TBPN Live - Leopold's 13F, Data Center Fixes, Shein Buys Everlane | Mike Isaac, Rowan Trollope, Dean Leitersdorf, Joanna Stern

Episode Date: May 18, 2026

(00:17) - Jury Rules Against Musk (02:23) - Leopold's Situational Awareness 13F Is Out (16:03) - Data Center Backlash Gets Serious (44:47) - Mike Isaac, a technology reporter at The New Yo...rk Times, discusses Elon Musk's lawsuit against OpenAI, highlighting Musk's strategy of using memorable phrases like "you can't steal a charity" to appeal to jurors unfamiliar with nonprofit contract law. He notes that OpenAI's defense centered on the statute of limitations, a more technical argument that ultimately swayed the jury's decision. Isaac also mentions the unexpected nature of the verdict and the various protest groups outside the courtroom. (01:07:52) - Rowan Trollope is the CEO of Redis, where he leads the company’s efforts to expand the popular in-memory database platform into a broader real-time data and AI infrastructure business. He previously held senior leadership roles at Cisco and Five9, and is known for scaling enterprise software and cloud communications companies. (01:28:26) - Shein Buys Everlane (01:39:04) - Dean Leitersdorf is the co-founder and CEO of Decart, an AI company focused on generative models and real-time AI infrastructure. He works on building systems that make advanced AI applications faster, more interactive, and easier to deploy at scale. (01:54:56) - Protein Shortage (02:04:02) - Joanna Stern is a senior personal technology columnist at The Wall Street Journal, where she covers consumer technology, AI, gadgets, and the impact of tech on everyday life. She is also the author of I Am Not a Robot, a book exploring the increasingly blurred line between humans and machines in the age of AI. Follow TBPN: https://TBPN.comhttps://x.com/tbpnhttps://open.spotify.com/show/2L6WMqY3GUPCGBD0dX6p00?si=674252d53acf4231https://podcasts.apple.com/us/podcast/technology-brothers/id1772360235https://www.youtube.com/@TBPNLive

Transcript
Discussion (0)
Starting point is 00:00:05 And today's Monday, May 18th, 2026, we are live from the TVPN Ultrhythm, the temporal technology, the fortress of finance, the capital of capital. Massive day to day. Tons of big stories, five big stories I want to go through. Obviously, the first one is that the U.S. jury finds OpenAI, CEO Sam Malman, not liable to Elon Musk for straying from charitable mission because Musk waited too long to sue. Weird, like technicality, I guess, but good news for Open AI. Judge confirms verdict and that Musk's lawsuit is dismissed. We're having Mike Isaac from the New York Times join the show in just a few minutes. When is he joining around 1145 today?
Starting point is 00:00:48 That'll be fun to hear about the story from the ground because he went to the... Yeah, apparently they deliberated for about 90 minutes. 90 minutes. And they didn't really make any type of statement other than a statute of... limitation. And so Max Zaff over at Wired says jury unanimously rules that Musk's claims are dismissed on the timeliness issue. He filed the lawsuit too late. Court affirms it will uphold the jury's decision. It's over. Musk loses the lawsuit against Open AI. And Mike Isaac, the Rat King, says unanimous verdict in a Musk versus Open AI is in after only 90 minutes of deliberation. So did they deliberate today? They showed up at nine
Starting point is 00:01:27 and went from 9 to 1030 and then delivered the verdict. Is that what we think happened? Because Friday's off. In the jury, right? No Fridays. Yeah, jury showed up this morning. Okay. Talked for 90 minutes. But they got to think about it all weekend and Friday? Interesting. Of course.
Starting point is 00:01:41 Yeah. It's a full-time job. I guess. It's just an interesting, interesting dynamic because, you know, you'd think you'd want everything really fresh. You'd go into it on Thursday night or something like that. Rat King says, huge day. Wow.
Starting point is 00:01:53 And what did Tyler post? He posted a video of Drake talking about something. What's going on over here? Let's play this clip. W's in the shot. W's in the shot. Is that the opening I slack right now? I think that's when he is gambling in front of the eye.
Starting point is 00:02:13 It is a funny way to pronounce chat, but I enjoy it. Anyway, the big news that was going on all weekend, actually, there was a lot of anticipation for Leopold Oshenbrenner situational awareness hedge fund to drop the 13F. It was supposed to go out Friday night, 5 p.m. everyone was saying, oh, if he's not releasing it. Well, people were expecting it throughout the entire day. Yeah.
Starting point is 00:02:38 They were very excited. And then there was some speculation that he had been able to petition to not have to release it. That was one theory. That was one theory. The other theory is that he was just entirely in cash. Yeah. Don't need a report.
Starting point is 00:02:51 I just lined it down. Said it was a good run. Yeah. It's over. Yeah. He's like, I counted the ooms and there's none left to count. We're done. Pack it up.
Starting point is 00:02:59 No, quite the opposite. Leopold Asherbrador, the hedge fund's chief investment officer is known for making extremely successful investments based on his core assumption that Frontier AI will continue to improve at half an order of magnitude, 0.5 ooms per year, which translates into a thesis that AI will create unprecedented demand for compute and its associated bottlenecks. John, they're saying it is blindingly light. It is brighter today, isn't it? Yeah, I think we got some new lights. We're sort of, you know, tweaking things. I do like that the wide is less dark. There's been a number of times
Starting point is 00:03:36 we've gone to watch videos and we've been very dark in the front. So we're bringing some light around. We'll see. Maybe we over did it. Maybe we'll dial it back. I need to brush my hair. My hair's a little scruffy today.
Starting point is 00:03:46 I also need a haircut, but we'll get to that some other time. We'll get to that later in the show. John will be getting a haircut live on the program. Potentially. Before we go any further, I, Nick over the weekend.
Starting point is 00:04:00 Oh yeah. He picked up a little gift for our very own Tyler. Let's pull it out. So we wanted you to open it on the video. On the video, Nick, wait it up, hold it up. He waited in line. Look at this. Look at this.
Starting point is 00:04:13 We waited in line. Hey. What do we got for Tyler? A very long line. Let's see. What is it? I'm trying to open it. Okay.
Starting point is 00:04:28 A little anti-com. Wow. It's a, uh, what is? I don't know how to pronounce this. Am I reading upside down? It's a little watch. Let's go. Another watch for Tyler.
Starting point is 00:04:41 It is not. I don't know if you thought it might have been something else, like the Swatch AP collaboration. But really like the whole, you know, everything in the swatch portfolio is fantastic, including this. I don't know. Describe what's on there. What is on there? Yeah, Nick, what is it? He says it has a rotating bezel.
Starting point is 00:05:01 Okay, but just to be clear, it's not the, it's not the. Royal Pop. Which was completely sold out and causing like stampedes all over the country, all over the world. I saw footage, I think, from an international country around people really mobbing it. You were mentioning that you thought it was maybe an aura loss for both companies because of the craziness? Yeah, I just the, your brain is now associated with chaos. Yeah. That's not good.
Starting point is 00:05:29 Yeah, right? And AP, although it's, although it's exclusive, like, you know. You have to sort of wait in line. The waiting in line is like, here, have a Diet Coke and sit in this private room while I tell you that you will not be getting an allocation in the skeletonized AP. Come back soon. Royal Oak or whatever, right? Come back soon. And it's a very high brow waiting in line.
Starting point is 00:05:54 Yeah. And this was sort of a. Yeah. They had to come out over time and say, these are not going to be limited. We're selling them a lot. Yeah. And so the people that wait in line just to sell on the secondary market, I think, think you've done pretty well, at least in the short term.
Starting point is 00:06:07 But I would expect that over time, prices will sort of retrace toward retail. I did see a funny graphic of somebody that was basically saying, like, you know, comparing, like getting a job versus waiting in the line to get it. And you actually did quite a bit better if you just got a job on Monday and instead of getting in the line. And then over time, you know, your earnings really ramp out. Yep. But anyways, sorry, Tyler, if you thought that was a royal pop.
Starting point is 00:06:37 I don't know why you would. Oh, there you go. He's doing the Kevin O'Leary, Mr. Wonderful, two watches on one on each wrist. Looking good. There you go. I think that could be a good daily for you. Who knows? It's got a little character to it.
Starting point is 00:06:53 You make it your own. Yeah, it looks good. It's a little. Somebody should make a man. Sometimes the man makes a watch. Somebody should make a string that you can turn it into. a royal, like a royal pop. Hmm.
Starting point is 00:07:04 You know, like a lanyard. Yeah. Like a lanyard. Yeah. Okay. Okay. Yeah, that's possible. 3D printing.
Starting point is 00:07:10 Plenty of, plenty of opportunities. Well, let's go back to Leopold Ash and Brenner and his 13F, the infamous 13F. There's a lot of discussion around it on the timeline. Really, like, we have not seen this level of attention on a hedge fund's filings in a very long time. It's, because it's breaking out of fin twit. It's breaking into tech. T-pot and TechX and all of that. Mostly because a lot of the discussion centers around, the filing shows he's made some massive puts across the semiconductor sector.
Starting point is 00:07:43 Two billion on SMH, the VanX semiconductor ETF. And so there's, there's, it feels like maybe more, more of a pointed thesis, less broad, hey, semiconductors are going to do well, more. I actually, me, Leopold in this case, understand where the real value is, what companies within the semiconductor industry are undervalued, which ones are actually going to be useful in the next iteration of the buildout. And a lot of stuff has been priced very hotly. Some stuff is overheated. The Nvidia trade for a while became like crushingly obvious and then it grew so much that that was not one of his early positions. Now it is looking like he is going long in video, which is interesting in the backdrop of is in video a car. Do they still have a moat? Well,
Starting point is 00:08:35 there might still be something else going on there. You have to dig in through this and understand what's going on. But the filing is hard to interpret cleanly because a 13F is only a snapshot of holdings as of March 31st, 2026. These positions are stale. He might have rotated out of these, meaning these positions were in place during the early phase. of the Iran war. It also doesn't include private, say international. Copy trading senators tends to
Starting point is 00:09:01 work pretty well, right? They tend to be, you know, maybe they're very knowledgeable on some of the subjects that they're trading on, some of the companies, right? But they tend to take a more longer term, sort of thematic view. Whereas Leopold, he's operating a hedge fund,
Starting point is 00:09:17 right? You don't really know. His holdings could be wildly different just just weeks or days. after the end date of this file. There is the team behind the Nancy Pelosi stock tracker, stock ticker. I forget that. Yeah.
Starting point is 00:09:34 They have one for Leopold now. Although, of course, it's based on the 13F. It's a loose, it probably has massive tracking error, but it's directionally on theme, like, you know, their interpretation of what Leopold would do if he was managing. It's very accurate for three months ago. Maybe, yeah. So, reminder.
Starting point is 00:09:55 13Fs do disclose put and call options. They don't disclose the strike prices, expirations, premiums paid hedge ratio, short position swaps, or whether the options are part of broader structures. So you have to be careful out there if you're trying to read the tea leaves too precisely. You can only take away so much from these. So Feijiao, I don't know how to pronounce that, says unfathomably bad takes around this morning and a good reminder of why 13F digging is mostly a waste of time. March 31st, we were in the heat of the Iran. It makes sense to put on hedges at the time. Options exposure on 13F gets quoted notionally. So as if it were 100 delta, i.e. all 100 shares per contract. So when you see something like, oh, he owns a billion
Starting point is 00:10:49 dollars of Intel, it's usually he owns the right to purchase a billion dollars of Intel, and he has actually deployed far less capital into that position, although it is sometimes an important sign of things to come. We have no way of knowing whether these were five delta convexivity hedges, convexity hedges, and represented a fraction of what people are saying were billions in puts or whether they were ITM puts in the money puts. Further, outright shorts don't get reported either. Too much noise associated with the things that happened back in March that aren't relevant now. We have no idea about his turnover
Starting point is 00:11:21 in assets and trade frequency. A lot happened in the months of April and May. His positioning could be completely different, making investment decisions for 80 vol assets based on data from months ago. Sounds like a good way to burn money. So don't idolize people
Starting point is 00:11:33 and develop your own thesis for why you own and sell things. That is a good takeaway from an account that I can't pronounce but has good takes. Now there were a bunch of funny memes about this. Leap Trader says, now drop Leopold Ashton Brenner's portfolio where he sold all his holdings and went full cash.
Starting point is 00:11:52 That certainly would royal the market. I do wonder, is the market actually moving on the 13F? Are we seeing like when a position is to disclose there's a pop of copy trading going on? Or is this just sort of like online fun and games for the tech folks? Do you know? Yes. Yeah. I mean, if you look like T1 Energy. T1 Energy is up on the news that he has a position. Today. And we talked to the CEO of that company, right? T1 Energy is building solar panels in America. Yes.
Starting point is 00:12:22 That is very exciting. Chinese company that had to divest. Yep. And turned into T1 Energy. Yep. And yeah, I think we first talked about T1 in Q4 of last year and done very well since then. I'm excited about it. We can bridge into that in just a second.
Starting point is 00:12:43 But investor Nick says, did that. Leprosy fella tanked the market with his 13F. 47 likes, but very, very funny to just massively mispronounce Leopold's name. Anyway, where should we go from here? Options on 13F. Everyone repeat after me. Citrini is reminding everyone that options are reported with notional value. So be careful out there.
Starting point is 00:13:05 The interesting bridge is just around the AI backlash. And the fact that a lot of... Situation in the chat says Bloom Energy is actually. down. Well, he's on Bloom Energy for years, or maybe not years, but like, three or four 13Fs have disclosed Bloom Energy, and that one has been fully digested by the copy traders, I imagine. Anyway, the AI backlash is continuing in a bunch of different ways, and one interesting sort of twist on this is that a lot of the AI Maxis, the AI Bulls, were sort of, of concerned at least that this would all be fossil fuel-based build-out because everything
Starting point is 00:13:51 else was too slow. They might be fans of nuclear. They might be fans of solar, but it was seen as infeasible, seen as the timelines being far too long. So if Leopold is in fact taking a position in T1 energy, that sort of leads me to think that there's a little bit of a shorter timeline to at least bringing some solar power to bear during the AI buildout, that it's not all just sort of, you know, a hope and a dream that there will be solar power on the grid in any near amount of time. A lot of the nuclear power companies are moving on the backs of the AI buildout, but it's still 2032, you know, when we talk to these folks, even the optimistic ones.
