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Technology's daily show (formerly the Technology Brothers Podcast). Streaming live on X and YouTube from 11 - 2 PM PST Monday - Friday. Available on X, Apple, Spotify, and YouTube.
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Speaker 2:Today is Wednesday, 11/19/2025. We are live from the TVPN UltraDome, the temple of technology, the fortress of finance, the capital of capital. Ramp.com. Time is money. Save both.
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Speaker 3:What do we got today, John? What's your take?
Speaker 2:My take is, does do you want iMessage in Gemini three? Do you want iMessage in your AI assistant, in your personal superintelligence? After Meta Connect, we left saying, wow. The virtual reality the Call of Duty heads up display is here. It's arrived, the Meta Ray Ban display.
Speaker 2:And and the technology was really cool. Like like, the glasses didn't look that crazy, and the heads up display, like, actual HUD was really high quality. Like, you could actually read what was going on there. But where we left it was, wow. If it doesn't work with iMessage, I can't imagine wearing that because my whole life is iMessage.
Speaker 2:Yep. And and I and I was just kinda reflecting on this idea that, like, iMessage has kind of emerged as my personal ERP system. Remember when VCs used to be like, oh, we need a personal CRM. Yeah. And it was like, you're you've just turned every one of your personal relationships into a business relationship.
Speaker 2:And now New transaction. Should be using an actual CRM. And many VCs do use actual CRMs even if it's like catching up with coffee with a buddy from their MBA program or whatever. Like, people will track that because it makes sense. These are professional relationships, so they should be professionally managed.
Speaker 2:Maybe in a CRM like Adio, adio.com.
Speaker 3:The AI native CRM.
Speaker 2:The AI native CRM. Where is Adio here? I have a new, I have a new list.
Speaker 3:I'm getting I'm getting the blood flowing this morning.
Speaker 2:I'm glad.
Speaker 1:I'm enjoying some some movement.
Speaker 2:But personal CRMs never took off, and I noticed that, like, iMessage has kinda become, like, my personal data lake, my personal ERP system. Like, it's my single pane of glass. Like, if it's it's the it's the source of truth. Yeah. It's like the it's like the system of record for my personal life.
Speaker 2:And also, we use it for business and stuff. I don't know how unique I am. I feel like a lot of people are are are stumbling into this world, sleepwalking into this world where, they they bought the iPhone. They were like, yeah. It's cool.
Speaker 2:It's got all these apps. Like, I I could switch to a different phone. And, like, truly, you can't if your whole life is in iMessage because there's so many different chats. There's so many different, like, you know, the images and, like, iMessage has really, really grown to the point where it's not just, like, one on one text messages. It's all these group chats.
Speaker 2:It's sharing of locations and and documents files. These that were shared,
Speaker 3:you know, PDF that was shared over a year ago.
Speaker 2:Totally. Totally. And so and so my question is, like, it seems like like iMessage is important for the heads up displays for the for the smart glasses. Yeah. Will it be important for Gemini?
Speaker 2:We were debating this. Like, right now, iMessage, when you go in there, like, the the the only AI experience you see is, like, those Apple intelligence summaries, which are sometimes very funny. I I was laughing at that. It it's summarizing one is it declared over because, you know, someone if someone says it's so over
Speaker 3:It's so
Speaker 2:will just, like, rewrite these. It doesn't get the jokes. Other times, it'll just say PNG image shared. And, like, sometimes it's funny. Sometimes it's it's a little bit useful.
Speaker 2:But, in general, I think that all the Apple intelligence features will get better with Gemini three. We saw in the benchmarks. We demoed the product. Gemini three is definitely a great model, the best model potentially right now. Apple will be able to implement that all over the place, and they just won't have to worry about, like, do we have a good foundation model to build on?
Speaker 2:Yep. So they'll be able to stuff it everywhere. But what does the actual flowback look like? Because Google and Apple are famously like walled gardens. Like, they Yeah.
Speaker 2:Can't really just interface with them. Some of
Speaker 3:the best walled gardens of all time.
Speaker 2:Some of the best walled gardens of of all time. And I was wondering about if you if I'm if I'm using so the average consumer will just see Apple Intelligence, and they'll really just see Siri. And they'll be like, when I ask Siri the history of the Roman Empire, it does a great job giving me the history of the Roman Empire. It doesn't necessarily get confused and hallucinate because it's using Gemini three under the hood. But the the consumers, I don't think, will ex will expect if they wander over to Gemini three hosted on Google Cloud Platform or Google AI Studio.
Speaker 2:Go to aidot, go to we Gemini three Pro, Google's most intelligent model with state of the art reasoning, next level vibe coding, and deep multimodal reasoning. Ai.studio/build. That's the URL. The, I I I think I think people won't necessarily expect that that if they're interfacing with Gemini over in Gemini world, in the Gemini app or in Gmail, they won't expect it to connect to their iMessage even though it's the same model that's Yeah. Powering both of those.
Speaker 2:And Apple will say that that's for privacy reasons, and consumers won't know to ask. But I'm kind of curious about that because that would be an interesting feature, and I don't know if you would even want that. Like, would you want to be able to go to the Gemini app and have it be able to pull a, you know, a file that was shared with you in an iMessage group chat and then do something with that in the Gemini app. Is that a feature The
Speaker 1:only thing that I can
Speaker 3:think is I I feel like, my entire life runs on iMessage. Mhmm. And it doesn't feel like Apple is super motivated, like, actually building for power users. And so if there was a way to get more value having that data within Gemini. Right?
Speaker 3:Like, hey, draft me, like, text message responses to people that I've texted, you know, more than more than one day that I haven't responded to in the last two weeks and have draft a bunch of messages that I can then just go through and at least, like, look over and respond to. Yeah. But I I don't know. I'm I'm I I have zero faith that there will
Speaker 2:be Any sort of
Speaker 3:any portability.
Speaker 2:Any jumping of the of the wall.
Speaker 3:And the reason for that is Apple's paying Google To
Speaker 2:white label.
Speaker 3:To effectively, yeah, white label the model, leverage Gemini in the next version of of Apple Intelligence.
Speaker 1:Yep.
Speaker 3:And they're just gonna be focused on integrating it within their ecosystem deeply. Yep. And I think if if they weren't paying for it, Google would have been able to negotiate for quite a lot more and potentially more interoperability between between the products.
Speaker 2:Yeah. I I I feel like there there might be some magic that comes out of, you know, like a deeper integration between these two things. It does feel very different than Google Search because the models are actually intelligent and could it it's I I think that the the obvious, like, you know, draft a summary, like the example that you gave, draft a response to a text message. I don't know if anyone would even want that. And Yeah.
Speaker 2:And I do think that Apple Intelligence will be able just to do that out of the box. I I I'm imagining more of, like, of, like, when I when I go to an LLM to prompt it for a gift guide, if it has access passively to iMessage, it can understand, oh, like, people have been sharing these links with you to things that could be gift. Here's the context around the context. Maybe they shared that link with you being like, lull, I would never buy this someone for Chris for someone for Christmas. Or they could have been from a family member saying, you know, this has been like, I'm I I would write to Santa for this, and they're like alluding to the to you actually wanting to buy them for that.
Speaker 2:So Tyler, what do you
Speaker 4:I I think like when when when I think of like AI in like communications generally Yeah. Think it's more like the vision is like, let's say I'm trying to set up a meeting with Jordy. Mhmm. It's like I have an agent. My agent talks Jordy's agent.
Speaker 2:Yes.
Speaker 4:They sort everything out, if we should meet, when we should meet, where we should meet, and then it's kind of like done completely separately from like iMessage even. Yeah. So I I think that's more of like my kind of ideal vision of like what LLMs and messaging like look like. Basically, like, I'm not even doing actual messaging. I just
Speaker 2:So
Speaker 4:I'm just not sure how important it actually is that it interfaces with iMessage. I mean, obviously, it's, like, good to search through your messages. That's, like, useful. But
Speaker 2:Yeah. I just wonder, like, like, the reality of everyone's life is that they use multiple messaging systems. They use email and WhatsApp and Signal and then iMessage and Twitter DMs. And there's never been a successful unification of these. But I was laughing to myself thinking about, like, a humanoid robot because, like, a humanoid robot, you could literally just, like, be like, here's the phone.
Speaker 2:Here's the passcode. Go respond to every message on my phone. And, like, it could do that, and it would be impossible to, like, there's no, like, data wall that you can put up at that point, really.
Speaker 3:Yeah.
Speaker 2:I mean, maybe if you're, like, world coin scanning constantly to, you know, like, eyeball scanning to get into the actual the actual app or something. But it reminded me of, like, George Hots was saying that, like, at a certain point, the the the full self driving, like, it's like, you don't need to worry about car compatibility because it's just a humanoid that gets in the driver's seat. You want a driver? I thought that was such a funny take. Because it's like yeah.
Speaker 2:Like, right now, Toyota I believe it's Toyota, but a few of the carmakers are basically saying, like, no third party self driving kits. Like, we are encrypting our OBD two ports, like the actual port where you control the car. We're not gonna let anyone build on top of us because we wanna own the self driving stack on top of our vehicles. Yep. So no no third party kits.
Speaker 2:And, it's just very funny to imagine, like, well, how are you gonna stop a robot from just sitting in the driver's seat and shifting the gears and, and and and pushing the pedals? Anyway, Restream. One livestream, 30 plus destinations. If you wanna multistream, go to restream.com. Lisa Lisan Al Gayev has more Gemini context.
Speaker 2:Said Gemini three Pro is the first LM to beat professional human players at GeoGuessr. Wow. We gotta watch who's that who's that the the amazing GeoGuessr guy? I does he just go by GeoGuessr? What's was Oh,
Speaker 3:I know who you're You
Speaker 2:know what I'm talking about? The greatest game of GeoGuesser. This is the guy he's in
Speaker 3:the Rainbolt.
Speaker 2:Rainbolt. Yeah. Geo Rainbolt. I'd I I I wanna see his his reaction to that and see how how he's doing. He's just crying
Speaker 3:crying on stream.
Speaker 2:He's done a he's done a few like
Speaker 3:This is this is one of those things that I think is actually still gonna be wildly entertaining Mhmm. Even when even when they could like chess. Right? Like watching him figure out where something is Yeah. Down to a single street is still gonna be impressive and and probably entertaining.
Speaker 2:It's a pretty cool benchmark. I'm I'm surprised by this. But, what is this? Oh, so it got a higher score but lower country percentage than a professional player. That's fascinating.
Speaker 2:I wonder I wonder what that says. So so it outperformed on score, but it underperformed on guessing the country. And I wonder if that's something like, it's using different heuristics that are, like, less intelligible. Because a lot of the heuristics that you'll watch the, the the geoguessers use, the really good professionals, is that they will be able to identify, like, this color of of signpost is only used in this country. Yeah.
Speaker 2:So even though it looks like it's a tropical, like, that helps me understand it's this country and not that country. And that might be something that, Gemini three Pro is not picking up on, but it's do it's still doing a better job of understanding just the the references. Also, I mean, it this this feels like it has to be, like, overfit on geoguessing because, like, didn't Google create all the geoguessers, like, data source? Right?
Speaker 4:So Yeah. It's all just Google Maps.
Speaker 2:It's Google Maps, like and it has to be in the training data, like, perfectly. So even if it's, like, not intelligently thinking like, the beauty of watching someone play geoguesser is that they're they're not just doing memorization. They're not just like, oh, I know that street. I know every street because I've memorized every street. They're they're actually applying a whole bunch of heuristics and patterns and matching.
Speaker 2:What what
Speaker 4:do mean to
Speaker 2:Yeah.
Speaker 4:That's probably true. But also, I I remember with the I think it was the g fifty five release. Yeah. People would would, like, submit just a picture they took, like, on their phone Yeah. Of, like, themselves.
Speaker 4:It's like, where am I? Mhmm. So that's not, like, actual that's I mean, not from Google.
Speaker 2:Yeah. It's not over fair.
Speaker 5:And it
Speaker 4:would still do, like, really well. Okay. Yeah. Yeah. That makes sense.
Speaker 4:Also, this is Yeah.
Speaker 3:They nerfed that pretty quickly because there was so much there was it was could easily be abused. I remember I uploaded a picture of of outside of my house Mhmm. And I could I could tell I could tell by its response that it knew exactly where it was
Speaker 2:Mhmm.
Speaker 3:Even though there's no street view Mhmm. Because it's a private neighborhood. And, like, I it was basically, like, saying where what like, I knew it knew exactly where we
Speaker 2:I knew it knew.
Speaker 3:But it was it was just wasn't giving, like, specifics, but it so much it's like, it it was within, like, like, at least, like, a mile.
Speaker 2:Yeah. 2.6 m says we should play a round of GeoGuesser on stream. We should. We should get we should figure out how to actually wire up, like, games. We've done it once before, and it was pretty fun.
Speaker 4:I'm also curious where the DeepThink model ends up on this because this is still just this is just three pro.
Speaker 2:DeepThink must be doing even better. Right?
Speaker 4:Yeah. I mean, you would imagine. Yeah.
Speaker 2:So yeah. What how how would you benchmark the the three Pro versus GPT five? Because it seems like three Pro is not equivalent to to five Pro. Five Pro is more like DeepThink.
Speaker 4:Yeah. If you're looking at, like, price and, like, the How long it takes to an output. Yeah.
Speaker 2:So three pro is, like, five instant, or is it, like, five thinking?
Speaker 4:It's five thinking.
Speaker 2:It's five thinking. And then
Speaker 4:three flash, if that comes out
Speaker 2:That will be instant.
Speaker 4:Yeah. Like like 2.5 light or flash or there's flashlight Yeah. So Yeah. Okay. That's more of the instant model.
Speaker 2:So it it feel yeah. It feels like most of the labs are coming out with, like, three variations on speed right now, maybe, something along those lines. Yeah. And then maybe a deep research product adds, like, a fourth to the end. But that's, like, more more of a specific
Speaker 4:Yeah. Like, Anthropic has Sonnet, Haikyu, and Opus. Mhmm. Those are, like, the three. And then there's, like, thinking on all of those, but it's kind of a similar breakdown.
Speaker 2:Yeah. I wonder I wonder if Gemini will do a model switcher at some point. Like, right now I mean, I guess, like, AI mode has some of that, but maybe they just are they they they just don't have to worry about the actual GPU cost at this point. So they're
Speaker 4:not Yeah. Yeah. Authority needs it. He he couldn't figure out how to find the the thinking model.
Speaker 2:Yeah. You need the you need the switcher. You need the switcher. It is it is funny that
Speaker 3:To to This happened to the asked the model, what model are you? And then it said that it didn't have access to Gemini three.
Speaker 2:Yeah. It is that is something that they should, like, hard code in because it is very frustrating. It's happened a number of times where
Speaker 3:It just makes it feel not intelligent.
Speaker 2:Yeah. Where where where so, like, okay. Like like like, I want to use the latest and greatest. How do you actually do this? They should, they should definitely, like, make that URL or that explanation, like, available in the prompt so that it it can answer questions.
Speaker 2:Like, you need to sort of, like, bake in an FAQ since you imagine that people will be interacting with the chat directly.
Speaker 4:Yeah. Well, it seems like there's some difference between the naming conventions, right, where, like, the the, like, lab, like, DeepMind wants to come out with it's like a new model. Right? So it's three. It has a number.
Speaker 4:But then on the product side, you see it's like numbers are kinda confusing. So they want the consumer to just see, like, faster thinking.
Speaker 2:Yep.
Speaker 4:But then for people who, want to use the new model but they're using the consumer product, it's, like, pretty confusing.
Speaker 2:Yeah. I mean, the the the the name scheme is is very funny right now. There's I mean, everyone has, like, different different models, fast and thinking, but then there's also, like, deep research, which is deep. And then there's deep think deep thinking and deep research, and that's very hard to communicate the difference between there unless you're following this stuff very closely. Yeah.
Speaker 2:And then the create videos with VO. But then instead of create images with nano banana, it's the nano it's the banana emoji and then just create images. And so there's, like, not a lot of, like, symmetry in The U in the way the UI is laid out because I think everyone's moving so fast in this category that it's like, just get it out, ship the code word. Oh, the code word leaked. We gotta go with it.
Speaker 2:Like, there are still people who know strawberry in the context of OpenAI, which is like a wild thing to to be at the level where, like, no one knows, like, the code word for the next iteration of the Diet Coke can or whatever. Like, I'm sure that internally, there was some project for this, but, like, there aren't, like, diet people following the industry that closely. Maybe there are, but certainly not on the consumer side.
Speaker 3:Yesterday, Google announced Google Anti Gravity, their new agentic development platform. Marvin von Hagen, one of the most powerful names in tech, said which IDE did they use to build AntiGravity, Windsurf or Cursor? And Silas over at Cognition said, so Google just forked the Windsurf codebase and they even forgot to remove the Cascade branding. In some places, Cascade is a part of Windsurf's product, which is obviously now by Cognition. This is funny Yes.
Speaker 3:That they kind of missed this. And I think it's fair for the Cognition team to dunk on it. Yeah. That being said, they, of course You paid a lot money
Speaker 6:for it.
Speaker 3:You know, spend however many billions Yeah. On on acquiring the the Windsurf IP. So Yeah. Not super surprising.
Speaker 2:But, yeah, I mean, you'd think, like like step one is find and replace. You know? Just find and replace and just, like, anywhere in the code base, remove the old branding and put in the new branding. Do you have
Speaker 4:Varun was moving quick. Actually kind of hard to do this. I remember, like I mean, it should be really easy. But I I remember, like, months after the Twitter ex takeover
Speaker 1:Yeah.
Speaker 4:You would still find on on Doc's Twitter branding. I mean, that was still, like
Speaker 2:That's true.
Speaker 4:Months ago, I would I would see
Speaker 2:That is that is true.
Speaker 3:Yeah. But less imperative to actually make those changes, in my opinion. Right? It's like
Speaker 2:Also, that's a living that's a living, breathing service. And and, like, that might be a little bit difficult if it's like, you know, twitter.com is baked into some DNS. And if you switch it live, like, you're gonna have a bunch of downtime or something like that. Like, the like, this is a new product. Like, you can you can just like, the code base is just dead.
Speaker 2:It's just sitting there, like, waiting to run, and then you're just about to ship it. You'd think you'd do control that.
Speaker 3:I think was was in their launch
Speaker 2:AI, if you got the best AI, you think you'd say, hey. Go and fix this. Go make this change.
Speaker 3:Also think this was a part of wasn't this a part of Google's launch? Wasn't it in the launch video? I'm pretty sure the screenshot is from the launch video.
Speaker 2:Oh, really?
Speaker 3:Which makes it No. No. No. No.
Speaker 2:I don't think so. I I I I I don't think this is in the, the launch video. The launch video is, like, very minimal. And and and this is, like, clearly has, like, a streamer in the corner, like, looking at it. But, anyway, whether you are, excited and bullish or bearish on Google because of this, head over to public.cominvesting for those that take it seriously.
Speaker 2:They got multi asset investing. They're trusted by millions. Kyle Chan says this is the big story here. Google trained Gemini three Pro on Google's own TPUs. No mention of NVIDIA chips.
Speaker 2:This is pretty crazy. I mean, they've been doing this for a while, but NVIDIA's announcing earnings today, and it's pretty crazy. Like, the biggest score in AI is not is not really relevant to the biggest company in AI.
Speaker 3:Best model ever created Yeah. From a benchmark standpoint.
Speaker 2:Yeah.
Speaker 3:Didn't use didn't use NVIDIA chips, which are supposed to be a monopoly. Right? Yeah. And and so yeah. I don't know.
Speaker 3:I it just doesn't feel fully priced in yet Mhmm. To either company.
Speaker 2:Yeah.
Speaker 3:But then again, right, it's so hard to predict demand over the next five, ten years that, maybe maybe it doesn't even matter.
Speaker 2:Yeah. I wonder I wonder how much. Because if if TPUs are not for sale, NVIDIA does have a monopoly. Like, you can if if there's, you know, a monopoly on if if NVIDIA truly is the only seller in the market because Google is not a seller, then, yes, they still extract they still extract monopoly power from every other buyer. Yeah.
Speaker 2:Because every other buyer said, yeah. I'd love to buy TPUs, but I can't. So Yep. You're the only game in town still. Yep.
Speaker 2:But it's a very weird dynamic where you do have two very clearly performant products that are not, that are not actually driving down cost. It must be very frustrating if you're somebody else. But that's why every other all the other labs are working so hard to
Speaker 1:Yeah.
Speaker 2:To develop their own chips or, you know, bring AMD online. There's a whole bunch of different efforts in this.
Speaker 3:Do you know the background here on on, from this post? It's extremely Google that a flagship consumer product is named as a reference to inner org drama that happened three years ago.
Speaker 2:Well, there's lots of people saying that they require context. Let's see if anyone.
Speaker 3:Antigravity.
Speaker 2:No. Oh, oh, is antigravity the reference? Zodiac the zodiac Gemini refers to twins Google's Gemini is a reference to two formerly distinct labs, Google Brain and DeepMind, that were merged into one lab, Google DeepMind.
Speaker 3:There we
Speaker 2:go. Think that's it. Yeah. And I guess the inter org drama that happened three years ago was just this idea of of, you know, DeepMind was acquired in, but Google Brain was still running. This is isn't this reference to Gemini as in the constellation of the Gemini twins referring to the consolidation of twin organizations?
Speaker 2:I like that. That's actually a pretty good name. And, I mean, it it this original post makes it sound much more dramatic, like, in your org drama.
Speaker 1:Yep.
