaker 4:Right? And they the private credit fund takes the keys, and they're just trying to hope they're hoping to get anything back.
Speaker 1:Sure.
Speaker 4:And you could see a special situation there.
Speaker 2:Zito saying, like, I think I think Yep. You know, some of these deals, you're gonna be lucky if you get $30.40 cents back.
Speaker 1:And is that is that technically a bankruptcy or just, like, the the shareholders get wiped? Like like, would you be declaring bankruptcy in that scenario?
Speaker 4:Equity is taking a zero almost undoubtedly. Right? And depending on how the restructuring plans pans out, it certainly could end up that way.
Speaker 1:Okay.
Speaker 4:But you can see where a very like, a bending spoons, like, entity might come in Mhmm. And say, you know, hey, creditor. We're gonna help you out. Mhmm. We're gonna we're gonna run this thing.
Speaker 4:We're gonna shift it to the constellation model.
Speaker 2:Help me help you.
Speaker 4:And, yeah, help me help you. Right? And we're gonna shift this to the constellation model. And you
Speaker 2:know what? We're not gonna focus
Speaker 4:on growth, and we're just gonna focus on dividends. Yeah. And we're gonna try
Speaker 7:and get you out.
Speaker 4:Right? That's something that that that a special situations investor might look at. Right? And I think that's a much more reliable way to derive IRR than we're gonna turn around this thing that went flat and isn't profitable at all today. And I think AI enables some of these special situations a little bit more, and that's kind of the whole bending spoons thesis on some level.
Speaker 4:Right? So I think there will be a new wave of software special sits people that come in and try to do something with these over leveraged software codes with the creditors
Speaker 1:Yeah.
Speaker 4:In some form. Right?
Speaker 1:Okay. So so, mean, you you said that, like, software PE investors might be, like, heading for the door, but isn't it possible that they spin up a new fund that does special situations, like, develop new expertise, like, reorient their strategy around, you know, curing logjams.
Speaker 4:Yeah. I mean, well, you're already seeing people leaving Constellation to pursue this. And I think that when LPs evaluate who's the better option, they're gonna go with the guy from from Constellation. Since then Sure. Somebody from, you know, Telma or TA or I was thinking about
Speaker 1:I I was thinking about, like, internal to They're like,
Speaker 2:it was a special situation when I paid 50 x ARR
Speaker 1:for this.
Speaker 2:It was quite special.
Speaker 4:Look, you just didn't see my vision for this thing. Okay? What's Sorry. Your Go ahead.
Speaker 2:What's your take on on Zuck's move with Manus? You were an early Manus Oh. Paul.
Speaker 6:Got it. Deal.
Speaker 2:When when I was when I was kind of when I saw that acquisition, I I was thinking, hey, obviously, a talented team have built a product that can get to real revenue. But I think there were some questions of like, is Zac just gonna kind of wind down whatever they were doing acquisition. Was it a talent acquisition or product acquisition? I was leaning more toward product acquisition. They seem to continue to be Yeah.
Speaker 2:Investing a lot in it which is of interesting because it's a product category that Meta doesn't they obviously serve a bunch of small businesses Yeah. But Manus is being positioned now as like an agent that can help you with your business or Yeah. School project. And it just feels like, you know, obviously hyper hyper competitive. But what what's your read on on their strategy with Manus and and their odds to actually compete in productivity against OpenAI, Anthropic, you know, Microsoft Zuck.
Speaker 2:And these other players.
Speaker 4:Zuck, if you're listening right now, you got a deal, man. That was good. That was a good deal. I love it. I think that Zuck looked and he saw an absolutely magical product that he could have, and he bought it.
Speaker 4:Right? It it's magic. I have spent thousands and thousands and thousands of dollars on Manus, and it was worth every penny. There are simply things that you can do with Manus that no other inference provider or product can do. And I would reveal them, but they're they're It's Manus,
Speaker 2:by the way.
Speaker 1:Manus. Manus. It's There's new an American company now.
Speaker 4:Yeah. And, like, there's there's, like, literally things you can do with it. I'm sure you saw the same thing. Like, they figured out context, like, LLM context in browser before anyone else ways that no one else has yet to figure out. Right?
Speaker 5:That's
Speaker 4:cool. And I think that I think that if Anthropic had figured it out, they would have pushed it. I think if OpenAI could figure it out, they would have pushed it, and they simply haven't yet. And he saw a piece of magic Yeah. And he said, what do we gotta pay to get the magic?
Speaker 4:And I think it was right because it's generational. Like I said, there's a lot I've spent a lot of money on Manus. There's things Manus can do that no other inference provider can do, and they benefit from open source. But there's another cheeky thing that Zuck realized. Right?
Speaker 1:Like, we don't broadly an open source bowl. I feel like a lot of people are saying, like, all the money flows to the frontier. You know, everyone just wants to use the best model, the Opus four six, the GPT 5.4. Codex desktop can do a lot of these things. Have you have you actually tried the other harnesses and found, like, a real big delta?
Speaker 4:You know, I I am a Manus and Anthropic Maxi, so those are my two favorites. Okay. So, like, I use Manus for certain tasks and Anthropic for others. But, like, I'm certain that what Zuck realized Yeah. Is that I don't have to care about inference and who's giving it to me because these guys figured out some really special stuff Mhmm.
Speaker 4:Around how does an LLM process data in and around a web browser. Mhmm. So he realized, okay. This is kind of inference provider proof. Mhmm.
Speaker 4:They figured out this piece of magic, this secret about LLMs using a browser. I'm gonna pay whatever price I need to get it. Mhmm. And now, if you've heard, he's already integrating it with, like, Ads Manager.
Speaker 1:Yeah. I mean, that that that makes so much sense.
Speaker 2:Manus. Right? I wanna grow my business. Alright. Give me every dollar in your bank account right now.
Speaker 2:What
Speaker 4:is Yeah. Yeah. And so No.
Speaker 2:Credit to Zaslav for kind of Sounds nice. Either knowingly or accidentally realizing that harnesses would be quite important. Yeah.
Speaker 4:Yeah. And and like I said, there's a lot of stuff Manas can do that no other, like, AI related product can do for me.
Speaker 1:Yeah.
Speaker 4:And it's just it's just really special. So, yeah, I think you gotta steal. You pay whatever price you gotta pay. Within, like, the like, the history of m and a. Right?
Speaker 4:I think it's a good bet. It's a great bet.
Speaker 1:Yeah. Especially for their scale and and and the compute that they have. Like, there's a lot that they can deliver there that another buyer would not be able to. Just I
Speaker 4:actually think Manus is, like, one of the most underrated AI products today.
Speaker 1:I'm I I I just downloaded it. I'm gonna I'm gonna try it out. We we we had our intern Tyler download it and give it a try yesterday. We'll get a review from him later on the show.
Speaker 2:Well, I'll read out one. Somebody in John in the YouTube chat says, would be interesting to have Jeremy Gaffan back on with Carried, no interest. Do a sort of roundtable style back and forth on here. I like Carried's analysis so much, feels up
Speaker 1:Jeremy's
Speaker 2:alley.
Speaker 1:Oh, that'd be cool.
Speaker 2:I totally agree. We should do that sometime.
Speaker 1:That'd be great. I've heard of
Speaker 2:that guy. I've heard
Speaker 4:of that guy before. Smart smart cookie. He's a
Speaker 1:good You're you're both former TBPN guests. He's been on the show.
Speaker 2:Great. Well, we'd love to have
Speaker 1:you back. Let's let's throw a smoke grenade and get him out of here.
Speaker 4:Yeah. Do it. Oh, it's so sweet, guys.
Speaker 7:It's great to
Speaker 2:see you, Carrie.
Speaker 1:See you later.
Speaker 4:I'll get behind this for now. We'll see
Speaker 2:you soon. Goodbye. An honor.
Speaker 1:And let me tell you about MongoDB. What's the only thing faster than the AI market? Your business on MongoDB. Don't just build AI. Own the data platform that powers it.
Speaker 1:And let me also tell you about Cognition. They're the makers of Devon, the AI software engineer. Crush your backlog with your personal AI engineering team. Mid journey's financials are crazy and underrated. This is from Amrit.
Speaker 1:Did not raise anything. Built things in house. 200,000,000 ARR as of late twenty twenty three. Confirmed $500,000,000 in ARR as of 2025, maybe. I don't know.
Speaker 1:That sounds reasonable to me.
Speaker 2:Yeah. The question is, like, how was the how was the Meta deal structured? Right? I assume that Meta was just, like, we're gonna give you a ton of money Yeah. Every year for some period of time.
Speaker 2:So I'm sure that that that that factors in.
Speaker 1:Yeah. Yeah. They must be making a fortune.
Speaker 2:Okay, John. Said yesterday, I wanted to order beef Okay. From a ranch with a livestream Yes. To put to allow me to be Yes. Present
Speaker 1:Yes.
Speaker 2:Where where the cattle are Yeah. Being raised. And this Indian startup lets you own a mango tree for a $111 per season.
Speaker 1:That's cool.
Speaker 2:Allows you to rent a mango tree and enjoy the entire harvest. There are three types of trees you can rent. Base, you can get a base tree.
Speaker 1:Base tree. Standard tree. No. No way.
Speaker 2:You can get thirty thirty kilograms to 60 kilograms of mango.
Speaker 1:That's lot of mangoes. I love mangoes. We're going through them.
Speaker 2:This company is operating in three states in India. You select your favorite tree, pay the money. You will get a dashboard with all the information about the tree you rented, connecting farmers with direct customers who love chemical free fruits. Is awesome. Very, very cool.
Speaker 1:I love it. Yeah. We should definitely get a mango
Speaker 2:Tyler Mango tree. Can you can you can you get us one mango tree on the Max plan?
Speaker 1:On the the mango Max.
Speaker 2:I want the mango Max plan, please.
Speaker 1:So so did you Tyler, have you had a chance to fire off a a Manus prompt and see what it's cooking?
Speaker 3:Yeah. I mean, I I think so far maybe I'm just not creative enough, but it's felt very similar to, the co work kind of thing, which is essentially just like a wrapper around
Speaker 1:Yeah.
Speaker 3:Cloud Code.
Speaker 1:We're like, in your desktop.
Speaker 3:Can interact with my desktop.
Speaker 1:That's cool.
Speaker 3:Interact with local files.
Speaker 1:Yep.
Speaker 3:I I haven't tried, like, using computer use yet. I'll do that now. I
Speaker 1:have, like, no local files anymore. I I like, everything's either in the cloud or camera roll or, like, I just I I get a new computer. I don't even, like, transfer anything over. I just sign in to Gmail. I'm, like, I'm good.
Speaker 1:It's it's a very, very thin client these days. But, you know, if you do have a lot of files, I guess that makes sense. And and I still do like that that idea of, like, color grade every image off this off this off this thing. And, I mean, just there's a ton of different research things that you can do. Anyway, let me tell you about Railway.
Speaker 1:Railway is the all in one intelligent cloud provider. Use your favorite agent to deploy web app servers, databases, and more while Railway automatically takes care of scaling, monitoring, and security. So the AI boom has exploded. The San Francisco housing market, I believe we will have an expert coming on the show soon to discuss Rohan Dhar
Speaker 2:is gonna be joining next week to talk about this.
Speaker 1:But The Wall Street Journal has a report today that we will read through some of it. At a Pacific Heights open house in January, a line of people made their way up the steps of a two bedroom, one bath co op. There were 85 of them. Steps, not people. Eight flights, no elevator.
Speaker 1:The property received 14 offers and sold for over 1,620,000.00, more than $400,000 over the asking price. While much of The US housing market has been stuck in a rut slowly elevated by mortgage rates and slowed by elevated mortgage rates and home prices Francisco are rebounding in a big way. The AI boom, a new mayor, and other changes in municipal leadership have helped bring the city back, reversing a years long slump that was compounded by the ripple effects of the pandemic, crime, and persistent struggles of homelessness. Rents citywide were up 14% year over year in February. And this is what we dug into when we looked at Daniel Gross's AGI bets.
Speaker 1:He was saying, is San Francisco the new Detroit? And it was unclear if he meant the current. Is is San Francisco new going to be like the current Detroit or what Detroit was in its heyday as a boom town. Well, it certainly feels like the latter. An uptick in demand with the city's notorious lack of housing supply, they gotta build the cube.
Speaker 1:They gotta build some cubes. They gotta build some skyscrapers. This is the way.