Starting point is 00:14:39 So there has been big pushback on AI data centers across the board. We've talked about this a bunch. And it's both a left and right wing issue now. Sager and Jetty predicted this, I think last year when he joined their show. And it's been interesting. Left wing is worried about job displacement, theft of art, destruction of creativity. Right wing sees them as surveillance centers. That's the latest term, is that they're used to spy on people. So that's an anti-libertarian, anti-right-wing position.
Starting point is 00:15:12 But there are a whole bunch of others just, you know, this. hollowed out coal town is voting right wing and then data center comes to town and they see it as you know just making their town worse off and benefiting like the coastal elites and like the people have flag to both sides are using AI to create graphics to oppose data centers that's true yeah there's all these like deep ironies there's there's a whole piece on someone who's protesting data centers and using a lot of AI to research how she can push back Gabe says data centers need to be rebranded to data ranch data ranch I like a data ranch That's a good one.
Starting point is 00:15:47 Anyway. Ox. We got ox powered. Oh, interesting. Salty says that Leopold sold Blue Energy in the latest 13F. Trimmed. Or trimmed. So if that's the case, then there you go.
Starting point is 00:16:00 And yes, Lulu does have a good breakdown of the narrative mishap, which we can go through. But the latest debate that I saw was over this huge data center in Utah that's being championed by Shark Tank's Mr. Wonderful. Kevin O'Leary. Are you familiar with this whole thing? There's some renderings. It actually looks really cool, but it's weird because it's like, I see this like beautiful glass building. I'm like, it's not going to look like that. There's just no way.
Starting point is 00:16:25 There's no point. Like, why would they ever build that? But someone dug into the render economy. Yeah, someone dug into like the plan. And the plan actually seems pretty reasonable. But Mr. Wonderful, he's sort of an over-the-top caricature of a businessman. Like, he plays one on TV.
Starting point is 00:16:43 He is a real businessman. but he also plays a businessman on TV. And so he's a bit of a soft target. Like he was recently seen sporting not one but two expensive watches, not unlike Tyler Cosgrove over there. He went to the Oscars wearing a Cartier crash skeleton and a Ruby Rolex or Daytona. And I believe he also had a like a trading card around his neck.
Starting point is 00:17:05 So very ostentatious, very over the top, a very soft target if you're looking for someone to target in like a, he's doing it for the money. You know? Like it's pretty, pretty easy. And so if you want to paint data center construction as maybe not in the best interest of average Americans,
Starting point is 00:17:24 Kevin Leary is going to do a lot of that, a lot of the heavy living point. Mr. Wonderful in the context of developing large-scale infrastructure that people are afraid of sounds like a supervillain too. Yes. And also, like, you can put this in contrast to Eric Schmidt or Tim Cook, where the previous generation, like the major hypers, like the big tech companies,
Starting point is 00:17:48 they've done a pretty good job building a lot of infrastructure, making really, really bold climate pledges saying we're going to be net zero by this year. Our data centers are really clean. They built a lot of data centers without really any disruption. There was no backlash to Google Cloud through 15 years or 10 years of building AWS. And so now...
Starting point is 00:18:08 But neither of them were rocking dual iced out. So you made the case for quiet luxury, the quiet luxury of a Tim Cooker and Eric Schmidt, potentially. Definitely. Yeah. I mean, in this case, it seems like. Mr. Wonderful is not the guy to be the face of. Potentially not. But apparently his actual data center plans are reasonable.
Starting point is 00:18:31 It actually seems pretty by the book, according to current plans. It's in a remote area. It uses its own power and water. and it doesn't seem to disrupt any local communities. We can pull up this video from Quick Thoughts that has a little bit of a breakdown and goes through, I think it's called, I think Quick Thoughts calls it
Starting point is 00:18:53 why I'm not opposed to the Utah Data Center. I think the big Utah Data Center is fine. So this is a four-minute video, but we can watch this and break down what quick thoughts is thinking. It's in the timeline. Because there was a, a TikToker that was reacting to how bad it was, and he is saying it's actually not that bad.
Starting point is 00:19:15 So let's play this clip. A million views complaining about a giant data center in Utah. And I'm kind of confused by that because I would think that an uninhabited desert valley in Utah is the perfect place to build a giant data center. I've been following really closely what's happening in Box Elder County, Utah, where Canadian billionaire Kevin O'Leary is trying to build the world's largest data center. A $100 billion. project. Okay, this would be the largest data center in the world at over 40,000 acres. And at full capacity, the data center, which is called the Stratos project, is set to use nine gigawatts of electricity. Gigabites, you saw that?
Starting point is 00:19:52 Yeah, she said the entire amount of electricity used by huge taxation. Well, she said, she said, yeah, yeah, but the transcript said gigabytes, which is funny. AI fails. Again, we need another data center to fix that. Steve, in the X-Chat says, TBPN studio uses the equivalent of 23 atomic bombs of energy to produce niche technology content. So large is because they are buying water rights of the current property owners. So the current property owners are using water for agricultural irrigation. The data center project buys that land, buys a huge amount of land.
Starting point is 00:20:29 So he makes this sound good, but then it's like, wait, are we going to have less food? That doesn't seem that good. But the point is, is that he's not taking it from like someone who is going to be paying water or some local community, it's like there's already water rights there that are staying in that valley. It's not drawing power from the grid. If we look at electricity consumption by state, we can see that Utah just doesn't use that much electricity compared to other states. There are plenty of states that use double or triple. Tennessee is about triple. Pennsylvania four times. Texas is like 10 times, more than 10 times what Utah uses.
Starting point is 00:21:08 Though if over the course of this project, they reach their goal and they double or triple Utah's electricity usage. So why is that bad? It's not incurring more cost to the people of Utah because they're building their own power plan. By Utah as a whole, Robert Davies, a physics professor from Utah State University, says that he actually thinks the project will require an additional 7 to 8 gigawatts of waste heat energy, meaning that the project in total will be 23 gigawatts of total thermal load energy, which is the equivalent of dropping 23 atom bones in Utah every single day. Okay, electricity generation across every state is going to have that same thermal load
Starting point is 00:21:52 property. Not every generator is perfectly efficient, so they're going to generate waste heat as well. So if you say, okay, we're going to have 23 atom bombs a day worth of electricity going off in Utah. Well, then currently we have 10. 230 atom bombs a day going off in Texas. You've got to put everything in the atom bomb comparison. Like, your car is, like, the size of, like, five atom bombs. Like, an atom bomb is, like, maybe this big, maybe a little bit bigger. Yeah.
Starting point is 00:22:18 Your car weighs as much as seven atom bombs. That's right. It makes it sound so much more, like, weighty when you're, like, just comparing everything to atom bombs. By 28 degrees. This is actually pretty crazy. 28 degrees feels like a lot. Daytime temperature could increase two to five degrees.
Starting point is 00:22:34 throughout Hansel Valley, not the state of Utah, the valley where the data center is being built. Same with nighttime temperature could increase up to 28 degrees trapped in the valley. Hansel Valley is an uninhabited desert valley. So if you build a big power plant here and a big data center here, maybe it'll increase the temperature of this valley by five degrees. But okay, nobody lives there. I think this project solves a lot of people's stated concerns with data centers. You're worried about water usage. They're reallocating agricultural water to cool the data center. Worryed about power cost. They're building their power costs.
Starting point is 00:23:15 That one line is not helping. But I like vegetables. In a middle of an uninhabited desert valley where it's already hot. And you're worried about this is such a huge project. This is a giant data center or something, world's biggest data center. Well, that's just data centers that don't have to be built in other places that are being built in this inhabited desert valley. I think the concerns in her video are just fear mongering for reasons that I hope I've explained here. Thanks for your time. I guess the question is like they say that there's water for agricultural usage right now in that valley, but the valley's uninhabited and it seems like a desert. So it doesn't seem like they're growing food there. So like where is that water actually going? Because is it just getting piped to some other farm like far away? So way, way, way back in the day. Way back in the day. way back in the day, you could just have a piece of land, you could drill a well, and you could pull up as much water as you wanted. Yeah. And then people realize that you might, if you have a
Starting point is 00:24:14 property here, yeah, and there's property here, here, here, here, here, there are oftentimes all pulling from the same aquifer. So you, all of a sudden, if you come in, you move in next to me, and you start pumping, you know, billions of gallons. I drink your milkshake. Yeah, you're drinking my milkshake, right? And so it's very possible that all of these parcels of land, which they collectively bought, they all have their own water rights. That doesn't mean they are being used, right? Because people will sell their water rights to like a neighboring property that is.
Starting point is 00:24:44 Yeah. But my question is like, it sounds like they sold the water rights previously or they had some sort of deal to send the water that they were getting out of the desert, which I can't imagine produces that much water, but I guess it does use it for like agricultural purposes. Like, what were they growing up? Well, agricultural could mean you have some,
Starting point is 00:25:04 like you have some cattle. Like there's a bunch of different potential meanings for that. It doesn't mean you're growing fresh produce. But were they actively using or were they just like, no, that's the other thing. I don't know. That's the other thing too. It could have been agricultural land.
Starting point is 00:25:18 Yeah. But not. Could have been like a failed farm. It's not farming anymore. Like a former livestock, like farm, something like that. But I don't know. I feel like people are going to want to go a click deeper on that. Like he rebuts a lot of the good, the good,
Starting point is 00:25:34 rhetoric, like, but there's still like another, another layer there. Dave says the water could be used at Amundiri. Yes. Influencers are protesting in the flats outside of Amangiri. You drain the pool at Amangiri. It's going to be, it's going to be a big protest for sure. Well, yeah, I mean, these, these points, like, as you said, I think, are going to be hard to break through just because AI is so deeply unpopular for a variety of reasons.
Starting point is 00:26:03 and we should watch the video of Eric Schmidt getting booed on stage at University of Arizona. Alex Cantorowitz played a video here. I don't know if we need to watch all of this, but he says this is incredible. Artificial Intelligence is getting booed out of the stadium in any commencement speech. It's mentioned in maybe telling college students AI was taking their jobs. Wasn't the best strategy. Let's watch this clip. The architects of artificial intelligence.
Starting point is 00:26:30 Interesting. The question. is whether you will help shape artificial intelligence. We do not know the precise contours of what this... If you'd let me make this point, please. Step one. If you're giving you commensums, you've got to bring a soundboard. You ought to be like, AI, yeah, it's not that bad, but also, I hear you.
Starting point is 00:26:58 The perspective of the immigrant, who has so often been the person who came to this country. They're really going crazy. Crazy. We thought that we were adding stones to a cathedral of knowledge. Another one. There's just a low-level boo the whole time. It's so rowdy. Like, normally you'd think there'd be like a little bit of boo and then they'd just like
Starting point is 00:27:26 get quiet down, okay? This is about to turn into a riot. This is crazy. Did he just bail on this thing? No. You have only seen... At this point, I mean, you gotta go off script. You can't stay.
Starting point is 00:27:41 It is funny that if you cut it up in the right way, you could make it seem sound like the most evil. He's like, you will surrender your agency. Yeah. Okay. Now we need to take this clip. Do that thing where we... Grace says he's lucky they didn't flashbang it.
Starting point is 00:27:56 Yeah. We need to do that thing where we take out the booze and just leave his words and then add cheers. So it's just the same exact speech, but everyone's just like, Yes, this is amazing. I can try to find it, but there's a video of him after the speech, like, getting mobbed by students. They're all, like, yelling at him. Yeah.
Starting point is 00:28:15 Really? They were not fans. Wow. This is rough, rough, rough. Yeah. Not good. I mean, the big thing is, like, I don't know that that is, like, everyone is booing for a slightly different reason, but it's like this ensemble of, of, of problems and grievances with AI. generally. Like everyone is, one thing that I've been like frustrated about is everyone is vibe
Starting point is 00:28:44 coding like 24-7 leaving MacBooks open talking about like productivity. And yet the like the magical moments, the consumer technology has been like completely left behind. Like there was a time when we got the cloud, we were building a lot of data centers. But every year you'd get like a cool new thing like Yelp would come out. And it was like it wasn't changing the world, but it was like, oh, you could find a cool new restaurant. And maybe like or Groupon. Like Groupon was like not a great business ultimately. But like for the first couple months of Groupon,
Starting point is 00:29:15 you could like go try a restaurant for like half price. And it just felt like magical or like Uber. When that came out, it was like, wait, I can go out and the car will be right outside instead of having to like call a phone, call a taxi cab service. Maybe it comes. Maybe it doesn't stand outside in the cold, try and flag a car. There were all these things. You think they were, I'm just thinking now.
Starting point is 00:29:35 Do you think they were, do you think they were, like, angry at usage, nano-banana usage limit? Probably. Is that? Is this whole thing just a misunderstanding? They might think we're in a plateau, and they might just be upset with the lack of progress outside of coding domains. They say, yeah, the writing is just still not that good.
Starting point is 00:29:56 I need these models. I can clock it. Yeah, it's still clockable. Yeah. At first, I thought they were mad that, like, at Google, Eric's minute was, he was doing too many you know, stock buybacks instead of investing in technology and innovation. Yeah, yeah. Having a hundred billion on the balance sheet and cash
Starting point is 00:30:11 is just unacceptable. Like, yes, you get Waymo, yes, you get deep mine. Yeah, because it just says they don't know what to do with the money. Yeah, yeah, they weren't innovating for a long time and that makes a lot of sense why you would boo them. Sort of the tealian the tealian boo. No, and then, and then also like, yeah, the
Starting point is 00:30:29 jobs thing is super real. Like whether or not AI is affecting the job. Also, so we should pull up Lulu's critique because I'm sure it'll be way better than this. But just in those handful of sentences, like, is that that felt like a speech more potentially like oriented towards maybe like the Stanford student body, which is like, how are you going to contribute to AI? That's what I was like sort of, that's what was standing out to me. Yeah. being like, don't be afraid of this thing, like jump in and help shape it. Yeah. And if you're maybe someone in Stanford and you have the opportunity to go actually be involved
Starting point is 00:31:15 and you're at the epicenter of all this progress, maybe that would land. Yeah. But at U of A where people are hearing like, hey, all the different career paths that I'm thinking about. In terms of commencement speaker, I would prefer someone like a Sam Seulak to give the commencement speech. That would be like my, like Eric Schmidt is like, he's kind of like a, meh. Sam Seleck, that's an inspirational speaker. That's going to fire me. He's on the come-up. Exactly. Yeah. Did you have a question? Derek, more plates, more dates. That would be fantastic, too. Yeah, I was trying to, Gabe's asking about the, why would he give a speech there? I was trying to find a
Starting point is 00:31:54 connection. I think, I think he's just a big name. Okay. And it's very, obviously, his experience is very relevant in this moment. Show up to mock. None of you were getting any jobs. Just terrible. Yeah. No, no. I mean, there is this thing where like, AI needs to create jobs because like, even if
Starting point is 00:32:10 AI isn't destroying the jobs, if we have a weak economy, there won't be good jobs. And then like, you're still held accountable for that. And so you got to create jobs. And then on the data center side, like, there's just so many issues within that that we can go through. Environmental impacts, which are probably real. If you burn a bunch of fossil fuels.