Speaker 2:But in fact, it's it's it's sort of a way to keep, keep the lore going, basically. Anyway, let me tell you about adquick.com, out of home advertising made easy and measurable. Say goodbye to headaches and out home advertising. Alex, he has a q and a with Dennis over at DeepMind. He says world models
Speaker 3:Alex are is on a tear.
Speaker 2:He's on a tear. Sources.news.
Speaker 3:Averaging like, the challenge when writers go and when journalists go independent Yeah. Is actually figuring out a way to get enough scoops Yep. To justify a subscription business model Yep. As a stand alone company. Alex has been Yeah.
Speaker 3:It's basically been at least a scoop a week or or, like, in very interesting content.
Speaker 2:Big stuff too. Yeah. And I don't know. Just like it it it's interesting seeing, like, there's a bunch of there's a bunch of interesting things. I mean, he did that interview with Mark Zuckerberg that was, like, seated hour long, you know, in-depth interview.
Speaker 2:There were some big scoops that came out of that, some funny takes about the, about the the the bubble, basically. I think Mark was saying, like, yeah. We might overspend, and that was sort of a viral moment. And then doing some q and a's. Also, it feels like maybe great timing to just you look at the stats on this, 57,000 views, 477 likes, link in the core image.
Speaker 2:I mean, I I get it. It's like it's a it's an important scoop. It's an important story. But it feels like a year ago, this post would have been buried by the algorithm. And so We
Speaker 3:will show it to
Speaker 2:three people. Like, like, really, really great timing on that as well. So just just catching, you know, different opportunities and capitalizing constantly. Yeah. So very, very exciting.
Speaker 2:But the actual quote that Alex Heath is sharing from his piece in sources on news, which you can go subscribe to, is he says, world models are the thing I'm spending most of my research time on. I'd love more TPUs. You look at seed rounds with just nothing being tens of billions of dollars is not quite logical to me. Taking shots. Shots fired.
Speaker 2:Do we have the gun? Do we have the gun? No.
Speaker 3:We removed that.
Speaker 2:Oh, we removed it. Okay.
Speaker 3:We have have to add it.
Speaker 2:I like that. I we need a taking shots one.
Speaker 3:Jumping in just a note on TPUs. Yeah. Alex says, when you talk about the constraints, Google has more computing access with TPUs than most companies. I would think that Google could just go all in on your team's work.
Speaker 5:Mhmm.
Speaker 3:But Google also gives TPU access to other startups Yep. And even rival AI labs. Do you ever just go, give me all the TPUs? And, Dennis says, I'd love more, but there are business requirements to balance. There's short term and long term revenue, and all of these things need to be balanced and smoothed out.
Speaker 3:It's a huge advantage. We have TPUs in our own stack, and we co design the TPUs with the TPU team based on where we know we're going software wise. But, yeah, there isn't enough compute in the world, as we all know, for everything that we wanna do. There are always competing things, and then there's the question of what is the return on that amount of compute. It can be a research return, a new product investigation return, or direct revenue.
Speaker 3:Genie is still in the exploratory phase in terms of what we may eventually do with it. And so, anyways
Speaker 2:Well, if you're looking to manage a bunch of TPUs, get linear, meet the system for modern software development. Linear is a purpose built tool for planning and building products. Greg Brockman looks like he's hanging out in DC. This has to be Washington DC.
Speaker 3:This was last night.
Speaker 2:This was last night. So he says the future is bright, and he's pictured with David Sacks, his wife, of course, Elon Musk, Jensen Wong. What a fantastic photo.
Speaker 3:Tyler did a green line analysis on this.
Speaker 2:I think Tyler was getting a little wild with this one. It's barely barely a
Speaker 3:feeling. Know you
Speaker 2:got a
Speaker 4:chance to not beating the
Speaker 2:I don't know. I I think he's standing up pretty straight. He's a little leaned over. Not full.
Speaker 3:Pull it. But We gotta pull it.
Speaker 2:Elon is the is standing up extremely straight with some wild shoes. People are saying, what are those shoes for?
Speaker 3:It's NVIDIA earnings day. You gotta look for any signal
Speaker 2:This is
Speaker 4:they the
Speaker 3:possibly get. Yes. Pull it up. It's at the bottom of the timeline squad. Here we go.
Speaker 2:Yes. Here
Speaker 3:we go. So everyone is is pretty You
Speaker 2:did him dirty with that line.
Speaker 3:I drew the line perfectly. No. No. I'm I'm with Tyler. He's it it's all about center of gravity.
Speaker 3:Right? Center of gravity? Anyways, I I'm I I I think Jensen will put on a show later right after the show ends. So we will look forward to finding out more.
Speaker 2:Okay. Well, Elon was was pictured wearing some very, very crazy shoes. These are his SpaceX shoes. I don't know who made these. Look at these, Jordan.
Speaker 2:Woah. Would you rock these? Could you pull these off? The think likes them. I'm not I'm not I I don't think
Speaker 3:Are these are these like were they made in collaboration with some another brand or these just I don't know. Is he vertically integrating drip?
Speaker 2:I don't know. Yeah. Did he make his own shoes? I have no idea. These
Speaker 3:would these would go for I I have a feeling they'd go for quite
Speaker 4:a lot.
Speaker 2:Yeah. They seem pretty cool. Let me tell you about fall to build and deploy AI video and image models, trusted by millions to power generative media at scale.
Speaker 3:Bobby says they look like Yeezy's. They do
Speaker 2:look like Yeezy's. That's right. Quarter. The Quarter app has dropped the oh, I gotta follow them. Why am I not following them?
Speaker 2:Quarter app has dropped a, an announcement that the NVIDIA earnings call will be tonight at 5PM eastern time. As soon as we log off this stream, you can head over to quarter and start streaming the NVIDIA earnings call. Jensen is there pictured, all eyes on Wong. And, they've done a fantastic job developing this image style. I feel like, it's been 2025.
Speaker 2:The the meta on X has been exploding in terms of, like, image macros. We've had a ton of fun with the trading cards. These have done extremely well. Anything where you can bring design and just tell a little bit more of a story, give a little bit more context, texture, something breaks through. And every time they post one of these 3,000 likes, people love them.
Speaker 3:Yep. Scroll down if you can. If somebody ran this graphic through mid journey and it's crazy.
Speaker 2:Crazy. So bad about comparison.
Speaker 3:I mean, it's it it still goes pretty hard.
Speaker 2:Yeah. So, I mean
Speaker 3:The arm there is looking a little
Speaker 2:It's also an interesting testament to, like, I I know that the quarter designers use mid journey. They use AI, but they are really, really deep in the SREFs. They obviously have a whole bunch of different stylized prompts. And then it seems like they're also doing a ton of work in post processing, layering text on top of it. They've they've they've created, like, a visual style that's distinct.
Speaker 2:I'm sure people will copy it, but, it's definitely created, its own its own sort of style and broken through. And at the same time, like, it it doesn't feel like, yes, there's AI involved, but it doesn't feel like, if you just threw, you know, this this prompt at a random person. Okay. Hey. Go make one for, you know, Coca Cola next week.
Speaker 2:I don't know if they can necessarily pull it off even if they had a mid journey subscription. Like, there's still a lot of, like, inspiration to understand, like, what is the texture? What is the what is the style?
Speaker 3:Right now on Polymarket, will NVIDIA beat quarterly earnings is sitting at an 87% chance? The real question is how will it trade
Speaker 2:after the fact? Yes. It it feels like we're in this weird market where you can beat on earnings and then sell off because nothing is ever good enough for the Street right now. But but we we we will see. The the the NVIDIA NVIDIA is such a big story now that just the fact that they are going to have earnings is essentially front page news, at least of the business and finance section.
Speaker 2:NVIDIA and jobs data coming. Reports will provide key signals for investors after a market pullback. The fog masking the direction of the American economy and future of the artificial intelligence boom is starting to lift After mounting scrutiny of stratospheric tech investments, as well as a blackout of federal data during the longest government shutdown in US history, Wall Street awaits two reports that stand to reshape its outlook for the months ahead. AI poster child NVIDIA is due to report earnings after the closing bell Wednesday, offering a snapshot of demand for chips that are in that are a linchpin in the tech mania that has lifted markets and helped buoy the economy. Also, with the NVIDIA news, it's like, how much can you actually read into AI demand based on NVIDIA earnings?
Speaker 2:Because I feel like we're we're projecting out, like, these deals five years in in advance. We buy the chips, then we install them. Like, are we really seeing, like, it like, the the that whole rumored, you know, decline in or deceleration in ChatGPT growth? Like, if that is real and that's happening and and ChatGPT usage is starting to plateau from 800,000,000 weekly to, hey. Next year, it's gonna be at, like, 900 mill a billion.
Speaker 2:Like, it's not gonna be 5,000,000,000 next year. If that's happening, are we expecting that to show up in the NVIDIA data this quarter? Like, probably not. Right? Because, like, OpenAI has projected out five years of demand for GPUs.
Speaker 2:So I don't know. It seems hard to actually read into NVIDIA's earnings as a as a as a real, snapshot of demand. Mean, I I guess demand for chips, certainly.
Speaker 1:Yeah.
Speaker 2:A sell off in NVIDIA has dragged down indexes with Peter Thiel's macro hedge fund and others dumping shares. Sort of crazy that that's that that's in the journal.
Speaker 3:Yeah. Especially especially when when it's the equivalent of, you know, the average person in tech selling like a a $10,000 position in the company. Yeah. It's like not not like super notable.
Speaker 2:With no statement either.
Speaker 5:With no
Speaker 2:It's not like he's, oh yeah, he was also on Rogue and told
Speaker 6:him trash.
Speaker 2:It's like nothing. The tremors extended beyond other AI names into crypto, gold, and more. Even Warren Buffett's latest big bet on Alphabet hasn't staunched the bleeding. America's richly valued stock market has retreated in similar fashion several times during its years long run ups. In every instance, bargain hunters snapped up stocks, tech companies out profits, and the economy kept on motoring ahead.
Speaker 2:The fact that there's yeah. We can move on
Speaker 3:from Yeah. I mean, the reason there's fixation, Nvidia's currently wait. It's like 8% of the S and P 500.
Speaker 2:That's crazy.
Speaker 3:So, like, it it just it matters more than any other Mhmm. This this feels like the most important earnings call of the year
Speaker 2:Yeah.
Speaker 3:Given given the sell off Hold
Speaker 4:the one.
Speaker 3:Hold on the the clouds, given the the just like pressure and debate around OpenAI, given given Google's investment and progress with TPU. I mean, there's so many different factors. Mhmm. In related news, it got announced this morning, Musk's XAI and NVIDIA to develop a data center in Saudi Arabia. Mhmm.
Speaker 3:It's a 500 megawatt data center in Saudi. XAI is working with NVIDIA and a Saudi Arabian partner to develop a data center in the kingdom. Musk said Wednesday at an event with the crown prince. They're teaming up with Saudi Arabia's AI company Humane. That's gonna be 500 megawatts or enough electricity to power several 100,000 homes for a year.
Speaker 3:The announcement came at the US Saudi Investment Forum. Of course, the crown prince announced a trillion dollars of investment in The US, yesterday.
Speaker 1:Mhmm.
Speaker 3:President Trump touted Saudi's investment in The US and the partnership between the two companies countries. My question is, like, what like, there's no information here on on how this data center is gonna be used. Is this do do we expect x AI to be operating and and competing as, like, a AI cloud? Or is this gonna be something that they're they they wanna have a local version of Grok? Right?
Speaker 3:And I would and and and to me, it seems much more likely that they're just gonna be in the like, they just wanna be in the in the in, like, the data center business.
Speaker 2:Yep. Oh, that yeah. That's a very interesting
Speaker 3:And to me, that's always made sense because Elon is clearly
Speaker 2:better to get that.
Speaker 3:Pretty much best in the world. I mean, was he was mocking Microsoft for bragging about how many a million work hours. 15,000,000 work hours. More than more than And so clearly very good at at like large scale infrastructure build outs, getting getting access to energy, doing things on a ridiculous time horizon. And so in order to support XAI's valuation, I could see them trying to get into get into that game.
Speaker 2:Yeah. Yeah. I mean, there's also the the possibility that if there is strong US inference demand, but latency is not an issue, like, might be valuable to actually just co locate the the data center next to the oil. So because maybe the energy is cheaper. Midjourneys, I believe, have been doing that for a very long time doing in inference internationally because the data center demand during peak hours in The United States is more expensive than
Speaker 3:Yeah.
Speaker 2:Across the world.
Speaker 4:Let's pull up this
Speaker 2:let's pull up this video. And while we do, let me tell you about graphite. Dev. Code review for the age of AI. Graphite helps teams on GitHub ship higher quality software faster.
Speaker 2:Let's go to Elon Musk saying AI and humanoids.
Speaker 3:Will actually eliminate poverty. Eliminate poverty. And Tesla won't be the only one that makes them. I think Tesla will pioneer this, but Humoid robots. But but AI and humanoid robots will actually eliminate poverty.
Speaker 3:And Tesla won't be the only one that makes them. I think Tesla will pioneer this, but there will be many other companies that make humanoid robots. But there there is only basically one way to make everyone wealthy and that is AI and robotics.
Speaker 1:And we can't talk about robotics without AI
Speaker 2:What do you think? All
Speaker 3:all problems in the world solved by one product. I love it.
Speaker 2:Mean, it's it's not the craziest take, over over a long period of time. You know, you'd give everyone, the ability to sort of marshal anything. It it it, you know, it it I wonder if we'll redefine poverty at that point. Yep. Poverty will be not having a beachfront property, a beachfront mansion or something.
Speaker 2:Something that's truly scarce that Yeah. You know,
Speaker 3:even even This is my land thesis.
Speaker 2:Even an army of humanoids can't necessarily give out this this
Speaker 3:is you know, we joke about land a We joke about it being the most undervalued asset by the current generation of investors. But land is the one thing that even with an army of humanoids, like you can't as easily like copy and paste. Right? Like it's just it's just it truly is scarce. It's not like land.
Speaker 3:It's not like land on the blockchain where people are like, no, like you can buy this plot of land on the blockchain and that's yours forever. And somebody's like, what if I just make another blockchain?
Speaker 2:Exactly. That was the most ridiculous.
Speaker 3:And I can also get this piece of land encrypted. I can get from this piece of land on this blockchain to this other piece of land on this blockchain in a second.
Speaker 2:Yeah. It's ridiculous.
Speaker 4:If you have enough humanoid robots though, then land is actually not that hard to to get.
Speaker 2:Why? Because you're like,
Speaker 3:you would just put enough dirt in the ocean and, like, it's like, oh,
Speaker 2:you're good at Mars.
Speaker 3:You had this ocean or you have a robot army.
Speaker 2:Putting Robot army? And then you just steal?
Speaker 4:Then you just take
Speaker 2:But the the I think I think what Elon's saying is, like, if you assume universal basic humanoid army, everyone gets 10 humanoids. And so the humanoids can cook for you. They can give you shelter. They can clothe you. They can give you health care.
Speaker 2:So you get everything that, you know, would typically be bucketed in poverty, but there's still scarce resources. There's only gonna be one Mona Lisa. And so you gotta fight over that.
Speaker 3:Orange is in the chat. So the humanoid form factor is silly. Make it an r two d two. I'm actually Matic. Surprised.
Speaker 3:Yeah. So one Matic. I gotta I gotta give a shout out to Matic. Dude, they re crushed you. Matic has been running in my house every no.
Speaker 2:They test too. Yeah.
Speaker 3:Personally, been running it in my house daily for months now.
Speaker 2:That's fantastic.
Speaker 3:And it has worked flawlessly. Yes. Go check it out. I I I had like, you know, I I feel like everybody's been disappointed by like a vacuum robot Sure. Over the years.
Speaker 3:And so I didn't have the highest expectations even though setting it up was like fast and and it But got to work I've been shocked at at how just well it's worked and haven't had to think about it. You replace the little bag every once in a while, and it's great. Yeah. But r two d two form factors, we'd love to see more of that.
Speaker 2:What do you want out of an r two d two, though? Because the Roomba form factor, the Matic form factor where it goes and cleans, like, that's pretty useful. But if you like, is it an r two d two that it can fold your laundry? Do your dishes? Like, you you need to sort of define a few different.
Speaker 2:Because clearly, we're we're gonna be in the age of, like, spiky intelligence and also, like, spiky humanoid usage. Like, they're gonna be good at some stuff and
Speaker 3:bad So I think in r two d two form factor, you could reduce the number of motors that you need.
Speaker 2:It could Yeah. But what does it carry more
Speaker 3:weight. Right? Carry?
Speaker 2:So it's gonna carry you?
Speaker 1:Are you gonna ride it?
Speaker 2:No. No. Explain what it's doing. Because in the classic Star Wars, r two d two is like basically just like a hard drive that like carries like a video. Like, that's all he does, the whole movie.
Speaker 3:Yeah. But I you can imagine
Speaker 4:it has it has it
Speaker 3:has a number of different
Speaker 2:What does it have? What does RTDs you have? Have you seen the movies?
Speaker 3:Doesn't have, a screwdriver? Screwdriver.
Speaker 2:It has a screwdriver. It's basically a USB cable that comes out and like plugs in when it's like you could just use wireless to hack the network or whatever.
Speaker 3:That's true. No. The the other the other challenge with that form factor is is
Speaker 2:That's a screwdriver.
Speaker 3:You have so so it's great if you have like a one story home. If you have a 2nd Floor Yeah. R t D two is kinda cooked. R T D two starts asking like
Speaker 2:Totally cooked.
Speaker 3:He's like, hey, we're gonna need some more CapEx. I need some I need Can we get an elevator? Elevator, please. Elevator, think please. If you're
Speaker 1:Robot, right?
Speaker 4:What? I think the opt if if you wanna talk about like Star Wars form factors, I think the optimal is General Grievous or whatever the one that has a bunch of arms. Yeah. Because, basically, he can walk around like a normal human except it has more arms.
Speaker 2:No. No. I completely agree with that. Bunch of arms form factor is
Speaker 3:Everybody wants to make a humanoid. Nobody's trying to make the the general grievous.
Speaker 2:I know. General grievous is Six. Way better. Six. Way better.
Speaker 3:More lightsabers.
Speaker 2:You you distilling r two d two as it has a screwdriver. It's like the most it's completely mugged. But I mean, truly, other than just being like cute RGD two can't even speak English. Think about that. RGD two.
Speaker 2:It just goes beep, boop, beep, boop, boop, boop again.
Speaker 3:I mean, the the implication was like the general form factor of something that can roll around Yeah. And has the a lot of different capabilities built in.
Speaker 4:It doesn't have any capability.
Speaker 2:It has no capability.
Speaker 3:What are the capabilities? Can we pull up general Grievous on the screen?
Speaker 2:We need to be Grievous. Hacked the Death Star and saved Luke from the garbage disposal. Yes. Two things that could have been done with wireless networking. It didn't need to plug in for that.
Speaker 2:Why? It is a hard drive. I'm I'm getting into battle with the with the chat right now. We can we tell the story of us risking our lives yesterday? We really should.
Speaker 3:Yeah. This was this was truly incredible stuff. So we were looking we're we're in the Ultra Dome here for at least another year. But, we're starting to think about our our second, our the the next Ultradome
Speaker 2:v two.
Speaker 3:We wanna get slightly more space. There's a number of different things that we want.
Speaker 2:There's general grievous. That is the ideal humanoid form factor. And if you're not building that, it's a zero. What are you thinking? I think I I think Optimus would look way better with six arms.
Speaker 2:Not scary at all. It is crazy. They they they do the quadrupeds, but no one's no one's really working on, like, the six legged, six arm, like, the really crazy creepy stuff. Been a couple humanoid robots that look really scary where they're like, wow. Let's put it up on the meat hooks.
Speaker 2:Remember that one? That was crazy. That a wild one.
Speaker 3:Was that the video where it started going
Speaker 2:No. No. No. That's a different one. But this was a company that was like, here's our here's our presentation.
Speaker 2:Like, we're we're ready to release our humanoid, and they were, like, hanging it up on meat hooks. And it looks so spooky spooky. Because it it was using muscle fibers, basically.
Speaker 3:Nathaniel Smith is very bearish on r two two. A cell phone can do most of what r two d two
Speaker 2:Completely agree. I r two d two cute for sure. Like, definitely, like, fun to have around, but more of just like a toy companion. And and, you you know, we we looked at that lamp, and that lamp, we we were kind of like, what is that lamp? And I think there's just like, it's just delightful.
Speaker 2:Like, it's just nice to have around. It's like this this turbo puffer here thing. Search every byte. You know, for serverless vector and full text search built from first principles on object storage, fast, 10 x cheaper, and extremely scalable. Like, the the turbo puffer, I mean, obviously, we're sponsored by them, but but this is something you might have in your house just because it's cute.
Speaker 2:People people people like having cute things
Speaker 3:Many people have asked.
Speaker 2:In their house. And and having an r two d two in your house would be cute until it runs into a stair and goes tumbling down and smashes into a million pieces.
Speaker 3:Anyways Tell us we're looking so we we found a space that we love.
Speaker 2:Yep.
Speaker 3:It it's it's dome like. We're looking for a space in LA that is fit for the UltraDome. There's not a lot of things that qualify. And so we had looked at the space a couple times. I had seen it with Ben.
Speaker 3:John and I drove by it, and then we went back to look do another walk through.
Speaker 2:You were
Speaker 3:I'm like extremely excited about the space. I'm pitching John on
Speaker 2:Oh, don't you
Speaker 3:Here's where this thing goes. Here's where goes.
Speaker 2:You made us on the way to the show in the morning, you make me poke poke poke my head through the window. I was really selling John on keys. We go in.