Speaker 3:The Munger Munger dorm? Yeah. Out to general dorms.
Speaker 1:They should build one of those. They should tear down those those houses. They call them, like, the painted ladies. You know? They they should tear those down and build, like, the Munger dorm with no windows.
Speaker 1:That would be ideal. Maybe, maybe BlackRock should start investing in San Francisco real estate. I feel like they would have a good financial opportunity here if, wow, everything's booming.
Speaker 2:Condo prices, which had been sluggish for years, grew 12% year over year as of February ahead of the spring peak according to Real Estate Brokers Compass. The median sale price was 1,230,000.00. Single family homes Mhmm. Prices are up 23% with the median price at 1,960,000.00.
Speaker 1:It's just Scott.
Speaker 2:By comparison, the year over year median increase for existing home sales nationwide is just point 3%. Last month, 16 homes in San Francisco sold for 5,000,000 or more, a 220% year over year bump. It's just skyrocketed, said Kelsey Carson, 34 years old, an attorney who is expecting her first child in June, and has been house hunting with her husband since April. You're more likely to get outbid by an all cash offer. Carlson was outside a packed open house for a three bedroom, two bath condo on Buchanan Street in Pacific Heights.
Speaker 2:The area known for its breathtaking views and mix of mansions, Victorians, prewar apartment buildings has long been sought after. Carlson and her husband have been outbid on four properties so far, brutal, even a house in nearby Presidio Heights that needs hundreds of thousands of dollars of work. With AI, everyone's coming in with these huge salaries. She says, we just can't keep up the pace.
Speaker 1:There is some good news. There's a housing market that I think is potentially better than San Francisco growing a lot slower. It's only up 4% this year, year over year, as opposed to San Francisco, which is up 12%. Right. It's Alaska.
Speaker 1:And I would highly recommend, if you've made your money in an AI, pick up pick up a house in Anchorage. Average house prices up there, $400,000. Doing very well. And you can throw a star
Speaker 2:Let's move San Francisco. The locals will love The locals will Yeah. John has a long history with
Speaker 1:I shouldn't even joke about it. I need to lock my account again. If you see me with a locked account, you know that I've I've
Speaker 2:Bissed off the Alaskans. Anyway, so we're we're gonna have Rohan who is an SF realtor Yeah. And has a 100,000 followers on X just talking about investing and advising clients.
Speaker 1:Let me tell you about Plaid. Plaid powers the apps you use to spend, save, borrow, and invest securely connecting bank accounts to move money, fight fraud, and improve lending now with AI. And let's see what Adam Neumann is up to because he's in Miami and was featured in an Instagram reel, a TikTok collab with none other than Caleb Simpson, Caleb w Simpson.
Speaker 3:Adam Neumann. What's up, man?
Speaker 1:Nice to see you, Caleb.
Speaker 6:How much do you pay for Red Miami? In Miami, I don't rent.
Speaker 2:That one. Okay.
Speaker 1:It's all cool. He's a developer.
Speaker 8:I to
Speaker 4:show you.
Speaker 6:I come into the main lobby.
Speaker 1:I want your review on this. Did you live here?
Speaker 7:What's going on over here, Adam? You
Speaker 1:should do one of these in Alaska. Yeah. I don't know why he's doing it in Miami. Rent the tower.
Speaker 3:He's getting after it.
Speaker 1:It's like, right? Yeah.
Speaker 3:I've always wanted to do one of those glasses.
Speaker 1:You can go as low as 2,000.
Speaker 2:Okay. That's price.
Speaker 8:What if you oh my gosh.
Speaker 1:What does 2,000 get you in Alaska? Yeah. Does Safety. Clean. After it's safe and clean, now we can talk about community.
Speaker 1:And if you can
Speaker 6:put those three things together, you're starting to
Speaker 1:have a great experience. It was a company once. It was a television show, but it was called WeWork. I think it looked like this.
Speaker 2:Still got bits. Still got bits. Smells
Speaker 7:so good. That is so important. It smells so good.
Speaker 6:Yoga. Yoga. That coffee shop was downstairs.
Speaker 1:That's open and even the public can come into it. This is just for the residents. All these products are all
Speaker 5:flow products done
Speaker 1:in the flow kitchen.
Speaker 6:And there's a
Speaker 7:whole spread out there.
Speaker 6:Everything is fresh. And whatever one sell today will go to the residents at the end of the day. There are people working out in this
Speaker 1:gym all day long. I've
Speaker 6:lived here since I started,
Speaker 5:but I've lived in this building for about four years.
Speaker 4:He was here before we bought the building.
Speaker 1:You gotta see the building before and after.
Speaker 6:When I moved here, it was a great building, but it's turned off way better now.
Speaker 1:The same price in Alaska, you can get a whole house. There you go. But k. It's probably fly in only or there's limited road access.
Speaker 2:Limited road access. Limited road access. How many square feet?
Speaker 1:At least, like, 2,000 square feet.
Speaker 2:Okay. Okay. Not too Yeah. Mean, so
Speaker 1:He's been
Speaker 2:around this for a long time. I think Adam will create a great apartment living Yeah. Experience. Yeah. The question is how different is it from every other kind of apartment complex in Miami?
Speaker 2:How much do residents care about, you know, a cafe, differentiator, workout classes? A lot of a lot of apartments have gyms. They have, you know, spas, things like that. So I just think yeah. Ultimately, think Adam will be successful with this
Speaker 1:Mhmm.
Speaker 2:Purely because he has high energy. He cares a lot about
Speaker 1:Yeah.
Speaker 2:Customer experience. And then the question will, like the question coming out of WeWork is, like, can he keep the cost in control? Because it's if you don't wanna make it if you're if you don't care about making money Mhmm. It's really easy to deliver, you know, the best, like, apartment living experience in a given area. Yeah.
Speaker 2:The question is like, can he can he deliver services and keep the margin? Because I think ultimately, this time around, the business will be valued. Maybe there's some brand equity with to the to the Flow apartment platform, which is which is the brand that he's doing this around. But ultimately, it will be valued based on earnings given given enough time. So I think it'll just be a balancing act, but looks looks nice.
Speaker 1:Yeah. Well, Florida man Florida man sold his house in just five days after letting Chatuchipiti handle the entire process instead of a real estate agent. There's some community notes on this. He he he didn't go to Chatuchipiti and say, sell my home. He, like, you it's the same thing as the dog cancer story.
Speaker 1:He used Chachi BD to conduct market research.
Speaker 2:Everybody wants to move the goalpost.
Speaker 1:And yeah. But you can just sell your house. That's that's good news. I I was listening to Ryan Serhant talk about the opposite scenario, which he said he was working on, I think it was, like, a $50,000,000 house sale. And he and they'd spent he was like, our agents had spent, like, months getting these two buyers very educated about the value.
Speaker 1:They'd agreed to a price. And then the seller went to Chachi Beatty and said, like, this should I sell, or is the price too low? And and Chachi Beatty said, you're absolutely right. It is too low. You should pull out.
Speaker 1:And then the buyer went like, is this a good deal or is the price too high? And Shyam Sankar you're absolutely right. The price is too high. And so they both walked away and Ryan Serhant was like, what the like like, now I gotta get these people to come back. This is, like, a funny thing.
Speaker 2:Yeah. Who knows?
Speaker 1:I think they need to be in a shared chat window going back and forth arguing with the AI, the same AI
Speaker 2:in the same in the
Speaker 3:You can do that. Yeah.
Speaker 1:Yeah. Yeah. That would be the the correct way because then they can fire back and forth. Anyway, let me tell you about Figma. No matter where your idea starts, Figma may clog code codex or a sketch.
Speaker 1:The Figma canvas is where ideas connect and products take shape, build in the right direction with Figma. Reorg in the copilot division of Microsoft. So company scraps divide between consumer and business app teams. AI chief, Mustafa Suleiman will focus on AI models. He's freed up, apparently.
Speaker 1:So Microsoft is reorganizing the teams that work on the different versions of its flagship Copilot AI product. This is from The Wall Street Journal. Altering a strategy that some employees said created a disjointed user experience and consumer confusion. The software giant is unifying the teams that work on its Microsoft three sixty five Copilot productivity offerings and the consumer version of Copilot, according to a memo from Satya Nadella. Jacob Andreu, who leads product and growth for Microsoft AI
Speaker 2:The man who has sat right here.
Speaker 1:Yeah. He brought back Clippy. He's the man that brought back Clippy.
Speaker 2:Yeah.
Speaker 1:He will become the executive vice president of Copilot, of just Copilot itself. That's the brand. And it's a great it's a great brand name. I'm a big fan
Speaker 2:of Copilot. Yeah. These are prosumer tools. Prosumer tools? There's they kind of they're just they're gonna be used in and out of people's work life.
Speaker 2:Yep. I think businesses Yep. Everywhere kind of realizing that individual employees are bringing their own AI tools Yep. To work just because they help them do a better job. Yeah.
Speaker 1:And This is what happened with Apple. I mean, Apple was like a consumer company, and then all of a sudden, everyone was using Macs at at work. And I think that's also a little bit of what's happening with the the the OpenAI story about, like, refocusing on business enterprise API. It's like, no. Like, there TBP was also saying he's gonna put Codex in Chateappity.
Speaker 1:So you're just gonna be like, build me an app that does the thing that I want.
Speaker 2:There's a
Speaker 1:lot of times when you go to Chateappity and you ask something to, hey, go go transcribe this YouTube video. And like, within ChatGPT, it might not have that tool. The codex could absolutely one shot it. And so bringing that ability, whether it's cloud hosted or running on your computer, bringing that over makes a ton of sense, and Microsoft should probably follow a similar strategy. So, exciting news there.
Speaker 1:Let me tell you about the New York Stock Exchange. Want to change the world? Raise capital at the New York Stock Exchange. Just do it. And we have our next guest, Shyam Sankar, from Palantir.
Speaker 1:He is the chief technology officer in the restroom. Let's bring him in to the TV. Shyam, good to see you.
Speaker 7:Good to see you guys. How are you, Jordy?
Speaker 1:Welcome It's been far too long. It's been, like, almost a year. You were one of our earliest guests, a huge moment for the show. Thank you so much for joining then, and thank you so much for joining today. How are
Speaker 4:you doing?
Speaker 2:A big day.
Speaker 7:I'm doing great. I'm pumped to be back on Saint Patrick's Day, but more importantly, on the book the book launch day on, on Mobilize.
Speaker 1:Yeah. What's the
Speaker 2:sent a green package.
Speaker 1:Should we open this? Can we open this on the
Speaker 7:You gotta open it.
Speaker 2:Okay. Oh, yeah. You sent us a green package because you knew
Speaker 1:Needs to take
Speaker 2:it was Because it was gonna be on Saint Patrick's Day. Levels. At this thing.
Speaker 1:Look at this thing. So it opens.
Speaker 2:This is so This is a real piece of hardware.
Speaker 1:It's so big.
Speaker 7:It's an actual, US Army ammo box. It it held 20 millimeter electrically primed munitions, and now it holds mobilize. There you It's got some schwag.
Speaker 1:There's amazing
Speaker 2:stuff. You've outdone yourself. So cool.
Speaker 1:Much. I'm gonna step back.
Speaker 2:You're you can move that.
Speaker 1:Just might get in the way of the camera. Anyway, so, I mean, you've been working at Palantir for a long time. Has it been almost two decades now?
Speaker 7:It's it'll be twenty years this Friday.
Speaker 1:Wow. Oh, this Friday. Congratulations. Success. Amazing.
Speaker 1:So, when did you when did you feel like, okay, now is the time to write the book? What was the thesis? What was the what what was the moment where you were like, okay, it makes sense to actually take all that experience and distill it down into something that can be instantiated in an actual book?
Speaker 7:Well, the book is the long form version of the 18 Theses. So I I put the defense reformation out there in October 2024. Mhmm. And that was kind of the welling up of all these feelings I had working more or less in the bowels of the Department of War for eighteen years.
Speaker 5:Yeah.
Speaker 7:Watching deterrence slowly erode Mhmm. Seeing that we had a problem. Like, you you go back in time and you say we had the annexation of Crimea in '14. We had the militarization of the Spratly Islands in '15. Breakout capability for Iran to get the bomb in '17.
Speaker 7:We've had a pogrom in Israel, the invasion of Ukraine, conflict with the Houthis. What's going on here? Like, we're spending a trillion dollars a year. Where is our deterrence? And that that's one piece of it, and you and you look at what what's looming with China.