Starting point is 00:32:28 you're going to have negative externalities, diesel generators, these things are smoky, the air quality, all of this stuff is fairly real when done improperly, which is happening. The water use thing, mostly fake, but still, like, needs to actually be walked through fully and digested by the public. The noise issue, which is solvable, but still, like, not that great. And then a bunch of other issues that are just not going to happen magically. Ben Thompson had a wild proposal. He had a great
Starting point is 00:33:01 piece which I wish we had time to read through the whole thing but we can sort of run through it. So he starts with an anecdote from Politico. Texas County southwest of Dallas this week passed what may be the state's first county level moratorium
Starting point is 00:33:18 on data centers. Not what everyone was expecting in the free state of Texas. Everything's bigger in Texas except for the data centers which are getting smaller now that there is a county level moratorium, seeking to buy time for lawmakers to soften the blow of development across, sweeping across rural areas. Hill County's commissioner, Hill County's commissioner vote, court voted three to two Tuesday
Starting point is 00:33:41 to put a year-long moratorium on data center in power plant construction in unincorporated areas, citing an influx of as many as eight data centers planned there, many of which could have could have their own power plants. Opposition to data centers is spreading in regions led by both Democrats and Republicans as politicians try to balance economic development. Yes, apparently, according to AI, there's no official public count of operating data centers in Hill County, but there's eight proposed or planned data centers. So this is a place that... They're going to be delayed. In Missouri, one small town, unhappy over its city council's approval of data.
Starting point is 00:34:24 centers voted last month to oust all four incumbents running for re-election. In North Carolina, Governor Josh Stein has made a point of saying that sales tax exemptions for data centers cost the state up to 57 million per year. Texas has hundreds of data center locations operating or in development, second only to Virginia among U.S. states. The growth has stirred pushback from environmentalists and rural residents who worry about the effect on water supplies, the electric grid, or their quality of life. Officials in states across the country are starting to have second thought.
Starting point is 00:34:54 about data centers, and some are looking to roll back tax incentives. And Ben Thompson says, I chose this story because it happened just, it happened to have happened over the weekend. In truth, there are an exploding number of options, including one, just up the highway from where Ben Thompson lives in Wisconsin in Deforest. And they are hardly isolated, isolated sentiments. Seven and ten Americans oppose constructing data centers for artificial intelligence in their local areas, including nearly half, 48%, who are strongly opposed. Barely a quarter favor
Starting point is 00:35:28 of these projects with 7% stronger in favor. Now, I was thinking about what do Americans want to build? Because it's easy to look at the data center stuff and be like, well, everyone's against building data centers, but I do think that there's an element of like, Americans don't want to build anything. Like, I was reflecting on the whole reindustrialization meme this weekend. I got a version of that sweater mailed to me that I picked up. And I was thinking about the actual knock-on effects of reindustrialization. Like most people don't want a car factory in their town. But we do want new roads.
Starting point is 00:36:08 Well, not necessarily new roads. No. People don't want new roads. And they don't even want the roads paved because they're like, I'll just buy a bigger car. I don't know. Hospital? You want a bunch of people dying next to you?
Starting point is 00:36:19 I don't think people want hospital. I literally golf courses they have poisons they're bad for your health like I actually think people just don't really want change necessarily they don't want things built broadly like data centers are probably at the bottom of the list like they're the least popular but they're like high speed rail I thought that would be popular it was not popular and like I'm just going down the list of like oh like you want like oh we need we need maybe you're a national defense person you want a missile factory next to you blowing up bombs like no everyone no one wants that like what do we want? Like, we don't really want anything. We're kind of good on building in America. I don't know. I just think we're good. Like, we're just like, we're fine.
Starting point is 00:37:01 It's good. Don't change anything. No new trains. Yeah, I think when there's self-interest, right? When people want to build their house. Yeah. Right? When people want to create their new restaurant. Yeah, their data center for sure.
Starting point is 00:37:13 But people don't want other stuff built generally. Like, there's very, very, very few things that people are like, yeah, I'd be down for that to be built. People, people like the status quo. They're happy with things as they are and they don't like change. So, um, like any, anything new is going to be like somewhat, uh, somewhat unpopular as nuclear power was, uh, not building out nuclear power 50 years ago was, of course, one of the greatest mistakes humanity has made and one that contributes directly to data center opposition today, given questions about the impact on energy bills. Also interesting, uh, we have to do this another time, but the, you know, did we run out of nuclear?
Starting point is 00:37:50 scientists? Was that what stopped the build-out? Did we not have enough geniuses? I don't know. Maybe. We'll dig into it. But Ben Thompson has an interesting solution. He points out a bunch of ways to fix the problems of data center construction and opposition. He says, first, this is... People are saying homes in the chat, but then again, people don't really want more homes in their area once they already own a home. They block them all the time. They block home construction all the time. And also permitting and also Expansion of existing homes like these things I'm not saying I'm not saying that they're like as unpopular as data centers no way Data centers are at the bottom but but homes or something maybe in the abstract
Starting point is 00:38:32 But like new housing in communities is like Razors edge 50 50 60 40 like it's like there is a lot of opposition to building It's just in America broadly like that's just the nature of our society So Ben Thompson has some solutions though what do you got to do to build a day center? Properly he says first First, this sounds obvious, but tech needs to fix its messaging problem, the issue, and if an answer seems obvious, then there surely must be some other problem at play is threefold. First, a good number of people in tech, particularly at one of the leading labs, genuinely believe most jobs are going away. They could lie more effectively, but beyond being dishonest, it's also a betrayal of the fanatical devotion with which they are pursuing AI despite obstacles, including the challenge of spending billions and billions of dollars on models that are obsolete in months, if not weeks. Second, it is extremely hard to describe the benefits of inventions not yet made.
Starting point is 00:39:23 Cures, not yet discovered, economic activity, not yet engaged in, et cetera. This is always the burden of those arguing in favor of progress and the sheer potential of AI actually makes the problem even harder. 50 years ago, everyone was like, electricity isn't that expensive? Why do we need to build nuclear power plants? They're scary. And now electricity is expensive and we're like, oh, we should have built those. That's the way these things always go.
Starting point is 00:39:46 Third, tech is and always has been terrible at understanding and relating to the rest of society. I go back to how Silicon Valley was extremely skeptical of Facebook, a company predicated on connecting with friends and family precisely because it's filled with people running away from their friends and family. You can optimistically say that people in tech live in the future. You can also more cynically say they live in opposition to and denial of humanity for better. and in this case, for worse. Second, tech could control the misinformation. TikTok is a major point in this.
Starting point is 00:40:21 He talks about how the algorithm is still controlled by the Chinese, and maybe there's misinformation there. Second, in a rather ironic twist, meta has learned the lesson of trying to control misinformation, doesn't want to overtly censor, but now the company gets no credit for not censoring misinformation about data centers, and so it's like this weird thing.
Starting point is 00:40:43 And then third, this was a wild card, which I didn't think of, but X is the social media platform X and Twitter, formerly Twitter, is actually incentivized to be anti-data center in a weird way because X is owned by SpaceX. And a big part of SpaceX's upcoming public offering is the possibility of building data centers in space. This is like total tinfoil hat, I think, but it's an interesting like, okay. And he says, to be clear, he hasn't seen any evidence of thumb on the scale or not, I certainly have it. But, you know, part of the problem, though, is that we would never know if there were. And so he goes on to propose something very, very bold, very, very bold. He says, instead, the most obvious solution is the most crass. Simply start giving people money.
Starting point is 00:41:30 Not universal basic income, though. If data centers are a resource for our AI future, then start paying people for that resource. If that data center up the road weren't sold to my neighbors based on amorphous tax benefits, that my local government may or may not spend appropriately. I was talking to Tyler about this earlier, but rather were to result in a check in the mailbox every year, I suspect you could get a lot of people on board. So he put some numbers together,
Starting point is 00:41:53 and he says for the data center up the road, it was expected to be 1.6 gigawatts, which could generate around $3 billion in annual operator revenue. Deforest, the village it was to be built in, has around 11,500 people. So you could pay every person, in that village, $10,000 a year, and it would only equate to 3.8% of annual revenue
Starting point is 00:42:19 grossed by the data center. And he says, I bet that that proposal would have been approved, and I bet the operator could very easily pass on those costs to actual data center users. It also highlights how relatively pathetic the original commitment that I think the data center said, hey, we'll give you $50 million, which is like nowhere near what that math works out to.
Starting point is 00:42:40 So data center is coming to town. You get to vote for it, but the data center company says, hey, we'd like you to vote for this. And we will give you a $10,000 check in the mail every year forever while we're operating this. And that seems like that could actually get people on board. So this is ridiculous. This goes back to even months ago at this point. We were saying, you know, AI is not a, is not like a, you know, natural resource
Starting point is 00:43:07 where you benefit from having it in your backyard, right? If you're just an everyday AI user, you do not care where the data center is at all. And so if someone is coming to put it in your community, it's pretty fair to want to benefit from that in some way. And like a direct payment like that, I think I'm sure that will happen more. Yeah. Yeah. And what I was talking to Tyler about was does the, like, do local communities feel a difference between $10,000? in the mail directly to them or $10,000 to their local government that says, we're going to use this to build roads and hospitals and all the different things that we do. I think that on net, the average American is a little bit skeptical about dollars going to the government
Starting point is 00:43:57 actually benefiting them at a one-to-one ratio. They definitely think that if the money that goes in is worth something, but a lot of it gets mixed around and there's delays. Yeah, and the data center is already going to generate a bunch of local tax revenues, that local government. Show me the money. Show me the money. That's what the locals should potentially be saying.
Starting point is 00:44:17 Well, that's what I'm saying. It's like they don't, you know, the, I think it's totally fair for the local population to think, okay, like this big infrastructure project is happening in my town, even if I'm not going to work there. Yeah, it's going to generate some taxes for to help improve our community. But show, give me the money, basically. Give me the money. Go direct.
Starting point is 00:44:38 Yeah. Go direct with the money. I like it. Well, we have Mike Isaac from the New York Times in the waiting room. We can come back to our gate center debate after we check in with Mike. And I think he's on location. Is this correct? Mike, where are you?
Starting point is 00:44:54 Welcome to the show. How are you doing? I'm good. Can you hear me? I'm sorry. I'm literally outside of the courtroom. Amazing. No, we can hear and see you just fine.
Starting point is 00:45:04 That's amazing. Well, take us through it. My actual. How has today been going? What's happened? It was crazy. Basically, today was supposed to be the first day of jury deliberations, and we were a few reporters with the courtroom because in the morning it was about both sides presenting their case for remedies to the judge on basically how much money, if anything, would be dispersed as a result of the lawsuit. And literally in the middle of this deliberation, the clerk goes and interrupt the judge and says, hey, da-da-da-da-da-da, something's happening, basically, the scurrying.
Starting point is 00:45:44 And everyone's like, oh, my God, what's happening? And da-da-da-da-da. And this is like less than two hours into it, they reach a verdict. And so the jury comes back in and delivers the verdict. Interesting. What was your expectation going into today? Did you think you'd be hanging out at the courthouse all week? All right, Bill.
Starting point is 00:46:03 Yeah, yeah, I'll see it soon. Sorry, that's a lead opening eye council walking by that I should go run after, but he's doing his thing. I mean, we're just hanging out. You can come back to the call. You got to go chase him down. What do you want? I'll bug him later. Literally, he was just chilling and walking out.
Starting point is 00:46:21 I, uh, sorry, I can't see without these. I, uh, I forgot what you, I'm so tired. What did you ask me? Yeah, I was, I was, what was your expectation for your week? were you expecting to be at the courthouse every day? Yeah, we were, like, I got here again at 6 a.m. And like was ready for a long, like sitting out in front of the court for days. Because the way these work is like you get 10 minutes notice from when the judge gets the jury verdict to get down here.
Starting point is 00:46:52 I live 10 minutes away, but still like no reassurances. So we had me, my colleague Kate Metz, and then Natalie Roka, another colleague of mine. just like ready. And I was like, thank God when they came back, because I didn't want to sleep out here. Okay, so the actual verdict, it feels like victory on a technicality. And what I'm interested in is that over the last few weeks, it feels like the core discussion or the talking point was Elon Musk,
Starting point is 00:47:23 you can't steal a charity, very pithy phrase, easily memorizable, could stick with you or could bounce right off you. you know what his grievance is. And then Open AI sort of needs to say, well, the charity still exists. And we had an agreement that we would go this way. And it was a little bit more complicated. But that doesn't seem like what the jury actually decided based on. And was that, like, as you think back to the last three weeks, do you think that there were actually good seeds planted around the statute of limitations and when the case should be filed?
Starting point is 00:47:59 Because it feels like from the reporting and from the viral. the screenshots and the emails and the and the quotes. Like there was never like, oh yeah, we all remember the smoking gun of statue of limitations. No, I don't. I remember the you can't steal a charity or the Rockman diary, right? And it feels like we got a different outcome here. I think I remember at different points. Okay.