Speaker 3:Really selling John on It's a beautiful beautiful space. It's, like, a few minutes from where we are now. Made a lot of sense.
Speaker 2:Ethan says r two d two was the original digital guy. True. Yeah. Digital guy is incredible for sure. Sorry.
Speaker 2:Anyway.
Speaker 3:So so anyways, we go for the third time to this space. Yeah. And I'm just selling John on every every inch of the space. I'm like, this is what we're gonna do here. This is what we're gonna do here.
Speaker 3:Here's where the truss is gonna go. Here's where the production team's gonna go. We're just walking around kind of get getting getting a feel for it. And we're basically wrapped up. Like, we're super excited about it.
Speaker 3:Not necessarily ready to make an offer on it, but but certainly, like, we're, like, okay, this is by far the best option that we found. Yeah. We've we've looked a bunch of a
Speaker 2:bunch of the boxes.
Speaker 3:Checks a lot of boxes. And right as we're about to leave, John, like, looks over and there's like a closet door with a key in it. And you just like walk over. I just walk watch you walk over and like open it up and you start looking looking around. And first, I make the joke.
Speaker 3:I'm like, oh, this is like the the intern closet. Because it's like this really long narrow like hallway thing that's just like a it's it's like the worst room you could imagine. And and so the idea of putting Tyler in it was was was at least entertaining. And then we're like, wait, what's that humming sound? And there's like this box that's like covered up and it's just like this like, not super loud, but just like constant humming sound.
Speaker 3:And we asked the the broker, we
Speaker 2:say It's super weird because it was drywall. Like you walk into this to this big room. It's a big room. And then within that big room is a massive drywalled box. And so no entrance.
Speaker 2:No entrance to the box, but it's drywalled. Like, you don't usually see drywall inside of a room that's not doesn't go all the way to the ceiling. And so it was very
Speaker 1:clear
Speaker 3:And
Speaker 1:so we
Speaker 5:walked into this
Speaker 2:room while they were hiding something, basically.
Speaker 3:There's no see. There's no purpose to the room Yeah. Other than it just stores the box
Speaker 2:It stores the box. That has no entrance. That has no entrance.
Speaker 3:And it's humming. Yes. And and we looked around, and John's like, what's in the box? And The real estate agent. The broker says the broker says, oh, that's just the machine that cleans the soil.
Speaker 3:And we were
Speaker 2:No. No. No. She said she said, that's just the machine. That's just the machine.
Speaker 2:And we're like, oh, like
Speaker 3:What kind of machine?
Speaker 2:Kind of machine is in there.
Speaker 3:And she's like, don't worry about it.
Speaker 2:She's like, don't worry about it.
Speaker 3:It's not a big deal.
Speaker 2:Yeah. Just like, you know, buildings have machines sometimes. There's a machine in there. It's a
Speaker 3:machine. It's always on, but you don't. It's it we took shit out of the square footage. So don't worry.
Speaker 2:Oh, yeah. That was a wild one.
Speaker 3:We did that. We wouldn't bill you for it.
Speaker 2:And and and so we
Speaker 3:were like, yeah.
Speaker 2:Was like, what type of machine is it?
Speaker 3:And she and then she goes, it's a machine that cleans the soil.
Speaker 2:And we're like, is this on, like, some sort of haunted burial ground or something? Like, what are we doing down Is
Speaker 3:a hazardous waste site? And she goes again, really not a big deal. I would worry about it if you were gonna buy the place, but since you're just planning to lease, don't worry about it. And then we were like, okay. Like, the more you tell me not to worry about it, like, I I kinda wanna know more.
Speaker 3:So what's it cleaning up? And she's like, oh, I mean, there there there's it's it's 85% of the way clean. We're like, what's what's getting clean?
Speaker 2:Process start? How long will that go? Has it been going for a hundred years? Is
Speaker 1:it We're
Speaker 3:like, will the box years Will the machine
Speaker 2:start an hour ago, and it's just gonna be fifteen more minutes? Like, you gave us no context to actually project out what 85% of the way means. And and finally, she's like, there's a there was a laundromat here ago, and we start piecing it together. And we kind of, like, don't wanna press her on it too much, So we leave and start doing some googling. We figure out that it's not a super fun site, but apparently, there was a, there was a, laundromat there that was using toxic chemicals that No.
Speaker 3:It's a machine shop.
Speaker 2:Oh, a machine shop. Oh, that's what we figured out.
Speaker 3:Yeah.
Speaker 2:So they said laundromat. And, apparently, laundromats can give off toxic chemicals that if they get in the ground can be very cancerous for a very long time. This was apparently a machine shop, like, almost a hundred years ago or something. Yeah. And they're working on cleaning the soil.
Speaker 2:But I still don't even understand how you clean all of the soil under any massive building without causing a collapse. Is it like a whole bunch of tunnels that
Speaker 3:are digging around It's bunch of r two d two robots.
Speaker 2:Maybe it's a bunch of r two d twos, honestly.
Speaker 3:Anyway, so so she's she's still saying, yeah, I really wouldn't worry about it. It's just, like, not that big of a deal. It's just a machine. It just runs. You won't even know that it's running.
Speaker 3:We'll keep the door closed. And the and granted, the machine would be, like 10 feet from the set. So we'd be sitting here doing the show and and you just have the the death machine running right there always. So It
Speaker 2:was very, very bizarre. It was one of the funniest like just like jump scares ever. Very, very good.
Speaker 3:It was just it was such a good bit too because I'm I'm far more health conscious, I think, than you. And even you were were thinking, there's no way we're gonna lease an Ultradome that has a death machine that always needs that needs to run.
Speaker 2:I I just I I found it so fascinating that it could sit there and clean the soil for years with a massive machine the size of a giant room. I wanna learn more. I wanna know what that machine is. I wanna know what company makes that company. Exactly.
Speaker 2:That's what I wanna figure out how to make money on CEO of whoever makes that machine on the show. I wanna get to the bottom of it. We need to do a deep research report. Tyler, can you fire off Gemini three Pro deep thinking max twenty four seven mode where it works for ages? It works for eons.
Speaker 3:So so I found two groups, CDE group, soil washing equipment.
Speaker 2:Okay.
Speaker 3:Our wet processing equipment extracts maximum value from hazardous soil.
Speaker 2:But so is it just that corner that has the hazardous soil? I what I wanna know is is is it going under the building and then over so that underneath us over here, the machine's here, is it digging a tunnel that goes underneath the building and then washes over here too? Are are there is there a network of tunnels under that building? I have to know. We have to go back.
Speaker 2:We have to lease this thing. We have to buy the building just to get to the
Speaker 3:to the bottom of it.
Speaker 2:I have to know. I I I I'm I'm I'm ravenous for information.
Speaker 3:Yeah. The cool thing is they use physical and chemical methods to separate heavy
Speaker 2:metals from soil. That's super cool.
Speaker 3:That's exactly what we want.
Speaker 2:That's exactly what we love. Well, in in other news related to water
Speaker 3:found I think I found the machine. We gotta pull it up. I can't can't leave people hanging. Yeah. I dropped I dropped one of the makers of these machines.
Speaker 2:It sounds like fracking. Right? Yes. If we could if we could frack directly some natural gas out of the soil and then use it to power a natural gas turbine that we use to, you know, run the show and power us. I'm I'm down for that.
Speaker 2:While you're looking that up, let me tell you about fin.ai, the number one AI agent for customer service. If you want your AI to handle customer sort, go to fin.
Speaker 1:So Okay. Pull up this.
Speaker 2:In the water news. Okay. You wanna pull up that and then we can go into the water news. I gotta talk about Andy Masley at some point this show.
Speaker 3:I I just I wanna see your reaction when you start to see the scale of this contraption and how it pretty much perfectly fits into the box.
Speaker 2:Yes. Yes. Yes. Fracking with extra steps. Language, please.
Speaker 2:Was someone swearing? I don't know. Anyway, let's pull that up. Let me also tell you about ProFound. Get your brand mentioned in chat GPT.
Speaker 2:Reach millions of consumers who are using AI to discover new products and brands. Let's see about this water story. Andy Masley is going back and forth with what's his what's her name? Karen? The the AI and the environment, somewhat related to our own environmental story that we could kind of go through.
Speaker 2:If How we doing, boys? But
Speaker 3:There we go. Look at this, John.
Speaker 2:Okay. Okay. Yeah.
Speaker 3:This is from GN separation. Core equipment for contaminated soil washing. And you just look at this machine. This is pretty much exactly what this is exactly would have been in the in the the room.
Speaker 2:Soil washing. You have to wash all the soil.
Speaker 3:And there's a graphic if you scroll down
Speaker 2:a little bit more. Wanna know so
Speaker 3:much more. Like Give
Speaker 2:me this. It's a simple process. It's 85%
Speaker 3:done.
Speaker 1:It's 80
Speaker 2:It's 85% done.
Speaker 3:We just need to get the hazardous waste into the decanter centrifuge and then get it into the non acceptable solid second wash, then take the acceptable solid up to the coarse screen into the washing fine screen, and then take the washing chemical and bring it up into the washing reaction tent tank, put it back in the centrifuge, push it down into the soil, filter press, dewatering screw press
Speaker 1:Okay.
Speaker 3:And then move it back up through the hazardous waste, John, and you're you're good. You're good.
Speaker 2:You're So so Doug is asking, if it's behind drywall, like, is that because it generates fumes? We have no idea. Maybe it does generate fumes.
Speaker 7:We have no they access it without locking down all the drywall.
Speaker 2:Yeah. We don't know how they access it. Is the drywall just up? And, also, I really wanna know, like, was there another entrance someone could go into? Like like, what if the machine breaks while we're there?
Speaker 2:Does someone come by and change out something? Does this machine need to be turned off at night? Does it require is it fully automated? Does it just run for years? Would we have never seen a technician come by?
Speaker 2:What if it gets jammed? Like, is it just the most flawlessly built machine in the world that never breaks? That seems unfathomable. All machines break. All machines need some level of of attention from time to time, but maybe it's the most perfect machine possible.
Speaker 3:Machine is, of course, made by Hebei Jian Solids Control Co, which is a China based company.
Speaker 2:Wow. Well, we don't know that this is the actual machine, but who knows? Anyway, let's go over into the environmental impact of artificial intelligence. There was a very funny post from Henry Thunberg who says, woah. I had no idea that AI uses 5,329,584 water per year.
Speaker 2:That's insane. Like, it uses just one water. Yeah. People are all over the place with the water thing. It's so interesting because no one is debating that it uses a lot of energy.
Speaker 2:Like, you could just have all the same discussions about energy. Like like, we're actively burning natural gas for a lot of this AI stuff. Like like, all of the old school don't don't, cause global warming by burning fossil fuels. Like, all of those all of those, like, claims apply to AI today. Like, you could just make those claims.
Speaker 2:But instead, everyone seems to have been, like, caught up in this water. Oh, the water usage is so bad. And it's like You had
Speaker 3:play right
Speaker 2:here, which was, like, like, we're burning fossil fuels, and that's bad.
Speaker 3:Is it because water feels more scarce to people than electricity? Maybe. It's energy in general.
Speaker 2:It's like it's like if I can't drink water, I die. But if I can't access natural gas, like, I can still live, maybe?
Speaker 3:Yeah. Or or the sun beams energy on the earth daily.
Speaker 2:Yeah. And maybe it's easier to spin move out of that being like, well, we're doing nuclear and solar tomorrow. Next year, we're doing we're doing nuclear and solar. So so, like, you don't it's not a gotcha that I'm using natural gas today because tomorrow, I'm gonna be using nuclear and solar. Maybe.
Speaker 2:Maybe. Whereas the water issue might be, like, more like, it's not as concise to to wrap up in a bow. Yeah. But, anyway, we covered this story yesterday a little bit, and I and I wasn't able to pull up the original post. Andy Masley called me out.
Speaker 2:He put me in the truth zone. He said, John, you follow me. How do you not know where the story broke? I broke the story. And, Andy Masley, you did break the story.
Speaker 2:And so we wanted to run through a little bit of this post to actually understand the claim about what he's saying went wrong here. And, basically, the the high level is that he says, this is the single most massive factual error in a major book I've ever personally noticed on my own, and I think I'm the first person to notice it. Empire of AI asserts that a data center is using 1,000 times as much water as a city. In reality, it's 22% of the city's water. And so the chapter turns to Chile.
Speaker 2:We talked about this a little bit. It's a unique combination. Look at this line again. So the line says, in other words, the data center could use more than 1,000 times the amount of water consumed by the entire population of Surrilos, that Chilean city, roughly 80,000 residents over the course of a year. Howe justifies this number in the notes saying, in other words, the data.
Speaker 2:The goo the Google environmental impact report to SEA stated that the data center could use a 169 liters of potable water a second or 5,000,000 oh, it's right there. That's the same number. Five mill 5,000,000,000 liters a year. According to the water service authority in Cerrilos, the municipality consumed 5,000,000 liters in all of 2019. The Google, the year Google sought to come in, 5,000,000,000 liters a year divided by 5,000,000 liters equals 1,000.
Speaker 2:Something isn't adding up here. It doesn't make sense that you could use 1,000 times the amount of water used by that city. And so Andy Masley has successfully put these this book Empire of AI in the truth zone. And we thank him for his service. Let's go back to the timeline.
Speaker 2:But first, let me tell you about numeral.com. Let numeral worry about sales tax and VAT. Newworld.com. New product from Travis Kalanick. That's exciting.
Speaker 3:Big.
Speaker 2:Try picnic.com/requestpicnic. Travis says, I'll come out of Twitter retirement for this one. Picnic at work, LFG. Great job with you.
Speaker 3:Picnic is delivering lunch directly to your office floor with no fees and no tips every day from 50 plus restaurants. Sign up your office for free.
Speaker 2:Okay. Wow. There's only one benchmark for this stuff. We gotta look at the benchmarks. What's the max amount of protein?
Speaker 2:Is it over 200?
Speaker 3:Are they protein maxing?
Speaker 2:Or is it over 200? Because we saw the a major, major jump in in in the amount of protein in a bowl yesterday with Sweetgreen. Sweetgreen's at one zero eight now. This is the most important benchmark in the bowl economy, which I'm a huge fan of. But, are we seeing acceleration?
Speaker 2:Are we seeing a fast takeoff in the amount of protein? I wanna be seeing 200 grams of protein, then a thousand, then 10,000, then a 100,000. It should be 10 x every year. Just 10 x that. Yes.
Speaker 2:Exactly. So
Speaker 3:Everyone's always talking about fast takeout, but we need to be talking about a fast
Speaker 2:casual takeoff.
Speaker 3:No. Just a fast takeoff in
Speaker 2:In the amount protein.
Speaker 3:Per serving. Yes. Anyways, I I think this is What do mean is this this has to be built on top of
Speaker 2:Goes to kitchens?
Speaker 3:Cloud kitchens.
Speaker 2:Cloud kitchens.
Speaker 3:I wonder if it's a separate company
Speaker 2:Mhmm.
Speaker 3:Or it's just a subsidiary, kind of a front end for Cloud kitchens. And but either way, I think people just don't like paying delivery fees. Mhmm. And and tipping too is still, you know, debated.
Speaker 2:Yeah. So you you get sort
Speaker 7:And of and and one
Speaker 3:part of it part of it is like I feel like a lot of a lot of these things, if you just build it into the cost of the food, people feel better about it. Mhmm. But when you're when people are forced to make the decision around tipping for something they wanna do every single day and it's like, you know, maybe maybe it's great sometimes, maybe it's not, but you're setting these things oftentimes before. Yeah. So.
Speaker 2:Yeah. A lot of the tipping stuff, it just it needs to be, like, injected in the UI at the right time. And a lot of the apps don't necessarily, like like, prompt for the tip at the right time. Like, if you ask if you ask for the tip before the service is rendered, it's hard to use the tip as a as a quantitative feedback mechanism Exactly. Or
Speaker 3:how so I will when I order I order delivery, from a grocery store
Speaker 2:Yeah.
Speaker 3:And I tip Front. Upfront.
Speaker 2:Do they see the tip? That's always
Speaker 3:And that's the other thing. I don't know. Don't surface that. In theory, I'm like, I'm gonna tip because I want you to not go the other grow the drinks a Yes.
Speaker 2:Exactly. Exactly. I do that. But but then yeah.
Speaker 3:Getting the the fact that we've just normalized getting getting an exploded bag of drinks in a in a in a bag is just is Wild. Funny.
Speaker 2:Back to the press release economy. Today's press release is out. Brookfield today announced the launch of a $100,000,000,000 global AI infrastructure program in partnership with NVIDIA and the Kuwait Investment Authority. There are tons of press releases going out every single day. Daniel Tenriro says running a business is all about partnerships.
Speaker 2:It's all about announcing partners.
Speaker 3:It's not even about you don't even necessarily do need to do the partnership. You just got to announce the partnership.
Speaker 2:The partnership economy is going crazy right now. Like, the prediction markets are obviously the the the the the most heinous offenders with a new a new partnership, like, every single day. It's hard to keep up with. We obviously are partnered with the Polymarket, and we wanna celebrate them when they do great things. But, there's a lot of these things going on.
Speaker 2:And so we tend to give you a little bit of a higher level review.
Speaker 3:On the prediction market front, somebody just leaked a bunch of screenshots from Coinbases
Speaker 2:Oh, yeah.
Speaker 3:Coming That's at right. Prediction market. Yeah. Everybody's getting into this game. There's different approaches.
Speaker 3:Some are some are like partnering with existing prediction markets. Mhmm. Others are building it, you know, entirely themselves. Yeah. I'm interest I'm more interested to see if when Coinbase does their prediction markets product, how are they actually running it under the hood?
Speaker 3:Are they taking on the responsibility of actually managing the markets, being a market maker? What is that actually going to look like?
Speaker 2:They should just hire a guy on the other side of every trade, where you just go to Coinbase and you say, look, I want I want $50 on the Eagle. And the other guy says, like, yeah, I'll take that. Well, I think they're gonna lose. And that's how it goes. He should be a guy that you call.
Speaker 3:Almost like a bookie.
Speaker 2:They should acquire mybookie.ag.
Speaker 3:Mybookie.ag should pivot to not having any digital experience and just lean into be no. No. No. Lean into just the guy mess.
Speaker 2:Oh, the guy. Yes. Actually, a guy. Become a guy company. Well, Cloudflare had unfortunately had an outage yesterday.
Speaker 2:We were not affected. Although, Tyler, do you wanna take us through how we we seem to be dodging exposure to Internet outages lately? What's going on?
Speaker 4:Well so, I mean, to be clear, all my systems were fine. But I I recently moved some of the kind of back end processes we use onto, like, a local machine. Yeah. Then used prem. Yeah.
Speaker 4:On prem. And then I used Cloudflare tunnels Yeah. To, like, help do, like, API stuff.
Speaker 2:Okay.
Speaker 4:So I was worried for a second that I the stuff that I moved off AWS onto on prem was actually gonna go down because of a cloud. Yeah.
Speaker 2:Because AWS was down, what, two weeks ago, three weeks ago or something?
Speaker 4:It was I think it was Maybe
Speaker 2:maybe longer. Half. That one was rough. I I feel like I feel like that day, we actually did cancel a bunch of guests. There was there was a lot of stuff going on.
Speaker 2:We've had a few we've had a few rough outages, but let let let let's read a little bit of the of the post mortem from the journal because it did make front page news. Obviously, sending our best to Matthew Prince over at Cloudflare and the team hoping for a swift recovery because we love the Internet and we love them. An outage that knocked swaths of the Internet offline was resolved Tuesday after drowning social media sites, disrupting retail sales, installing transportation networks. Users visiting sites including X, Chateapiti, DoorDash, IKEA, Metropolitan Transport Authority in New York City were met with error messages related to Cloudflare, a cloud provider used by major companies for security tools that protect from cyberattacks and traffic surges. A spokesperson spokeswoman from Cloudflare said an unusual rise in traffic to one of its services at around 06:20AM eastern time caused traffic passing through the company's network to experience errors.
Speaker 2:The bug was fully resolved by 09:30. She said in an update, for several hours Tuesday, users were unable to access sites and services from retail and social media to financial services. The outage echoes problems with AWS. Cloudflare and AWS services were effectively invisible to users, but their tools underpin many people. So I don't know if there's a full, full breakdown here.
Speaker 2:Last year, a bug in a in a tool used by, cybersecurity company CrowdStrike upended computer systems across the world. There's just a lot of these going on. But, we'd I don't think we have, like, a full postmortem. I would love to know exactly what happened. It's always interesting.
Speaker 2:We failed our customers.
Speaker 3:I'm interested to know what what happens to the business when they have these outages. Yeah. Because on one hand, it's a great way to tell the world that the entire world runs on Cloudflare or at least like a large amount of
Speaker 2:Sort of Super Bowl ad.
Speaker 3:Yes. It's like super very, very much so.
Speaker 2:Yeah. You guys just, gave everyone call.
Speaker 3:And then and then you talk about the stress from the Cloudflare team where anybody that's, you know, built a a software product has experienced the product going down and the stress around that. Yep. But it's it's like when your product goes down and then and then, you know, many of the services that people use and love across the country and the world also go down. It's even more stressful. Yep.
Speaker 3:But it also probably brings like a ton of, you know, ton of traffic to the site. Yep. And people might might start evaluating some features and say, hey, maybe this is a good solution. I'm gonna watch I'm gonna sign up and see how they kinda react to this.
Speaker 2:Well, I mean, the the Cloudflare team, like, reacted very well. They got a lot of praise for their response. Dane Knecht here says or Nacht. He's the CTO of Cloudflare. He says, I won't mince words.