Speaker 7:Mhmm. Then you look at the other side of this and you really look at history and you say, like, what what gave us deterrence in World War two and the Cold War? How did this stuff work? You recognize how much the industrial base that won World War two and the Cold War is not the industrial base we have today. Mhmm.
Speaker 7:You know, the the astonishing statistic is that only 6% of major weapon systems were built by defense specialists in '89 when the Berlin Wall still stood. So 94% of spending Yeah. Went to companies that were what I call dual purpose. Yeah. Chrysler was the prime on the Minuteman intercontinental ballistic missile.
Speaker 7:So Chrysler made missiles and minivans. Ford made satellites. General Mills, the serial company, more made torpedoes. We had an economy that was equally invested in freedom and prosperity. But the corporate story is not enough.
Speaker 7:So that that's a precondition. You could kind of say, well, what were these companies like? You know? Because we think about it today as Northrop Grumman and Lockheed Martin, but it was Glenn Martin. It was Jack Northrop.
Speaker 7:It was Leroy Grumman. It's something that your audience would understand foundationally. It was founders. You know? Our entire industrial base was made up of founders.
Speaker 7:What's happened? You know, at the end of the Cold War, we had this enormous financialization of defense. These companies became run by the third, fourth generation, buybacks, dividends, financial engineering over real engineering. And by the way, that's not specific to defense. The same thing happened to Intel.
Speaker 7:The same thing happened to a lot of great American companies. The rejuvenation of our economy comes from the heretics. And then I started researching a lot about the the history of innovation in defense. It's like nothing worked because of the system. Everything that worked worked despite the system.
Speaker 7:You look at Andrew Higgins, the Scots Irishman in Louisiana who built the boat that won the war. 92% of all boats in World War II were Higgins boats.
Speaker 1:Wow.
Speaker 7:But the Navy didn't let them compete. Then when they finally let them compete and he won, they stole his designs and failed to copy it successfully. In the end, after all these things, like, fine, we'll buy the fricking boat. And the boys in Normandy landed on Higgins boats. You know?
Speaker 7:And you go story after story. Hyman Rickover, who built the nuclear navy, his first office was a women's restroom. And I think part of documenting these stories of the heresy because, of course, the navy wanted to humiliate him into quitting. And then think about the chutzpah. Like, Oppenheimer told him, this is not gonna work.
Speaker 7:You're not gonna be able to build a nuclear powered submarine. And in the face of Oppie telling you this, you're like, no. You're wrong. I'm gonna do it.
Speaker 1:That's bold.
Speaker 7:You know?
Speaker 1:Yeah. Bold. Bold. Have you heard the story of Ball Corporation? They make mason jars.
Speaker 1:You know you know those Mason jars? Also a defense contractor.
Speaker 4:Yeah.
Speaker 1:I I think they eventually sold it to BA Systems in 2024. But for a long time, the company that made the Mason jars that are in every, like, hipster millennial burger joint in America was also making, like, satellites and sensors and all sorts of stuff. And so there's just endless stories about that of of reindustrialization. I'm wondering, like, there's a huge boom in new start ups that are saying, we're gonna build boats from scratch. We're gonna build missile systems from scratch.
Speaker 1:We're gonna build satellites from scratch. But is there some underrated industrial capacity in America where we haven't actually gone to Chrysler and said, can you do these days? I know Chrysler might be a bad example because it's an older company.
Speaker 7:Yeah. Great idea.
Speaker 1:Is there still some latent industrial capacity where, in the worst case scenario, like, America can actually adapt?
Speaker 7:Well, that's the important part of the book is we're telling the story that it's not the facile version of the story where we flipped a switch in World War II and then Yeah. Bam, the automotive industry started making munitions. It was actually a journey. It took eighteen months to retool and rebuild factories to produce war materiel.
Speaker 2:Yeah.
Speaker 7:And so you you really wanna get moving early.
Speaker 1:Yeah.
Speaker 7:And now I we have a lot of this latent capacity. You think about GM produces a new Escalade every ninety seconds. You know, right about now, we need some SM sixes, SM threes, and and Tomahawks rolling off the line every 90, too. Yeah, yeah. And so how do we take the kind of exquisite, artisanal approach to a low number of munitions we've built and start scaling that out?
Speaker 7:And you're going to need a breadth of approaches. You're going to need the new entrants building entirely new classes of things, and you're gonna need to make the exquisite things much more quickly. What makes this stark is we have eight days of weapons on hand for a major fight with China. Nobody thinks that's deterrence. Nobody thinks that's enough, right?
Speaker 7:We need eight hundred days. How do we really fire up the arsenal of freedom here
Speaker 1:Yeah.
Speaker 7:And get serious about building? And we're gonna have to build those things in new ways.
Speaker 1:Yeah.
Speaker 7:And a lot of that skill exists in El Segundo. It exists in the modern American manufacturing economy.
Speaker 1:Yeah. Yeah. I I'm always reminded of this this, like, Palmer Lucky take about, like, the younger generation throughout the February got obsessed with building consumer software, ad platforms. We love ads, but I take the point. Is there something similar going on right now with AI?
Speaker 1:Because AI can be important for the military, but also you see there's only a few caterpillars or electricians, and they're working on data centers. If that doesn't become critical to the defense and deterrence of the nation, it actually just winds up being more just juice for the economy, which is probably good. But at the same time, it might be sucking capital, sucking human talent out of true industrialization efforts.
Speaker 7:Well, I think so. Like, I think with AI, we have to remember that we have huge human agency. AI doesn't do x or y. Humans use AI to do x or y. Yeah.
Speaker 7:Let's pick intelligently. Let's pick things that are in the national interest that give the American people prosperity, that actually propel civilization forward and aren't AI slop and, you know, AI slot machines. Yeah. And I think the promise in front of us is that AI is an opportunity to give the American worker superpowers. How do you make the American worker 50 times more productive than any worker anywhere in the world?
Speaker 7:And that solves the math equation of like how do we re industrialize economically? This is actual.
Speaker 2:Yeah. How you rate the current re industrialization process? There's a lot of founders you mentioned in places like El Segundo that are working as hard as they possibly can. Mhmm. But but is are we 10% of the way there for what
Speaker 1:Oh, yeah.
Speaker 2:You wanna see? Are we 20% of the way there? Are we 5%? Like Mhmm. Where where do we stand right now given all the effort that has already started, but Mhmm.
Speaker 2:We're still, you know, early in this process?
Speaker 7:Well, I'd say three years ago at the first we industrialized, there was an aspirational aspect to it. Now I think we're closer to 5%. Like, this is happening. Yeah. We're in the early innings of it, but it's happening.
Speaker 7:Yeah. And I think people are starting, you know, one of the amazing things about the American spirit is people just roll up their sleeves and get busy trying things. You know, and I'm working with people on the factory floor every day who are using AI to change how they do production. One of our submarine parts manufacturers actually added a third shift. They were able to use AI to automate the planning process, which meant instead of having to have tools down while they did planning and quoting, they were actually able to get that done in ten minutes.
Speaker 7:They needed to hire a third shift because there was more work to do. And these are the sort of narrative violations that aren't being reported. And I think the underlying phenomenon is that we are listening so these revolutions are always tools driven revolutions, not concept revolutions. And it the the impact of the revolution is determined by the people who wield the technology, not the people who invented it. Galileo did not invent the telescope.
Speaker 7:He used the telescope to discover the planet's and planetary motion. The microscope, the power loom, the personal computer, thing after thing. It's the wielder of the technology that determined its impact on society. Today, we are way over indexed on listening to the inventors of AI. They're very smart, but just like their creations, they have their own jagged intelligence.
Speaker 7:And the future of AI is going to be written by the the American worker.
Speaker 1:Yeah. Yeah. How how do you think AI interfaces with the reindustrialization effort? There's there's also, like, yes, use AI in the factory, but I imagine that retraining is a really underrated opportunity. I've already heard just years ago, I was talking to Chris Power about from Hadrian about how he was able to hire someone and just get them forklift certified and teach them how to use these things.
Speaker 1:And reskilling has always been happening, but it feels like we're going through an accelerator phase of reskilling. But what are you seeing on the reskilling side related to AI?
Speaker 7:Well, enormous thing. So, I mean, I think Chris has really led the way with Hadrian here. He's gonna have a huge factory opening on Friday. Oh, yeah. Hopefully, you guys get a chance to cover that factory The Panasonic Energy, I work with them.
Speaker 7:So they're located in Sparks, Nevada, inside the Gigafactory. They make every cell for the Gigafactory for Tesla.
Speaker 1:Interesting.
Speaker 7:The population, your employee base there are prior casino workers. And this is high end, exquisite Japanese equipment. It used to take three years of apprenticeship to learn how to be a battery technician for this equipment. Yeah. With AI, it now takes three months.
Speaker 1:Wow.
Speaker 7:So that's a very concrete example of the reskilling. More profoundly, I'd say, you know, one of the things we cover in the book is the story of Colonel Kukor, the father of Maven. And it's one of it's the newest heretic. He's a contemporary. He's alive today and, you know, obviously.
Speaker 7:And what I think is really compelling about that story is that this is actually the most consequential AI system in the world today. But because it exists in the Department of War, it's not something that broadly the Valley interacts with or thinks about. And I think one of the reasons it's so consequential is the stakes are existential. People are not they don't have lane goals like, how do I get 10% more efficient or, you know, reduce head count by x or y? It's really like, how do I have complete dominance and overmatch?
Speaker 7:Mhmm. And as a con as a result of this, the people who are building it are not just formally trained computer scientists over here, but it's become a platform that the vocational expert, the intel warrant officer, the FIRES officer, is able to really encode their knowledge, build agents that are their kind of team working with that to get things done. And so the efficiency, the speed, the scale of what's going on, like, really, we're learning more from those users today than we are anywhere else.
Speaker 1:Yeah. How are you how are you thinking about the role of the forward deployed engineer in the AI boom? It feels like there's the capability overhang, an incredible amount of, you know, genius intelligence from the machines, and yet there's so many processes that are still manual. I went to the doctor recently, and I had to fill out a paper form. And so in many ways, like, there's still a capability overhang just from, like, HTML web forms.
Speaker 1:And it feels like as amazing as the AI is getting and has we're seeing so much progress there. There's still something that needs to fall into place to actually get systems deployed.
Speaker 7:I think that's right. There's a huge I mean, I I in air quotes, mockingly, I'll call it the last mile problem.
Speaker 8:You know?
Speaker 7:Yeah. Everyone's the first 80% of the problem was building the genius technology. Second 80%, it turns out, is actually how do you implement it for economic value. Yeah. And that's where we're that's kind of our jam.
Speaker 7:It's what we do for a and I think it's never been more fun to be a forward deployed engineer than right now because the speed at which you can take new product ideas that you're learning, systematize them, generalize them Mhmm. It's crazy. And I I know Ted's talked about this, but, like, we have to reinvent forward deployed engineering as we go right now. Things that we used to think would take four weeks, take four hours. And so the amount that you can get done, how you go to market with that, let's it's just sit in a room with the customer that there's no sales meeting.
Speaker 7:Sit in the room. Let's go build agents that are automating actual workflows today. By tomorrow, you decide.
Speaker 1:How do you think about advice for young people? I imagine that you'd say, you know, come work with you, but also it seems like there's some potential alpha in being a young person that goes to a Chrysler and says, I'm gonna be the AI czar. I'm gonna be the forward deployed engineer fully foiled deployed because I'm just gonna work at this company. Where where are the opportunities for young people in during this, like, tumultuous AI revolution?
Speaker 7:So I'll I'll give you two two answers to that. The first is what would I tell my children? Like, what should they learn right now?
Speaker 1:Yeah.
Speaker 7:And really, what I'd want to cultivate in them is agency. Extreme agency. Like, think all the other skills you'll able to figure out as you go, but, you know, do you really believe that your human effort can make a dent on the planet? Yeah. And how do you experience that and live that?
Speaker 7:Yeah. Then where would you spend time? I I think Palantir is an amazing platform to have impact on the world. The things that you do in the commercial sector impact the government and vice versa. And, you know, you have access to the problems.
Speaker 7:You're in it. But second to that, it's like when you're thinking about being inside of a company, I think AI is going to be the antidote to the managerial revolution of the twentieth century. All this power that was sucked away from the frontline worker who actually knew what they were doing to an amorphous blob of middle managers, And even, actually, they sucked power away from the senior leadership. That's being reversed because all of the bureaucracy is getting cut. The agency that someone has.