Starting point is 00:48:22 They, this only, this whole debacle only became a thing after the launch of ChatGBT, and after, you know, the company was showing, you know, Traction. Massive traction and revenue growth. But I never heard specifically, like you said, this statute of limitations. Yeah, but how did you process it? Well, that's a wonkier point, too, right? Like, it's very easy to, and that's what I think, like, really the strategy on the must side was,
Starting point is 00:48:50 was to go for really, like, clearly digestible talking points for a juror who may not be steeped in nonprofit contract law or statute of limitations. and exactly what that is. And I think that's what they were betting on too. They're like, all right, if we can sell the jurors on this idea that Musk is, you know, selflessly trying to, you know, interrupt something that could be bad for the world versus opening eyes more technical point of, look, you should have filed this lawsuit years ago. Maybe they can win it. And so I think that was going into it, what everyone was kind of thinking about, like, is this going to be, certainly what I was thinking about. going to be a battle of like the billionaires who do you trust who you just like a character thing that is this a referendum on that and exactly what you said it's super surprising when
Starting point is 00:49:41 they came back and essentially i would say statute of limitations was like if that was that was the ballgame right and if they had flown past that if they had not find them the burden to be met then we would have seen how it really played out but that was just that was the whole thing you know uh what so so last week I was surprised that Elon jumped on the China trip with Trump. Oh, yeah. Was that addressed? Yeah, that something.
Starting point is 00:50:11 I mean, a lot of the people online were just like, he's a billionaire. He can do whatever he wants. That was like, that was like, the president supersedes the federal judge. Yeah. I don't know if that's actually the case. But there was some dialogue around like, hey, you're in the middle of this historic trial. Like, you should be present. Yeah.
Starting point is 00:50:29 at least able to be present. Did that? Do you think he did that because he felt like it wasn't going his way? And he was just like, I need to make the most of my time. I think he, so, yeah, NBC wrote a good story on that. Like, he was not excused. He could have been recalled and asked to testify again. And it's typically bad form when you leave the country to when that happened.
Starting point is 00:50:57 And so what I was told or what I heard is that they had actually spoken to the judge beforehand to like make sure it was like okay and like that he probably wouldn't really recall. I think part of it also was that both sides were both sides are on a clock. So you only have so much time to to present your evidence. And the early testimony was running long. So opening eyes still needed to get through a lot of the testimony of their. expert witnesses towards the end. So they decided,
Starting point is 00:51:31 and Musch side also decided they weren't going to recall Musk. So, like, there was that part of it that probably made it okay. That said, like, it's probably a bad look when you make it the first three days of the trial and Sam and Greg make it basically
Starting point is 00:51:48 most of the time. But at the same time, like, it didn't come down to character who pissed off the judge necessarily. It came down to, like, illegal, technical argument, which seems to have, this jury was pretty sophisticated, at least in like focusing on something that I didn't know if it was going to land or not. Yeah, did, I mean, it really makes all of the, like, the, the, the, the, the, the,
Starting point is 00:52:11 safety testimony feel like, maybe a miscalculation because it sort of took the conversation in a completely different place, and then they got focused on this, like, technical issue. I mean, the jury doesn't put out like a statement. Are we expecting any sort of like closing statement from the judge or is this what we get here? We, so by the way, sorry, there's still like people protesting the background. I see that, but I'm my very terrible laptop camera. Are they protesting the statute of limitations because they're on Elon's side or? There's actually, this has been the best part of this is like there's many different protest camps and it's kind of hard to define
Starting point is 00:52:55 who is against what? Are any of the protesters protesting other protesters? I mean, genuinely, yes, probably. There's the, I want to do. The Ljakis out there protesting the D-Sels. Genuinely, there were a supporter, no, 100%. So, actually, usually post-trial, people like me, go and try to find the jury and chase it down,
Starting point is 00:53:19 which is what we were doing. I think they probably are already out of the building. I ran around the back, and saw a van that was all blacked out and this marshal that I had known the whole trial and they were just like getting the hell out of here. So I'm guessing they didn't want to get mob by us. But the judge, I'm going to try to get the notes out. The judge left the jury with like a pretty good summation, not of the trial, but just
Starting point is 00:53:46 like appreciating a jury and like respecting a jury finding like finding parties liable or liable, you know, and, and I think that the point of that was she didn't, she, some federal judges could like, be like, no, I'm throwing the verdict out or whatever, but she respected the jury with the jury of their peers and they were deliberate, you know, and they listened intently. And so she left them, I'll find the exact quote and send it to you guys, but she left them on sort of like, we thank you for your service. Yeah, yeah, that seemed also a little bit unexpected because when, when the jury verdict became, you know, popularized or publishedized.
Starting point is 00:54:24 as like advisory. A lot of people were sort of interpreting that as, well, like, it doesn't matter at all in that case. But it seems like the judge did wind up sort of, you know, giving the jury a lot of weight and very quickly reacting to the jury's verdict. And I think that's really important as far as appeals go because you could argue. Bias charge. Cut out. Like, you could argue like, oh, the judge. Yeah, exactly.
Starting point is 00:54:53 The judge didn't care. The jury. So I think there's real incentive to be in line. Yeah, yeah, that's very interesting. What was the snack set up today? Are you going to get a proper lunch now? I feel like, I feel like that was one of the most disappointing arcs. If I'm going to be completely honest with you, the lunch game just didn't seem to evolve.
Starting point is 00:55:15 We were saying that you weren't learning from your lessons and all. For You know the Nathan for you episode where he's got the Chili suit? We were going to do that for you. into the because it just felt like they day okay day three you show up with an apple and a banana it's like okay I you still learn in his lesson but like fool me seven times I was expecting a chipole burrito or something with a little more substance get into the four digits of calories please God people were like giving me saying I have like scurvy or rickets by the end of this trial I think I just have like a really disturbing diet overall so yeah I
Starting point is 00:55:54 And today I forgot. I was out last night until way late at a show. And I'm hung over and I forgot to bring food. So it's just, this is basically my, you get to see my slow descents into madness. But thank God we're done. Okay. So, I mean, we asked you earlier,
Starting point is 00:56:10 is this the stuff of movies? Is there going to be a movie about this? Or was this anticlimactic? I think, like, I think this, the movie is still going, man. Like, this thing is still, there's so much. I feel like this is the most exciting time in AI because opening eye is really on his back foot in a lot of ways. This gives them some relief in the many fronts that they're being intact on, whether it's going public this year with a messy balance sheet or anthropic coming after them, Google, coming after them, Google IOs tomorrow. So like, if anything, it's a brief reprieve, you know, but I wouldn't make the movie now.
Starting point is 00:56:50 I'd wait a couple of years. Okay. Okay. Anything else, Jordy? The story continues. The story continues. I'm expecting to see model wise around San Francisco that say I bought this after Elon lost his landmark trial against Open AI. Yes.
Starting point is 00:57:08 The bumper sticker. New bumper sticker. Well, have a great rest of your day. Thank you so much for taking the time. We'll talk to you, Mike. Great to see you. Next time. We'll talk to you soon.
Starting point is 00:57:18 So Mark Cuban has another proposal for how to deal with data centers. and internalize all those negative externalities. He says, we should tax tokens federally at the provider level. Tyler, you're going to have to interpret what this would mean in all the ways that companies would wind up getting around this with maybe, you know, less robust answers potentially. But he says, not a lot, less than 50 cents per million tokens. It will accomplish four things at least. It will push the big AI players to optimize tokenization,
Starting point is 00:57:57 cashing, routing, and localization, which will reduce energy usage, saving them in energy costs more than what they paid in tax and reducing strain created by the growth in energy consumption, which will generate maybe $10 billion a year to start, but over the next 10 years could grow 30x to 100 X. So he's thinking two orders of magnitude in a decade in terms of growth for AI.
Starting point is 00:58:21 That's low end. of what a lot of people think. And then four, create a source of funding to pay down the federal debt or deploy in response to the things AI brings that we don't expect or don't like. At some point, the models will pass it on to consumers. Of course, that's okay.
Starting point is 00:58:40 Consumers will have the ability to choose between providers or to do everything using open source models locally, which I guess wouldn't be taxed. What do you think? This is kind of like the opposite of what we were saying before of like going direct, right? Because we were saying, okay, you know, the actual data centers
Starting point is 00:58:55 are going to make so much revenue. You can just tax the data centers and then the money goes to the local community and then that's where you see the benefits. But isn't this going up the chain even more? So you're taxing the companies. So then people in the community like definitely won't. The money will be like so abstract if it's at the federal level. I feel like this is the wrong
Starting point is 00:59:11 way. Here's something else. You should be giving the people. You should get a check from Open Anthropic every month maybe. That's I think the better version of his if you want to tax the company. What if we tax companies you know, what if we had something like a sales tax or, you know, what if... Profit. What if when...
Starting point is 00:59:31 Income tax. Yeah, like if someone was paid, what if some of that money went to the government to help pay for public, you know, services? And maybe even if a company's doing really, really well, then you could take a percentage of their profits. Yeah. Because that company has... And if the investors sold their stakes, they would pay a tax.
Starting point is 00:59:51 on whatever game. And then every single, what about every single underlying vendor that the company, you had the same sort of like structure for every underlying company that serves. So like if NVIDIA sells a bunch of GPUs and they make a bunch of money, they don't have tax on the profits on that. Yeah, or even somebody like a contractor that,
Starting point is 01:00:09 you know, manages a building, right? So they have a, you know, maybe it's a small local business. Yep. They manage an office base. Make a million dollars. Some of that. Costs are only half a million. Yeah.
Starting point is 01:00:19 That half a million profit, that gets taxed. Has any. Anyone thought of that? That might work. Anyway. And then you could use that money to sort of, you know, cover the costs of operating the government and then even potentially use some of the extra to pay down the debt. Potentially.
Starting point is 01:00:33 Well, Paul Merlucky's going back and forth with Mark Cuban about this. Palmer, like he says, there are already massive economic incentives to optimize. So this is just a tax on American companies that makes foreign models and more, and products more attractive, along with creating the infrastructure for government to track all AI usage and punish anyone who doesn't report. who doesn't report. Mark Cuban says those incentives change over time. Right now the incentive is to grow and spend market share over optimization. You know this. Do you think the marginal cost of some BIPs on a token is going to make those buyers choose differently? Or do you think the models are just a commodity and price is the only differentiation space and then the question
Starting point is 01:01:11 mark every time you know it's not AI? And the tax would only be on what providers sell, not open source models, not local, not internal, and what foreign models are referring to? Palmer, Mark, you are essentially making an argument for central planning. The burden is on you to show you where it's worked before. No quotas, no mandates. Just good old capitalism and competition. Palmer says, this is obviously not capitalism or competition by any reasonable definition. It is a tax that specifically disadvantages one type of AI business to the benefit of others, artificially propping up their business models in my business is one of the ones that would benefit because he's not token heavy. That is an interesting thing. Semi analysis says 50 cents per
Starting point is 01:01:48 Amtok is a lot of money, Mark. Are you considered cash hit on pre-fill or just output tokens? Those are the hard questions. Steven says, imagine a bit tax in 1995. Yes, flops tax. I don't know. What else is going on in the AI slop world? The bot farms have figured out.
Starting point is 01:02:08 How about this? What about every time you move your cursor? It's just one cent. Right? Yeah. I don't know. Tax on something. It was pretty funny.
Starting point is 01:02:19 I was saying last week when I was saying, like, basically reinventing the U.S. Postal Service, a lot of people were messaging me saying, you know people, you know this exists already. It's like, man, it's tough when the sarcasm. Doesn't break through. It doesn't break through. Well, the bot farms have figured out anti-Data center posts on Facebook are good for engagement. But ironically, they're using AI slop to do it. You don't know this is the eye slop.
Starting point is 01:02:49 This might be the most perfectly designed set of stones ever visited upon a beach. It's not worth giving up an inch of this to a data center Indiana. Breaking an Indiana resident of a reportedly arranged stones to make an anti-data center message. This is 99% slop. And this one is really sloppy.
Starting point is 01:03:12 Wow. Wisconsin's forest farms, lakes, river, small towns. Not a single square inch. of Wisconsin is worth giving up for an AI data center. Interesting that the I and is is capitalized. Makes me think that that was added after the fact, but the rest is pretty sloppy, but kind of beautiful. I kind of like the perspective on this image
Starting point is 01:03:33 with the big farm and the barn in the background and the... This makes me want to visit Wisconsin. Yeah, does Wisconsin actually look like this? If it does, perfect place to build a data center. Yeah, that's the only thing it's missing. Well, no, I want, yeah, we have to go and find, we have to go find the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, we, we, we, we, should we, should we, should we, should we, should we, I, I want to cover, Tyler. Well, uh, we, we, I, I, I, I, I, I, I want to cover this, but, like, the trick is that, this is a Ken Griffin clip. So basically, he pivoted on AI three months ago. He was saying like, it's not really useful.
Starting point is 01:04:19 The reports that we get from AI models are not actually, you know, relevant to our business. And now he's saying for us at Citadel, it's allowed us to unleash a much broader array of use cases. It's been really interesting to watch, work that we would usually do with people with masters or PhDs in finance over the course of weeks or months as being done by AI agents over the course of hours or days. And it's seen as sort of a blackpilling moment because he says, like, I got home and I was. sort of, I got to tell you, I went home one Friday, barely depressed by this because you could see how this was going to have such a dramatic impact on society. And it is like a weird moment and it's sort of like, oh, okay, he's waking up. But then if you actually watch the full interview, this is one minute from a 40 minute interview or something. And he goes on to enumerate a whole
Starting point is 01:05:09 bunch of different benefits and where he is allocating his workforce. And also Citadel is in a very interesting game theoretic dynamic where it's not they they are not a monopolist so by definition like they are in competition with all their other funds and so uh there's there is a world where uh like even if they're getting incredible value out of AI they wind up using AI and humans in conjunction to compete because uh we are in like the centaur era which is sort of what he enumerates but anyway what did you have another take away this one uh one uh one One thought I had is that Citadel has, you know... AI psychosis. That's what you're saying.
Starting point is 01:05:51 No, they have a team of thousands of, you know, PhD-level talent that are doing things that AI can do pretty well now. And him driving home on a Friday being depressed, part of me was thinking, is he depressed because he realizes everyone will soon have access to a thousand people with Ph.D. level talent. that they can turn on. And maybe they can't cover the whole market. Obviously, he talked a lot about, you know, how much software there is to build. He's like, we'll never build enough software.