Speaker 2:Earlier today, we failed our customers and the broader Internet when a problem in Cloudflare's network impacted large amounts of traffic that rely on us, the sites, businesses, and organizations that rely on Cloudflare depend on us being available. And I apologize for the impact we caused to transparency about what happened, and we plan to share a breakdown with more details in a few hours. In short, a latent bug in a service underpinning our bot mitigation capability started to crash after a routine configuration change we made. That cascade into a broad degradation to our network and other services. This was not an attack.
Speaker 2:That issue impact impacted caused and time to resolution is unacceptable. Work is already underway to make sure it doesn't happen again, but I know I caused real pain today. Oh, I know I caused I know it caused real pain today. The trust our customers place in us is what we value most. Just taking full responsibility here.
Speaker 2:Lulu says, well done response, and the comments reflect that. People in the comments are very happy. Mert, of course, always having fun. Okay. Thanks, Dane.
Speaker 2:But have you considered that blockchain's handling 0.00000001 of your load did not go down? Very funny.
Speaker 3:There was another company there was another company.
Speaker 2:Has a picture here from, from Shogun, I think. It says Cloudflare's comms playbook. I ask permission to commit seppuku because they're just like fully throwing themselves down. Just being like, yeah, we're a 100% responsible. We won't mince words.
Speaker 2:Pretty sweet.
Speaker 3:We should get into mister Hobart's piece.
Speaker 2:We should. He's joining the show in just a few minutes. Tyler, what's up?
Speaker 4:Before I there are breaking news. OpenAI new model.
Speaker 3:New model?
Speaker 4:Yeah. GPT 5.1 Codex Max.
Speaker 2:Okay.
Speaker 3:So it's They're firing back.
Speaker 2:They're firing back.
Speaker 3:I knew it was Max. We were debating. Did they have the juice?
Speaker 2:Well, what what's interesting is that Gemini three, the one benchmark that it didn't outdo open Anthropic on, it was better in a lot of benchmarks, but it wasn't better at Sweebench. Correct?
Speaker 4:Correct.
Speaker 2:And so and so that was, of course, a testament to, like, Anthropic being really, really great at doing just something special in code. Obviously, that's aligned with their mission of reaching superintelligence through self replicating code, essentially. But a fascinating, like, you know, durability of their business that even with this Gemini three thing that's so good at all these different things, Anthropic's still on top in Suitebench. But do we know how OpenAI is faring in this bench? Is there any reaction?
Speaker 2:What can you tell us about the latest model from OpenAI? Because we gotta get to the bottom of it. Yeah. While you look it up, I'm gonna tell everyone about Vanta, automate compliance, and security with the leading AI trust management platform. Also, Suno raised 250,000,000 to build the future of music.
Speaker 2:I'm gonna hit
Speaker 3:the That.
Speaker 2:While Tyler pulls up the reaction. Great
Speaker 3:hit to open up the day. While Tyler gets into that, there is some breaking news. Glu has hit the public markets. Christian Tech Group tests investors faith in AI deals on Wall Street debut. Shares in a company backed by formal former Intel chief Pat Gelsinger waiver after scaled back IPO.
Speaker 3:I didn't realize that Glu was IPO ing.
Speaker 2:No. I didn't know.
Speaker 1:Pat. It's
Speaker 3:cool. Pat's company. Shares in a company developing AI software to connect Christian organizations across The US wavered in its Wall Street debut Oh. Following a scaled back initial public offering.
Speaker 2:That's very cool.
Speaker 3:Glu counts Pat Gelsinger as exec chair and video rental store Blockbuster's former chief operating officer Scott Beck as chief executive rose as much as five percent after it began trading on Nasdaq on Wednesday morning, having raised 73,000,000 from investors. The average, share price pop for a US IPO that has raised 25,000,000 or more this year is nearly 25% according to Renaissance Capital Management. Founded in 2013, Glu hopes to pull the Christian faith into the digital age by using values aligned generative AI to distribute content and sell marketing services to ministries and community outreach groups. There's an imperative to shape technology for good. On its own, it isn't good or bad.
Speaker 3:The question is what it's used for, Beck told the Financial Times. And anyways, so wasn't tracking this one, but really enjoyed having Pat on the show a while back.
Speaker 2:Yeah. No. He's he was amazing. I'm very I'm very excited for for him. He's just like I don't know.
Speaker 2:Just fun to fun to talk to. Let's run through Bern Hobart's economist piece. But first, Tyler, did you give us any
Speaker 4:Yes. So previously, the Codex SuiteBench verified was 73.7. Mhmm. And now with, like, the highest reasoning, it's at 77.9. Okay.
Speaker 4:And then SONNET four five is it it's always kind of hard to tell, like, what exactly it is because people measure it differently.
Speaker 2:Yeah. Don't they take some of the questions out sometimes?
Speaker 4:Yeah. Sometimes they do that for. So so Sonnet four five is, like, officially 77.2. So that's lower. But then with parallel test time compute, it's at 82%.
Speaker 4:So it's kind of unclear what that, like, really means. But it it's definitely better. Yeah. So this is a big improvement
Speaker 3:Compute just like a real guy who's just kinda sitting there being like, oh, don't Actually, don't do it like that. Do it like this. I I
Speaker 4:I think that the the the main headline is that they said
Speaker 2:Yeah. Tool use. You kinda find you find a dude who's a bit of a tool and you tell him, hey, I can't solve this. You gotta do it, human. You I gotta kick this one out to you.
Speaker 2:Do this arc puzzle for me.
Speaker 3:Age AJ, our our incredible brokers in the chat, he's talking about the the office debacle. He said, l m a I. I still can't believe that happened. Maybe something the landlord brokers should disclose before tours. I can't
Speaker 2:believe he's in the chat watching us talk on
Speaker 3:the screen. AJ's been incredible finding us every every possible every possible space in the greater Los Angeles area for the next UltraDome. Highly recommend if you're in the LA office market.
Speaker 2:Yes. I also recommend Figma. Think bigger, build faster. Figma help design and development teams build great products together. So we have our update.
Speaker 2:We will keep monitoring the the GPT 5.1 Codex Pro Max.
Speaker 4:More thing. What else? There's an interesting headline. They said there were some tasks they found that the model worked for more than twenty four hours. Oh.
Speaker 4:Which is, like, that's it. You know, if if you're if you're following that that one meter chart
Speaker 2:Yeah.
Speaker 4:Yeah. Yeah. Where it's that time horizon
Speaker 2:That's super interesting.
Speaker 4:Definitely a good sign.
Speaker 3:Okay. Have you ever worked for twenty four hours straight?
Speaker 2:Buckle up, buddy.
Speaker 4:It depends on how you
Speaker 2:Time to take it for a spin.
Speaker 3:Yeah.
Speaker 2:See what that
Speaker 3:Tyler bench. I
Speaker 2:I would love to know what it actually is doing for twenty four hours. I wanna know the prompt, and I wanna know the output.
Speaker 4:Yeah. That's
Speaker 2:what people are asking. Yeah. Of course.
Speaker 4:They didn't say that.
Speaker 2:Okay. And the press release Because it I mean, it's like just just sit there, like
Speaker 4:It was working on the easiest problem just trying to debug it because it's so Yeah.
Speaker 2:Or I mean I mean, there is a world where it's like, hey. The prompt is like, just go take a crack at every single open GitHub issue on every repo for as long as you can and work on it. And then you're you're basically just wrapping another for loop around it, and it's like, is that one continue like, there's obviously a lot of
Speaker 4:But even if it is like, having a task that you can continuously work on Totally. Just having a, like, kind of plan that you can maintain that you don't get lost
Speaker 2:Totally.
Speaker 4:Is still like a
Speaker 1:Yeah.
Speaker 4:Yeah. Yeah. Yeah.
Speaker 2:No. I mean, in general, I I would imagine that there there's maybe some SaaS productization, but there's also just some a ton of value to having agents that sort of roam through your organization continuously and clean up data or or look for different errors or just do opportunistic tasks. That seems very valuable.
Speaker 3:Trey says count for twenty four hours. Yeah, Tyler. Yeah, count for count for twenty four hours?
Speaker 2:MrBeast has literally done that. Count there's a YouTube video up. It's I think it's twenty four hours.
Speaker 3:What about doing one wrap of one thirty five every five minutes for twenty four hours? That would probably get That would probably really hard. Get absolutely brutal by I don't know. I don't I don't know if that would Matt? I think I think you would I think most people sounds like it doesn't
Speaker 2:It's yeah. It sounds very easy, but I I imagine it'd be very difficult.
Speaker 3:Anyways, we have to talk about Bern Hobart. Bern Hobart, the legend
Speaker 2:The king of bubbles. The
Speaker 3:of Bub Talk.
Speaker 2:Yeah.
Speaker 4:He wrote the book on bubbles.
Speaker 2:He wrote the book on bubbles.
Speaker 3:Yes. He's he's
Speaker 2:an economist.
Speaker 3:He says, how I learned to love financial bubbles by the author of a book on bubbles.
Speaker 2:So he says, tech stocks have sold off this week over fears of frothiness and artificial intelligence, AI. I love that.
Speaker 3:Economists adding that in.
Speaker 2:Some investors were no doubt surprised by this. But for the others
Speaker 3:Some some some reader out there is just like
Speaker 2:Thank you.
Speaker 3:I I never put that together. That artificial intelligence was the thing that people were talking about.
Speaker 2:Broad audience of The Economist, but I I absolutely love The Economist. I have been a subscriber for probably over a decade. The signs of an AI bubble have been there for some time. Cluely, whose original product was a tool for using AI to cheat during Zoom job interviews, raised $15,000,000 and then dropped its cheat on everything tagline and pivoted to being a more benign AI meeting assistant. More serious AI labs have been able to raise 10 figure sums and 11 figure valuations, not just pre revenue, but pre product.
Speaker 2:Individual researchers have reportedly been offered 9 figure signing bonuses. And in the past year, the spending commitments made by a single company, OpenAI, total about 1,400,000,000,000.0, a sum equal to 1.2% of global economic output. A frenzy like that is enough to make you long for the relatively sane and responsible days of the pets.com sock puppet or the synthetic CDO squared. You wanna continue reading?
Speaker 3:Yeah. But bubbles are tricky things. The default school of thought is that they're driven by irresponsible speculators who aren't trying to invest in great companies, but to buy something they can flip to someone more gullible. A more benign theory is that there are wealth transfer from rich investors to everyday consumers. People who bought telecom firms junk bonds in the late nineteen nineties lost their shirts, but the rest of us were blessed with bandwidth cheap enough to support the likes of YouTube and Netflix.
Speaker 2:This is one of my favorite takes of his is that is that in the bubbles, like, when bubbles pop, rich people actually get hurt more than Main Street, which I think is not how it's framed most of the time. It's because, yes, there are some retail traders that go crazy and put you know, they they have a 5 figure net worth, they put it all in the most risky NFT, and they do lose it. Like, there are some anecdotes like that. But in general, most people have a pretty diversified, you know, you know, asset base, whether it's their house or their, you know, their stocks in a retirement fund. And those Yep.
Speaker 2:Just fluctuate a lot less than someone who's in the most risk on positions.
Speaker 3:Yep.
Speaker 2:We're Dogler, the first AI native dating and vibe coding platform for dogs. We've raised a $150,000,000 as part of our seed.
Speaker 3:As part as part of our seed. Part
Speaker 2:of our seed. Dogler is
Speaker 3:a That's good hilarious. Okay. Good day. There's some truth to this. To this day, America and Britain benefit greatly from rail networks whose construction turned out very badly for the original investors.
Speaker 3:But there's another way to look at bubbles. The participants in the AI race are all building products that are economic complements to one another. You need the turbines to power the grids Mhmm. That power the chips that run the models that power the products. And you need firms to build their growth and hiring plans around the expectation that ever more of their work will be done by AI, but that every company and every employee will be automating different sets of tasks.
Speaker 3:Mhmm. If TSMC builds hugely expensive chip factories, but the big AI labs all decide they've spent as much as they need to, those factories are a stranded asset. But when asset prices are loudly signaling that the technology is real and economics will be compelling, it encourages those complementary investments that actually make it happen. There are countless historical examples of this. The car industry's growth implicitly subsidized oil production and vice versa.
Speaker 3:Electric electrification followed a similar path. Appliance manufacturers had to operate on the assumption that utilities would wire up more households and those utilities had to bet that once power was available, GE, RCA, and the like would give people something to plug in. During the heyday of Moore's Law, chip companies raced to build ever more powerful chips and software companies rushed to ship products that would use them. It's hard for any of this to happen without entrepreneurs getting excited about a business based on its hypothetical future rather than its present profits. It's impossible for this process to keep going unless investors too get excited.
Speaker 3:Naturally, one side or the other will overshoot. This hasn't been a technological revolution in history. There hasn't been a technological revolution in history that didn't at some point get overhyped. There's always there That's always obvious in retrospect, but less so when we are in the cycle. An investment researcher once circulated an essay called, A Home Without Equity is Just a Rental with Debt.
Speaker 3:Warning that house price appreciation was driven by loosening underwriting standards and would inevitably lead to collapse, but it was dated June 2001.
Speaker 2:Wow. That's crazy.
Speaker 3:At the post
Speaker 2:so early. This is true. But Yeah. Seven years too early or six years too early.
Speaker 3:Even at the post crisis low a decade later, the Case Shiller Index of American house prices was still 18% above its level when that piece was public.
Speaker 4:Wow. Wow.
Speaker 2:I mean, this is like the Bitcoin bubble stuff. Yeah. Well, it was like, yeah. It's gonna crash. And it's like, yeah.
Speaker 2:Crash from a 100 down to 90 or whatever.
Speaker 3:Similarly, media coverage of .coms described trading as nutty and quoted an investor saying, I don't really know anything about the company. But that article, The Wall Street Journal on the Netscape IPO, was published in the 1995. Yep. At its post.com low in 2002, the Nasdaq one hundred was still 40% higher than it had been then. Signs of a bubble aren't necessarily signs that it's time to sell because they precede the peak of the mania by an unpredictable amount.
Speaker 3:Anyone who read the quite cogent arguments against buying a house in 2001 or buying tech stocks in 1995 would have benefited financially from completely ignoring them.
Speaker 2:Love it.
Speaker 3:The famous dictum apo
Speaker 2:Apocrypha Apocrypha
Speaker 3:Apocrypha fully attributed to John Keynes is that markets can remain irrational longer than you can remain
Speaker 4:solvent. Didn't
Speaker 2:Keynes didn't say that? That's funny. I always thought that.
Speaker 3:Really?
Speaker 2:Yeah. I mean, that's
Speaker 3:that's That's like his
Speaker 2:Well, no. It's not.
Speaker 3:No. I know. But Apocryphally Yeah.
Speaker 2:Attributed to him. We'll have to get to the bottom of that.
Speaker 3:Yeah. But this presupposes that everyone has the same information and that irrational traders are simply ignoring it. It's more in the spirit of Keynes to argue that the economic growth is partly a matter of believing that it will happen, recessions, and when people and companies start to spend as if they're over
Speaker 2:Animal spirits.
Speaker 3:And booms persist when some participants are building the infrastructure that others need to make that boom happen. When OpenAI announces a splashy new scale up, or Meta declares that it has found yet another opportunity to raise its planned capital expenditures, they're signaling to AI users, coders, lawyers, writers, whoever, that they'd better be prepared for smarter models. The more people and organizations gear their behavior towards a world in which AI is even more powerful and ubiquitous, the more they're locking in the demand that justifies all of those eye popping expenditures. In the end, a bubble functions like an industry cluster that exists in time rather than space. If you want to be a movie star, you move to Los Angeles.
Speaker 3:Of course. If you wanna start a hedge fund, you move to New York. And if you want to be if you want a part of being first to something in AI, first to build, first to use, first to profit from asset prices, or insisting that
Speaker 2:Now is the time to act. I love it. Fantastic.
Speaker 3:Oh. Someone in the chat was saying earlier they were expecting Nvidia to beat and then trade down five to 10% Yeah. Which I feel like is the consensus view
Speaker 2:It is.
Speaker 3:Now, which means that I'm I think I think we might see something else
Speaker 2:Something else happened. Who knows? It's all been very unpredictable. Well, I'm I'm very excited for Bernd to join the join the join the show. I'm also excited to tell you about julius.ai, The the AI data analyst that works for you join millions who use Julius to connect their data, ask questions, and get insights in seconds.
Speaker 3:It really is like a UAV for your business.
Speaker 2:It is. Although, that's night vision. That's not the UAV sound. Play the UAV.
Speaker 3:UAV online.
Speaker 2:That's the UAV for your business, baby. Crazy crazy
Speaker 3:while I was reading telephoto. Ben got me this. What what do we what do we got here, Ben?
Speaker 2:That's Bub Talk, baby. Wow. That's actually a lot of bubbles. I like that.
Speaker 3:Woah. Alright. So We will be ready to go when when Burn joins the show. Hopefully, those don't get on the camera.
Speaker 2:There is a there there there's some pretty crazy news. The founder of an ADHD startup is found guilty of conspiracy in an Adderall case. What a crazy story. Ruth Yahi
Speaker 3:Gotta give credit to Will for, like, predicting this, like, years ago at this point. He was just saying, like, all this stuff is Yeah. Like, seems deeply
Speaker 2:So back in 2021, Will Menidas said on 10/03/2021, so four years ago, he said, Telemedicine psychiatry startups have driven an unprecedented wave of amphetamine abuse. So he was worried. He was sounding the alarm bells four years ago about ADHD medications being overly prescribed, too easy to prescribe. He said, after tweeting this, an executive at helloahead.com DM'd me from an anonymous account details of my care history with them asking that I delete the tweet or caveat that they are not bad. This is an unimaginable violation of patient privacy and
Speaker 3:an off threat. Just the worst person to
Speaker 2:It's also insane because he didn't specify. He didn't call anyone in particular out, but then he got a threatening message from one person in particular. So that was very rough.
Speaker 3:Just say you're responsible.
Speaker 2:Yeah. And so Will has followed it up and said, worth remembering that in 2021, 2022, many major health care venture investors funded a cabal of Internet pill mills that operated with mafia tactics to silence regulators and drive an unprecedented wave of amphetamine dependence in The United States. Well, today, there has been some justice I'd done, I suppose, for these a these ADHD startups. And so a jury found Ruthia Hee guilty of conspiring to distribute controlled substances after her startup. Dunn Global became a ready source of Adderall prescriptions for more than 100,000 patients.
Speaker 2:The jury found he and Dunn's former top doctor, David Brody, guilty on two conspiracy counts and four counts of distributing controlled substances. The former CEO was found guilty of conspiring to obstruct justice. So the company was the subject of a series of articles in The Wall Street Journal from 2022 to 2024. Maybe they got maybe they're It's interesting Well,
Speaker 3:that's that Tech and Venture basically decided, like, doctors were a bug, not a feature.
Speaker 1:Yeah.
Speaker 3:It's like, yeah, why waste time talking to a doctor just to get the medication that you want and that you know you need? Yeah. It's like, oh, actually, having somebody that is like, if it's slower, having somebody that's there and actually understanding the patient and having some personal connection with the patient feels very much more and more like a feature.
Speaker 2:And also having the economic incentive of the doctor being like, they get paid a lot of money and live a great life just to give great advice and follow the Hippocratic Oath and be like a pivotal To give great care. Member of their society. Not to increase conversion rate. Exactly. As opposed to like piecemeal, how many scripts did you write?
Speaker 2:That's the pill mill model.
Speaker 4:Yeah.
Speaker 2:And so, defense lawyers argued that he had, so during a seven week trial, prosecutors argued that he sought to enrich herself by making it easy to get Adderall and other stimulants while the government classifies this as a controlled substance with high potential for abuse. The startup collected more than a $100,000,000 in revenue, and, and the defense lawyers argued that they just wanted to make it easier to get the drugs when there was a shortage of providers. Quote, I think the goal we want to optimize is to help patients manage their ADHD in a convenient way. And there's some good reasons for that. Not everything.
Speaker 2:Sometimes you actually can't get to a doctor. There were good arguments on both sides, but, in this case, it does seem like they, they, pushed it way too far. There's some crazy, crazy quotes in here. Whoever is the first person to get arrested, I'll buy you a Tesla, Ruthie told concerned staffers, saying, like, don't worry. You know, bend laws.
Speaker 3:Wait. Don't if you get arrested
Speaker 2:A former company executive
Speaker 3:I'll buy you a car.
Speaker 2:Testified that the CEO encouraged staff to, quote, unquote, bend laws. Okay. I'm encouraging everyone here who works for TVPN. Never bend the law
Speaker 3:within the law.
Speaker 2:Operate within the law. Tyler? A 100%. Looking at you. Tyler.
Speaker 3:Operate within the laws of physics.
Speaker 2:Yeah. Don't bend
Speaker 3:the laws of physics. You're studying physics, make sure to operate within the laws of physics always.
Speaker 2:Yeah. Whoever is the first person to get arrested, I'll buy you a Tesla. That's a crazy thing
Speaker 1:to say.
Speaker 3:Was that in writing, or was that just like, a quote from from one of the employees?
Speaker 2:No. That was the former custom former executive testified on the stand that this was said by the CEO, which is pretty pretty crazy. Anyway, fortunately, the bubble in ADHD medication is winding down as the justice is being served. We have Bern Hobart in the Restream waiting room. Let's bring him in to talk about other bubbles, more positive bubbles, more beneficial bubbles.
Speaker 3:Is great to have you here.
Speaker 2:Good have you on
Speaker 1:the show.
Speaker 3:Brought the bubbles.