Speaker 7:I was thinking about this because I in the military, I'm seeing incredible AI application developers who are not formally trained computer scientists. And I was like, what happened? I've been doing this for twenty years. This feels like a discontinuity. Where do these people come from?
Speaker 7:And I realized, like, oh, they've always been there. The thing is, like, what would this guy have done ten years ago? Make a PowerPoint, try to convince some program manager that his ideas were good only to be told they weren't? Knowing full you know, he's too smart for that. He's not gonna waste his time.
Speaker 7:Now he just goes away in a corner for two weeks and builds it. And he's arguing about something that's empirical. And the commander's like, shit, this works. Let's go. You know?
Speaker 2:And Yeah. That's like the most underappreciated part of this moment. I mean, we've been covering we covered a story yesterday of a guy in Australia who's gone on like a year journey to try to cure his dog's cancer. And he had experience building in AI and ML but didn't have any experience in biology or pharma or any of these things. He and just by leveraging the models, he was able to kind of figure out the right path to go down, figure out like even he took a recommendation from JADGBT of like which professor to go to in their sort of local university system to get help with the problem that he was working on.
Speaker 2:And he ultimately has been able to show real results on this sort of experimental vaccine. And you apply that to that type of, that sort of nationwide realization that you don't need to be an expert. You don't need to have gone to school for a certain thing. You don't need to be a software engineer to build software. Or you don't need to be an electrician to start figuring out how this stuff works.
Speaker 2:And I think that that that unlock across the entire world where just like bringing down the kind of like knowledge boundary around so many different tasks is going to dramatically transform huge parts of the economy.
Speaker 7:Yeah. I couldn't agree more. Mean, and talk about an example of agency. He couldn't have started that unless he thought, I could do this.
Speaker 1:Yeah. It's gonna work.
Speaker 2:Yeah. Like you said, wasn't like he just one shotted anything. And that's the That's what people need to realize. It's like, if you just want AI to one shot everything, that's like saying, like, I want results in life and I don't want to have to work. And it's like anybody throughout all human history, you want results and you're not willing to put in the work, you're going to have a bad time.
Speaker 2:But now, there's never been a better moment in history to want to build something, to do something, and and have a better shot at actually achieving that or or learning how to do that than right now. And so again, it's all it all comes down to agency. I guess the question is like, you teach agency or is it is it is it innate? Know, I I I find like if I'm talking with somebody about their career, sometimes it's like you know in your head all the different moves that you should make in order to achieve the outcome. And yet, some people just think, Okay, I'm just going to go back to submitting resumes that never get answered because that's like the straightforward path.
Speaker 2:Anyways, we're gonna find out
Speaker 7:if wanted to go back in. Remember? You he wanted to eat a steak steak.
Speaker 1:Yeah.
Speaker 7:And I think you you look, obviously, there's a part of it that's innate, but there's a part of it that you can cultivate.
Speaker 1:Yeah. I agree. Yeah. I was I was talking to my wife about my five year old last night and was talking about AI and sort of, like, what he might do for a job and how it is nervous. It's a it's nerve racking.
Speaker 1:It's like, okay. What? Like, what if I try to predict and set him up for success in some particular career, like, how is that tractable at all? And then I was reflecting my own career, and I was like, well, for, like, ten years, I sold things online. And when I was born, ecommerce literally didn't exist because I was born before amazon.com and before Webvan.
Speaker 1:So it was not fathomable to click a button the
Speaker 2:door door protein sales.
Speaker 1:Exactly. And now I have a livestream, which was not a thing before the Internet. And so, like, every career opportunity I've had has been adjacent to other things. People sold things before, but or and they talked on, you know, TV before. But the actual shape of the career has been wildly different.
Speaker 1:And so I felt very relaxed at the end of this conversation. But it is nerve racking if you really do want to just think, okay. Yes. Like, doctor, lawyer, merchant, chief forever, and it will never change. But even the lawyer's role has changed a ton with the with the electronic revolution, the information revolution.
Speaker 1:But, yeah, it's a it's just a fascinating time. Where should we go next, Jordy? Do you do you
Speaker 2:have any Any other any other stories that stand out? And you can give kind of like a trailer so that, you know, we want people to go and buy the book. So just give us another trailer. I know there's so
Speaker 7:many trailers. We can talk about Colonel Cukor a little bit more. Think about it. So you have this Marine colonel who's just the I call them heroes and heretics.
Speaker 1:Yeah.
Speaker 7:You know, because it is somehow their rebellion that gets all these things started. So he had this seminal experience where he was on a helicopter waiting to land on Mount Sinjar to evacuate the Yazadi who were fleeing ISIS. Oh. And there was a young Marine who thought he saw a rocket propelled grenade. And because of this misidentification, this human error, they waved off the landing.
Speaker 7:It was obviously unsafe. And you have order of a thousand people who were raped, tortured, and enslaved from this small little decision. And so this is the sort of thing that he was like, there has to be a better way of doing this. And that kind of set him up on this crusade to go figure out how to bring AI to the department. What I think is interesting we document is how everyone tried to kill him in doing this.
Speaker 7:I mean, up to the point, know, the services were threatened by it. The bureaucracy was threatened by it. People filed IG investigations. People claimed he was housing Iranians in his basement. Here, you have this devout Mormon with four daughters living in a 1,400 square foot home that has no basement.
Speaker 7:They actually sent investigators out to go do this. But just through that, never giving up. And that's what you see consistently. When you see Rick over, you know, you see in his memoirs that the humiliation of the women's bathroom, all these slights. It's not that they didn't get to him.
Speaker 7:You know, he documents how much they did hurt. But despite that, he would push through and get all these things done. And perhaps one of the greatest heretics, Billy Mitchell, who's the father of the Air Force, he didn't even live to see the creation of the Air Force. His his little rogue act of rebellion was the Navy was trying to sink a ship at this they call it a sync ex, an exercise to do it. And they were failing to sink this stationary ship.
Speaker 7:And he's like, you know what? I'm just gonna drop a bomb from a plane. And there was no permission. There was no rules. And, you know, you get this, like, feisty he sunk the ship.
Speaker 7:But before then, people thought air power was about sending messages back and forth across the front line. Nobody thought about actually using these as weapons of war. It's totally crazy. And so I think hearing these stories, what I I really hope is both the heretics inside and outside the building are inspired. Because your country needs your heresy right now.
Speaker 7:In every one of these wars, it really comes down to Churchill and the tank. You know, as the head of the Royal Navy, Winston Churchill funded and built a landship. He could only build ships, of course. That's the tank. Because the British Army was like, we got horses.
Speaker 7:We're good, dude. No thanks. You know? And so you start discovering these stories and you get emboldened to say, like, I gotta pursue what I think is right here. And you go back to World War II, we built a 154 different airframes, different types of aircraft.
Speaker 7:I think 10 really mattered. But just, you know, in in the sense of, like, the American free market system, like, obviously, you can't know. You need a market for competition. And that's part of what makes defense really hard. It's a monopsony.
Speaker 7:There's a single buyer. People have a penchant for control. I like to quip that, you know, everyone's given up on communism, including Russia and China, except for Cuba and the DOD. You know, we have this deeply centralized planning approach that we thought would provide for what the what the what we needed to win wars, and it's just not the case.
Speaker 1:That's a good hot take. Do do you think that the next batch of defense tech companies should go public earlier? Like, what advice do you have for the current crop of private defense tech companies? It seems like there is appetite in the capital markets. Palantir has obviously done very well in the capital markets.
Speaker 1:At the same time, the private markets seem to be able to find any amount of money in the couch cushions, especially if there's AI attached to the narrative. But how are you talking to leaders of private defense tech companies right now about the markets broadly?
Speaker 7:My advice to them is all the same. I think one of the hardest things about doing the defense tech thing is you need to hold two contradictory ideas in your head. One is, like, you need to run towards the pain. Like, you you need to run towards proving real results operationally. But that's not your buyer.
Speaker 7:And so, you know, you could say there ought to be a mark to market moment right now. Who's in the fight today in Iran?
Speaker 1:Sure.
Speaker 7:Where have these companies started bending the curve? What are the opportunities to prove this capability? You know, I'm hearing about incredible things Shield's doing in Ukraine right now.
Speaker 1:Sure.
Speaker 7:That's really important. You're not going to get paid for that. But that's the validation you need. And then you have to figure out how to get programs of record, all those sort of things. But if you just focus on the programs of record, if you just focus on treating the Defense Department as a buyer, you'll lose the magic.
Speaker 7:You'll you'll lose your own heresy that leads to the innovation.
Speaker 1:Yeah. There was a
Speaker 5:little bit of
Speaker 1:that in the space economy where we saw there was a there was pretty quickly a bifurcation between space companies that were doing a lot of interesting work in signing deals and then other space companies that were like, they got on the rocket and they went to space. And I feel like that was like an important binary. I don't know how I you know, the binary is sort of coming down now as more companies get to space, but it felt like, you know, actual deployment where the rubber meets the road. It's always important in startups, but it it feels especially important in this scenario.
Speaker 2:What about stories from history around copying what works? Stand out? And the reason I ask is because one, it was somewhat surprising seeing that we have American made versions of the Shahed drone. And I feel like America has always prided itself on being inventors, you know, these sort of like zero to one zero to one projects. Right?
Speaker 2:You said a 100 something, you know, airplanes created or airframes created during World War two. And yet here, it felt like the smart decision was like, hey, this is like a battle tested form factor. Like, we can just make the thing that is delivering results Mhmm. On the battlefield. But are there any other kind of stories that stood out around America kind of swallowing its pride and saying like, hey, this thing has worked.
Speaker 2:Fast follow. Let's fast forward.
Speaker 7:The best one that also speaks to the importance of founders and people, the primacy of people, is Operation Paperclip, you know? As the Nazis were losing, we started we actually had two competing programs. We had Fiat and Paperclip. Fiat's theory of the case was, we don't need these people, these dirty Nazis. We're gonna go steal all the technical papers, and we're just gonna be able to figure it out just by having all the papers.
Speaker 7:That was an abominable failure. It did not work at all. Instead, what worked was you go get the founders. You get Wernher von Braun. You get the people who actually know.
Speaker 7:Because there's something more three-dimensional to the knowledge than what's on the just the Judy paper. And I think that's a huge swallowing of pride. It's something we had to really hold our nose to these Nazis and recognize that actually it delivered ICBMs, it delivered the space program, it delivered a fundamental offset against the Soviets. But we did that other times as well. Think probably the most famous one is there was a North Korean defector.
Speaker 7:I don't have the dates exactly right. He flew out of North Korea on his MiG.
Speaker 1:The MiGs Personal were killing MiG. Korean War.
Speaker 7:Flew out on his MiG. Okay. You know, they tried to shoot him down. He escaped. Fortunately, we didn't shoot him down.
Speaker 7:We figured out he was defecting.
Speaker 1:Okay.
Speaker 7:So we reverse engineered the MiG and built our own, I'm forgetting, was it the F86 or the Wow. Something like that. And that actually helped us restore air dominance once again. So, you know, is the primacy of winning. Whatever it takes to win.
Speaker 7:It's it's you know, you you don't wanna be like, hey. I didn't steal anything from from the adversary, but I died nobly.
Speaker 1:No. Yeah. That doesn't make any sense. Yeah. Well, thank you so much for taking the time.
Speaker 1:Congratulations on launching the book. Yeah.
Speaker 2:I can't wait I can't wait to get into it. Yeah. We're gonna have to we'll share our copy.
Speaker 1:We will.
Speaker 2:We'll fight over it. I think it's
Speaker 1:too in here.
Speaker 2:We maybe got to.
Speaker 1:But thank you so
Speaker 2:much. Yeah. Congratulations on the launch.
Speaker 1:We'll talk
Speaker 2:to you soon. Great to catch up.
Speaker 1:Goodbye. Cheers. Let me tell you about Graphite. Code review for the age of AI. Graphite helps teams on GitHub ship higher quality software faster.
Speaker 1:And let me also tell you about Restream. One livestream, 30 plus destinations. If you want to multistream, go to restream.com. The timeline is in turmoil. Doug O'Laughlin at Fabricated Knowledge is going back and forth with Buco, capital bloke over Google.