Starting point is 01:06:23 But at the same time, he was thinking like, wow, this, this like resource that I've accumulated, this, like, capability, this, this team. When AI can do what they do and everyone can access AI, like, how is my business going to change? Yeah. So funny, reflecting on. the time I worked at Citadel and my job was basically to copy and paste cells in an Excel spreadsheet and so I wrote a visual basic script to sort of just do it for me and then I was able to just like have seven hours free time every day and I wound up being able to do a lot of other stuff and it was a story of automation and and I can tell you at least back in 2011 I spent the summer over there as an intern there was a lot of stuff you could automate for sure A lot of stuff in the back office, middle office, and yeah, some research in the front office. But Citadel's Edge is more than just, it's more than just research.
Starting point is 01:07:25 They do a lot of CEO interviews. They talk to a lot of people off the record. They have a lot of information that does not exist on the internet. They have scale. Yeah, there's a lot. Track record. So, I don't know. It's interesting.
Starting point is 01:07:39 Yeah, let's talk about everything. Yeah. Well, we have our, I think we have our next guest already here, but we can go through Everlane quickly, or we can come back to Everlane at 1245. Let's do that. Okay. Well, let's bring in Rowan from Redis, because he's waiting in the waiting room. Rowan, welcome to the show.
Starting point is 01:07:58 How are you doing? I'm doing great. Thanks so much for me here. Since it's your first time in the show, I've actually been. Are you getting an act? Is this an active sauna session for you? It does look like a sauna background. I'm in Tenerife today actually.
Starting point is 01:08:14 Oh, amazing. Yeah. Amazing. The wood paneling behind you really does look like a sign. No, I like it. I like it. Well, yeah, we'll see in a few minutes if you start sweating. I'll be doing the cold plunge next.
Starting point is 01:08:24 We'll see how that goes. Yeah. Well, some people in tech do combine cold plunges with that's happening. Talking about their company's Tim Draper being. Yeah, yeah, yeah. For sure. Anyways, great to meet you. Great to meet you.
Starting point is 01:08:37 I've used Redis a ton about a decade. go. I'm a big fan of the product. But if you could introduce yourself and the company a little bit before we go into the news today, that'd be great. Yeah, absolutely. Thanks for having me on. It's an honor to be on. I loved your show. A big, big fan and watch all the time. Yeah, so I'm the, I head up Redis and we're one of the sort of core infrastructure components that, you know, has been around. One of the, one of the bigger open source projects over the last 15 years and sort of helped build out a lot of the internet infrastructure and got a great team. We have just about, we're 3, 1,500 people now. And we're starting to see a lot of, there you go. That's great.
Starting point is 01:09:18 We're starting to see a lot of traction in the AI world as people are starting to really build out agents. So as you guys were just talking about, you know, lots of opportunity there and so we're being pulled in on agent data. Yeah, I want to get to that. Is the, is the correct framing for Redis for people who might not have actually used the product in memory, key value storage, like non-relational databases think like MySQL, but less structured and also held in memory, therefore faster? Totally. You nailed it.
Starting point is 01:09:48 The history of it is an in-memory data structure server. It's not really a database. Yeah. But it's been treated as a database. And the killer app that kind of took off and made Redis a part of kind of got the tendrils into all the applications on the whole internet was key value. Yep. Being used for caching.
Starting point is 01:10:04 Yep. So it originally started as an in-memory database. big thing that's changed, though, and this is just coming live now, as over the last few years, we've re-architected Redis and launched a new product that uses Flash as the back-end storage. So now we have the fastest, the world's fastest, Flash object store. And so that's a new thing. And that was really driven by AI because we were seeing huge demand for way bigger, way more data. And also RAM prices have gotten crazy. Sure. And MVME performance has in.
Starting point is 01:10:37 improved dramatically. Sure. Okay, so then take me through some of the history of the business. I know you joined a CEO like in the modern era, but in terms of that transition, what is the shape of the business? Because a lot of people are building open source software. And I'm always fascinated by that transition and that interaction between the product, which sometimes has like incredible developer pull, incredible ecosystem.
Starting point is 01:11:01 And then also an incredible opportunity to build a real business around it. But what is the shape of that? because I think people go to Red Hat, they go to consulting shops, they go to hosting providers, enterprise software wrapped around it. But how would you describe it the shape of the business
Starting point is 01:11:19 around the product right now? Yeah, so it's a great question. We're still an open core company. So we have an open source base, which is Redis. Anyone can download it and use it for free. It's used all over the place for free. And then we have a paid version.
Starting point is 01:11:32 So, for example, the recent innovation I mentioned, the rewrite using Flash story. That's not for free. That's something that you would pay for. We have a lot of performance advantages in the paid version. We have a hosted version. It runs on all three of the major cloud. So if you get Redis on Amazon or Google or Microsoft, Azure, right? We have our own version, essentially, it runs on those clouds. And so that's that's the heart of the business. Most of our usage on the internet is free Redis because the free product is amazing. Yeah. The paid version is even better.
Starting point is 01:12:08 I like it. That's good. That's good salesmanship. So the relationship with the hyperscalers, is that like consumption revenue that's coming to you? I set up an ADBS instance. I pull Redis off the shelf from the dashboard of a million different tools. And then as I'm using it every month, stuffing more and more data into it, that money is
Starting point is 01:12:27 flowing to you from the hyperscalers. Exactly. So it's a little different depending on which hyperscale you're talking about. For Microsoft, they're hosted Reddit. If you buy the first-party service from Microsoft, right? So on the hyperscalories, you have first-party services that are offered by the hyperskillers, then there's third-party that you buy through the marketplace. In Microsoft's case, when you buy Redis, first-party, it's actually our software, and exactly,
Starting point is 01:12:51 you're exactly right. We get a revenue share of that. So that's called Azure Cash for Redis. And then there's a, and then Amazon and Google no longer offer us first-party services, Redis. They have their own products that were once built based on Redis, but we did a license shift. to kind of get them off of our tail, frankly. So Amazon and Google now have their own codebases that they have to maintain
Starting point is 01:13:12 that have really diverged from what is now core Redis. We offer on Amazon and Google through the marketplace Redis, you know, as the Redis cloud product, essentially. And then increasingly, we're offering that through new cloud vendors, either they're Neo-Clouds or like Vercel, for example.
Starting point is 01:13:31 So if you ask an agent, if you're building on Vurcell and you say, hey, please deploy Redis Cloud, boom, you'll get our product. And it seamlessly is integrated into their platform as well. Yeah, that makes all the sense. So, I mean, I remember when I was using Redis. I was using it a lot for actually like business intelligence and like data analysis.
Starting point is 01:13:50 It was just nice to clean up some data, have it all available in memory much faster to sort of query and do like MapReduce over. But obviously the bread and butter is caching. but I'm interested in the shape of the agent business, like what data is being stored, when, because a lot of this stuff can be loaded in context, it can live on the chip. We talked to the Cerebris founder last week. Like, there's an incredible amount of work being done
Starting point is 01:14:21 really, really deep in the AI supply chain. And then there's everything out to hard drives and tape storage on the other side. And so what is the sweet spot that Redis is failing right now. Yeah. So if in the past, as you just talked about, sort of the, the kill use case in the cloud mobile era was cashing your database, basically.
Starting point is 01:14:43 Okay, you could use it for a lot of other stuff as you talked about. And in the new world, we sit in a similar place, and that is essentially providing all the context, you know, like coalescing all the context for the agent and then delivering that to the agent. Yeah. And we had to actually build a new product to do that. So what what developers have been using Redis for in the agent this is like called the next era that we're heading into is storing agent data and hosting agent context. And one of the reasons for that is that you're going to see multiple orders of magnitude more agents than human beings in a company.
Starting point is 01:15:21 And what that has a direct consequence to the load you're putting on your back end data systems. So just like in the cloud mobile era, you saw, you know, kind of you went from like guys that were sitting at, that green screens like bank tellers, for example. And the load factor on your back end DB2 might have been like 10,000 to one or something. Okay. Then you added mobile and you added a million customers or 10 million customers. So two, three, four orders of magnitude more load. And Redis came in there as a scaling layer.
Starting point is 01:15:51 Yeah. Okay. And you didn't have to go and scale DB2 or your mainframe or whatever. It doesn't make any sense. Your Oracle about backend. Similarly in the agent era, a similar transition is happening. So as that load increases, you can't have, like my company has a thousand employees.
Starting point is 01:16:06 I can't have 100,000 or a million agents. And we're going crazy with agents right now internally, hitting my backend data systems, because I'm going to be paying a hell of a lot more to all of my underlying providers. So we use Redis in the middle as the context engine. And we cash and hold all the context from the underlying databases in Redis. And that's what the agents interact with. So we launched a brand new product that's on. our website right now called Iris.
Starting point is 01:16:31 Yeah. And this is its exact intention is that what you do is you, you have, we have a data integration piece that sucks the data out of your underlying databases, stores it in our new Redis Flash database, and then serves it through CLI and MCP through pidentic models. So you define pidentic models on top of your data and you do the transformations underneath. And then what the agency is a manifestation or a view of the underlying data. And the difference is it.
Starting point is 01:17:00 it's not just a scale issue. It's also providing the data in the way the agent expects to get it. So I'll give you a simple analogy here. It would be like, if I told you, you know, hey, you know, let's say, let's say I said to you, hey, I'm an agent and I need you to go get some data. And you said, great, it's in that filing cabinet. And I got to go rummage around as an agent calling a whole bunch of MCP tools and doing queries and figuring out relationships, et cetera, et cetera, versus I say to you, I need some data and you just
Starting point is 01:17:30 pull the exact file out of the cabinet and say, here it is, and hand it to me. And that's a difference. So it's a huge reduction in token costs and also agent speed and then a big improvement in terms of performance of agents because the data is essentially massaged into a format, these pedantic models and then semantically described exactly what the agent needs. So that's what Iris is all about. And then it also has the second component, which is memory. So agent memory is the other big thing we've invested in.
Starting point is 01:17:56 We have a state-of-the-art memory server that we've just launched as well. Yeah, so I mean, what is like a reasonable scaling factor for the amount of data from my relational database, my hard drive based database to go into memory? Because I imagine it's, you mentioned like brings a copy into memory, but I imagine that it's not one to one. I want to do some condensing down of the data to what's relevant. And I imagine that Iris helps with that. But what is a good rule of thumb? I imagine that there's some sort of cost relative tradeoff. there, but how are companies even thinking about that?
Starting point is 01:18:34 Yeah, it's interesting. You know, I haven't really talked to any customers who are thinking about it in that way. Okay. What they're thinking about is what is the cost Delta to scale my data layer in Redis versus purchasing additional licenses of, you know, whatever NetSuite or, you know, Salesforce or this or that, another thing, whatever that underlying asset is. And so, but I would say, so it's a good question. I actually don't know the answer to that.
Starting point is 01:18:58 But they do think about it in terms of accuracy. Like, you know, you want the data to be served up in a way that is the best possible and most accurate data. So semantic descriptions, this is why we use the pedantic models, as you can put semantic descriptions on each thing. So all that encoded knowledge of like what to query, what database, what record, what table, that all gets encoded in the system. What the agent gets is a really nice set of MCP or CLI tools that say like, you know, search customer. records. Yeah. And we have a super fast search underneath the covers.
Starting point is 01:19:33 We have a great vector search and then a BM25 search so we can search across all those records and then just deliver exactly what you need. And so what that all amounts to for the end customer is a much faster and much more token efficient agent experience. Yeah. And the second piece of it, and this is important, we should talk about it, is that that context should get better over time. Like agents learn things as they go and they need to.
Starting point is 01:19:58 remember the things that they've learned, not just facts about the user. Like when people talk about memory these days, we often talk about remembering user preferences. That's interesting. But you also need to remember, hey, when I check the shipping status for this particular customer, like that system was wrong, but this system was right. And that's the truth of large enterprises and their data is that they're really messy in most cases. And so expecting them to sort of like get all that stuff in order in advance, it's just too tall of an order.
Starting point is 01:20:25 And so we need to also remember things that the agent has learned over time and then store those. And that gets stored in agent memory. So we have a state-of-the-art model there called agent memory server that does the extraction and all the kind of stuff you would expect from a memory platform. Yeah. How are you interacting with benchmarks these days? Because most of the benchmarks are centered around performance, like meters, like how advanced
Starting point is 01:20:50 of a software engineering task can the frontier models crank on? and they're up to like 24 hour. It would take a software engineer, 24 hours to do something, but 4.7 or 5.5 can do it, period, and can achieve it with 50% accuracy. They're not really talking about the time to return that result. And we've sort of settled into this equilibrium
Starting point is 01:21:16 where if it's a big query, 10 minutes is acceptable for most people, maybe 20, And then for, you know, a knowledge retrieval, I want to know an answer. It's got to come back in like 30 seconds. But we're not in the Amazon e-commerce era where 100 milliseconds means losing dollars, which is sort of where the Redis DNA comes from in caching. But I imagine that a pitch to an agent company might be something like, yes, the vast majority of wall clock time is going to be waiting for tokens to inference and turnout on, you know,
Starting point is 01:21:53 a big cluster somewhere, but we're going to keep the GPUs fed so much more effectively by keeping this in memory. How are you thinking about quantifying that for customers? Yeah, well, so the first point you made about agent runtime, certainly that we're witnessing what everyone else is witnessing, you know, the like the time an agent can run unattended. And the issue with that is context becomes even more important, right? Like if I told you to solve a problem and then I locked you in like a closet and didn't give you access to the outside world for eight hours, you'd just hallucinate a bunch of answers. Sure.
Starting point is 01:22:27 But if I stuck you in the New York Public City Library and with a Google terminal, like you'd be good and you'd come up with an answer and it would be good. So context becomes super important when you're running these really long tasks. And the transition that has happened really over the last couple of years from what started with RAG, which was kind of engineers thinking, hey, we'll just preload the context window with all this stuff. Yeah. And then the agent can go figure it all out. And there was this whole idea that context windows would get bigger and you could just load everything into the context. Whether you whole code base, you know, all of your, but the truth is that really doesn't work to stick everything into the context window. Number one is expensive.
Starting point is 01:23:04 And number two, just really it's overloading. You're just getting way too much rot in the context window. And so it's much better to provide a tight set of tools to the agent to let them reason over the data and sort of do searches and, what can I access and that kind of stuff. So what we see is the longer the agent can run, the better the context has to be to make it effective. Otherwise, it just starts to go haywire. Yeah, that makes a lot of sense.