Speaker 7:Awesome. We brought bubbles. Great to be here.
Speaker 2:Bubbles. Wow. We're the bubble king. It really goes everywhere. For those who don't know you, please, can you kick us off with a little bit of an introduction on yourself?
Speaker 2:And and thank you much for taking the time to be here.
Speaker 7:Yeah. Absolutely. So, hey, everyone. I'm Bern. I am probably best known for writing the newsletter of the diff, which you can check out at the diff.co covering topics in tech, finance, everything adjacent to them, everything in between.
Speaker 7:Also, a partner at Anomaly, an early stage frontier tech venture capital firm. Also, coauthored with Anomaly partner, coauthored the book, Boom Bubbles in the End of Stagnation, published late last year by Stripe Press. So, yeah, I'm the Bubble Boy.
Speaker 3:The Bubble Boy on a roll.
Speaker 2:How can we talk how should we set the table? Do you wanna talk about, just it feels like you've been, sort of defined as, like, pro bubble. So I feel like asking you the question, are we in a bubble, is a little bit irrelevant. But would you agree that we're in the bubble, in a bubble, maybe at the start of a bubble, maybe at the end of the bubble, but we are it feels like we're in a bubble, and it's safe to say it now?
Speaker 7:Yeah. Totally. It's great. Yeah. And, yeah, that that is, like that that that is the curse of coauthoring a book that is trying to rehabilitate the image of bubbles is that every time the Nasdaq hits a new high, people start calling you and asking you if that's good.
Speaker 7:And, yeah, my my obligation here and and the the model advance in the book is to say that, yeah, it is pretty good. Like, not to say that stocks will always go up forever, not to say that everybody's options or their weird quasi equity participation units will all be valued at their present prices. But yeah. So the general argument we advance in the book, I guess you can rewind a little bit and say, you can you can go through these different ways of talking about bubbles. One is just say, they're stupid.
Speaker 7:It's when people are they just get overexcited about some new technology, or in the case of, like, housing, they get excited about some very old technology that is, newly easy to finance, and they just lose their heads. And by the end, everybody knows they're overpaying. Everybody assumes someone else will overpay more in the future. And just when you run out of stupid people, prices collapse. So Yep.
Speaker 7:That's like the bubbles are really stupid vein of thought. And then there's one which is a little bit more nuanced, which is, hey. Yeah. They're stupid, but they're actually a wealth transfer from hedge fund people, venture capitalists, etcetera, to everyday consumers. So I lived in San Francisco in 2015.
Speaker 7:I remember I I I long for the incredibly cheap, you know, universal basic Uber where every you know, you could get anywhere for, like, $8. It was amazing. And, that was, yeah. It was great. It was, like, you know, wonderful wealth transfer from the, Saudi Arabian sovereign wealth fund to me, and I really appreciate it.
Speaker 7:Thanks, guys. But but that
Speaker 3:Well, it's also note notable that they they they've, you know, done fairly well even though there was like Yes. A it it was it was a it was a bubble. It wasn't necessarily sustainable, but it created a enduring business or at least one out of the out of the out of the two.
Speaker 7:Yeah. And that's that's often what we get. Now Uber's a little bit of an abstract case. What we often get from bubbles is we build too much infrastructure, but that means we have the infrastructure, and we have enough infrastructure to build the next thing. And that was the case in the nineteenth century with railroads.
Speaker 7:That was the case in the late nineties with telco infrastructure. And, you know, that that could end up being the case today with GPUs. But there is the pro bubble argument, which is that what bubbles really do is they coordinate different market participants, founders, employees, investors, regulators, customers, suppliers. They convince everyone that this is happening. It's happening right now.
Speaker 7:And that if you build something, if you overbuild for today's demand, you will still have underbuilt for future demand. And if everybody's doing that at every layer in the supply chain, then you actually do build enough to satisfy future demand. And so, like, making it more concrete, if if TSMC does not buy into the idea that AI is a really big deal, they're not gonna build enough fabs, NVIDIA will not be able to ship enough chips. The next models will not be quite as good, or we'll have to do more of a trade off between training and inference, and the whole thing slows down. But if everyone is wildly optimistic, then they do build all of that infrastructure, you know, all the way from the power generation to the end use cases.
Speaker 7:They're building all of that, and it all it's kind of like just in time manufacturing of the future. And the prices, like the crazy prices, they are the signal that this is the time. Like, it's happening now. If you build something on the assumption that OpenAI is going to keep shipping better models and that they will need a lot more compute and that will need a lot more power to make those models work. If you operate on that assumption, you're making the right call.
Speaker 2:How how was how have you been processing, some of Ben Thompson's maybe jitters around the idea that the infrastructure that gets left behind in this particular bubble might not be something that's with us for a hundred years. He's been advocating for, hey. Let's do energy. Let's do nuclear, solar. Let's build out a lot of energy.
Speaker 2:But if we're just like, yes. We way overbuilt, like, a bunch of h one hundreds, and then, you know, years later, we're like, those are those actually aren't that valuable. They don't they maybe depreciate over a few years. That's sort of how he's articulated some of the fear of the overbuild not being as durable. Do does that resonate with you, or how have you been processing that?
Speaker 7:Yeah. I think I think you can look at these different lags, and you also look at how generalizable the use cases are for these products. And so it is true that GPUs depreciate. But depreciation, it is this economic concept that's tied or it's an accounting concept that's tied to an economic concept. And it actually ties in a couple different things.
Speaker 7:One of which is just if you use a machine, there is friction. It it can break. It can overheat whatever. And so, eventually, it's trash. And then the other piece, more on the economic side and much more relevant to GPUs, is if the GPU is producing fewer tokens per watt and that just relative to newer ones, it could be economically worthless even though it can still actually do something useful.
Speaker 7:And so if the GPU build out slows down, that actually decelerates the depreciation for all of the world's existing GPU fleet. And power is harder to slow down. The just the lags are a lot longer. It takes a long, long time to build a new power plant and to build all the equipment that goes into it. So you could have this case where bubble pops, OpenAI has to do some weird recap at a much lower valuation, NASDAQ's down by half or probably not down by half, but down by a lot.
Speaker 7:And, you know, a lot of a lot of people who felt very smart as of, like, a month ago, looked pretty stupid, myself included. And, you know, we could have that, but there will still be this increase in power generation capacity because that stuff is locked in. And the, like, the gas turbine companies, they they can really lock their customers in because there are just not that many places you can go to buy one. And so if you're gonna buy one and they say, okay. But you have to actually guarantee that you'll pay for it even if you really regret this.
Speaker 7:That's pretty much what you'll have to do. So you could have this case where what actually happens in the aftermath is power cost decline. And so these GPUs actually become higher margin when they're doing inference. And so you get really, really cheap abundant intelligence at today's model capabilities, and that that is still
Speaker 3:Yeah. The the GPU is fully depreciated, and power costs have come down which would make them you know, the the the concern around depreciation is you just get a chip that is so much better that is just non economical to Mhmm. To run one of these old GPUs. But there the scenario that you're pointing out, you could they could potentially be valuable for for much, much longer.
Speaker 7:Yeah. So if you look at a company like CoreWeave where their business model is, we stack a bunch of GPUs in a data center. We lease them out to various people in varying terms. That is actually kind of a bet on this narrow slice where AI is not a complete flop. We don't find out that it was actually just Sam Altman typing those answers really fast all along, but it's also, you know, doesn't completely revolutionize things because that, like, if there's another generation or two GPUs or if TPUs take more share of inference, then maybe those GPUs end up being economically stranded.
Speaker 7:But if there's a world where we're not building many more GPUs, but we are using we do find all the use cases for the ones we have, That might be a world where actually Farweave is, know, reporting pretty nice gap profits, and, and their investors are happy. And when you look at Farweave, one of the interesting things about them is on their cap table. So they one of their big backers is this hedge fund, MagnaTar, which does incredible has done incredible things with structuring various bets. Like, if you control f, their, their prospectus, Magnatars actually mentioned more often than NVIDIA. And Magnatars likes to make interesting bets on, like, the relative volatility of different things or the time relative timing of things.
Speaker 7:And so you could see this as them making this kind of esoteric bet that AI is actually both a really big deal and somewhat overhyped. That was always always hard to, you know, read into like, what you're reading into is Yeah. Macintar is involved in this. They have done a bunch of really interesting deals
Speaker 2:Sure.
Speaker 7:Throughout the core reef cap structure. But it is it is still striking that they are very sophisticated about this exact kind of trade.
Speaker 2:That's interesting.
Speaker 3:What like, what is the right way to view a bubble? Is it this monolithic structure in which they're or or or are you viewing it as like because in in my view, it's like we we just read your article, or essay in The Economist right before you
Speaker 2:Congratulations, by the way.
Speaker 3:It's great.
Speaker 7:Thank you.
Speaker 3:Great kind of summary of everything.
Speaker 2:But High honor to be in
Speaker 3:But you're you're saying like there's pieces of a bubble that are feeding into each other and making the possibility for durable value creation to be higher and higher because you have all these parts combining. I feel like another view maybe is is similar but slightly different is like you have these rolling bubbles that are all kind of like building up. And right right now, there's like you have a private credit bubble. Maybe there's a a neo cloud bubble. Maybe there's an LLM bubble.
Speaker 3:Right? Like, we it's unclear if we need the fiftieth closed source LLM, right? I don't know. Maybe we do, right? It's hard to predict.
Speaker 3:But what is the right way to even just visualize the bubble, the AI bubble broadly?
Speaker 7:So I like a lot of other bubbles, it starts out as this really differentiated unique thing where most people do not know you know, like, five years ago, most people did not know or care that much about AI. It was kind of this thing where you would listen to the quarterly call from Google or from the company then known as Facebook, and they would talk about how they're an AI company. And you'd think, okay. Like, I'm glad you have your science project nerds, but I really care about more ad clicks and more dollars per ad click. So good luck with whatever whatever robot experiments you're doing.
Speaker 7:And then when it starts growing, what it starts doing is actually connecting with the rest of the economy. So now, like, the marginal dollar of AI CapEx is increasingly going into general purpose power generation infrastructure. So and and meanwhile, AI is getting much more broadly distributed. Like, initial initial use cases were, one, it was a really good autocomplete for coding, and two, if you needed to create original content in order to spam people or if you were replacing, like, the lowest value bloggers, you could do it, and it was cheaper. But then it became this thing where it's like a lot of there are just a lot of things where you wish you could apply a little intelligence to it.
Speaker 7:It's really not worth your time, but if you can get the right answer easily, then you should do it. And just like a lot of a lot of cases where you'd want it like, I use it a lot when I'm writing as a research tool where it's like, I want examples of this phenomenon or
Speaker 3:It's a research tool, not a writing tool.
Speaker 2:Yeah. Right. Are even using it as a first draft or is it more like you have a bunch of facts here and then you are actually typing out the sentences that you wrote?
Speaker 3:We're like bloodhounds for AI content and we did not and no no alarm bells went off when we were reading
Speaker 2:Oh, yeah. The Economist article was like I've just been I've been surprised most clearly human written
Speaker 3:thing It's so it's it's extremely notable that like using AI for writing has become the most low status thing that you can do on the Internet.
Speaker 2:It's like It just feels like disrespectful
Speaker 3:or something. Lower status than making just like sloppy memes. Like
Speaker 2:There are low status things you could do.
Speaker 3:Yeah. Okay.
Speaker 4:Adult content. Adult content.
Speaker 2:Or like, use my coupon code to sign up for a prize pack. So, you know, here's my parlay who's
Speaker 3:riding with me. Still, it just communicates it it it it it you know, people feel disrespected by it when because it's like, hey, you put this out. You wanted me to read this. And if it's completely obvious that that a computer generated it, it's like, well, was this even worth my time? Right?
Speaker 3:Like, if you couldn't have said it in your own words.
Speaker 7:So there there is this dynamic where they're just sometimes when there's increasing efficiency with something, we find out that some of the effort was load bearing, and that doesn't mean the technology is bad. It means we do have to adapt. And so in the case of writing, one of the things that used to be the social norm was if you can produce a grammatically correct lengthy document about some topic, that is an indication that you probably know what you're talking about. Totally. And to get into a position where you can do that, you have to read a lot, so you get you acquire knowledge.
Speaker 7:And if you wanna write something persuasive, you probably have to talk to a lot of people and find out what's persuasive to them, what's persuasive to you, etcetera. And if you can just just ask a model to admit that, then you can basically write at a level that is much higher quality than your ability to think. You can write well beyond your wisdom. It's kind of like when people use some peptides and steroids, they end up getting weird injuries because they're just like, mechanically, their body is not actually suited to lift the weights that their muscles can move. So they do get serious injuries unless they train pretty rigorously.
Speaker 7:So I have a nine year old who has, in the past, used ChatTPT to write emails to me explaining her side of a fight that she had with one of her siblings. And the email is very clear, very articulate, lots of em dashes, lots of it's not x, it's y. And it's you know, that I think if someone that age sat down and write this coherent letter explaining their side of an argument, that would actually be impressive. Like, you'd say, okay. This person's actually thinking seriously about what happened.
Speaker 7:But in this case, it's like she can write two sentences in ChatGPT and answer some follow-up questions from it and then produce this nice coherent looking document. So
Speaker 3:Do you how much do you worry about a new we have kids younger than that. But how much do you worry about potentially a generation of young people never maybe in a classroom setting, teachers can be like, put your phones in this box, and you guys are all gonna write a paper on this. And it's possible that writing will become, like, highly supervised because the only way to prevent somebody from just generating the written
Speaker 2:word It literally already is in many schools.
Speaker 3:But Yeah. Yeah. That's right. But but but even even then, it's like when I think about growing up and being forced to think deeply about topics Mhmm. Oftentimes, it was because I was assigned to write an essay on something, and I didn't have the world's best autocomplete tool.
Speaker 3:And I just had to sit there and kind of wrestle with an idea and actually learn about it. And I had to read a book or read a bunch of essays and really put it together. And I think it's possible that just a lot of time spent, like, deeply thinking is just fully lost forever.
Speaker 7:Yeah. I think it comes back to that load bearing effort question. So I do tell my kids that there is just a qualitative difference and also that when they get an assignment at school, it's not because the teacher has this burning desire to read an essay about, you know, whatever, about Charlotte's whatever something. Like, it's not like the teacher is absolutely you know, they have been pining for this. It's like, the the point is the effort, and the point is the way that you think about things.
Speaker 7:And that writing is actually just a very useful way to think something through. I I don't really understand why that is. Like, I don't know why it is that if you just try to talk to yourself for twenty minutes straight about a topic, you won't get to the same level of clarity that you do if you type it out, even though the typing it out process is just really similar. It is one word after another and then a little bit of editing sometimes. So I think some some of what the education system has to do, which different schools do to different degrees, is is actually explain to kids what the purpose of what they're doing is so they understand what that purpose is.
Speaker 7:And then we also do have to make this adjustment of sometimes there are things that it is it used to be necessary for basically every adult to be able to do, no longer as necessary, and fewer people will be able to do it. And maybe the ones who do it will still take a lot of pride in their craft, but they they won't strictly have to. So think of it as, like, I don't know, things like manual labor and, I don't know, wilderness survival skills, things like that. There there was a time when being physically strong and knowing how to like, being able to navigate in space and figure out which way is north if you're lost was actually a pretty important skill that a lot of people had to have. And there's actually
Speaker 3:probably be mocked. You'd be mocked if you could.
Speaker 7:Right. Right. And so then you go through this generation where there is a lot of mocking. There is a lot of bullying. Yeah.
Speaker 7:But the nerds are actually probably right that this thing is not so important. And then the next generation is only the hobbyists who who do this. Thomas Old had this this argument about, I think it's in, his book on knowledge and decisions where he's he's talking about how if you live in a really if you live in a subsistence level tribe somewhere, you actually have to have this incredible breadth of knowledge. Like, you've gotta know all landmarks, how to get from one place to another, all the signs of danger, everything you can eat, everything you shouldn't eat, and, you know, which which local tribes are friendly, which ones aren't. And you just don't need nearly that level of knowledge to survive in a modern city today.
Speaker 7:There are all kinds of things about where your food comes from and what is safe to do and not to do that you simply don't have to know because you're not exposed to any of the risk. And so we, we actually have just a much lower knowledge requirement in in more advanced societies. On the other hand, we have much higher returns from having that having unique kind of knowledge because now that whatever value you can create can be amortized over a much larger number of people, and there's just more stuff to go around. So the the rewards from being really, really smart are a lot higher. And, you know, I hope that when I talk to my kids about this stuff and I basically say, like, there's gonna be a cognitive overclass and a cognitive underclass, you can opt into one of them, and it's super easy and
Speaker 2:You tell
Speaker 4:your kids that it's amazing.
Speaker 3:You must escape the cognitive underclass.
Speaker 7:That way. It is true. Like, it is so easy to go through life without thinking, and it will only get easier. And so you you have to decide knowing that the thinking part is increasingly optional in a larger and larger number of domains. Do you wanna be the kind of person who thinks because you like thinking and you like creating and discovering new things, or do you wanna be the kind of person who has just a much easier, more relaxing time because they don't?
Speaker 3:Very there's that we could continue, on this conversation for a long time, but I wanted to ask you about what scares you about this current bubble. Like, things that are not necessarily, like, bad today but could get bad. To me, like, tech, you know, indulging in in, in leverage for the first time, may maybe as an industry or as, a lot of the leadership has not act they weren't in the they weren't participating in telecom bubble. They didn't get blown up. Maybe they've never gotten blown up by leverage, and and maybe that's a a concern, but I'm curious how you think about it.
Speaker 7:Yeah. I'm I'm less concerned about that. I I think the current generation of tech leaders there's a lot more tech history that they can know about, and they just seem more interested in tech history. You can actually go back and see that the people who were more obsessed with tech history tended to do better. Like, Steve Jobs was obsessed with the story of Polaroid.
Speaker 7:It's this beautiful consumer device, changes everybody's behavior, really simple tool. You look at it, you know exactly how to use it, you know what it does, and it does what it's what it looks like it's supposed to do. Jeff Bezos gave a TED Talk when that was a much cooler thing to do, I think, right after the .com bubble had rolled over, where he's talking about the early days of electrification and how the Internet is like that, partly in the sense that we we did not know how to use it. We didn't know all the applications. And he I think he he said I think that's where I I heard that the original appliances, like, you bought an iron, originally, it would actually plug into a light socket.
Speaker 7:Like, you'd unscrew a light bulb, screw in the iron, and then iron your clothes in the dark, and then screw in the light bulb again. Or I guess you'd iron your clothes during the day. But, anyway, like, we it was very janky. And so you could have looked at it at that time and said, like, this is just a clown show. Like, okay.
Speaker 7:Sure. Electric lighting. I get it. But what are you doing with all these other weird gadgets? And who needs that?
Speaker 7:Like, we already had irons. They were fine. So I I I think that a lot of tech people are actually pretty keenly aware of history. And a lot of them are just they're they're way more obsessed than you would think with the prospect of their company becoming irrelevant in six months and a total failure in two years. So I think we're know, you it is riskier to borrow than not to borrow, but we're probably safe on that front.
Speaker 7:I think one thing that could go wrong is some combination of, corporate behavioral norms and regulatory norms and investor assumptions where we decide that this stuff is really dangerous. We should not touch it. It will blow a giant hole in somebody's balance sheet because we know it happened, and it'll happen again. And it just becomes untouchable for a while, which did kind of happen in the .com space. And I think people underestimate that when they look at things like Mark Zuckerberg starting a social network in 2004 is that that was you could have looked at that as really like, now it looks really forward thinking.
Speaker 7:At the time, it kind of looked dated. It kind of like, the example I use is, like, if you if in 1999, you moved to Seattle to start a grunge band, like, you missed it. You were you were way out of date, and that's what it looks like. So it was still a kind of contrarian thing to do, and it was still a company that was started in the aftermath of this dot com bust when people were were cautious. So, but it it with AI, the capital requirements are so high that it is actually a really big deal.
Speaker 7:If investors decide that the space is uninvestable, progress actually stops. Whereas you just don't need a lot of capital if you're in your dorm room on your laptop just slinging PHP.
Speaker 2:Yeah. Or or you can at least monetize much much earlier, and you see that with, like, the Google, earnings, like, pre IPO. It was, like, a massively profitable business, undeniable, didn't need any permission. What do you think about Sovereign AI International, how bubbles spread internationally? I was listening to Tyler Cowen, talk about, one of the weird side effects of tariffs is that other countries might copy America's tariffs just for sort of memetic reasons.
Speaker 2:And America might be in such a powerful position that tariffs might not actually wind up hurting America because of its position in the global economy. But if another country says, oh, let's copy that, they might be hurt more. I'm wondering about how bubbles propagate, at the same time, a lot of the telecom, magnates in foreign countries that just kind of copied our telecom build out, well, they're the richest people in those company in those countries now. So how are you thinking about, like, the bubble spreading internationally?
Speaker 7:Like, I think it is a it is a really cool toy for petro states. It's something they've actually done some really impressive work. So, you you know, I I don't really begrudge on that. I'm not sure how many how many general purpose models the world needs. I suspect what the world needs is lots and lots of special purpose models.
Speaker 7:And that could be the level of, okay. This model just knows Rust, but it is insanely good at Rust. And it has not polluted its mind with any bad habits from c or c plus plus or anything else. It's just pure Rust. And then you could also have even more narrowly scoped models where it's like, this model is this one person.
Speaker 7:Mhmm. And it will give you the best approximation it can of what this one person does.
Speaker 1:Mhmm.