Speaker 1:So Fabricated Knowledge says, people keep thinking that their distribution, think Microsoft or CRM, is bigger than the technology AI. But the fact that people are buying Macs to mess around with Cloud Code and other coding agents, they're also good machines, tells me that this technology is much bigger than any distribution this time. Like, whatever distribution is there, if you're willing to go to the Apple Store, buy a MacBook Pro or a Mac Mini, wire it up, install OpenClaw on it, verify everything, like, you're you're willing to jump over the default of the distribution.
Speaker 2:Yeah. I think another another question here is how many small businesses in America don't pay for any AI Yeah. Products directly and yet their employees are bringing the product to work. Yeah. And it's just not appearing on the on the actual
Speaker 1:But who the capital bloke fires back? He says, these people are dweebs. It's like zero point it's like point 0000001% of the market that's buying Mac minis in his opinion. Distribution does matter. It will always matter.
Speaker 1:It doesn't mean the incumbents will win, but it certainly gives them an advantage. Just look at the sentiment shift on Google in one year. That's a good point. Doug Doug fires back. He says, I am going to just put the distribution friction as the only thing that matters if we are going to be software brain.
Speaker 1:I do not I do understand and appreciate it, but I do think this is a big thing, and making it all about distribution is such a technology loser way to look at it. Lots of great tech companies with Austrian with awesome distribution lost, like IBM had CTO relationships with every company. Almao. Buko says, yep. I am not pounding the table for Salesforce, but they're not dead yet, the long game.
Speaker 1:Also, IBM beat QQQ over the last five years by a lot. I had no idea.
Speaker 2:Let's give it up for international business machine.
Speaker 1:And they keep going back and forth. But let's give it up for IBM, and let's give it up for
Speaker 2:our burn
Speaker 1:it. Yeah. Cyber starts. How do I pronounce this thing? Let's bring him in to
Speaker 2:the TBPN UltraDome. Well, what's going on? Great to meet you.
Speaker 1:Help me pronounce your name. I don't wanna get it wrong.
Speaker 5:Gilly. Gilly. Hey, John. Hey, Georgie.
Speaker 1:Thank you so much for taking the time to join us. How are doing?
Speaker 5:I'm doing great. I'm speaking for you with you from Miami. We just finished first day of our annual conference, CyberSparks. Yeah. We had a lot of fun, lots of terrific guests and and speakers.
Speaker 5:We we just had Nikesh Arora from Palo Alto Networks We love Josh. Joe Coote, CEO of CrowdStrike.
Speaker 1:We love Joe.
Speaker 5:My friend from Sequoia Capital So we
Speaker 1:are flush.
Speaker 5:The founders of Wiz, a small company that you might heard of that completed a tiny acquisition last week.
Speaker 2:Yeah. Congratulations to to to to you on that one. Yeah. Great great outcome. Yeah.
Speaker 2:What what is what what are people talking about? What is what is top of mind for everybody at the event?
Speaker 5:Well, the the whole idea of CyberSpark is to bring together the top 300 leaders in cybersecurity. So you've got here about a 100 executives, you know, CEOs and founders of the top cybersecurity companies in the world and the the top practitioners, you know, chief information security officers of Fortune 500 companies. And the whole idea is to work together and talk about what's next and how we can work together in order to deal with what's upcoming. And there's lots of risk and, you know, on expanding the threat vector. You you probably talk a lot about artificial intelligence and the new capabilities that it brings in your show, but with every technology wave, you know, think about Internet, think about cloud.
Speaker 5:There are new risks introduced. So we kind of spent couple of days together to talk about, you know, the future of cybersecurity.
Speaker 2:Is there do you think there's more more fear or excitement around the technology shift? Because there's obviously this new kind of new threat vector, but at the same time, that creates opportunity for the industry, for product expansion. And so it's go time from a business standpoint to kind of meet meet the threat. But also, I'm sure some people in the room are a little bit scared of what's coming.
Speaker 5:I think there's mostly uncertainty about where this thing is going. You know, what we've seen in the past twelve months is accelerating, and there's there are so many things that we simply don't know. You know, I wish I could stand here and give you all the answers, but in the room, we had a lot of conversation around the uncertainty on one hand and the pressing need to make some decisions about safeguards to make sure that, you know, the Arnold Schwarzenegger Terminator movie Mhmm. You know, doesn't become a mild a mild story relative to the reality in in ten years. And I think that what we are going through right now, we call it, or I call it technological doubling.
Speaker 5:You know? If you think about technology used by us as as a as a race, as human, You know? And you think about, you know, what we have today as the 100 the 100%, and let's say the zero is the invention of writing lot like, four thousand years ago when we lived in caves and started to write in order to accumulate knowledge so so we can build tools, then the 50% is probably at the point in time of the invention of the steam engine, which allow us to build machines that would perform tasks too difficult for men. So the last doubling took hundred and seventy years. The next doubling would take place in the next twenty five years.
Speaker 5:That means that all of us, all the three of us and everybody who listen to or watch the show are the first people that would witness technology doubling within their lifespan. Mhmm. That never happened.
Speaker 2:That's the acceleration.
Speaker 5:Yeah. No living person have seen doubling of technology. And you know what? When you just follow the curve, you know, the curve I just described to you, that means that in, you know, twenty five years, we'll be at 200%, but in a hundred years, we'll be at 2000%. This is just math.
Speaker 5:It's it's not a prediction. This is this is just following the math. Meaning that people that would live a hundred years from now would look at our technology in the very same perspective. We look at the technology used by our ancestors living in a cave drawing something on the wall. That's exactly the same.
Speaker 5:So we are going through a radical change in technology that we have never experienced, and that would require dramatically different type of thinking about, you know, safeguarding the new capabilities. So because this is enormous opportunity ahead of us. You know? AI would change everything about health care. It would change everything about education.
Speaker 5:So there's an enormous opportunity conditioning under the condition that we can control it and that we are not losing control. So that was the core of the conversation.
Speaker 1:Yeah. Very cool. What is what is the shape of the cybersecurity threat landscape right now? It feels like there's entirely new capabilities when I think about, a phishing attack with a deepfaked voice on the phone that's like I guess you could get an impersonator, but it's sort of a new threat area. But then there's also just hammering a coding model to spam a whole bunch of, like, SQL queries or SQL injections, like, all the old stuff, but just multiplied.
Speaker 1:Like, where where are you seeing the biggest new threats emerge, or what are people discussing on on that front?
Speaker 5:It's all over the map Mhmm. Just because of what you said, because it it becomes very asymmetric, even more asymmetric than we used to see, because the guys on the offense, you know, threat actors Yeah. They have a huge advantage because they can simply take a cloth and and just apply it for new attacks Yeah. While the pace of adoption of AI tools and machines within for defenders, within large organizations, within the enterprise is significantly slower.
Speaker 1:Mhmm.
Speaker 5:So that makes you know, that puts AI in the hands of the bad guys much faster than it puts it in the hands of the of the good guys. Yeah. And if that's if that would be the case for long, then the bad guys would win. And the bad guys today are really bad. You know?
Speaker 5:You look at state sponsors attack and, you know, everything that's going on in the world, and that's that's a real risk.
Speaker 1:Is there a bit of, like, a white pill here in the sense that because there's a lag between open source capabilities and proprietary systems, And then you also have the big frontier labs, DeepMind, OpenAI, Anthropic. Like, they're definitely running agents over their user bases and their APIs to know, hey. This person just spent $5,000,000 on our API, and it's all cyber attack related prompts. Like, let's maybe turn them off or figure out what's going on over there. They have a huge incentive to sort of, you know, control their customer base so that their customer base is not using these tools maliciously.
Speaker 1:The hackers sort of wind up on the lagging edge, not on the frontier. But all of the cybersecurity companies like Palo Alto Networks, like CrowdStrike, like the folks that you've had at your conference, they maintain access to the frontier, and so they're always fighting with a bigger weapon. Is that sort of the equilibrium we should expect here?
Speaker 5:That's a great question. And and by the way, we did have today the two the two top cybersecurity experts at Anthropic, you know, the head of security and the head of product security Yeah. You know, sharing the road map and thoughts about about the upcoming capabilities of of Anthropic and and and other, you know, platforms. Yeah. I think the answer to that is our continuous investment in innovation in the space.
Speaker 5:We you know, it's not just about Whiz that I mentioned or or or Sierra, you know, those are large, you know, established startups. Yeah. But we we did we did have one company going out of stealth last week, Onyx Security. All they do is agent security. We had today a major launch out of stealth for another company, Self AI Yeah.
Speaker 5:Which takes care of, you know, leverages AI to to complete tasks much faster for organization. So Yeah. There's definitely there'll be a battle between machines, machines that are used by threat actors and machines that are used by the good guys. And we obviously our job in this world is to identify the best teams, the best talent, and help them build and utilize AI so we live in a in a in a safer, better world, and and that's that's the plan.
Speaker 2:What's the what's the general sentiment from guests around the actual competitive threat of the labs releasing various agents and security focused products. Obviously, they feel threatened when they announce new products because they tend to send the the stock price down or they have over the last month. But is there a real sense that this is a threat the labs could threaten their business models? Or is it more just kind of frustration with with how the market perceives the threat?
Speaker 5:We always overestimate the impact of disruption in the short term, and we always underestimate it on on the long term. So I I think that what we would see here is that in the next two years, the frontier guys, you know, they have different focus and different priorities, so their impact on cybersecurity companies would relatively be low. But in five, seven years, think that they they are they have a chance to to take over. And and I think that we would see three cohorts or three groups of competitors. You you'd see the traditional cybersecurity platforms, you know, the Palo Alto, the Wiz, the the CrowdStrike of the world.
Speaker 5:You'd see the cloud providers, you know, Google, Amazon, Microsoft, and there'll be a third group, which is the AI platforms. And, you know, we shall see who would hold keys for cybersecurity. And, you know, that's you know, if you if you ask me the same question twelve months ago, I would not even mention the third the third group. So it's really hard when technology moves a deep space. It's really hard to make predictions, and I'm not afraid of making a fool of myself.
Speaker 5:I do that almost every day. But but making predictions right now is is is guaranteed to make fool of yourself.
Speaker 1:I was just gonna ask you for another prediction, but I guess I'll table it for next time. Thank you so much for
Speaker 5:taking time. Can share you with you the predictions I made today in in a enclosed room. Please. But the predict you know, maybe I'll start with the predictions I made twelve months ago. You know?
Speaker 5:Sure. Twelve months ago, I told the room that my prediction and and keep in mind, it was really really the big wave of AI. I I told them that I I expect to see a million dollar ARR company, a million dollars in revenues company Yeah. With a single employee within two years. Yeah.
Speaker 5:And I told them that I expect to see a $100,000,000 revenue company with less than a 100 employees within two years. I was wrong twice because those two things materialize in less than twelve months. You know, we've seen we've seen base 44 with one founder Yeah. Who reached 3 and a half million dollars ARR before acquired by Weeks, and we have seen Kerser reaching a $100,000,000 of ARR with less than 20 employees. Right.
Speaker 5:So things are accelerating, and and and and therefore, the predictions I made today were that I anticipate that we would see Fortune 100 company with a security a cybersecurity group of less than 10 employees. Keep in mind that today, those companies employ thousands of employees. So I believe that we would see AI making huge impact on cybersecurity and would turn cybersecurity from a profession into function. That would be a major shift in the market.
Speaker 1:Yeah. Makes sense. Well, thank you so much for taking the time to come chat with us and we
Speaker 6:will talk
Speaker 2:to how these predictions play out. It was my pleasure.
Speaker 1:We appreciate you all day long.
Speaker 2:Risking risking here. Risking it all to
Speaker 1:to take care random prediction off the cuff business, and I completely agree with you. It's it's it's extremely hard to make predictions that hold for any amount of time right now. But it's an exciting time.
Speaker 5:I love it. It's entertaining. Thank so much. Really enjoyed it.
Speaker 2:Yeah. We'll talk soon.
Speaker 1:Have a good one. Take care. Goodbye. Bye. Let me tell you about Eleven Labs.
Speaker 1:Build intelligent, real time conversational agents. Reimagine human technology interaction with Eleven Labs. Let me also tell you about Gusto, the unified platform for payroll benefits and HR built to evolve with modern small and medium sized businesses.
Speaker 2:Alright. Someone in the chat shared this earlier Of but it is important to the Manus deal. Oh, yeah. In the New York Times, China ramps up scrutiny of a Meta AI deal. The country appears to be cracking down on people linked to the acquisition of Manus Wow.
Speaker 2:Singapore company with Chinese roots.