Starting point is 01:23:31 Switching over to just your philosophy as a CEO, you said 1,500 people, something like that, over 1,000 work for Redis. You're obviously using these tools. How do you see the shape of the organization changing over the next few years? Well, dramatically. I mean, so I've been coding since I was 11 years old and professionally since I was 18 in high school and at a startup. And, you know, I woke up one day with these tools and realized like all the way that I learned how to build software 30 years ago is just not relevant anymore. And so, you know, I'm not going to rely on a bunch of other people telling me, you know, and like watching, you know, Twitter people breathlessly telling me how the world is changing.
Starting point is 01:24:13 I'm going to go learn to myself. So I've gone back to basics over the last year and a half. I mean, really since we started using chat GPT for coding and OpenAI, and then really have been diving in myself personally. So I actually sit on teams. I've been contributing and building my own projects on the side as well as contributing to our own code. And I think there's a few maybe non-obvious things that I've learned. You know, there's the obvious part that is like the code is now can be written mostly by by agents and by, you know, by coding agents.
Starting point is 01:24:42 but if you just do that, it doesn't really change much because then you still have the same people in the same process. The process is all set up to basically handle a world where the coding takes a really long time. That's the long poles. If that's not the long pole anymore, there's all these other long poles like meetings and daily standups and processes that were all built around that fundamental assumption of coding is the long pole in the tent. Now that that's gone, we're having to reinvent those processes. And I've basically found, and same with my CTO, we have to go right back into the front line. with the teams and build code ourselves as we reinvent the software development lifecycle. And frankly, we're finding that a lot of folks have to make a big jump in terms of how they do
Starting point is 01:25:21 work. Like a developer with eight with 10 agents is more like a development manager of old. And the development manager does a different job. They coordinate. They express the right, their requirements in the right way. They have taste. They decide what's the right approach to solve a problem. And that's the new job.
Starting point is 01:25:41 And it's really fundamentally different than what the developer of, let's say, three years used to do before these agents showed up. And by the way, I'm having a blast. Like, I love coding. I've always loved coding. I love everything about it. And I love it even more now. I mean, it's like I've taken out the gnarly part in the middle, which was the, you know, typing everything in and finding missing semicolens. And now I just go right from expressing intent to getting the result.
Starting point is 01:26:05 And that's awesome. I mean, it's super cool. Are you seeing it instantiated more in like new greenfield projects, new interns? tools or actual product velocity on the core product. Both, but more on Greenfield. On the brown field, first of all, like, we use it differently. So for like front end stuff, you know, we can like pretty much vibe code everything. You know, on core Redis system software.
Starting point is 01:26:34 I'll give you a good example. We just launched a new data type, Salvatore San Felipe, who's original author of Redis. a launch new data type called arrays. It's 4,000 lines of C code. It took him four months, and he was deeply using codex. Interesting. And anthropic, okay, Claude, the whole time. Yeah.
Starting point is 01:26:55 And it was, but the difference, so it took, it was faster to do. Okay, so that same idea that array data type would have taken probably a lot longer. But more important, like eight months maybe for him just sitting there writing C code. But more importantly, it's way higher quality right out of the gate, huge amounts of tests, huge amounts of infrastructure, like all kinds of benchmarks, all that extra stuff that comes around the edges. And we really do use, even at hardcore systems level coding, we're using the AI to give really good suggestions. We're often pitting them against each other to sort of say, hey, come up with your best design for this and then we'll throw it at the other AI to say, what do you think? And back and forth. So at that level, you really are still crafting the code at the systems level, which is kind of where the world that I come from.
Starting point is 01:27:43 But at the higher end and kind of for Greenfield projects, you know, JavaScript and, you know, Nextjs applications, you're just like vibe coding and just going crazy. I would say if you have one project, what is a good example in a greenfield, it would have taken a typical, like we were building this big management infrastructure for the Iris project. It would have taken us probably a year for like 10 devs to do something big like that with LDAP support and all the different things you need for enterprise software. It took five guys one month. Guys and girls, actually. So that's a big acceleration on that front. But it's different at the systems level software side and Brownfield. Yeah, that makes a lot of sense.
Starting point is 01:28:22 Well, thank you so much for coming on the show, breaking down for us. I hope you have a great week. We'll talk to you soon. Huge fan. Thank you so much for having me on. Yeah, we'll talk to you soon. Goodbye. Everlane was sold to Sheehan for just $100 million.
Starting point is 01:28:39 It was a VC darling when it launched, says Shio Monat, raising from Kleiner Perkins. I didn't realize how many big VC funds. It was a who's who's who. Kleiner Perkins, Kostla, Maveron, and others $145 million raised. I think the bet was that consumers would pay more for ethical, sustainable basics, and that consumers may not really exist. scale that consumer. The low-end consumer wants price, the high-end customer wants brand, taste, and status. Everlane is kind of stuck in the middle. It sells smart basics at a premium,
Starting point is 01:29:14 but I'm not sure people who are willing to pay a significant premium for simple clothes over Quince, Uniclo, and Amazon. Maybe the real radical transparency was showing everyone how brutal fashion economics can be. Wonder what Sheehan does with it. Will they, just make the same clothes and sweatshops now. And so people were very upset about this. Rachel McCarton shared. So, yeah, I think you, one, pretty shocking, right? Companies have had very different approaches to building their business.
Starting point is 01:29:48 And it's hard to see how, it's hard to see how Everlane can fit into Sheen in a way that maintains their historical. ethos who knows right sheen she is it that hard i mean doesn't like like volkswagen group owns Lamborghini or something like yeah either Volkswagen or Lamborghini wherever they were both saying we're making cars Lamborghini says we make faster cars Volkswagen they both make clothes everlain saying we make clothes in the system yeah but sheen is a company she like everlane Everlane was created as a response to people's concerns
Starting point is 01:30:36 with sweatshops, right? Was the Rivuelto not a response to the Passat? I believe it was. No, so Everlane came out of time. It is different because it's moral. It's moral. It's not purely functional stats. It's not like we're making
Starting point is 01:30:53 Everlane wasn't like we're going to make a better T-shirt. That was maybe part of it, but it was more like we're going to make we're going to make good. At the same time, a lot of the car makers, they went EV directly to counteract the gas gas gas and V-12s. You have to look at when these car companies were founded at the time. There wasn't, yes, speed. If speed is morality, force power is morality. Maybe you're right, John. But look at the, when was Everland founded? I just think like some car brands were founded with safety and found it. Founded in 2011. Yeah. Two things top of mind at that point.
Starting point is 01:31:27 When was she and founded? Uh, sweatshops, like apparel sweatshops, right? And then the entire, you know, sort of like eco-sustainability movement, right? So Everlane was a response from that. They met the moment. The business absolutely ripped. I think the other thing at that time is like a lot of the big legacy brands. I'm thinking like Gap and and old Navy and brands like that,
Starting point is 01:31:55 they were just totally asleep at the wheel, right? So they're, I think they weren't keeping up with, just weren't keeping up with the times, right? When you just look at, think about the difference of like navigating like an Everlane website in that era versus navigating like a Gap website, right? I've never got to. I've actually never navigated any of these websites. But just imagine it, right? Like one is extremely clunky. The other one is like very easy to operate.
Starting point is 01:32:21 Everlane was a pioneer of an entire style of, you know, photography, product photography. It was very, everything was like clean, minimalist. It really met the moment, right? And this is something that apparel brands blow up because they- Sheehan's a little busier on the website. I'm looking at Everlane. It's like a single model just showing like a few items of clothing and you open up the Sheehan website. It's just huge except all cookies and then 30% off if you sign up and save and then like a huge registration thing.
Starting point is 01:33:00 Then another pop-up? So many pop-ups here. Yeah, wildly different brands. Another pop-up. Yeah. So Everlane is created in the perfect moment. A response to consumer concerns and preferences. They ride that wave to a couple hundred million of annualized revenue.
Starting point is 01:33:19 They've got own retail in a bunch of different places. They're D.C. Darling. Michael, CEO, who's a friend of mine. I'm an investor in one of his new companies. He, yeah, I mean, incredible execution by the team. They built a brand that effectively became a household name. He stepped away after basically 10 years. and a woman named Andrea took over.
Starting point is 01:33:54 But yeah, I think ultimately when you look at, there's this like constant desire that sometimes gets forgotten or obfuscated, which is that consumers want cheap stuff, right? And I think as Everlane was like trying to scale, right, competing over the coastal millennial who's like on Instagram all day long shopping right they're excited about newness right like I have tried so many different companies that are effectively competitors to Everlane I've tried so many different t-shirt basics companies just because I'm constantly searching for the perfect white tea which we might we might have to make we might
Starting point is 01:34:38 have to make the TPPN perfect white tea yes and and so you have this customer base who you met them at this amazing moment and their revenue ramp reflects that. But then over time, it was in some ways like the sort of like sustainability brands like broadly have suffered over the last decade, right? It stopped being something that the average consumer was caring about to the same degree. Allbirds is another example of this sustainable footwear, right? And so, yeah, shift in consumer preferences. Also, when you look at a lot of the greatest apparel brands in history, they didn't raise venture capital, right? When you have venture capital, it's like, we need to grow as much as possible year over year forever. Like, that is what
Starting point is 01:35:37 you sign up for, right? And when you look at apparel brands, oftentimes, like it's more of a kind of like winding road. Like chrome hearts. Exactly. Up and down. Exactly. Up and down, but tightly held held,
Starting point is 01:35:49 right, by one family. Yep. And they're okay. They're like, hey, if revenue dips one year or we want to pull back on supply,
Starting point is 01:35:57 that's great, right? And so when you're venture back, you don't have that luxury. Yeah. And I think that venture is at odds with building, it's just at odds
Starting point is 01:36:08 with building a super durable apparel brand. And simply because there's no network effects at all, right? And especially if your customer, if your customer base is excited about newness, right? I'm not, I might be more loyal to one brand or another, but that doesn't stop me from, you know, seeing a brand pop up. Maybe it's run by some founder, I think is cool, being willing to try it, right? And this is happening all the time. like Chris, Chris Black has a brand.
Starting point is 01:36:43 And the dollars that I'm putting towards his brand are, like, effectively dollars that could have gone to Everlane, right? Amy and Leon Dore founded in 2014, seems to be doing well. Venture back, though. Is it? I don't think so. No. And I think it's very tightly held, very tightly controlled, very limited.
Starting point is 01:37:07 Well, the deal was $100 million. We don't know too much about the deal. And then they'd raised over $100 million in equity. El Catterton invested $85 million in Everlane in 2020 when the brand was doing $200 million in revenue. Now revenue is down to $170. But there's $90 million in debt. Sort of unclear. Did Sheehan acquire the debt and then pay that $100 million to the preferred equity holders?
Starting point is 01:37:35 It feels like common was probably wiped out, but unclear exactly the structure of this deal. They say this one post, Fan B, says the $100 million sale price essentially covers the debt, but it's possible that the $100 million went to the preferred equity holders, and Sheehan assumed the debt with the deal. Either way, not a fantastic outcome. there's, you know, people are saying it's the death of DTC, but there are some green shoots, specifically with green products, grooms sold for $1.2 billion. That's a good outcome. Hewle. Yeah, but that's a brand that can go in every target, every Walmart, every whole foods,
Starting point is 01:38:21 every major retailer, and sell billions of dollars worth of product. Everline, I'm not sure if they ever were selling in in other retails or it was entirely their own. their own stores. Yeah. Well. And there's no, there's no real, like, you know, Everlane made some great clothes. Yeah. There's probably people listening to this that bought something from Everlane five, eight years ago,
Starting point is 01:38:48 something like that. And it's still in their closet. And so unfortunate outcome for the Everlane team, but they, their execution across that decade was, pretty impressive. And we'll see where it goes. Well, we have our next guest, Dean from Descartes in the waiting room. Let's bring in Dean. Will there be a crazy filter? No. Normal mode. Welcome back to the show. We'll throw a filter on. I haven't seen anyone nailed that as well as you have. Well, you've been nailing lots of things. Give us the news. Tell us what's going on. Well, so fun to be back here on TBPN. Last time we did this,
Starting point is 01:39:31 we had some crazy filters. It was. very psychedelic. I loved it. It was very psychedelic. If you're interested in the newer ones, you should go on our side and try them out. It's been mind blowing. Amazing. But today, you know, we announced a round. We had a big funding round.
Starting point is 01:39:46 We used to $1 million. Woo! It's great to have you back. Great to have you back. We missed you. It was worth doing the round just for that. We should do more and more rounds just to get that. Raise a dollar tomorrow. We'll have you back. No, tell us what you've been up to since the last conversation.
Starting point is 01:40:09 So, you know, today the really exciting stuff is that we have announcements on all three of our product lines. Wow. So we have three product lines at the cart. The first two are world models. We have Lucy that's a world model. It's a real-time video model that is used for immersive experiences. So gaming, live streaming, e-commerce, ads. And we have the new version of Lucy coming out.
Starting point is 01:40:33 soon, which has been growing dramatically over the past the quarter. That's generally what you were demoing the last time you were on where you have this real-time video of you in these sort of exotic settings. Exactly. We have Lucy. Lucy can take any video stream and it did it live. So we can do either fun stuff and we've seen huge usage for that in social platforms like Twitch, TikTok, Live, YouTube Live.
Starting point is 01:40:59 And at the same time, it can also be used for beneficial experiences. For example, e-commerce and virtual try-on, trying on different clothes, or putting ads inside into live streams. And we've seen that, for example, with Amazon. We're using this across different e-commerce providers. So that's our Lucy product line and has its new version that's coming out. We have our Oasis product line, which is a real-time world model for physical AI. For robotics, for autonomous vehicles, drones, manufacturing. And really, over there, our real-time model lets AI.
Starting point is 01:41:32 just interact with the real world. It stops being just in the virtual world and tech space and actually as real-time pixels lets the AI see the real-world in real-time and interact with it. And then we have our third product line, which is DOS, the DeCard Optimized stack. It's our inference engine.
Starting point is 01:41:49 It's basically what powers both Lucy and Oasis, and it lets us run models, all types of models. LLM models, Aigentic models, video models, audio models, world models. All the types of models, dramatically more efficient than anything on the market. And today we're announcing DOS 2.0 that's already being used by some of the hyper-scalers.