Speaker 7:And if you have a lot of different models and you and people who interact with models interact through a router where the first thing the router does is figure out which sub model to send things to, and it can do many iterations of that. And, eventually, it might be sending some things to you know, maybe delegating some things to an agent that ends up talking to an agent at some third party service. So, like, I'm thinking of things like if you are planning I mean, everyone says if you're planning trip. Let's say you're planning a really complicated tax sensitive global m and a transaction. So maybe you need, like, the French tax law bot to interact with The US tax law bot, and they both need to make sure that the economics of your weird tax thing also makes sense.
Speaker 7:In that world, you could actually have this great diversity of models with a great diversity of model use cases. But for the general purpose stuff, like, I don't I don't think there I I think that there is enough room for customization at the user level that we probably don't need 50 different models that are close to the frontier.
Speaker 3:Yeah. What are your labor displacement timelines? Because every CEO over the last year has has used AI as the reason behind layoffs, and I think everyone has been calling BS on a lot of that. It's just, like, they need a good reason to do a round of layoffs for other more real reasons. And everyone, I think, has seen the chart by now of of of job openings versus, you know, when when ChatGPT was released.
Speaker 3:And at the same time, you know, if you've used these tools, you're not a a lot of people
Speaker 2:It doesn't feel like a drop in replacement
Speaker 3:for Meanwhile, you have engineers. LMs are incredible at coding. And you have engineer if you're a talented engineer or even a high agency engineer, you probably have more opportunity than ever. And but I'm so I'm curious about, how you're thinking about timelines.
Speaker 7:Yeah. Like, this stuff takes a surprisingly long time to deploy because one of the load bearing inefficiencies is that if something required intelligence, there's a single there's at least one human being whose judgment is implicitly tied to the output of that product. And it's really hard to go from there is some specific person to blame. Like, if a mistake was made, some someone made it to if you scale up your work by, you know, 100 x, and now 95 of the time, you do just fine, and 5% of the time, you mess up. Is that your fault?
Speaker 7:Is that Claude's fault? We don't wanna blame Claude. Claude's so nice to us. We don't know. So, like, we actually have to rethink how people get judged.
Speaker 7:There's this sense in which everyone becomes a kind of engineering manager who like, everyone in software becomes this engineering manager who is describing what needs to get done and vetting what has been done but is writing less code themselves. On the other hand, LMs are actually pretty good at doing the opposite, where you are the junior coder, you are doing the grunt work, and what it's doing is looking at your overall architecture and telling you what things you missed and what design mistakes you have made that are just a lot easier to fix upfront. But with a lot of organizations, they they don't want they they don't want the risk of their workers are massively more productive, but they're also producing some mistaken things, and that's actually going to be a big hit to the company's reputation. So you'll you'll probably see what I think you'll see is that a lot of AI deployment is that there will be a legacy version of something. There will be an AI native version of that thing.
Speaker 7:The AI native version will sell to smaller customers. Those customers will grow faster than legacy companies, and then the AI native product gets sold to all the legacy companies. So this is kind of the Stripe model where they started out doing payments for early stage companies that had pretty simple requirements and had some tolerance for error. And then they as long as they stay good enough to maintain whatever their biggest customer is, they are necessarily building out the feature set for other companies the size of that biggest customer. So you get some deployment that way, but it has this it it actually takes a while because the big companies, they just they wanna be somewhat cautious on this.
Speaker 7:And you sometimes have this case where there's a top down mandate at a big company saying, everybody's gotta use AI. And there's also this bottom up insurgency of, I can use AI, and it makes my job more effective.
Speaker 2:And this
Speaker 3:is there's gonna be a dynamic there's a dynamic too, where we will see scenarios where employees say, well, I don't want to adopt this AI. This one's a little too good. I'd be worried about losing my job, right? And so I think we're going to see more friction between even with tools that actually can replace labor, truly not just being a copilot, and the friction to adoption because of the people that would be adopting them. And that's probably years out.
Speaker 3:Certain investors have been underwriting early stage private market bets. They're saying labor is the TAM. How do you view that framework? It feels overly simplistic to just say any dollar that is spent that goes out through any type of payroll system today is up for grabs. But there seems to be some element of truth to it.
Speaker 7:Yeah. There's a little truth to that. But I think it's in the same way that Netflix says that time is the TAM, and their biggest competitor is sleep. Like, it is broadly true
Speaker 3:It's marketing.
Speaker 7:That, yeah, like, it is marketing, but it's also a way to frame the scope of the opportunity. So Yeah. What I would say is that when the labor in question is mostly delivering value by producing a sequence of tokens, whether that is writing a document or building an Excel model or writing some code, that that is the addressable market for an AI tool. But the real world just has enough complexity that models have to develop a really good world model. And one way to think of it is in software, they have a really good world model because that is their world.
Speaker 7:Like, their world is this abstract world that is defined by whoever wrote the compiler. And to a lesser extent, that world has some complexities if you're actually working with real world physical systems where someone can trip over a cord and unplug one of the servers in your distributed system, and that is just not contemplated in the purely software world model. But as soon as you move out of pure software that is running on one machine for one user, you start to get some real world complexity. And then when you're trying to automate something like building up a financial model, you need pretty tight feedback between what assumption works in the real world, what actually maps economic realities, and then what assumption is the the most probable token in this cell that needs a token. And I think that we'll like, as AI gets deployed in messier parts of the world, what you'll actually see is that more of the world will get structured in an AI friendly way and that more more of GDP will be in that world that is already prestructured for AI.
Speaker 7:But then you still have the rest of the real world where it's just really hard to get Eagle onboarded. And you can actually see that with things like when when the company then noticed Facebook was growing internationally, one of the obstacles they ran into was in many places almost nobody has a computer. So that's one of the reasons that they went into mobile early, but they also realized they can market themselves through Internet cafes. And that the apparently, for a while in the developing world, if you went to any Internet cafe, half of the unused computers would have the Facebook log out screen, and that was actually a huge source of user acquisition for them in develop mark in developing markets. And then once smartphones came out, those people migrated onto smartphones, and then Meta was able to keep them and continue to sell them apps.
Speaker 3:So somebody would be on Facebook. They would use a computer in an Internet cafe. They would get up and leave, and then somebody would sit down and they'd be like, what is what is Facebook? And then they would just create Exactly.
Speaker 7:Yeah. Yeah. So there's there's a great story in chaos mark monkeys about this and about how they wanted to have an ad on the logout page because they're like, this is otherwise just wasted real estate. And it turned out the logout page is this mission critical thing in all the in many of the non US markets. So there's a big internal fight on that.
Speaker 1:Interesting.
Speaker 7:And they did end up doing ads on the logout page only in developed markets where growth had slowed down enough, and they were already a dominant market share. But, like, they they needed the outside infrastructure to catch up with the product. And once it did, the product was already there kind of waiting for that infrastructure and saturated it really quickly. But this is this is another thing that happens with bubbles in general, technology bubbles in general. It's like, you you don't consider the podcast an electricity company.
Speaker 7:Like, you don't think of yourselves as that business. But the business doesn't really function if you can't plug something into an outlet or use a battery and actually get power from
Speaker 2:it. Nothing can stop us from podcasting. Let's be clear. We will Okay.
Speaker 7:You can yell. Yes.
Speaker 3:Do it without microphones, without cameras.
Speaker 2:We'll just find a crowd of people and scream at them.
Speaker 3:Megaphone But, yes. On the rooftops.
Speaker 7:But, like Yeah. So in one sense, the nineteen twenties bulls who were like, I'm all in on electricity. This is the future. They were absolutely right. But if you transport a trader, a stock trader from 1925 to 2025, and you're like, okay.
Speaker 7:Go buy all the electricity stocks. It's like, well, that's everything. Like Yeah. Every company uses this. So there is no real way to make a direct bet on it.
Speaker 7:And the to the extent that there is, the direct bet is now a totally different bet. Now, actually, that that particular time traveler, if if he arrives in, in 2023 instead of 2025, actually, his his 1925 thesis of just buying levered power generating companies and put all your money to that, it's actually brilliant. Yeah. So these things the you know, the cycle does repeat itself a little bit. But Yeah.
Speaker 7:They as it as it disperses, you've got a little bit of AI and everything. And it's you know, Internet is the same way. Like, you don't consider Target an Internet retailer. They do a lot of e comm. All the physical reach basically, all the physical retailers do a lot of online sales.
Speaker 7:The the fast food restaurants do a lot of their sales through apps and through kiosks. So there is just this convergence where by the time the bet is such a big scary bet that you're like, the whole economy is dependent on this, you're also like, well, it's just mixed in with the whole economy. Like, you can't actually take the AI part out of The US economy and The US growth story without completely breaking things. And at that point, it kind of converges. It settles.
Speaker 2:Yeah. Makes a lot of sense.
Speaker 3:I have one more question that is probably, worthy of, like, a ten minute answer, but we'll see. We only have a few minutes. Okay. How how is it the CEO's job to disconnect the stock price from reality?
Speaker 7:Well, it's partly the market's job to tell employees where their equity where they should go if they wanna max out the value of their equity comp. And this is something that I I used to not really believe, and what happened with Meta in 2022 kind of converted me this view where Mark Zuckerberg did not actually have to care that his stock was under a $100 a share. Like, he it's not like the board is going to vote him out. Even if even if he didn't have voting control, they're just not gonna kick him out. But it did mean it was harder to recruit people.
Speaker 7:And so if your dream is we're all gonna live in the metaverse, we're gonna have this legless utopia, you could only hire the people who make that possible if they think your stock is gonna go up. Otherwise, you have to pay them entirely in cash Yeah. And then your stock goes down even more. And suddenly, you're in this position of making really hard decisions that you don't wanna make. So sometimes you, yeah, you take a foot off the gas pedal in terms of massive CapEx for something investors are skeptical of, and as long as you're still in the lead.
Speaker 7:And this is what like, investors would send hedge fund people, there was a great hedge fund letter. There was a big plaintiff. It was like, Mark, even if you cut your metaverse spending in half, you'd still be spending the majority of the world's metaverse money. You're still a winner. You still get the trophy, but please just give us some free cash flow and buy back some stock.
Speaker 7:It's cheap.
Speaker 3:Yeah. Feels like disconnecting your stock price from reality, at least to the positive, can be a massive advantage. You can see different like Palantir is a good example of this, or Tesla is a good example of this. And if you can keep it going, it's tremendously effective because investors want to be in companies where the stock price is not necessarily always gonna be tied to fundamentals and even employees can benefit. And maybe, like, the the opposite side of that is, like, is, like, Dylan Field with Figma.
Speaker 3:Like, I feel like he just wants he wants to be valued, like, like, fairly and, like, accurately and just wants to make the business better and better and better every day. But, of course, it's a double edged sword, and it's great when it's, disconnected, to to to the higher end. But, anyways, this was super fun. Thank you. Thank you so much for joining.
Speaker 3:We'd love to have you back on again soon.
Speaker 2:Yeah. Let's do it again soon. Is Absolutely. Time. Have a great rest
Speaker 3:of your day.
Speaker 2:We'll talk to you soon.
Speaker 7:Will do. You too.
Speaker 2:Before we hop on with our next guest, let me tell you about Privy. Privy makes it easy to build on crypto rails, securely spin up white label wallets, sign transactions, and integrate on chain infrastructure all through one simple API. Our next guest is Glenn Hutchins. He is the cofounder of Silver Lake Partners
Speaker 4:A legend.
Speaker 2:And, the chairman of North Island, North Island Ventures. I believe he's in the restream waiting room. We will bring him in the TDPN UltraDome. We're keeping him waiting just a few minutes.
Speaker 3:Keep the going. Available. How are we doing?
Speaker 2:If he is on the line Glenn, good to see you. Sorry for keeping you waiting. Welcome to the show. How are you doing?
Speaker 3:We don't have audio.
Speaker 2:We check?
Speaker 3:How are doing, team?
Speaker 2:Mute button, check on that and see if we are getting audio through the, the call. I will give some more context. He is the, chairman of North Island, North Island Ventures, the cofounder of Silver Lake, the vice chairman, lead independent director, Austin Tander.
Speaker 3:There we go.
Speaker 2:He's also the lead independent director of Corwyn. And he's here on the show. Welcome to the show. Thank you so much for taking the I
Speaker 1:just wanna say I'm a big fan of your show.
Speaker 2:Oh, that's amazing.
Speaker 1:So it's Thank so much fun to be here. Really a real pleasure to meet you guys in, almost in person.
Speaker 2:Yeah. Club. Well, next time you're in Los Angeles, please, feel free to stop by the, TV and UltraDome here in
Speaker 3:Los Angeles. We are so excited to have you on. So much so much to talk about.
Speaker 2:Yeah. Why don't we, start with, just a little bit of your career arc? I know we're gonna wanna talk about the the .com bubble, the .com boom, your experience there. But walk me through, your career up to cofounding Silver Lake in I believe it was 1999. Right?
Speaker 1:That's right. Just really briefly Please. Basically, when I got by Silver Lake was my third, and now I'm on my fourth essentially startup.
Speaker 4:Yeah.
Speaker 1:In and around investing largely called private equity. The first one, I was a junior partner to a guy by the name of Tom Lee founding what people look back on now and say as one of the first private equity firms. Then I took some time off, worked in the White House for Bill Clinton. And then was recruited to come to a young little firm called Blackstone
Speaker 2:Mhmm.
Speaker 3:There you
Speaker 1:that was getting into the private equity business and wanted to build a private equity platform. About five years later, the Blackstone guys helped me start Silver Lake, which was the they invested in it, which was the first large scale organization to combine private equity type of investing with technology. Mhmm. And now I'm on my fourth, which is my platform called North Island, which has one very difficult limited partner, which is me. The best guy.
Speaker 1:And we're doing and I'm doing so it's my fourth start of investing. Yeah. And you know, the maybe we can get into this a little bit later, but you know, the origins of private equity might be something worth talking about at some point if you'd like. But we'll come back to that. What's your next question?
Speaker 3:No. Let's start let's start there. I'd love I'd love your view on it. And and it really is quite quite funny to think that you couldn't have maybe picked better stepping stones across the whole across the whole journey. Must have been you must have had some good intuition.
Speaker 1:Yeah. You know, it's better to be lucky than smart. But so, you know, the one thing I would say is would you mind can I speak can I get a little geeky for you guy with you guys Of course? For a moment. We can come back to the more personal dimension, but there are four or five real advances in largely quantitative approach to finance that enabled the creation of kind of what I've done over the years.
Speaker 1:Especially in the early eighties when we started thinking about private equity. And the first was the capital asset pricing model, which allowed us to really do very good in-depth evaluation of equities, which had not been done before. The second was to were was Black Scholes option pricing model, which allowed us to value options and really understand what embedded options how to value embedded options inside of equity securities. Oftentimes when you bought a private equity company, you pay for the company and then you identify something inside the company, the real upside, and how to value that and how to pay for that was the question. Second or maybe third was understanding fixed income, a fellow by name of Marty Leibowitz came up with something called inside the yield curve, but let us really value fixed income.
Speaker 1:And then Mike Milken understood, really good work on understanding the risk reward associated with high yield securities, which became a tool that we were able to use to build these companies. Michael Porter at Harvard Business School did a bunch of research using standard economic analysis about the five forces that you could use to extract value from companies, which wasn't being done in a very systematic way in those days. And then finally, modern portfolio theory with Sharpe ratios and efficient frontiers were adopted by places like Harvard and Yale. And a key part of that was having an allocation of private equity and as that model was rolled out across first pension funds and then sovereign wealth funds, a huge amount of money flowed behind us.
Speaker 2:That makes a ton
Speaker 3:of sense.
Speaker 1:We figured out how to value the companies, we figured out how to use debt, we figured out how to extract value from the companies, and then we had a big flow of money coming in to back us doing it.
Speaker 2:Fascinating.
Speaker 1:So that was a that's a kind of one way to think about what happened over the last four How
Speaker 3:how quickly did those ideas and methods actually disperse? And tying that to the present, feels like, you know, with AI labs today, there'll be like some sort of innovation Really
Speaker 1:good that
Speaker 3:gets discovered, and then one of those people gets immediately poached to another lab, and suddenly, then then another lab is, you know, developing the same type of system or approach. Yeah.
Speaker 1:That's a really good question. Someone quote you could look it up. Someone quoted or I read a quote recently. He said, the future is already here. It's just not evenly distributed.
Speaker 1:And so, you know, innovators try to I've always tried to find the next way to be successful in investing and stay ahead of what people do who copy me. Of the things I say is that my very my my best ideas are ones that people dislike, and my very best ideas are ones they hate intensely. Because I know if someone really hates something, I'm I'm I'm thinking about it, and I know it's like, could be really good. And then by because by the time it turns out to be generally accepted, that's when I sell what I bought before to them. If you know what I mean.
Speaker 3:Yeah. So so basically, like if they if they if they think an idea is dumb or silly, that gives you a window of opportunity to
Speaker 1:That's a signal. To To be good.
Speaker 3:Yeah. Well, and it gives you a window to like, you know, get as much value out of that idea before it becomes common knowledge or an accepted approach.
Speaker 1:Occupy that territory before they get there. When they come there, then you sell to them the beachfront property that you've already purchased.
Speaker 4:That's
Speaker 1:right. And then but to go back to it, the half life of innovation on Wall Street, AI is a bit different. But the half life of innovation on Wall Street is a time it takes someone to read a prospectus. Yeah. And then a copy what you did.
Speaker 1:And so like, you know, someone does a SPAC and everybody does SPACs. Yep. Someone does a digital asset treasury thing and everybody wants to do a DAT. You know what I mean? Yep.
Speaker 1:It's like on in on Park Avenue, New York City, as soon as it starts raining, the guys with the umbrellas come out. It's almost like they knew it was raining and then blocks on either side of Park Avenue, everybody's with umbrellas. And gotta figure out something else to sell because the umbrella's already there. But the so, you know, when we first started doing in private equity was a way of exploiting value that was latent inside companies because you didn't have the financing to be able to purchase these companies Mhmm. And you didn't have the toolkit to extract value from them.
Speaker 1:Mhmm. That's those are the issues that we resolved with what I just what I talked about earlier. Yeah. Especially when Mike Milken untapped this high yield market that we could borrow from to finance these companies. Then then people rushed in and in part the Blackstone ID, you'd have to talk to Steve Forsman, was to build a platform that you could take to scale where you could raise an amount of money that people who'd come into the space couldn't match you.
Speaker 1:And so you could like mine a different vertical layer of companies that were immune yet to private equity disciplines by getting to scale Yep. In the enterprise. You see what I mean?
Speaker 2:Controversial in Silicon Valley to call that out nowadays. Right. There's a big discussion over platform funds and funds that might be doing exactly that.
Speaker 1:Yeah. Okay. We'll come back to that. Then and then what I and I decided that another path that the technology industry had reached a point where there were scale companies where you could use debt and more private equity style skill sets to buy the companies. First big one we did was something called Seagate.
Speaker 1:Yes. Where we borrowed a bunch of money to buy a big tech company. Yes. But if you look at companies like so and when I was coming up, people looked at companies like Microsoft and they said, oh, these are very risky companies. Steve Ballmer and Bill Gates were college classmates of mine,
Speaker 3:by the way.
Speaker 1:Yeah. We're all You're
Speaker 3:a lucky guy.
Speaker 1:Class of '77 at Harvard. Steve and I graduated. Bill did not, but he did better. So On on on the
Speaker 2:on the financial innovation, it feels like a lot of what you identified, Black Scholes, modern portfolio theory, Sharpe ratios, all all of that, that's all pre 1999. I'm interested in understanding what what what was the key unlock to bringing private equity to technology specifically? Were you thinking about Metcalfe's law, network effects, zero marginal costs? Were you looking at businesses that fundamentally differed from the traditional widgets business or industrials business and had different structures, or what what else was going on there?
Speaker 1:Really good question. So at that point, technology was in a a part of the transition. This is like the future is here. It's just not evenly distributed. Yeah.
Speaker 1:The it was thought to be an area of two things. One, it was thought to be an area of expertise where you and it was true. You really had to have specialized expertise to understand the companies to invest in them successfully. You couldn't just wander off of out of Wall Street with your pinstripe suit and so think you could figure go to a couple conferences and think you could figure out how to buy, you know, a tech tech technology company. Because the process of evolution was so rapid.
Speaker 1:Mhmm. And then secondly, to that point, people did not understand how technology companies had evolved. That point technology companies were big, they consumed huge amounts of cash Mhmm. In investing in R and D to build the products. Yes.
Speaker 1:And they had very volatile earnings streams as a consequence of being pioneers in a space that came and went very quickly. And so people looked at that from, there was a venture capital gig, but looked at it from a private equity perspective and say you can't do it. But at that point, Microsoft for instance, that's why I was talking about Microsoft, got to a level of scale where it was one of the greatest economic enterprises in world history. The where you make this piece of software that comes out of someone's brain has almost no capital expenditures and associated with it, no kind of fixed cost and sell it a billion times. Right?
Speaker 1:I mean, that's and it just this massive flywheel of cash comes into that company. And we I've know that like
Speaker 3:light bulb moment for you? No. But for you personally I
Speaker 1:I observed it in the nineties. I had the benefit then of living in Boston, the venture capital business was pretty pretty vibrant there then. It moved later primarily to Silicon Valley, but there was a big footprint in Boston in those days. Because remember, data general and digital equipment were kinda there, the the micro companies. The the some of the mini computer companies.
Speaker 1:And so you could watch it happen and you say, you know, that's a better way to make money than just trying to extract value from rationalizing legacy industrial companies that have been poorly managed.
Speaker 3:Yeah, widgets business.