Speaker 1:My
Speaker 2:name as President Trump prepares to visit Beijing. Chinese government is taking actions to penalize people linked to Meta's $2,000,000,000 acquisition of Manus in an apparent effort to discourage Chinese AI executives from moving businesses offshore. Sounds like they want to make an example of them. Officials at China's National Development and Reform Commission, a high level ministry that oversees economic planning including AI, called in Meta and Manus executives for meeting late last week to express concerns about the deal which was announced in December, said the people who declined to be named publicly. Mhmm.
Speaker 2:The scope of the Chinese government actions remain unclear but appears to include an effort to restrict Manus executives from departing China for Singapore. Beijing has issued exit bans in the past for corporate executives who are under scrutiny. The transaction complied fully with applicable law, said Andy Stone, a Meta spokesman, said in a statement, the outstanding team at Manus is now deeply integrated into Meta. He added, we anticipate an appropriate resolution to the inquiry. Manus did not respond, but they are owned by Meta and Meta did.
Speaker 2:So anyways, not not surprised to see China frustrated that one of their great AI teams just kind of Poached. Bounced, poached poached by the Zuck.
Speaker 1:But Zuck is
Speaker 2:hasn't been poached? Had had if you haven't had somebody poached from Zuck
Speaker 1:If you haven't had Zuck come to town
Speaker 2:Work harder.
Speaker 1:Not doing something good. Yeah.
Speaker 2:Work harder.
Speaker 1:Exactly. Shyam Sankar Raanan Loosaran, chimed in on the timeline. He said, what if AI doesn't need to show an immediate ROI but is instead is but instead is the plausible deniability companies use to rift 50% of the workforce they already knew did nothing? And says, the real question here is why is an allegedly cutthroat hyper capitalist economy with every large white collar firm maintained by a workforce three x the size it actually needs to run it run its operations, why would they stop now and not before? And that is a good question.
Speaker 1:Like, the private equity has been, you know, trying to find the right size for every company for a long time. I I I think there's, like, natural bloat that that happens. We were talking about the the the question of, like, you are an AI company if your revenues are accelerating. It would be very interesting to see what companies ramp up hiring this year, obviously, like the start ups and high growth companies are. But of the, you know, older school economy, what management teams are guiding towards more human capital needs, that should tell you a lot about where that management team sees the business and economy going in the AI era.
Speaker 2:Great story here. What happened? Formula One chief Bernie Ecclestone, the 80 year old billionaire, was badly beaten up in a brutal mugging outside of his Knightbridge office last month. I believe this is a this is certainly a historical But undeterred, he allowed his bruised face complete with an impressive black eye to be used in an ad for an Hublot brand of Swiss watches last week with the slogan, see what people will do for a Hublot. That's great.
Speaker 2:That is crazy. It's gnarly.
Speaker 1:Bernie Eggleston Gnarly. Bob Ogden's reaction was, how can I turn this unfortunate event into money? That's remarkable. Yeah. There's a there's a very there's a number of crazy Bernie Ecclestone stories.
Speaker 1:The f one acquired episode goes into a lot of Bernie Ecclestone's history and stories there. It's it's a remarkable episode you should go take a listen to. Let me tell you about finn dot a I, the number one AI agent for customer service. If you want AI to be your customer support, go to finn.ai. Noah
Speaker 2:Smith is sharing some unfortunate news. We certainly hit the gong when the GDP numbers came out in 2025. But the AI productivity boom story is gone, at least for now, according to Noah. Instead, it's all just AI CapEx. Data centers are the only thing keeping our economy afloat.
Speaker 2:Of course, the other thing keeping our economy afloat is all the economic activity that is still there Yeah. Despite a lack of excessive growth. Yeah. But certainly, data centers are making the overall picture a little bit rosier.
Speaker 1:Makes sense. Let me tell you about CrowdStrike. Your business is AI. Their business is securing it. CrowdStrike secures AI and stops breaches.
Speaker 1:Tyler, have you had a chance to fire up Manus again? Take it for another spin?
Speaker 3:I mean, I I had it organized my desktop.
Speaker 1:Okay. Was it effective?
Speaker 3:Yeah. I did a good job, but like
Speaker 1:because you look like a man with an organized desktop now.
Speaker 2:With a suit like that Yes. Can't afford not to have an
Speaker 1:What is what is a good benchmark these days? We need we need a bent a new benchmark like our comedy bench, like our what what is it? The
Speaker 3:The shrimp fried rice?
Speaker 1:Shrimp fried rice bench. We need a benchmark for desktop, something with a very disorganized desktop that would be difficult.
Speaker 3:I mean, I feel like that's not a good benchmark because that's, like, not. Yeah. Arbiter. You don't need to use that very often. But it's something like interacting with actual applications.
Speaker 3:Like, you open Premiere and edit a file? That's a great computer use
Speaker 1:Yeah. Benchmark. Yeah. Yeah. When I was
Speaker 3:We're very far away from
Speaker 1:that. Yeah. When when I was thinking about I was thinking, like, I would like to be able to pick a song and then have it go and find stock footage, AI footage, movie clips, and cut together a Vibreel to the beat like I see on Instagram from just a prompt. If I just have an idea of, oh, I like this song. I would imagine that this song with this footage would go really well together.
Speaker 1:That's like several hours of work. I could make a lot more of those videos, have a lot of fun with those. Meta Vibes is a little bit of that because you you pick a song and then you can generate one mid journey image and do some light animation on top, but it's not truly finding iconic footage from around the Internet. And that feels like something you could do in OpenClaw. You could do with a with a with a co work product or or Manus.
Speaker 3:Yeah. Because, I mean, you can just use, like, FFmpeg, and you can cut videos down from the terminal.
Speaker 1:Yeah. So it could They could do it
Speaker 3:without even using computer use technically.
Speaker 1:But the models could do that. Is that what you're saying?
Speaker 3:Well, I'm saying, like, Cloud Code could do that. Right? Yes. You can tell Cloud Code to edit a file Yes. And edit a video from this
Speaker 1:type of And it should download FFmpeg, do it, but who knows how good it is yet? That okay. That's our new benchmark. Music video driven by the. Let me tell you about console.com.
Speaker 1:Console builds AI agents that automate 70% of IT, HR, and finance support, giving employees instant resolution for access requests and password resets. And without further ado, we have Anna Patterson from Ceramic AI here. What's going on? How are you doing, Anna? Good to meet you.
Speaker 2:Great to
Speaker 1:meet you.
Speaker 4:You too.
Speaker 1:Thanks so much for joining.
Speaker 10:Are you guys, yeah, are you guys having a good Saint Patrick's Day? See the green.
Speaker 1:Yes. We're very Yeah.
Speaker 2:Greened up. Yes. Greened up.
Speaker 1:We we we have these bright green suits from a show we did on Black Friday about Shopify. And Okay. And I thought, certainly, I will not be using that until next Black Friday, but here I am on Saturday.
Speaker 2:Here we are.
Speaker 1:Here we Well, happy Saint Patrick's Day to you. Since this is the first time on the show, please introduce yourself and the company.
Speaker 10:Hi. I'm Anna Patterson. I'm a founder of Ceramic AI.
Speaker 8:Mhmm.
Speaker 10:I was a longtime Google engineer. I started in 2004, and I'm best known for building large search engines.
Speaker 1:That's amazing. Great. Yeah. Get give me the pitch for Ceramic AI.
Speaker 10:So Ceramic brings the cost of search in line with the cost of inference. As you know, inference costs have been going down and down and actually inference is faster and faster. But search is $5 to $15 per thousand searches. But inference is maybe $0.50 per 1,000 searches. So, the kind of analogy I like to use is tacos and salsa.
Speaker 10:Right? Tacos is kind of the meal and that's kind of what inference It's the thing that's really delivering intelligence to your application. But salsa kind of makes it better, right? But search is now the dominant cost. In tacos and salsa, you're adding search, but it's $5 to $15 per thousand queries.
Speaker 10:So it's really kind of time to bring salsa in line with tacos. So ceramic is 5ยข per thousand queries.
Speaker 2:Amazing. Right. So you're saying this historically, it was like you were getting a taco and it costs you $5, but then they were like, well, if you want salsa, it's gonna cost you an extra like 50 you know, $500. Yeah. And you're like,
Speaker 10:well, I don't
Speaker 2:not sure I really want salsa. It's a weird
Speaker 4:weird Exactly.
Speaker 8:Exactly. Okay.
Speaker 1:So help me define help me understand what search means. Because search can mean over the Internet that's already sort of baked into LLMs. It can mean web active web search, like, in a proprietary database or just the open web. How how are you thinking about the surface area of search?
Speaker 10:Yes. So we do have a 40,000,000,000 page web search.
Speaker 2:Mhmm.
Speaker 10:And that is the open web. Mhmm. And then we've built proprietary systems as well.
Speaker 6:Mhmm.
Speaker 10:So one of the things that we're announcing is this idea of supervised generation that as the model is generating, it's double checking what it's saying with search. And it's also double checking with search, what else should I say to make a comprehensive topic.
Speaker 1:Mhmm.
Speaker 10:And so that way, you can really enable new applications with 10 x fewer hallucinations, but also make the whole product affordable and fast.
Speaker 2:Okay. So who who do you sell? I sounds super valuable. Who do you sell this to? Is this gonna be maybe similar to kind of like the data labeling market where there's like five customers that like really matter and you wanna get all of them?
Speaker 2:Or are there a bunch of other applications that you wanna actually sell to vertical specific, you know, AI Yeah. Applications that can, vend you in in like kind of in line with their LLM products?
Speaker 10:Yeah. I think there are kind of two strategies there. You mentioned one of getting all the big players. That definitely would be nice. But we also have a self serve every agentic workflow.
Speaker 10:We have one startup. Their agentic workflows do 1,100 searches. So our search engine responds in fifty milliseconds. So it's both affordable and more real time than the other search engines. So we see a number of agentic workflows happening.
Speaker 10:But to your idea of a custom index or a custom application, we see that as well because let's say you're a pharmaceutical company or some banks are very privacy centric, they don't want their searches going out and they don't even want their searches to models going out. So they kind of host their own copy of a model. And here, they host their own copy of search as well so that they get their own proprietary environment for all their agentic flows inside their enterprise.
Speaker 2:Mhmm. Makes sense.
Speaker 1:Talk about just how the open web is changing in the age of AI. I I've seen some crazy stats about how much more of the Internet Googlebot sees because everyone has been indexing and been very friendly to Google for a very long time. Other publishers are getting more closed off. How easy is it to actually search the Internet broadly these days?
Speaker 10:So the 40,000,000,000 pages that we have are available on the open web. Mhmm. We do not disobey, or I should say we obey robots. Txt. So we don't actually crawl new sources that have blocked us, but we are in active talks to make deals to them and to have a proprietary API that costs more, but also reflects back revenue to those proprietary sources.
Speaker 1:Cool. Are are you are is there any value in having like, the I've always been interested in the flip side of Google Search versus Google Alerts where the search is happening internally, and then and then the information's actually getting pushed to you. That product is, like, probably, like, point 00001% as important to Google as search, but it's always been interesting to me. Is that interesting to you? Is that relevant in the age of AI?
Speaker 1:Does anything change about that ratio going forward?
Speaker 10:One of the interesting things Yeah. Is that inference, a lot of times with these MOE models, inference has a lot of spare compute because it's memory bound. And so with that, it means that it could be thinking. So as it's inferencing, it could be getting a stream of search results, searching all the time and actually bringing you sort of like alerts, only the most interesting information or information that it doesn't think that you already know by looking at your history. So I think it's going to enable a lot of new applications.
Speaker 10:Amazing.
Speaker 2:Jordan? Wildcard question. Hit me. How long until we see ads in Gemini? There's been some reporting this week.
Speaker 2:Obviously, Demis had come out and said, you know, why would we put ads in Gemini?
Speaker 1:For the record, on this show, we are extremely pro ad.
Speaker 2:Extremely pro ad.
Speaker 1:We love it.
Speaker 10:Exactly. Yeah.
Speaker 2:And and I and I we both expect ads to be in Gemini. I would say this year is my guess.
Speaker 1:But
Speaker 10:That's probably been my guess as well before the end of '26. Yeah.
Speaker 2:Let's go. Cool. That gives me a lot of hope.
Speaker 1:We're gonna be very excited
Speaker 2:about And excitement.