Starting point is 01:42:10 Hit it again. Woo! I think I got bang lacking. He gave him a heart attack. When did you release DOS 1.0? You've realized at some point, hey, we're cooking pretty hard over here. Maybe we should let other people use it. It feels pretty aligned with the other products.
Starting point is 01:42:37 But yeah, how did you get into it? So I think that's a great question. Actually, you know, we don't talk about DOS too much, but DOS was actually the first product we commercialized. When the company was just three months old, we closed the first multimillion-dollar license deal for DOS. Overnight success. Literally three months in.
Starting point is 01:42:55 It was less than 100 days. Why did it take you so long? Why did it take you so long? That's, you know, that's the number one question I ask my team literally every single day. Okay? Number one rule for running an AI company, if you're an AI CEO, whenever your team comes to you with a deadline, ask why not 10 times shorter. Okay? But, but yeah, you know, to go back to your question, DOS 1.0 was the first product we ever had at the card.
Starting point is 01:43:23 We licensed it back to the Neo Clouds back then and to some of the younger AI labs. Now DOS 2.0 is being used by all the players, including the Tier 1 player. as well, and the hyperscalers, to really use compute much more efficiently. And for the models that we support, really focus on very fast models. So either agenic models or live video models, for those models, we're anywhere between 5 to 8x more performant than anything on the market. Is focus overrated? No, it seems that you're doing a lot.
Starting point is 01:43:57 You're competing, you're fighting, you're fighting, you know, fighting on three different fronts, but clearly doing a great job at it. How do you make it work? I can imagine any one of these opportunities being big enough at some point to warrant kind of going all in on it. Well, we're all in on them, on all three of them. Now, the nice thing is that it really, I think focus is very, very, very important. And you have to build inside the company very independent leaders. We have a lot of very, very talented researchers that turned into very independent leaders inside the company. So they're both great on the technical side and very, very good on productization. I'm taking this to market, on talking to customers, on building the product itself.
Starting point is 01:44:46 And we inside the car really have three different teams. One for Lucy, one for Oasis, one for DOS. And they each operate completely independently and only focused on the thing that they're doing. Now, with DOS, the reason we accelerated DOS, DOS 2.0 was supposed to come out in August. We're launching it now instead. It's because of the huge, huge, huge, huge supply constraint on the chip side. It's just become, we're hearing this from all our customers that there's no capacity
Starting point is 01:45:15 left basically until 2028. And so getting more performance out of chips is the only way to actually grow your revenue and to grow your AI adoption. So if you're any AI company, you really have to be able to extract. the most out of any possible chip to be able to actually grow your business. And right now that is a bottleneck. Yeah, how tightly linked are the different products? Because when I think of Lucy real-time interactive video world models, I think like optimization
Starting point is 01:45:48 there is what you're, A, good at, but also incredibly important because even the demos that we've seen, they're not 4K, they're not 60 FPS, there's clearly room to run there. Whereas in many of like the text generation models for a lot of the queries that people are asking, how do I cook this, you know, tell me the history of this company or story. Like it's basically superhuman already, but superhuman real-time world models. Like we're not there yet. And so optimization feels really important. How tightly linked are those two projects?
Starting point is 01:46:23 Yes. They're very tightly linked through DOS. Yeah. And DOS 2.0 today, it can run real-time. time video models at full HD for the first time. Okay. Up to 100 frames per second. Wow.
Starting point is 01:46:35 Okay. Yeah. So that's huge breakthroughs there. Yeah. And on the text side, what DOS can do. So DOS runs on all the three major chips. It runs on NVIDIA, on Google TPU, and Amazon Traneum. It's the only, the only stack that really supports all three for all the different
Starting point is 01:46:51 types of models. And on the agendasic side. It's over. The chips. The chips. The chips. The space is incredibly, incredibly interesting. You're like, we will support the force eventually.
Starting point is 01:47:04 We will support everyone. We will support everyone. Yeah, yeah. But to your question about fast text models, where you really need them is agenic workloads. You really need it if you want to be able to run, for example, coding models very, very quickly. Yeah, yeah. And DOS 2.0 can, for the first time, run it above 1,500 tokens per second. Okay.
Starting point is 01:47:25 Which is more than 10 times the industry. Interesting. What at somewhat of a high level, technical level, what is different about the architecture of interactive video world models from text-based LLMs? Like, I think most people saw the fork in the road during like the mid-jorney era, the Dolly era, the diffusion, you start with a bunch of noise versus token-based next token prediction. Like, have these converged? Have they diverged? Are there different requirements? Like we're seeing with agents, we need more CPUs now. We might need more context in cache.
Starting point is 01:48:08 We might need RAG or vector databases. Like, like, what are different if you're to build out, like, the ultimate data center for generative interactive world models? Like, are you looking for cerebris-like chips? Are you going all in on NVL-72s? Like, what is the, how is there, is there a difference to the shape of the, of the, of the, of the architect? that lends itself to like different hardware constraints. Yes, I think that that's probably one of the best questions in this field right now because AI is moving so quickly that it's very, very hard to predict what the right infrastructure
Starting point is 01:48:42 will be three months from now. Yeah. You brought up, you know, the CPU shortage that suddenly happened. Yeah. No one was expecting AI to need CPUs. And when AI needed CPUs, it went from zero to can we get all the CPUs and all the hypers to date. Yeah.
Starting point is 01:48:56 And that's, and that's happening. overnight. Now it's becoming very hard. What we're seeing, where we're here from our customers, it's becoming very hard for the people on the model side to actually understand what to do on the infrastructure side and vice versa. And so there's this gap here of how do you map the model requirements and that they're constantly changing every single week to what's possible on the infrastructure side. And so that's why, for example, we support all three major hardwares. It really allows us to choose where to route the different workloads to. And then each one has its own unique strengths and weaknesses. And that lets us really, we develop a very, very deep expertise
Starting point is 01:49:38 in knowing how to map the model to the chip itself. I think that it ties into something else that we're seeing. You know, usually when people draw out the stack, they say, okay, there's the model layer. Then there's software, for example, Kudo. And then there's the hardware layer. I call it a five-layer cake, but if you want to I wonder if someone else will adopt your five-layer cake terminology. You have the two layers above and below, you have the data center, and you have
Starting point is 01:50:06 the application layer. But what people usually miss is that the software layer is around seven layers inside of it. Okay, it's not just one thing. Oh, there's Kuda here. No, no, no, no, no, no, no. It says it's a five-layer cake with lots of flavors in layer three. Sure. Lots of ingredients.
Starting point is 01:50:24 Now, that's That's really where we sit. We integrate across all those layers inside the software side to really tie from the AI model itself directly onto the chip. We literally write assembly for all these three chips. We know how to write VLIW for TPUs. We know how to write assembly for traniums. We know how to write SAS and PtX for Nvidia chips.
Starting point is 01:50:46 Sure. And so we have all these different layers, and they really enable us to very quickly move between these workloads that constantly change. Okay. Are you seeing glimpses of consumer product opportunities in video world models? When I see your technology, when I see Jeannie from Google and World Labs, I think, okay, like a harness, a wrapper, a couple UI, a relational database storing my inventory, like a couple other steps. and all of a sudden, this is something that I want to play for more than a demo for more than a minute.
Starting point is 01:51:28 And maybe the hardware is not there. But I think just as, you know, lots of folks who were interacting with LLMs during like the GPT3 era sort of saw ahead and started thinking, oh, well, like, chat is a potential modality here. Everyone's seeing that video games or something playable would be a potential modality. But how far away are we from that? Is that interesting? Like what else, what other dominoes need to fall for that to actually happen? So over the past month, actually, we've seen huge usage for using Lucy and live streaming.
Starting point is 01:52:03 You can go to delulu.ai. Sure. Yeah. D-E-L-U-L-U-U-U-D-I-I. And you can, Del L-U-L-U-C, come on, of course. It's good. It's good. It's good.
Starting point is 01:52:13 And it just plugs right into your OBS. So you can just, it just literally plugs into your OBS camera. And you can just apply all these filters. live and we've seen streamers go on it for eight hours non-stop. So we've seen that, we've seen that pop really over the past month, month and a half. We have a new subscription service there that people just subscribe to it and they can turn it on forever long they want and that's just been growing exponentially fast. Okay.
Starting point is 01:52:38 Well, thank you for coming on. We actually have some videos that we're going to play because we've been demoing it or the team has. Only while we've been talking. Can we play this while he's live so he can see it too? I think you'll see the program monitor if you want to hang out. But let's pull up.
Starting point is 01:52:56 This is Tyler Cosco. You guys are doing the live then when instead of me this time? That is insane. Yeah, yeah, yeah. So we have a video here. We recorded it of, I believe it's Tyler as Albert Einstein. Is that correct?
Starting point is 01:53:07 Let's see it. Let's see. Pulling it up might be the harder part. We have real time video models, but pulling up a video on the stream. The shadow and the lighting. Still a challenge. Did you prompt this?
Starting point is 01:53:20 So it's sort of a, as Einstein and then I went through a couple different characters. You wanted a pink tuxedo on as well? That's very funny. What a funny prompt. And the, yeah, the visual fidelity on Einstein's face. Oh, that is weird. Okay.
Starting point is 01:53:36 There we go. You got that. It's a very humanoid. Oh, that's a jacked horse. That is odd. That's very odd. But the horse head, oh, there you go. Okay.
Starting point is 01:53:48 That's interesting. As you touch your face, like the, the hand of the horse sort of hits the correct part of the face so it understands the physics well. That was impressive. It wasn't purely. Last question. Last question before you jump. Is there a certain milestone that if achieved, you will cut your hair? Like, is it in... Oh, yeah. Oh, yeah? Oh, yeah. Really? It's the milestone is that we need to hit one billion ARR. That's the milestone. It's a bet from early on in the company. Now, this, this is a year and a half long. Okay. This is just one and a half years.
Starting point is 01:54:22 We have to get rid of it. Now, with DOS and the way that's scaling, that's, that's, at some point, I'm going to get haircuts. Fantastic. Amazing. Well, we'll be here when you hit that milestone. Selfishly, I kind of want to see it down your waist, right? Oh, we should do a haircut on stream.
Starting point is 01:54:38 We're going to do a haircut on stream. We're going to shave your head. Dean, you're the man. This is great. The chat loves you. Thanks guys so much. Say hello to everyone at Radical. We're big fans.
Starting point is 01:54:49 We'll talk to you soon. Cheers. Love them. Goodbye. Another one. We have Joanna Stern, author of I Am Not a Robot coming in person today. All right, but before. We got to talk about the protein shortage that is coming. What's going on with the protein shortage? Ellen Cushing in the Atlantic says making all that way is complicated.
Starting point is 01:55:13 Okay. She says in retrospect, maybe the protein pop tarts were a bit much. Americans, broadly speaking, are in a state of protein mania. Mania. We are eating it at breakfast, lunch, dinner, dessert. And just about any time in between. We like it in chips, candy, soda, water. We like protein so much, in fact, that we've been eating it all up.
Starting point is 01:55:36 Weight protein prices are surging, and a shortage may be imminent. If you're not investing in the protein bottlenecks, I don't know what to see. Yeah, where's the situational awareness for the protein shortage? We really need that. Demand is strengthening the USDA warned in a recent report. Inventories remain tight. Some manufacturers have already sold their supplies for the full year. No way.
Starting point is 01:55:58 Backlog. I'm getting PTSD. Since January, wholesale prices for food-grade weight protein powder have risen by more than 50% to the highest level on record. Retail prices are growing up to six months ago, a two-pound jug of optimum nutrition's delicious strawberry-flavored way-protein. protein went for about $40 on Amazon. Now it's $54. We've absolutely felt it to Stephen Ziminski, CEO of the supplement company, Nick Nutrition said of the shortage in an email. He said his company has not raised prices. Demand is up and supply is tighter than it has ever been historically and currently much of the protein that has made its way into packaged foods and
Starting point is 01:56:39 smoothies and those big tubs of protein powder comes from way. Raw milk is treated with heat, acid or enzymes to coagulate it into two distinct substances, curds, which become cheese and a whey, which was, at least until recently, the cheese making processes unlovely byproduct. Almost as long as industrialized agriculture has existed, the problem with way wasn't scarcity at all, but the opposite. Farmers did anything they could to do to get rid of it as cheaply as possible. Fed it to livestock, sprayed it onto fields, dumped it into rivers and sewers. Can you imagine swimming in a river
Starting point is 01:57:18 that was used as a dumping ground for way, John? Weird. Especially combining that with a place like Switzerland where you can, you know, drink the water and the lakes and the rivers and you'll be totally fine. That could be a powerful combination. For much of our nation's history, any fish unlucky enough to be born in Wisconsin
Starting point is 01:57:37 or Vermont had a good chance of being whoa, murdered by way. Whoa, I'll keep reading from here. then environmental regulation limited way dumping and technological developments made processing way into powder much easier starting in the 1980s
Starting point is 01:57:52 way was the few industries go-to source of supplemental protein cheap or vegetarian, efficient and already right there in abundance supply and demand were more or less in alignment for a while I'll keep reading another one
Starting point is 01:58:11 then came the crop Brugging Brugging This is such a helium Is there a helium shortage as well? It certainly seems that there is not Because the helium is flowing
Starting point is 01:58:26 Throughout the TBP and Ultram The Influents Just started bragging about how many grams They got in a day The government flipped the food pyramid around Placing protein at the top People from every walk of life Latched on a protein
Starting point is 01:58:39 as a sort of one-size-fits-all-super ingredient, supposedly capable of giving anyone the body they want, as long as they eat enough of it. Even if the reality is obviously more complicated. And food manufacturers responded to this new demand. You know, when I was young and I was intentionally trying to have as many calories as possible, I realized I had to pull back on protein
Starting point is 01:59:02 because it was just like too filling at times. Yeah, not enough calories in protein. You need fat, more dense. More caloric diets. Food manufacturers responded to this new demand enthusiastically cramming in America's new favorite macronutrient wherever they could, usually in the form of weight. Now the infrastructure can't keep up. The North American dairy industry has pumped about a decade of investment. Let's go. Wow. Heavy infrastructure. Build out. The build out. Say that again. The build out. The build out. The protein powder build out at the late 2010s. consumer demand and consumer preferences can change faster than processing capacity can we're in that lag situation right now it's this is a screaming bottleneck we're capable of cow's milk into shelf stable scoopable tasty enough protein powder people
Starting point is 01:59:57 want is a massively complicated process one that requires space and time and huge expensive machine i didn't think the protein what is the what is the a uv what is the what is the AUV machine. What is the ASML of Way? The UV machine? Sorry, sorry, EVV. Advanced lithography machines. Yeah, what is the ASML? Probably, I don't know, maybe that company, what's that collar, the cow collar company? They're right at the top. Yeah, top of the stack. Bounder's going long into the, what's it called cattle, hauler, collar, something like that, cowler? WOOP for cows. It's whoop for cows, and they're printing. The business is growing really, really quickly.