Speaker 1:Right. And then the other thing is, remember is that people will be in thinking about exiting businesses. The market will pay you more for companies that have good this was an insight in those days, it's not now. By the way, in those days, to borrow money, you had to have assets to back it with. You know, like, you know, inventory, working capital
Speaker 3:couldn't use the company that
Speaker 1:Cash flows.
Speaker 3:And and and then yeah. You couldn't use the cash flows in this in the in a software business didn't you know, maybe maybe had a a lease for an office, but not a lot of, So
Speaker 1:what we had to do was teach the markets to lend against cash flow. Oh. Actually lend against assets. So cash flow lending became this kind of new thing Yeah. That we had to teach people how to do.
Speaker 1:And when then once you got that, then you realize that if you had a rapidly growing company like a Microsoft that had an extraordinary cash flow engine, huge barriers to entry. That point people when I came in the investment business, people said tobacco is the best business to invest in because I'm serious, Because it was very stable. It has stable cash flow, stable pricing, and it didn't vary with recessions. I said, me get this straight. The businesses that addicts and sickens its customers is better than Microsoft?
Speaker 1:No. I'm sorry. I don't agree with that.
Speaker 2:I like Right? I can Right.
Speaker 1:You gotta look at the modern world to understand that these cash flows are sustainable and these businesses are extraordinary because it doesn't because the the the product comes from someone's head. They don't have to build a factory to build the thing.
Speaker 2:Fascinating.
Speaker 1:And so we just built this built this business that got that that had that set of insights and as a consequence, we were able to build a so the other idea there was to build a strategic competitive advantage of commanding heights that you could occupy that made it very hard for anybody to compete with you.
Speaker 2:So So help us bridge to, some of the debt financing that's going on today. I think that there are a lot of folks in the tech community that are very used to a bunch of 20% dilution equity rounds, maybe a growth equity round. And, the idea of bringing on a partner like Blue Owl for some massive deal, it just doesn't map to the traditional, like, tech startup, like, path. And yet folks who are trying to understand where AI is going and where the big hyperscalers are working start have to grappling with a with with debt and how debt is coming into this generation of And
Speaker 3:Sam this Altman has has said, like, we maybe need new we he he said we need new ways
Speaker 2:Like financial
Speaker 3:innovation. Need financial innovation Yeah. Not just technological innovation. A lot of people have, you know, kind of shunned him for suggesting that. But I think based on what you've been describing of what enabled this wave of like value creation Yeah.
Speaker 3:And unlocking the value of these private companies and the value of their cash flows
Speaker 2:It can be done responsibly.
Speaker 3:It can be done responsibly. Yeah.
Speaker 1:Yeah. Wow. That's a really, really good question. And, you know, know we're we're maybe we'll have to do a second show just on that because Please. Because this is that's complicated topic.
Speaker 1:Right? Yeah. It is technology is very I like technology, you know, gotten into it full time for now, you know, twenty five years ago. I still feel like I'm new to like, I'm still new to golf even though I've been playing it about the same period of time. The but one of the things great about is this constantly changing, and you have to constantly adapt your thinking and develop new modes of sort of how of investing.
Speaker 1:And so this AI thing is come brings us back to the future. That which is it's technology it's a technology software driven LLMs, technology enterprise that requires a scale of capital investment that we've never seen before. And that's a really unique kind of challenge. It's one of the things that's drawn drawn me into the kind of investments that I've made there. It reminds me a bit look, historically, it reminds me a bit of when the the semiconductor companies went fabulous about twenty five years ago.
Speaker 1:Yeah. And the industry split into companies that designed semis and and basically TSMC. Right? And TSMC succeeded largely because the the country of Taiwan was willing to essentially lend them the credit rating.
Speaker 2:Mhmm.
Speaker 1:The scale of capital necessary to build a fab that could design these, that could manufacture these wafers with a nanometer scale that they had at prices that were cost competitive that could continue to drive adoption of, technologies based upon semiconductors was only approachable by a national credit rating.
Speaker 2:TSMC had a backstop.
Speaker 1:Basically had the gut the backing of the government of Taiwan to go get this done. There's a reason why it's in Taiwan. Yep. You couldn't do that in those days that the capital wasn't available to do something like that in those days at that scale for that kind of enterprise. Mhmm.
Speaker 1:So very similar today, which is the scale of financing that's that's required to do to build all these fabs, not fabs, I'm sorry, factories. Yeah. Data centers. I call them factories because I think they're factories manufacturing data now. And what I say to my people, what I say to my friends is America's now the leader in the world in advanced manufacturing because we're building these data centers to manufacture data.
Speaker 1:Yep. Right. And that's kinda what it is. It's a massive factory manufacturing LLMs and applications for both training and inference.
Speaker 2:Yeah.
Speaker 1:But that's kinda one. The second point would be you that people compare this to so the question is, are we in a bubble? Sure. That's kind of underlying the thing you raised, right? What kind of bubble is it?
Speaker 1:And the question you have, you have to make a decision whether or not this is more like sub primes in a way or more like the Internet in nineteen ninety nine, two thousand. Mhmm. You know, whether sub prime is just kind of something that's not real, it's gonna collapse and when you're left, you're just left with a bunch of debt and no value there because the home values all went down. Mhmm. I am more in the Internet camp.
Speaker 2:Yeah.
Speaker 1:Which means that, of course, there will be companies that will be formed that won't be successful. Of course, there will be investors who put capital in bad places and lose money. Of course, there will be some number of scoundrels and shysters who come in because money gets moved around and they get attracted to this. Right?
Speaker 2:But
Speaker 1:there are one major, you mentioned Blue Owl, the one major difference today between the build out so this what was happening simultaneous with the Internet was being when the .com companies were being built, could say the the LLM equivalent today, the fiber optic networks were getting constructed all around the country, the CLEC's and those all went to zero and people lost their money on it. The major difference between that and people use that as analogy today and and maybe the railroads is another analogy, but the major difference between that and today is every one of these data centers, almost all of them, has a counterparty, a solvent counterparty that is contracted to take all the output. They're built to suit. Yep. Not if you build it, they will come.
Speaker 4:Yep. Yep.
Speaker 1:Okay. Microsoft has, I think, the world's best credit rating. If you sign a deal with Microsoft to take the output for your data center
Speaker 3:Satya is good for it.
Speaker 1:He's good for it. Yeah. And by the way, Microsoft's gonna survive if that has a collapse at some point before it comes back again.
Speaker 2:That's a good point.
Speaker 1:It's a very different kind of financing structure. And the the last point I would make and just finish this new again, is that each of these deals so far as I understand it, is done in a way that essentially generates in the in the four to five year period of the deal, generates about a two times multiple of money on the cost of buying the GPUs and standing at the data centers.
Speaker 2:Oh, interesting.
Speaker 1:Right? So the con and they're about four to five year contracts. Yeah.
Speaker 2:Yeah.
Speaker 1:Yeah. And the output, it has a now and and then and talk about, okay, embedded options and how you value those. Right? Yeah. And the and then the owner of the of date the GPUs in the data center has an embedded option on the value of the used GPUs, which will be worth something.
Speaker 1:I mean, your five year old iPhone is still worth something.
Speaker 2:Of
Speaker 1:course. Even though people are buying the new ones. Right?
Speaker 2:Yep.
Speaker 1:And so the each of the model each of the business each of the contracts and builds right now has a commercial proposition in it.
Speaker 2:Mhmm.
Speaker 1:When done well, these companies that are doing this like CoreWeave are putting one of building a wall with one of those bricks on top of the other. Yeah. Do you see what I mean?
Speaker 2:Yeah.
Speaker 1:So it's not it's not analogous at all to the CLEX where they put a bunch of money in the ground and then went to get the customers and the customer weren't there. Sure. That's a very different thing.
Speaker 2:That's a really good point. I haven't I hadn't considered that. That makes a ton of sense. That's great. Yeah.
Speaker 2:I feel like a lot of people in, in tech are just struggling to you know, there's been this narrative for a while that, ChatGPT is the new Google, and then you look at how capital consumptive OpenAI will be before profit comes or cash flow comes versus what happened with Google where they were throwing off millions of dollars in cash, like, well before IPO, and the prospectus just looked so clean, this, like, super high margin business, very fresh out of the gate. And it's just a very different world that we're in where we're delivering something similar. It feels just like a website.
Speaker 3:Because It feels like web page. OpenAI has to compete with Google.
Speaker 2:Yeah. Maybe. Maybe. But it's just a different it's just a it's a capital consumption.
Speaker 1:It it so the model's changed a
Speaker 2:little bit. Yeah.
Speaker 1:Each wave of technological innovation, companies are created that don't obsolete the company that went before them. They do something completely different.
Speaker 2:Yes.
Speaker 1:Right? And they're sometimes very different. Yes. Like, you know, so you've got, you know, the the the soft the Microsoft software applications was unlike anything we had before because we didn't have the PC. Yeah.
Speaker 1:And then Amazon was not anywhere near like Microsoft, it was a whole different kind of innovation that was based upon the internet that was built. Yeah. And then Google and was different, was something new and Facebook was something entirely new. Yeah. So these aren't companies that say, I'm taking your thing away from you.
Speaker 2:Yeah.
Speaker 1:Right? Each one is a very different kind of unique unicorn type of business that occupies a niche itself and eventually obsolete the other businesses because they stop growing. Yeah. Right? Like Facebook might stop growing if consumers go to OpenAI, but it's not because they're going to OpenAI because it's a new social network.
Speaker 1:Yeah. It's because it has a different use cases valuable to them today.
Speaker 2:What's been the biggest learning surprise sort of update to your mental model from working with CoreWeave?
Speaker 1:That's a really good question. The the the pace of change, the scale that we talked about, I mean, thing that just amazes me is the scale at which this thing is growing. Mhmm. And the rapidity that you have to have in order to act act at to be successful at this kind of scale with this kind of growth. It's unlike anything I've seen before.
Speaker 1:You saw the adoption curves of you've seen the adoption curves of OpenAI versus Google versus other things. Right? And it's just like this asymptotic thing going to 700,000,000 customers right overnight. All the infrastructure to support that is like unlike anything we've ever seen before.
Speaker 3:Yeah. Yeah. And it it's it's still under discussed how much bigger and faster the outcomes can be when you have the Internet as a distribution as a distribution engine. So like during when, like, you know, you founded Silver Lake in 1999. I'm sure you've looked at a bunch of companies that had a lot of potential, that if there was already billions of people using the Internet, they would have done very well.
Speaker 3:And the challenge at that point is there maybe wasn't enough Internet users to support even ideas that were maybe, like, structurally good ideas just missing enough enough of a of a user base. How how much time do you spend finding and and meeting and backing new managers? I feel like every new technology cycle, you know, the the hottest hedge fund of the year is situational awareness or at least at least on x. And that feels like, I I imagine there will be more of those. And so I'm curious how how many how much like new fund formation you're seeing and and what you're most excited about on on the GP side.
Speaker 1:Yeah. So I've got so I have investments. I don't do venture capital investing per se. Mhmm. So I've got investments in some of the major venture capital funds, you know, in my investment platform and I know all the people and watch what they do.
Speaker 1:But my business model outside of that is to find a small number of companies where I can put a fair amount of capital and be engaged, helping to create the outcome. See, that's kinda where I spend my time. So I'm not doing the whole build the massive portfolio thing, I'm picking my spots. And you mentioned the European bank that I'm the lead independent director of. Yeah.
Speaker 1:You know, we've got that stock up 3.5 x in the last three years since I invested.
Speaker 3:Hit the gong. We have a gong
Speaker 2:here we'd love to hit.
Speaker 1:Right. So, you know, there you go. Oh, that's great. Thank you. Thank you.
Speaker 1:I don't buy I did I gotta tell you, didn't get founder mode, guys. I don't know. What else is going on here?
Speaker 2:Founder mode. Here we go.
Speaker 1:There you go. They didn't get founder mode. Come on.
Speaker 3:I'm waiting
Speaker 7:for that.
Speaker 2:We gotta do founder mode for you.
Speaker 3:I love it.
Speaker 1:You know, I've got children about your guy's age. I just love your generation. I love hanging out
Speaker 2:with them.
Speaker 1:It's a lot of fun. So by the way, you see this logo here on my shirt?
Speaker 2:Yes. Yeah. What is that?
Speaker 1:That's binary code. See that? You know what that's binary code for? 1000110. What's that?
Speaker 1:100110.
Speaker 2:100.
Speaker 1:It's binary code for 70.
Speaker 3:70? 70. What what
Speaker 1:It's my it's my seventieth birthday logo.
Speaker 2:Oh, very cool.
Speaker 3:There we go.
Speaker 2:Happy birthday. Thank you.
Speaker 1:Congratulations. So as I said, I've got I've got kids your age, and I really love hanging out with your generation. It's been my a great pleasure for me.
Speaker 2:Yeah. Thank you.
Speaker 3:So we love hanging out with you too.
Speaker 1:Yeah. You're you're asking me another question that we got distracted from it. Oh, so the what what I'm trying to do is find a small number of enterprises in which I can engage.
Speaker 5:Mhmm.
Speaker 1:Get involved with them at a senior level in both cases, Core Weave and Santander, I'm lead independent director. Yeah. It was another term for non executive chairman. There's usually an executive chairman and I'm non executive. Yeah.
Speaker 1:And then really work with the enterprises to build value. Yeah. That's kinda how I think about it. Right? And then I I let venture capitalists who I invest with, and I still invest with Silver Lake, be on the rock face every day building these portfolios.
Speaker 1:Yeah. The rock analogy.
Speaker 2:I like that.
Speaker 1:Right. Well, you're you're the mountain climber. Right? Yeah. Yeah.
Speaker 1:Right. So, although you're this you're height of a basketball player, probably the wrong sport.
Speaker 3:I think you're referring to, like, one of our early episodes where I I was joking. Remember about Yeah. About you climbing the those, like Oh, yeah. Yeah.
Speaker 1:Yeah. That's what I heard. I heard that. Yeah. Didn't I thought that was yeah.
Speaker 1:We just you were just kidding him. Right?
Speaker 3:Yeah. Were. Were. Messing around. Just because just the the idea of John, a six eight guy skating.
Speaker 3:Okay.
Speaker 1:Yeah. I agree. Yeah. Know.
Speaker 2:I I okay. Climb.
Speaker 3:You could do. I I believe in you, but I I I I'd be I think that would, that would probably buy the key man insurance.
Speaker 2:Yeah. Know.
Speaker 1:I definitely wanna be above him on the wall. I won't be alone, but anyway, so, you know, so I'm I'm trying to pick my spots and really add some value.
Speaker 2:Yeah. Well well, thank you so much for coming on the show. We have to have you back soon. This is fantastic. We could talk all day long.
Speaker 3:Yeah. There's there's so many so many more questions Yeah. I wanna ask.
Speaker 1:Congratulations on the success of the show,
Speaker 2:guys. Thank you get bezel.com. Shop over 26,500 luxury watches, fully authenticated in house by Bezel's team of experts. We're going to our lightning round. We got Yogi Goel from Maxima announcing a massive round.
Speaker 2:Let's bring him in to the TBP at UltraDome. Welcome to the show. What is your t shirt? Introduce yourself. What do you do?
Speaker 2:Give us the news. What's the latest?
Speaker 8:Absolutely. So nice to meet you both, Jordy and John.
Speaker 3:Great to meet you.
Speaker 8:Yogi here from Maxima. We are an enterprise accounting platform focusing on waging the war on the month end close process.
Speaker 2:Let's go.
Speaker 3:We
Speaker 8:are, in a short, focused we are core our AI agents are core writing
Speaker 5:Mhmm.
Speaker 8:The monthly financial package and preparing the data for the accounting team. Yeah. We've been around for now five, six quarters and helping incredible companies like Scale AI Mhmm. Rippling, SpotOn, the Press Juice. So a lot of companies in both tech and nontech world to to make accounting sexy again.
Speaker 3:Very, very on brand to count your the the time you've been in business by quarters. Yeah. Exactly.
Speaker 2:And what's the news today? Break it down for us.
Speaker 7:Yeah.
Speaker 8:So we just raised $41,000,000 in c plus series a. Alright.
Speaker 3:There we go.
Speaker 2:Congratulations. Very excited. Love it. Explain to me how this plugs in. Obviously, there's a lot of folks that have an accounting layer of record, a single plane pane of glass, an ERP, an accounting suite.
Speaker 2:Do you wanna just plug into that? Do you wanna rip and replace that? There's so many different folks eating around the edges, creating different solutions. I don't think anyone knows exactly how the market will play out, but what have you built?
Speaker 8:Yep. So we've built a system of action and system of intelligence which works with any system of record.
Speaker 2:Okay.
Speaker 8:So when you go to an in enterprise company asking them to replace the ERP is like asking them to do a brain surgery. I I was at ProPrint and I would not agree to that. Yep. And we say that, hey, your system of record is where your data should eventually sit. We do the we help with automating the human work of grabbing the data, from upstream systems, doing the manual, doing the, our agents do the automated work Mhmm.
Speaker 8:And then, eventually finding anomalies and errors. I don't know if you're following, but last year was the maximum number of companies in The US which had material misstatements and, like, up to 40% stock drops stock price drops.
Speaker 2:The most mistakes from accounting specifically last year, that's not good. Hopefully, we can fix that. I have one last question, and then we will let you go. I wanna know, give me some examples of where, the current crop of AI models really excels in finding these types of problems. And then where do you wanna still leave the human in the loop?
Speaker 2:Where do you want the human where what's the really intractable problem that maybe we'll solve in a few years with AI, but for now, you'd leave it with the human.
Speaker 8:Yeah. So, the
Speaker 3:You gotta ask somebody to fire.
Speaker 2:Who's the last guy in the accounting office, I guess, is the question. But, you know, I'd I'd love some examples of of of problems that really excel for AI and and problems that are maybe more intractable.
Speaker 8:Yeah. Look. I'll just start with saying a problem a we are not going after the human, labor salary. We are going after errors, inefficiency, and pain that I personally face both as an auditor and and and as an accountant for twenty years.
Speaker 4:Yeah.
Speaker 8:There are not enough accountants in the world that you can truly hire for the amount of work that's there. So in terms of where AIs are very good at today, they are very good at taking a defined set of instructions and following things over and over again for variety of transactions, provided you give them, deterministic operators, which we have built, that they will only use those tools and and then come up with the right answer. So we are using this hybrid approach where agents follow Maxima tools to come up with the exact same answer. And so when Deloitte and comes knocking looking at the, work that Maxima produced, they will they will do two plus five, and the answer will always be seven. It will not just be 15.
Speaker 8:So that's one thing we determined really well. Yeah. Second is it's really good at finding anomalous behaviors and errors that might happen because it is it looking at millions of transactions over time within the company, it can just see that, hey. This your legal bill used to be $50,000. Suddenly, it's $500,000.
Speaker 8:Turns out, Jim Jim had a late night, and he had one extra zero, and that's why it went up.
Speaker 2:Yeah. And and, I mean, artificial intelligence has been used in, like, fraud detection for years and years and years. And so applying that sort of heuristic based to stochastic based, more less less, deterministic, computing, more probabilistic computing. Makes a ton of sense there.
Speaker 3:I I love love the positioning around pain and errors. You gotta talk to the venture capitalists who are yelling loudly to anyone that will hear, we're gonna replace all labor. Give me more money to replace labor. It's like Mhmm. No.
Speaker 3:You can just you can you can show the the sort of optimistic It is. Like, positive, you know Yeah.
Speaker 2:Well well, thank you so much for taking the time. Congratulations on the massive round. We will talk
Speaker 3:to soon. I'm sure you'll be back on soon.
Speaker 2:And have a great rest of
Speaker 5:your day.
Speaker 3:Great. Sure. Too. Let
Speaker 2:me tell you about 8sleep.com. Exceptional sleep without exception. Fall asleep faster, sleep deeper, wake up energized. Our next guest is Sam Jones from Method. We will bring him in from the Restream waiting room into the TV PM Ultra Dome.
Speaker 2:Sam Jones, how are you doing?
Speaker 3:Welcome. Good to meet guys.
Speaker 9:Great to see you
Speaker 2:again. That sound effect kind of just I don't know. It doesn't have enough of a crescendo for me. Need We to work on that one.
Speaker 6:Anyway Working
Speaker 2:on it.
Speaker 3:Drum roll.
Speaker 2:Thank you so much for taking the time to hop on the show. Please introduce yourself. Introduce the company. Tell us what the news is today.
Speaker 9:Alright. Sam Jones, the CEO and cofounder of Method Security. Our mission is to deliver cyber resilience to the US government and critical enterprises. Think of what we do as building the command and control layer for autonomous cyber operations across defense and offense.
Speaker 3:Mhmm.
Speaker 9:And the news today is that we are announcing our 26,000,000 combined seed and series a investment from Andreessen Horowitz and General Catalyst.
Speaker 3:Incredible. Very good. I I I I I You remember we wanted to jump in preemptively last
Speaker 2:preemptive Gong.
Speaker 3:Preempted it happens. But we got you
Speaker 2:in the the ultra.
Speaker 9:To raise more money to to pay you back for that
Speaker 2:other Gong. I'm sure I'm sure you I would love for you to get me up to speed on how you're thinking about that that story in the Wall Street Journal about Anthropic. I'm sure you know the one. Yeah. AI on AI violence.
Speaker 2:Exactly. Is this relevant to your business? Are are you building a solution to that, or or do you even have a comment on it or anything?
Speaker 9:Can you
Speaker 2:just get me up to speed?
Speaker 9:Highly relevant. Great. And that's kind of the moment that we've been we've been building for for a couple years now. Like, we've known this is gonna happen. AI is effectively, you know, taking at the limit, taking cyber offense to infinity and taking the cost to zero.