Speaker 1:Faith faith in humanity restored. What's what's
Speaker 2:the story of the the pink guitar on the wall there?
Speaker 10:Yeah. Well, if you want to do something really humbling, learn an instrument from your children.
Speaker 1:Oh, okay.
Speaker 10:They're they're absolutely brutal with telling you to practice everything you're doing wrong. Yeah. And and so I kept borrowing my daughter's electric guitar Mhmm. After she taught me acoustic guitar. And so she decided to get me my own.
Speaker 1:Oh, god.
Speaker 10:Because she saw her guitar, like, laying on my couch. Mhmm. And she said a guitar should be hanging on the wall. And I said, I think I've been told to clean up my room by my child.
Speaker 2:So That's amazing.
Speaker 10:My work here is done.
Speaker 1:That's amazing. You're great. Well, thank you so much for taking the time to come chat with us.
Speaker 2:Yeah. Have a great
Speaker 1:rest of
Speaker 2:your day. Excited to follow Ceramic, and we'll talk to you soon, Anna.
Speaker 1:We'll talk to you soon. Thank you. Goodbye. Bye. Of ads, let me tell you about AppLovin.
Speaker 1:Profitable advertising made easy with axon.ai. Get access to over 1,000,000,000 daily active users and grow your business today. And without further ado, we have our final guest of the lightning round, Jake from Gecko Robotics. Jake, how are doing?
Speaker 2:What's going on? Doing great.
Speaker 1:Thanks so much for taking the time. I don't believe we've met, but I I've heard about Gecko for a long time. I think Trey introduced me to when I was at Founders Fund. But since this is the first time on the show, I'd love a little bit backstory. Like, how'd you get into the industry?
Speaker 1:How'd you start the company? And then we can kinda get up to speed on what's happening today.
Speaker 8:Yeah. Well, Trey's awesome. Yeah. And I'm so glad he he originally spoke so highly of us, I'm sure. So I founded the company about thirteen years ago out of a college dorm in Western Pennsylvania, about an hour north of Pittsburgh, Pennsylvania.
Speaker 8:And I was studying electrical engineering, really wanted to figure out how things like energy was created, and so I went to a power plant in Franklin City, Pennsylvania. Yeah. For those history nerds out there, was like the that was where the first commercial oil rig was drilled. Mhmm. And got to see a power plant how it was made, and I dove in headfirst through this little manhole that I could barely fit through and and I got to see what a boiler was.
Speaker 8:And so, that was like a football field sized room Yeah. That was completely covered wall to wall with these these steel tubes. Yeah. And this whole job of this boiler was just to get really, really hot. Yeah.
Speaker 8:And so, you know, shot water through it, got really hot. Anyway, this boiler kept on having failures. 30% of the year, it was shut down because of catastrophic failure that was occurring because the pressure vessels would keep keep exploding. And the only way to stop it from exploding was a guy on a rope a 100 feet up in the air, you know, trying to figure out, like, where the next explosion was gonna happen, and that just wasn't working. And that guy actually died that's a year before doing the job of gathering data sets by hand in the real world.
Speaker 8:Wow. And so I was like, my gosh, like, where is the tech innovation for these guys that are making sure that our homes stay heated and just begin to look more and more at just, like, how we understand the health of built the built world and and also what kind of technology exists for these sorts of heroes that are, you know, that are hidden behind the scenes, if you will, whether they're a port engineer or they're a a boiler they're a boiler engineer and folks that are just, like, helping us helping us do all this stuff. Yeah. And Silicon Valley, for the most part, forgot about them, and I decided to build a company that was specifically dedicated to to helping these guys out.
Speaker 1:Yeah. So, yeah, I'm imagining a big vertical tank sort of like what you might see at a brewery with, but instead of filled with beer, it's water that's boiling. Why not just fly a drone up and use video camera and and, you know, just be close? Why did you choose this? Like, what what decisions did you make technically?
Speaker 1:Yeah. And why did you make those decisions?
Speaker 8:You basically if you're diagnosing the health of build structures, whether it's a boiler, a pipeline, a bridge, you know, whatever it is, inside of a ship, you gotta actually get close to it just like you would for a sonogram. So you use jelly, and then you use ultrasound to see inside of a a of a belly for for the previous example. Yeah. Same kind of idea. Use ultrasonics as one way of gathering information datasets.
Speaker 8:In this case, you know, what you're seeing is some of the electromagnetics that we developed into the sensors. And the robots are just the vehicles by which we get sensors around places that are typically hard to reach.
Speaker 1:Got
Speaker 8:it. And then also localizing and seeing, like, where, you know, to track that year to year to year to understand how do you predict into the future. I mean, this idea of creating the minority report, you know, for the built world was kind of this idea of predicting, you know, what death or catastrophe was gonna happen for, like, build structures before it did and the precogs, in this case, of these robots. Yeah. You know, with with all the data is being collected and then, like, fed into the central source of truth, which is which is cantilever, and then we sell cantilever, you know, as our way of predicting and preventing catastrophic failures for, you know, the built world.
Speaker 1:Yeah. So what's the shape of the business? It feels incredibly dual use. I mean, we just saw a video of the the robot crawling along what looked like an aircraft carrier or battleship. But I imagine that the oil and gas industry, the industrials industry, like, there's huge demand for this.
Speaker 1:What's the mix of the business, and what's what what are the best practices for working with both the government and private businesses?
Speaker 8:Yes. We started in the energy sector in those power plants. I actually bootstrapped for three years and then, was down a $100 to the name. And Oh. I know.
Speaker 8:It was bad. I was, like, homeless, living with my best friend, sleeping on the floors. Yeah. Really roughing it. And, you know, I was out in Pittsburgh, Pennsylvania.
Speaker 8:There's no VCs out here. Yeah. And so then then before I got an acquisition offer and then, you know, and then the folks over at YC in 2016 were just like, you're gonna build a huge trillion dollar company and, like, this is you're having incredible success. Come out and
Speaker 1:do that. That's awesome.
Speaker 5:So I
Speaker 8:was like, I'm already poor. I wanna this
Speaker 4:is an amazing thing. Poor. Why not? I
Speaker 8:already know what the worst feels like. You know what mean? So it's like, what could you what could be worse than this? And so, you know, just decided to do that. Got one up to California.
Speaker 8:And, yeah, we're in this YC batch. It's like really, you know, just like black sheep of the batch, you know, working in energy and robotics space in Pittsburgh, first time founder. So, you know what I mean? It was just like a it was kind of kind of wild and crazy. Mhmm.
Speaker 8:Then we came out like one of the top companies. But, you know, we started in the energy sector because that
Speaker 1:What was what we were was the actual first company? Because, like, energy sector could be, you know Power. Meeting with the CEO of Axon, or it could be, like, the local guy who just, you know, wants to buy these as, like, a prosumer tool almost or something. Like Yeah. What was that actual deal like?
Speaker 8:It was it was this group of power producers called IPPs. Okay. So independent power producers. So these guys were just like they actually don't have these, like, massive contracts utilities have that they can just, like, rest and just, like, pass all their losses down to me and you to pay more bills if our, like, if things just, like, blow up and don't work. Yeah.
Speaker 8:And so these these folks, like, actually have to be making sure that they make stuff. Yeah. And so in PA, there's a lot of them actually in the tri state area. And so we actually get a lot of access, you know. So the first three years I was bootstrapping, I was every single day at a power plant, for the most like trying to build the robot, like actually in the environment.
Speaker 8:And that was a really core thesis and core principle for the company, and we kind of still hold that today of build technology in the in the real world, not in the labs. Actually, of the most prominent investors in Silicon Valley said, don't don't leave, throw this in a lab, make it autonomous, and then launch. I was like, that's fucking stupid. That's so bad. A tough idea.
Speaker 8:And today's like, you know, top three investor in the world, you know, saying, well, the company's worth, you 10 plus billion. Was like, me and my Just
Speaker 1:one shot it.
Speaker 2:Yeah. Don't don't iterate. Don't don't don't talk to your customers. Trial and error, just one shot it in So, the
Speaker 8:anyway, so Power Man, it was like, I moved back to Pittsburgh because it was close to these customers. And so, that was the core ethos of the company, was build tech for the people in the environment. And so, that led us to oil and gas. Then next, we can apply the same kind of technology to diagnose the health of structures that make the oil and gas, assets go. And then it was mining and metal metal manufacturing, and then it began to get into things like building and manufacturing, for, building ships or or or submarines.
Speaker 8:Yeah. Injecting the same kind of tech and robotics and software, you know, there because we're one of the like, we have the most data about the health of and the material science of the world. And so applying it, you know, for actually building and welding. And then and then on the Navy side, you know, what we what we're doing there is helping to achieve readiness. 80% readiness is the secretary of Phelan's objective.
Speaker 8:And, you know, right now, it's about, you know, two out of every five ships are are stuck in dry dock somewhere, and that's a global issue. And so our technology allows for us to be able to make up that difference, by getting as so much information, you know, in some cases, two to you know, two, three, four months faster be able to get these ships out of dry dock in time and and then begin to plan it for the future. It's just like idea of if you always had a living, breathing understanding of the health of these these sorts of assets, my goodness. Like, maybe you'd never have to shut down. You know, that's that's the thing that I'm trying to build and, you know, these you can you can see now that some really large energy companies, like the ones that we work with
Speaker 1:Yeah.
Speaker 8:Are beginning to adopt this, you know, the data doesn't exist. In order for the data to be able to drive AI models to actually be impactful, we actually have to go out and gather information and datasets. And, oh, by the way, maybe one day, maybe not into the distant future, you can actually begin to augment these, you know, these very commoditized sectors and industries that are very capital intensive, you know, with robotics native, AI native approaches and operating systems that make commoditized industries less and less commoditized. And so, that's the kind of vision that we're trying to build, you know, being the company that's, you know, that's very pragmatic in its results and, you know, aren't just promising in five or ten years all this impact, but, like, actually delivering the thing today, you
Speaker 1:know? Yeah.
Speaker 2:Where are you are you I'm assuming the same kind of VC that told you to just one shot the product in the lab, make it autonomous, and then go to market would also ask you, are you doing anything in data centers? Can you? Can you get any Mhmm. Are there any tailwinds there? I can imagine I can imagine one of your robots just crawling around a data center.
Speaker 2:But what what are you seeing on that front?
Speaker 8:Yeah. Well, it's a it's a good question. You know, what ended up happening was we ended up really putting a lot of effort and energy into customers where when things weren't working, it was extremely painful and expensive. You know? So think of oil and gas when you're down for a day, could be $30,000,000 you're losing.
Speaker 8:Or if you're down and if your ship isn't, patrolling, you know, the, certain places in the Pacific or Atlantic, you know, that's really expensive and and harmful too. So that that's where we focus. But what's happening now is, like, all the attention on AI infrastructure, has actually put a large attention on how efficient can you run your your power plants, in this case, a lot of natural gas, and then also how reliable are those assets. And also the assets that were depreciated, not invested in for ten, twenty, thirty years because banks wouldn't fund continuation of, you know, putting capital into coal facilities or even natural gas facilities because of, you know, these these carbon these carbon strangleholds on these companies. It ended up creating this really interesting opportunity for companies that are really good to understand the health and the value of assets and can convert them into something, you know, really much more valuable, something that we're taking a very close look at.
Speaker 8:So, anyway, power and the ability to make a power plant run more efficiently and also more reliably is core competency of Gecko for a long time. And so we're putting, you know, a lot of efforts into that. You'll have a big launch, you know, in the in the middle of for the two hundred and fiftieth anniversary on and coming out on July 4 on the power production side. So it's you know, we've got something that's you know, if we pull off what our what our, you know, six or or or so trials have have proved, we might be able to, in the thermal fleet, be able to increase by 15 to 20 gigawatts the amount of power in The US without even building a new a new power plant. Woah.
Speaker 8:It's it's this kind of efficiency, man, that, like, is
Speaker 4:possible. No.
Speaker 8:Yeah. It's alright. We have a $71,000,000
Speaker 2:I know. Let's hit the gong. Let's hit the gong. Let's freaking go. Where's the mallet?
Speaker 2:That's a big number.
Speaker 8:Hit hard. Hit hard. Oh my god. Feel the vibrations over here.
Speaker 2:I know. We're feeling it. I wish I wish you were here.
Speaker 1:For for a much dumber question, how how do you actually stick a robot to the to a vertical surface? I mean, Gekko, I imagine suckers, but are magnets involved? Like, I imagine that there's some surfaces where suction cup won't work because maybe it's, like, stuck out or something. Like, do you have multiple tools in your toolbox? Like, talk me through that.