Speaker 9:And this is bad news for good guys, bad news for the defenders as our adversaries are essentially eliminating their requirement or or limitation on human headcount. So what we do is essentially allow organizations to safely become the threat to test their own defenses be some before some adversary does. Yeah. And the best offense is the best defense has always been a notion in security, but AI is really the unlock to do it at scale. The hard part is you need to do so safely, ethically, legally, and that is the infrastructure that is, like, needed to do, and that's what we build specifically.
Speaker 9:So, like, in that report, it's almost like no news to anyone in the security trenches. Like, obviously, this has been happening. Obviously, that wasn't the first
Speaker 3:autonomous attack. Without without naming names or any details, like, on on the individual companies that were attacked, like like, are I'm assuming when you see anytime I see a report like that, I'm like, okay. This must be happening like a ton and just a lot of it just never never hits like headlines. Yeah. But what what are what are some of the most like kind of common strategies that bad actors are using today in the context of AI to carry out whatever their goals are?
Speaker 9:If you think about pre AI malware, it was already autonomous, but it was basically reliant on, like, if then decision making. What AI basically allows it to do is to, like, a broader nondeterministic path planning that allows it to harness a multitude of tools, thus do a lot more damage. That's what's different now. And I guarantee you, the most sophisticated actors are not using vanilla Claude code to run their operations. Mhmm.
Speaker 9:That's ludicrous. They have our adversaries have better models at home that they make themselves that they're using that we have no, you know, telemetry on. And so they're essentially using it to scale and speed up their operations, which for us and why we have, like, a a national cyber resilience urgency moment on our hands is that all of these exposures that we've left out on the Internet and in our, you know, in our enterprises are now, you know, easy easy takings, for these types of attacks, and that's essentially why it's so urgent that we focus on resilience.
Speaker 2:Can Can you talk to us a little bit about traction? What unlocked this $26,000,000 fundraise across these two rounds? Yep. Are you doing like, yeah, just walk me through, how you actually show progress in, you know, you're building a a product, but you're also trying to do deals with the government. That can be very difficult.
Speaker 2:What what does progress look like?
Speaker 9:So we are deployed in production with a number of organizations to include the Department of War Sure. US federal government and Fortune 500 organizations. That's probably the biggest hallmark of traction. Sure. And we're doing so across defensive and offensive use cases that get to the heart of resiliency.
Speaker 9:Yeah. And so that's the, I think, the the unlock that we were able to do that. And we've had this hypothesis and mission from the beginning that in order to secure what matters, you need to be dual use. Mhmm. And we set out to basically pick what is the what is the most intense, hardest government customer we could go after from the beginning, and what's the commercial equivalent.
Speaker 9:Those were our first two customers. And then basically just continuing to build on that. You know, from the adversary's perspective, they do not discriminate between public and private, and neither do we. And that's why the I think the ultimate game changer solutions will come from dual use companies like ourselves.
Speaker 2:Now I'm thinking about the the the hardest to hack, Fortune 100 and and government.
Speaker 9:Necessarily always the hardest to hack. A lot of times, it's the hardest to sell to. Hardest to deliver
Speaker 2:for. Okay.
Speaker 9:Yeah. Think about the government done accreditation, deployability, like interoperability Yeah. Huge technical challenges. That's why startups would never dare touch there.
Speaker 1:But Sure.
Speaker 9:Sure. When you think about what matters, they are what matters, and that's what we built this company to serve.
Speaker 2:Yeah. Let me What
Speaker 3:were you doing before this again?
Speaker 9:So I started my career actually seeing this problem firsthand at The US Air Force. So I was a cyber operator, in many ways, rebuilding the tools that I wish I always had. I joined Palantir about eleven and a half years ago, you know, preproduct and building out both their cyber commercial and DOD business. And then I was also at Shield AI pretty early. And so you can kind of think of this company as Yeah.
Speaker 9:We were the users. My CTO and cofounder also started his career at NSA. His last name is Hacker, by the way, if you want
Speaker 2:Oh, there we go. About destiny. Did that. And then also, I want an overnight success for
Speaker 7:Yes. Overnight
Speaker 2:success. For being in this industry for fifteen years already.
Speaker 9:He had no choice other than to work at NSA, but we met at Palantir and did great work together. But we're combining our our knowledge of, like, we were the users. We know how to build hard care hardcore software and dual use businesses. Yeah. And then we built AI before, you know, it's become a meme and certainly a no fail scenarios, which I would group, security in for sure.
Speaker 2:Yeah. This is Very cool. Anything else?
Speaker 3:I'm very bullish.
Speaker 2:Yeah. Extremely bullish. Thank you so much for taking the time to come chat
Speaker 3:with us. Great to get the update.
Speaker 9:Sorry. My my background wasn't as good as Glenn's mahogany. I I showed
Speaker 3:the start up wood.
Speaker 2:Brought a wood, which is Yeah. Which is
Speaker 3:Thank you for bringing it. What what's the biggest fish you've ever caught?
Speaker 2:Yeah.
Speaker 9:Yeah. Probably nice nice walleye, I'd say. Okay.
Speaker 3:There we go. Good answer. Good answer.
Speaker 9:Midwest shout out.
Speaker 2:Thank you
Speaker 3:so Congrats on all the progress.
Speaker 2:Yeah. Have a great day. Cheers. Talk to you soon.
Speaker 9:See you.
Speaker 2:Bye. Let me tell you about wander.com. Book a wander with inspiring views, hotel grade amenities, dreamy beds, top tier cleaning, and twenty four seven concierge service. Our next guest is already in the room. We have Ali Madani.
Speaker 2:Welcome to the show. How are doing? Awesome. Thank you so much for taking the time to come chat with us. Please introduce yourself.
Speaker 2:Introduce the company. Tell us what the news is today.
Speaker 6:Sure. Absolutely. So my name's Ali. I'm I have a PhD in machine learning from, UC Berkeley. I've been working in the space of biology and AI for almost a decade now.
Speaker 3:K.
Speaker 6:Previous to this, I I led a moonshot at Salesforce, pioneering language models for biology. Yeah. And what started out as a purely scientific endeavor to develop transformer models for sequence generation has led into ProFluent specific ProFluent specifically where our mission is, to make biology programmable. And I'm happy to kind of break that down.
Speaker 2:Yeah. Yeah. I I I've seen, obviously, there's there's a ton of, just like momentum in the space. AI curing cancer is like a buzzword that a lot of people are throwing around. How are you, thinking about concretizing what you're actually how you're trying to fit in?
Speaker 2:Are you a tool? Are you a drugmaker? Is it uncertain? Like, who are your customers? How how much are you in, like, a science project world?
Speaker 2:Like, you know, you could be a nonprofit in another in another era versus, like, you're ready to commercialize. You're going to market. And not that there's one path that's wrong or the other, but I'd love to know how you're thinking about the business right now.
Speaker 6:Totally. Yeah. I think there's a a lot to unpack there. Yeah.
Speaker 3:I I think
Speaker 6:the meme that came to mind specifically, I don't know if it's a self marketing or otherwise where it's it starts with, like, build something, and then there's a dot dot dot question mark, and it's make profit.
Speaker 2:And then profit. Yeah. Step one Yeah. Step one, you know, make biology programmable. Step two, bro down with your boys.
Speaker 2:Step three, it. Right?
Speaker 6:And and I and I think a lot of folks, you know, right now, there's an incredible amount of excitement around AI.
Speaker 2:Yeah. It's kind
Speaker 6:of like step one is make a chatbot, for example. Right? And then it's question mark dot dot dot Mhmm. And then solve, you know, disease or cure cancer specifically. Whereas what we're actually trying to build here is actually tackle on the disease, head on specifically.
Speaker 6:So what we do is we build language models. So the same language models that have enabled, g p d two, three, and four, and ChatGPD specifically, These incredible models and algorithms that can learn on sequences. What we can feed instead of words in a sentence is actually amino acids that are strung together to form a protein. And, why that's actually important? Why making biology programmable?
Speaker 6:Maybe take a step back. Like, people usually shut off their brains when it comes to biology. Yeah. And and when it comes to, like, rockets landing on a platform in the ocean, we're amazed. Right?
Speaker 6:Super easy. And that makes sense. Right? Like, it's
Speaker 2:You see it.
Speaker 6:These are man made machines. We can see it. They're incredible. But, honestly, biology is not that much different. Mhmm.
Speaker 6:There are these molecular machines called proteins that enable us to breathe and see. They're responsible for everything in human health and disease, and, also, they sustain the environment involved in daily daily products, like even our detergents to begin with. And how let let let's actually stick to drug discovery in particular. How we've gone about finding these solutions, these molecular machines that we utilize day in, day out, has actually been through random discovery. So, you know, that middle school example of Alexander Fleming coming across penicillin.
Speaker 6:Right? He had a petri dish they molded, for example, and then they found the advent of antibiotics. And now after you get a cut on your skin, for example, where bacterial infection happens, it's no longer a death sentence. Right?
Speaker 5:Mhmm.
Speaker 6:That actually is not the exception. It's the rule in which we've gone about finding life saving medicines. Even fast forwarding to today, CRISPR Cas nine was actually found in a Danisco yogurt facility where people found these interesting bacteria doing these interesting characteristic having having these interesting characteristics. It was taken a molecule, plucked it from nature, and then crammed it within human therapeutic applications to actually save lives. And, honestly, like, let's put this really in rudimentary terms, that's that's kind of absurd.
Speaker 6:It's almost caveman like in terms of our technique, our techniques that we have and methods that we have available for us for drug discovery. And what we're trying to do is actually move away from random discovery and finding a needle in the haystack and relying on nature altogether and using AI to design bespoke medicines from scratch. And that's, you know, like, that's our mission to really gain control and mastery over biology and perform bespoke design. So in terms of your question of, like, where are we with respect to, you know, is this just a science project or or how's the commercialization looking specifically? I would still say we're in early days.
Speaker 6:Sure. Like, the equivalent of GPT eras of, like, maybe GPT one and GPT two, but we've already seen incredible amount of traction. So we have this project called OpenCRISPR specifically, where we took is, we took these, these language models trained on gene editing proteins specifically and generated a novel protein from scratch called OpenCRISPR one that thousands of people use now in pharma, large pharma, small biotechs, academics, and and industry, users, and scientists as well. And over thousands of people use this over worldwide today. And I think that's like it's amazing to actually see us solving problems today that have lead to commercial traction and that we have partners both from, therapeutics to diagnostics to biomanufacturing, even agriculture that are utilizing today.
Speaker 6:So
Speaker 3:can you talk about, like, how you create feedback loops as a company? Because, you know, there's no shortage of people in AI that talk about the opportunity of, like, curing various diseases. Many of them aren't saying that from the standing in an actual lab. You are standing in a lab. That makes me more excited about what you're doing because you're not just kind of, you know, like, it there's you're not just saying, like, oh, like, the next version of the model, we'll we'll just do this.
Speaker 3:Like, don't worry about it. It's like, no. Like, we're gonna run a lot of experiments. But, yeah, talking about, yeah, like, you know, using AI to, to to learn and and generate, you know, potential approaches, but then actually bring it into a lab setting.
Speaker 6:Absolutely. Yeah. We operate within a pretraining and post training paradigm within proteins similar to NLP and natural language processing as well. So the pre training step really involves in similar to how, we have all of the Internet that we can scrape from and can learn these underlying principles and grammar and semantics as to what makes human generated text. We've actually collected a tremendous amount of data of proteins that have naturally evolved through nature for selective reason selective pressures and evolutionary kind of pressures that have shaped those proteins specifically to make a functional protein.
Speaker 6:And just to put that into context, AlphaFold three was trained around it was exposed to around two to 3,000,000,000 proteins. What we've actually trained to date so far at ProFund is over a 100,000,000,000 proteins. And to put that into to tokens, that's over 20,000,000,000,000. Exactly. So there's there's an incredible amount of data for pretraining purposes that we utilize.
Speaker 6:And then what you see behind me as well is the data that we're doing, the assay labels, labeled examples, meaning actually taking protein sequences and then measuring their function, not just in vitro and test tubes and petri dishes, but in human cells and relevant cellular contacts and seeing how well they actually perform, and we could feed that back into our models. So I think that's you know, the future is really an integrated future where you're building frontier AI models and having, the the the the closed loop specifically with respect to the wet lab, which is what what's behind me today, to actually test these and feed them back into our models to get better and better over time. So
Speaker 2:Well, congratulations. I wanna ring the gong for you. What's the news Gershner
Speaker 3:and Bezos. I mean
Speaker 2:Yeah. How much?
Speaker 3:How much? Potentially the coolest cap table the cap table of the year.
Speaker 2:How much was the deal? Yeah.
Speaker 6:Absolutely. Yeah. It's a $106,000,000. I I think what's more important what's more important than number are these legendary investors that we have. I mean, Jeff Bezos is a legend.
Speaker 6:He's transformed industries. And I think what's exciting for him and for us as well is that biology is the next frontier for AI specifically. That will have tremendous impact. And it really honestly is the most important question in our lifetime. Thank you.
Speaker 2:So much for stopping
Speaker 3:you will be back on very soon.
Speaker 2:And congratulations on all the progress.
Speaker 3:Great great to meet you.
Speaker 2:We'll talk to you soon.
Speaker 3:Talk soon.
Speaker 2:Have a good one. Our next guest is already in the restream waiting room. We have a hard stop at two. We gotta run. We gotta hop on with New York.
Speaker 2:So let's bring in a meet from Luma AI with some massive news. How are you doing? It's been too long. Great to see you again. Welcome to the show.
Speaker 3:What's happening?
Speaker 2:Give us the news. What happened today? What happened? Break it down for us.
Speaker 5:Yeah. So we did two massive things. One, Luma raised a a 900,000,000 series c. Okay.
Speaker 2:What was the second
Speaker 3:I'm I'm sorry. I'm sorry. What like, what was there not another 100,000,000 lying around? You couldn't just like, you couldn't you gotta you're gonna make us wait for the the luma $1,000,000,000 round? Come on, dude.
Speaker 5:I'm happy to accept friends and family checks.
Speaker 2:Okay. Oh, yeah. If you got a 100,000,000 in for your for for from your friends and family to round out, that'd be fantastic. And then what yeah. What is the second thing?
Speaker 5:And, yeah, the second thing is basically along with Humane, which is a a you know, the this AI company being built in Saudi Arabia. Yeah. Are building a two gigawatt compute cluster Wow. That we are going to use to train, you know, multimodal AGI.
Speaker 2:This is the big news. The the
Speaker 5:This is actually the
Speaker 2:big news. This is much bigger. This is much more important. Is what we actually need to compute.
Speaker 5:So, you know, the TLDR, what happened here is basically, you know, so far, LLMs and LLM Labs have had the right resources. And multimodality, world simulation, these problems actually were side projects for most companies. Now there is a lab and there is a company in the world that has this level of resources and is going right after AGI that can help us in the physical world, AGI that can help us simulate and and and and, you know, generate the universe. So I think that's actually what happened, basically.
Speaker 2:Amazing. So How
Speaker 1:do you think about
Speaker 3:how how how do you explain the scale of two gigawatts? Because it sounds like two is not a big number.
Speaker 2:And and and is that is it two gigawatts because you're expecting two gigawatts worth of inference, or do you need a particularly big cluster for some sort of pretraining run that you're planning on doing?
Speaker 5:So it's both inference and training. Okay. But inference is actually you know, majority of the workloads, as we go forward, right, like, you know, as AI deployment goes forward, as we mature from just text only models to models that are able to, like, you know, generate videos, models that are able to explain things to us in video, what's going to happen is most of the workload and tokens will will move to video understanding and video generation. And video tends to be, you know, computationally much more intense than than than language. So we need this level of compute to be able to deploy this technology and to be able to train.
Speaker 5:But this is mostly inference, honestly. Even today, Luma's inference to training compute ratio is two is to one already. And and we are seeing that ramp actually growing further and further and further while we we do deep research and and train some some of the largest models in our space. Inference is the one that is actually taking off.
Speaker 2:Okay. React to this, this take I got from someone who's also building a world model, a generative world model. He told me that he believes that, it's more likely that AGI, something fully paradigm shifting emerges from world simulation than merely scaling up next token prediction GPT five four five six seven eight nine. Getting away from text is actually somehow foundationally important to the next major breakthrough in AI as we know it as a whole.
Speaker 5:I think getting away from text is a mistake. We need to build models that combine audio, video, language, and image. So, like, you know, we need to build things that, like, operate like human brain. Mhmm. If you remove text, you remove the entire interpretation of of the the the human logic and and, like, you know, reasoning and those kind of things.
Speaker 5:So we need the physics that comes from video. We need the causality that comes from video, and we need the text that which which actually makes all of this interpretable and logically connected, you know, across the world. So, no, I I think what we need to do is build these joint unified models. But on the simulation side, I agree. And I think that's really, really important because think about robots or think about systems, you know, how they would operate.
Speaker 5:Right? Like, they need to be able to understand the world. So this is world understanding, which is where world models are going to be very, very powerful and multimodal models are going to be very powerful. And second is simulation, being able to run the the process or idea in your head and and and drawing out conclusions. Right?
Speaker 5:Like, you know, what if I go 20 meters this way? Would I fall? Right? This is a simple question. But as robots become more general purpose and day to day in our lives, we need this level of simulation capability in their heads.
Speaker 5:So generative models give you simulation capability. Right? Simulation is extremely important. Second thing is LLMs are really good at things that can be represented more or less fully in text code analysis, these kind of things. But when we think of the physical world, especially acts like designing, manufacturing, these kind of topics.
Speaker 5:Like one of the things we think a lot about at Luma is manufacturing of a jet engine, right? Or manufacturing of a rocket engine. These are one of the most complex things humans do and it takes a decade to build one. Imagine having models that are able to run these physical simulations and get to an answer. It's not about the visuals, it's about getting to the right answer.
Speaker 5:People do that in CAD, people do that in like, you know, software today, but it's like very inaccurate. But if you're able to build models that can accelerate building of these complex systems, humanity has a chance at like, you know, building better and better things for ourselves, for for our planet. So that is why simulation is really important, and that's why multimodality is really important. The text is just the first step. The text is, like, you know, nineteen nineties Internet.
Speaker 5:Then we got images on the Internet. Then we got videos on the Internet. And today, like, you know, videos is the Internet for humans at least.
Speaker 2:Yes.
Speaker 5:Right. AI will not be any different.
Speaker 2:Yeah. Last question from my side. What is the actual timeline for building a two gigawatt cluster?
Speaker 3:Yeah. And where where where where will the majority of the infrastructure be?
Speaker 2:When can I see it? When can I go inside? I can be trusted.
Speaker 5:So some of it already exists. So by the way, we are building this, with in partnership with Humane in Saudi Arabia.
Speaker 2:Yep.
Speaker 5:And today, it was announced here. So we we are in DC right now for for the US Saudi Investment Forum.
Speaker 2:Oh, no way.
Speaker 5:And it was announced by president Trump and and conference Mohammed bin Salman. So the data center is gonna be built in Saudi Arabia. Quite a lot of capacity is actually already available, and Luma is actually an active customer and using that today. But the deployment of two gigawatt is going to take time. That's an absolutely colossal amount of power and infrastructure that needs to be built.
Speaker 5:Starting with 2026 and and currently we believe that like you know, by by '27 or early twenty eight, we will have majority of the capacity at hand and and like you know, we'll go from there.
Speaker 2:Fantastic. Well, thank you so much. Incredible progress. Time while you're traveling to come chat with us and break down what's going on. Congratulations on the amazing news, and good luck with the next phase.
Speaker 2:I'm sure there's a lot going on.
Speaker 3:Next time you call in, come call in from, from Saudi. That'd be amazing.
Speaker 2:We'd love
Speaker 3:to From the desert. That'd be amazing.
Speaker 5:The first time actually I was on TPPN, I was in Saudi.
Speaker 3:Oh, no way. There we go. We already did it.
Speaker 2:We'll check that box.
Speaker 3:Next time next time I want I want, you know, one of those four by fours that, you know, call call in from the desert from from humane. I want I want I wanna be live on the ground with you. Yeah. Amazing. Good.
Speaker 3:Great to see you again. Congrats on the progress.
Speaker 2:Thank you so much for jumping on. We have to hop on with New York. No. But first,
Speaker 3:have one post we gotta pull
Speaker 2:up. Post.
Speaker 3:And it's a post that I made
Speaker 2:You made.
Speaker 3:Right when I saw that
Speaker 2:NVIDIA beat. Beat earnings. They have traded up. The stock is up 3.8 3.91%. Massive
Speaker 3:massive at the very bottom.
Speaker 2:There were signs. This is your prediction. One of your one of your many predictions. But
Speaker 3:This is all is the only the only data.
Speaker 2:This is the only data you
Speaker 3:need to know.
Speaker 2:You know you know, you said this. I think he's gonna be in earnings because he's drinking beers. And, and Ev was like, yeah. You belong in a pod shop. And he was saying it, like, sarcastically.
Speaker 2:Like, you know, to be in a real hedge fund pod shop, like, you have to be much more quantitative than that. Turns out you don't.
Speaker 1:But but
Speaker 3:Turns out the vodka analysis works. It
Speaker 2:all in. Absolutely. All in. Thank you to everyone for tuning in and watching our show. Leave us five stars on Apple Podcast and Spotify, and we will see you tomorrow.
Speaker 3:Global economy continues.
Speaker 2:Continues. The party continues, folks. White suits tomorrow.
Speaker 3:Gabe in the chat. Gabe's getting drunk. Goodbye. Drunk responsibly. See you.