Speaker 8:Yeah. Good question. Well, I really was desiring to use nanofibers just like a gecko we use. Unfortunately, we can't live up to the biology. And so we we actually use neodymium.
Speaker 8:Why not just
Speaker 2:get hundreds of geck actual geckos on? Yeah. Yeah. Chariot Chariot
Speaker 4:It's a
Speaker 1:biotech company, actually.
Speaker 8:I actually That's a great idea.
Speaker 1:That's a you use so you use rare earth magnets. Is that right?
Speaker 8:Yeah. So we put them in a in a Hallbach array instead of wheels to optimize, you know, pull force, so magnetic force into the typically, like most of our infrastructure is Yeah. Is carbon steel, so it's magnetic. If it's not, if it's concrete or if it's, over stainless, then we'll use the best adhesion is actually suction for nonferrous materials. Like, you were right.
Speaker 1:Okay.
Speaker 8:So it's actually just like creating a really great a really a really great Like a vacuum. Vacuum. Yeah. And and so that can actually
Speaker 1:So you have air pump that's sucking in air and and creating that, like, waste?
Speaker 8:Yeah. Literally, it's a dice that vacuum, like, you know, head. And and then you create, like, a nice chamber, you know, for for, you know, for you to get a stick. Yeah. Unfortunately, it's, like, it's not great for dusty environments, but, so like, you know, you can so it's, like, it's less but, also, there's less less cases.
Speaker 8:Like, so so the food and beverage space, a lot of stainless steel. Sure. And so there's there's not much dust there either, and so you can actually apply cool there.
Speaker 1:Okay. That makes sense.
Speaker 4:But it's
Speaker 8:the same kind of concept too, which is, like, you don't want a crack. You don't want a failure to occur.
Speaker 1:You don't want to damage the material you're inspecting with some crazy, you know, rock climbing shoe with spikes on it. You're not going be ice climbing up the side of something that's gonna Totally.
Speaker 8:So, really a really geeky cool thing that we built for the Navy was you typically have, like, you know, when you when when Maverick lands his, like, fighter jet onto the plane onto the onto the aircraft carrier, he's landing on this, like, this this coating, this non skid is what it's called. It's, like, really rough and grainy. You have to actually remove all that stuff every, like, three years or so to be able to evaluate how healthy, you know, is the platform, is the flight deck itself.
Speaker 1:Because there might be a crack
Speaker 8:that And would need
Speaker 1:you gotta take all that off. Got it.
Speaker 8:Totally. And so, just so you even know, like, you know, how healthy the structure is underneath it. Yeah. So, we've developed technology that actually, you know, uses electromagnetics to excite the surface. You can actually measure how healthy things are underneath the the non skid.
Speaker 8:Sure. You know, it's like things like this where it's like, you just remove this, like, you know, this this orthodox way of thinking about how do you figure out, like, what's wrong, what to fix, what budget to put to use, and where does the supply chain, you know, how does the supply chain meet my need in terms of what what things I need to fix and get this ship, you know, back back patrolling and deterring conflict. Yeah. It's like this kind of stuff that we we, you know, have just, like, built, you know, such an expertise and now models around, you know, ensuring we we get all this information data set. So it really is like you know, it's going from, like, a three to four month process to to now click a button and you know exactly, like, where to make all the repairs.
Speaker 8:And and the next time you do it, you know, you you spend a lot less time in in the in the in the dry dock, and you should do the stuff more and more just, like, as it's as it's, like, you know, patrolling and on duty. So, like, robots, you know Yeah. Specialized robots that can be, like, on these ships, that's, like, that's the that's the way
Speaker 1:we wanna work towards. In the in the basic example of, like, there's a boiler, the gecko robot climbs up, finds the crack, let's say. I don't know exactly what happens, but
Speaker 8:Erosion, corrosion, cracking.
Speaker 1:Erosion, corrosion. Are you thinking about an act two where you're actually doing repair as well? Are you already doing repair? Or is that something where it's like, oh, that's a two that that that's a big technological challenge?
Speaker 8:It's a great question, and and it's something we're working on right now on the manufacturing side. But it I think the key was with the first idea was, like, what if you could own the health data of the world's most critical piece of infrastructure?
Speaker 1:Yeah.
Speaker 8:That that'd be cool. That's amazing. That seems like a pretty interesting, you know, thing to do. And then Yeah. Yeah.
Speaker 8:You know, then, like, you just begin to get into if you could understand the health of things, then you begin you know, then, like, you can just pull an existing information, time series datasets. Like, these are the kind of things we pull in now. And so, you know, if you're a power if you're a power plant or you're a refinery and you wanna you wanna really capitalize on the fact that, you know, oil oil barrels per day is a $100 a $100 right now. And so you want to maybe increase production. Mhmm.
Speaker 8:Well, you don't know if you can. This is where, like, the consultant with software and AI companies really have a hard time is because you don't know if you push the asset, will it, like, break down and not work and explode. So that's what where the missing dataset that never existed for that atoms to bits side of the robots allows for us to be able to make these operational changes now, and which is a lot more valuable than actually fixing the problem right then and there. So we're going after, like, these big these big, like, value propositions. And if you think about the ability to run functional, finite element analysis, so, these, like, ANSYS type of, like, you know, models.
Speaker 8:Like, these are sorts of things that Gecko's working on because we have this unfair data advantage that are we've been collecting for thirteen years on, you know, half a million assets now inside of cantilever. And so Over what are you getting So what we're trying to get to, man, is like, you're you're totally right in, like, where the where to go. Like, you wanna find the problem, fix it right then and there
Speaker 1:Yeah.
Speaker 8:Using these tools. And so you wanna build towards that, but, you know, you wanna be really smart on, like, the best techniques, the best kinds of approaches, and the ways to fix things, and solve the most valuable thing first, then, you know, keep on solving more problems that your customers offer you. Awesome. But, man, I think the future is really gonna be it's gonna be interesting because these, like, capital intensive industries
Speaker 1:Yeah.
Speaker 8:You know, that can adopt technology, like we're talking about here, really, really quickly are gonna be so unfairly advantaged. And this is, like, where, you know, we're we're, like, in those rooms creating the strategies, you know, for the top, you know, top 10, top 15, like, you know, large oil and gas companies in the world, helping them with, like, how do we make and optimize the future because, you know, the future's gonna really belong to folks that can figure out how to, you know, run this stuff unfairly.
Speaker 2:Yeah. Well, the chat absolutely loves you. Yeah. So come back soon. Yeah.
Speaker 2:This was really this was really fun. Congrats congrats to the whole team on the new contract. And I'm so glad you started this company fourteen years ago. You're not coming on just pitching pitching the concept, but you've actually done the the heavy lifting to figure this stuff out and and, yeah, the opportunity Yeah. That's amazing.
Speaker 2:The scale of it is is just insane. Yeah. Have a good appreciate it.
Speaker 8:Nice to you, brother. Jake.
Speaker 1:Thank you. Happy You too, man. Saint Patrick's Day. We'll talk to you soon. And I will tell everyone about Turbo Puffer, serverless vector and full text search built from first principles on object storage, fast, 10 x cheaper, and extremely scalable.
Speaker 1:We have some good news. For just $3,900,000, you can fly private to every ray every every f one race on the calendar this year. It's called the ultimate experience. All 24 races, $3,911,100 per person. So don't think, oh, they're flying me.
Speaker 1:I'll just have my buddies tag along. You're gonna have to put your buddies in a really large suitcase.
Speaker 2:It's so funny because you for some people, they would have to pay them $33,900,000.0 to go to all 24 races because because it's not just if you're really doing this Yeah. On a fixed schedule, it's like at least four days a week, 20 Yeah. 24 times a year. Yeah. And this feels like a full time job.
Speaker 1:Also, there's
Speaker 2:There's some AI researchers out there that would just say, like, I can make
Speaker 8:Yeah.
Speaker 1:There's So a wrinkle here. They they they they're saying, hey. Flat price for private jet to all the races. And they're like, well, like, even if you're based in Paris, like, there's a race over there. You know, you're based in Miami.
Speaker 1:There's a flight over there. They haven't considered the fact that if I live off grid in Alaska, it's gonna cost them $10,000,000 to fly the the private jet to some remote landing strip that I've constructed outside of Nome, Alaska.
Speaker 4:Is there is there, like, a
Speaker 2:SaaS company that could take advantage of this? Basically, being like, we know we've got a bunch of buyers here. We're gonna take our top rep Yeah. And we're gonna have them jetset around the world 24 strike missions.
Speaker 1:Yes.
Speaker 2:Make make it math out. Yes. If you're selling bigger deals,
Speaker 1:spend it in a conference room
Speaker 4:Exactly.
Speaker 1:Doing demos the whole week.
Speaker 3:The whole time.
Speaker 2:Yeah. Demos and
Speaker 7:then hop
Speaker 1:back on the jet. Somehow, I think that if you price these flights individually, it would be cheaper. But it is very funny. I'll I'll Not like much you're
Speaker 2:talking about going. It depends on the jet.
Speaker 1:Yeah. I mean, it's a lot of it's a lot of travel, a lot of miles, a lot of hours. But $4,000,000 is a lot of money, and I think it gets you a lot
Speaker 4:of flights.
Speaker 2:This this 20 going to all 24 races
Speaker 8:Yeah.
Speaker 2:If you're not a driver
Speaker 1:Yeah.
Speaker 2:Or working does not sound fun.
Speaker 1:Also, they didn't they just said private jet travel between every race in the calendar. They didn't tell you how many other people are on this private jet.
Speaker 2:That's what I'm saying.
Speaker 1:Imagine you hop on and then there's 17 stops while they pick up other people.
Speaker 2:Unfortunately, standing room only this
Speaker 1:Southwest vibe.
Speaker 2:Yeah. You know?
Speaker 4:Yeah. Yeah.
Speaker 1:Yeah. Anything could happen. Anything could happen. But we'll let you decide whether or not you want to spend $3,900,000 to fly private to every f one race on the calendar. If you do, let us know.
Speaker 1:Give us a review. Send us a message. Subscribe to our newsletter at tbpn.com, then email us when we email you the newsletter and tell us how the private jet experience was, the ultimate experience.
Speaker 2:Last but not least, the new Roadster is apparently gonna be unveiled next month.
Speaker 1:Hopefully. Hopefully. If Elon puts hopefully in a tweet, I'm
Speaker 2:He says very will be it will be a banger next level.
Speaker 1:I'm very excited. And Travis Kalananik said, when I've run into people who are in the know, I inquire. They tell me nothing, but their eyebrows raised and their eyes widen in a way. They can only mean something of sorcery and magic is coming. Hopefully, it's a flying car.
Speaker 1:And I hope that it is revealed in April. He should he should unveil it in April 1. Everyone would be so confused. It'd be very funny. If not, I can wait till May.
Speaker 2:Flying car.
Speaker 1:Can wait till May. But I'm I'm very I'm very excited for the next Roadster. Expectations are incredibly high. There have been so many electric supercars. A lot of depreciation.
Speaker 1:Not a lot that have filled that, like, special territory. Even the electric sports cars have not done well. The Boxster
Speaker 2:My sense is, like, it's not gonna be, like, a true supercar. Yep. It's gonna be a Turbo S, the kind of thing that people are gonna just daily. Daily. And it's super fast.
Speaker 2:Yeah. It's fun.
Speaker 1:But it probably I mean, I would expect the kind of thing
Speaker 2:you keep, like, with low miles because you expect it to appreciate.
Speaker 1:I I I would basically expect, like, a Remasto Nevera, like, a which is, like, a $2,000,000 car, but at, like, a $200,000 price point. Everyone's like, it goes zero to 60 in one point six seconds or something like that. Like, this it's going to have some headline stat that everyone debates and is like, oh, well, technically, it wasn't this. Blah blah blah blah blah. But it will be, like, shocking in its own way, and I'm sure people will have a lot of fun with it.
Speaker 1:So excited to track that story. Well, thank you for tuning in to TBPN today. We will see you tomorrow.
Speaker 2:Go have The best day. Maybe have
Speaker 1:the stars on Apple Podcasts and Spotify.
Speaker 2:It's been an honor.
Speaker 1:Why don't you throw that flash bang? Throwing flash bang. We'll see you tomorrow. See tomorrow. Goodbye.