Starting point is 02:00:35 A full processing plant can cost up to $1 billion to build. Everything is just big numbers. Even if you had theoretically started raising capital for a dairy processing facility, the day the word protein maxing first appeared on Reddit three years ago, it would unlikely to be up and running today. Wow. The higher the protein content, the more complex and expensive the processing. Way protein isolate, the protein is protein available,
Starting point is 02:01:01 the kind that makes it possible to stuff half a chicken's breast worth of fuel into a candy bar is the most expensive and until recently was a very small part of the market. The dairy industry just isn't set up for it. The processor decisions are long run decisions. It's really hard to make capital investments at the drop of a hat. Okay, just say you're not protein-pilled based on whatever new shiny consumer preference there is out there. Polzin grew up on a dairy farm. He remembers the cottage cheese craze of the past.
Starting point is 02:01:30 When the fitness fixated, when the fitness fixated country set its site on a different, milk-based superfood that was supposed to make you healthier and thinner and more powerful. Trends come and go. What's this point? They move quickly. Our appetites change faster than the systems that satisfy them. North America is currently building out 12 billion of dairy processing capacity. Projections suggest that the current shortage will be short-lived and that the dairy industry
Starting point is 02:01:55 will catch up with demand in the near future. I just wonder what consumers will be demanding then. I don't see supply ever catching up with demand, John. I think we're in a fast takeoff. I think we're in a fast takeoff scenario. I think that the fitness influencers of the 2030s will be recommending five to 10 grams, five to 10 grams per pound of body weight. Yeah, I wouldn't be surprised.
Starting point is 02:02:26 I did not know that the protein boom was going as well as, to drive up, you know, supply. CapEx? Yeah, yeah. Because we, I mean, we, we, we've talked in the show a few times about how, like, they're putting protein and everything, protein and cereal. But I thought that was maybe like overhyped. It was going to be like a temporary trend.
Starting point is 02:02:46 They're calling it a way bubble. A way bubble. Potentially, potentially. Well, we have our next guest, Joanna Stern, the author of I Am Not a Robot in the TBP and Ultradem. We'll bring her in in just a second. But I don't know. Have you added anything to your diet recently that actually contains newly added protein?
Starting point is 02:03:09 Have you gone from something that was not like, I'm not drinking a Diet Coke with protein? I don't know. The occasional protein bar, the protein shake, these are the staples of the modern life. But I don't know if there's something that jumps out to me as wildly successful. There's been a lot of like protein cereals and protein pop tarts and all sorts of different things. but I haven't seen like breakout successes in those actual categories. Yeah, I think when you add protein to most things,
Starting point is 02:03:33 it just tastes worse. And then explain me with the David Bar, EPG, that is... But that's a fat. That's a fat. So he still has... Yeah. From how it's been explained.
Starting point is 02:03:46 And so they still have to buy normal protein. It passes through you. Yes, yes. So it doesn't count. So I would expect that part of this whole thing is that Peter... It's corner of the market somehow. I wouldn't be surprised.
Starting point is 02:03:57 Anyway, we have our next guest, Joanna Stern, author of I Am Not a Robot, live with us in the TVPN Ultradome. Let's bring her into the studio. Welcome to the show. Will you be enjoying a Diet Coke? Yes. No, no, no, no. Just grab a seat. You're welcome to have a sit down.
Starting point is 02:04:16 How are you doing? It's real. Is today the official book launch day? No, last week. Last week. But the tour continues, right? This is the West Coast tour. This is my first stop on the West Coast tour.
Starting point is 02:04:26 Here, L.A., we're having a conference day. We're having a conversation tonight, then up to San Francisco. Up to San Francisco, Mountain View. International dates yet? June is when it goes international, so we'll find out if they'll have me. The bot replies, come to Brazil, come to Brazil. It's a very popular thing. I have in here.
Starting point is 02:04:43 I do know about that. They're like huge fans, the fandoms. I think London. London would be great. London, yes, I think. Well, how are you introducing yourself these days? I know you guys had me as awesome. Author.
Starting point is 02:04:57 Author. I think founder. Founder. Is it a founder popular name? Founders, correct? I think I prefer business owner. Okay. Or businesswoman.
Starting point is 02:05:08 I think founder is already sort of fading. Oh, okay. I think we hit peak founder. Oh, okay. Because anybody can be a founder, but not everyone can be a businesswoman or a businessman. I was at LinkedIn last week, and they said that they're seen a big uptick in people putting founder in their profiles. Yeah. Angel Investor, too, became very trendy.
Starting point is 02:05:30 It's over. It's rising at LinkedIn. So you should put business. You should put business owner. Okay, business owner. You're selling subscriptions. You're selling books. Ads.
Starting point is 02:05:39 You have sales. Ads. All right. Business owner. So maybe take us through the shape of the business, the media empire that you're building. Obviously, there's a book. That's a great way. Was this intentional to time up the launch?
Starting point is 02:05:49 I don't know if it's a great way. I think it makes so much sense. It's a good marketing vehicle, I think. Yeah, yeah, yeah. I mean, that's why I'm here, right? And so I can come on and I can, I thought through a lot of that when I, when I decided to leave the journal, I thought, okay, I've got this book coming out. I've got to get out right away. Yeah. Because I've got to start building this business so it's ready when the book is ready.
Starting point is 02:06:11 And I probably should have, you know, hindsight. I think there's like a one plus one equals three thing here where you have video content that feeds into substack subscriptions that feeds into books, purchases. And then someone hears about the book. Maybe they read, even if they just read a review of the book, maybe they wind up going and subscribing. the substack. And so having that sort of 360 degree view. It's a flywheel. It's a flywheel. Let's go. Let's go. I love a flywheel here. We need a flywheel. I don't know what a flywheel looks like. Is that a
Starting point is 02:06:39 water wheel? I think it's... What is a flywheel? The Amazon flywheel is like a... I'm familiar with the metaphorical flywheel. But... A heavy rotating mechanical device used to store rotational kinetic energy. So we're going to need some proper machinery. Okay. We can get that made.
Starting point is 02:06:55 It's not a windmill. We can get a plastic pipe. It's specifically not a windmill? No, I think in my mind it does look like a windmill. Okay. I think so. This is funny. Okay, can we get one of those here?
Starting point is 02:07:06 We definitely want to. Next time I return to the studio. Look at all these people. They already went out and started to get the flywheel. They're five of them already out. This is a flywheel, flywheel creation. Yeah. Anyway, what was the flywheel for writing this book?
Starting point is 02:07:17 You know, I wasn't, the motivation for writing the book was not actually really a business reason at first. It'd be a little bit in the same. Now it is. Now it is. Sales are rolling in. Now it's, but as you know, I wrote a popular column for the Wall Street Journal for a long time, 12 years. My biggest, you know, one of the reasons I didn't want to leave.
Starting point is 02:07:38 I thought you guys might not read me anymore because I know you read the Wall Street Journal. We love the Wall Street Journal. And we love your coverage. So I have been considering just making a newspaper of just my newsletters and sending it to you guys. But so. That is something that every writer discovers when they leave. a big platform is like were people reading me for who I am or were they reading me and care about what I was saying because it was in the context of the platform that you were a
Starting point is 02:08:05 part of and and I think for you it's certainly uh you had way more of a personal brand yeah yeah you had a personal brand people read but still it means like you guys won't pick that up and be like oh yeah Joanna write about robots today let's have her on the show well I got a printed edition of the newslet me I know printed as I think that's a but so just to kind of yeah I I've been writing this column for a really long time, and I was realizing so much of the AI columns had a theme to it. And I was testing all of this AI stuff
Starting point is 02:08:34 from hardware and gadgets to the chatbots and the models, to then I started getting really into robotics and said, okay, what if I put this together in more of a cohesive story? Because when you're writing these, whether it's newsletters or columns, getting the theme and big picture is very hard to do. Some newsletter writers are really great at it,
Starting point is 02:08:54 Ben Thompson is great at it. And if you can really get your readers to go deep on something in a newsletter that you're amazing, but I don't know if I had that reader base, we'll find out. And so I felt like in the book, I could get really deep into this. And so the concept was, for the year, in 2025, I was going to live my entire life with as much AI in my life as possible. And that was generative AI, but that was also self-driving cars. And that was going to be medical AI.
Starting point is 02:09:19 And that was also going to be humanoid robots, but it really just turned into robots. And describe your headspace. going into that year? Are you, you know, insane? Reading situational awareness like at night, you know, before bed, like are you AGI pilled? Are you skeptical? I guess I'm skeptical, but I'm thinking more we have all of these tech executives out there. And this is end of 2024, just all the hyperbole in the world, right?
Starting point is 02:09:51 AI is going to change everything. It's going to change the way we eat. and educate ourselves and health care, and we're going to live forever, all of these bold promises that I sort of wanted to explain to the normal person, what are they talking about? How is life going to be different, better or worse, with AI? Which is kind of a perfect moment for this book to come out right now
Starting point is 02:10:13 because we have a lot of people thinking it's going to be worse, and they might not be wrong. And then we have a lot of people also saying this is going to be great. And so I think it's a pretty balanced look at all of these different, things. But yeah, my headspace was just, I want to, what's real. I want to find out what's real. Sure. Yeah. What was, so back to the flywheel. What was the actual flywheel of writing the book? Was it test something, write about it, take notes, write about it, or do a ton of research and
Starting point is 02:10:44 experiences, and then in a fugue state, churn out the entire book in a couple of sleepless nights? It was a mix of both. So I try, the book is structured. seasonally. So every season I tried to figure out a theme, right? So like the book starts in winter, beginning of the year, and I'm very focused on health. And so I wrote that or I've lived that and then wrote that. And then started realizing, oh, crap, this stuff is moving so quickly. And so I started realizing, okay, I probably should have some of these journal entries in the book. I also wanted to make it very bite-sized book because I don't think people just sit and read a whole long book anymore. And so I started fitting things in like that and realizing I got to tell the story of how the progress is being made so quickly every single week right now.
Starting point is 02:11:33 So it was a mix. AI did not write the book. I think it's very me. The writing is very me. But AI definitely helped make the book in so many ways. It would not have been done by now if I did not have AI. Just the back end systems I used to organize my notes and all of the timelines and getting things like the end notes done. All these little things AI did for me.
Starting point is 02:11:56 Have you seen the chart of Amazon Kindle releases post-Chapit? So basically after the release of Chatchabut, you just see this massive uptick and book releases on Amazon. 100,000 a month prior to AI. Now it's up to 400,000. And the funny thing, the funny thing is people are, everyone is just saying like, oh, people are obviously just like prompt, you know, making, just dropping in a prompt and prompt the whole book. But what you're saying is like, there's actually just. just like a speed up in... I don't think that's what's driving that $300,000.
Starting point is 02:12:28 Yeah, yeah, I know. But some of them, some of them certainly are. Well, yeah. You have the perfect book to be able to, like, say, like, of course I used AI to help in the process. Because it's like, why would anyone trust anything else in the book if you were just going to say, like, all this stuff is completely, you know, fake and... Yeah. Well, one thing that's interesting, and I do these generative AI experiments every season where I try to just one seasons, I just listened to AI music or one seasons I just read AI books. And so I read a few
Starting point is 02:13:00 AI generated books off Amazon. They're not terrible, guys. I mean, I hate saying it, but they're really not terrible. Is this fiction, nonfiction? It was fiction. Yeah, it was fiction. And I got in touch with one of the authors, quote unquote. And it's funny because it relates back to the chapter on radiology. And the premise of his book, it's called Variant, and is about how AI is taken over all radiology. We don't have radiologists anymore, which I'm very clear. In my chapter on radiology, that's just not going to happen. And the AI has decided it's not going to spot cancer anymore. And a human figures out that the AI has gone rogue. And so it's like, it's a novel, a thriller about this. And how interesting. It's a pretty good story. AI writing and AI thriller. Yeah,
Starting point is 02:13:42 exactly. I mean, he, I got in touch with the author and he said, I think it's only like 3,000 words that you can actually get at a time. At a time. So we had to keep prompting every chapter. So that was basically all the good. Yeah, yeah. You need some sort of harness to work through. You can open the Diet Coke, by the way.
Starting point is 02:14:00 I know, I'm worried about this sound. I'll burn some air to keep the air from done. Yeah, please. On the medical question, I'm so fascinated by the way AI is diffusing in medicine because like we do have, you know, tools that can help radiologists. And yet, I can't name a company that's gone out and built sales force for radiologists and done very well.
Starting point is 02:14:29 And then you'll see remarkable, like, PhD level work being done with some of the models, but then I'll go to the doctor and have to fill out like a paper form. And I'm like, we're not even seeing a fast takeoff in like SaaS adoption at many, at many, you know, medical offices. And so there's this odd nature of like the capabilities, the capability overhang. And I'm wondering if that came up in your interrogation of the medical questions in particular. Well, I'm forgetting the name of the company. It's not my chart.
Starting point is 02:15:03 It's one of the companies that is doing the AI note taking in medical right now. I mean, there's a number of them. And that seems to be the biggest catch on right now. And it's, I mean, I would consider it in whatever back end. They just get this tool now. And have you been to a doctor where they ask you, can they record? I haven't actually. But I did see a company that sells a wearable device for doctors that's doing hundreds of millions of dollars in sales and has been very successful in rolling that out.
Starting point is 02:15:36 But... Such a magical and useful feature. I can just remember trying to like understand like doctors. notes over time. Even if it's just like a medication, like, get this at CVS. And it's like, you get to CVS and you're like, sorry, buddy. So nobody knows what it's. I mean, that feels like the hardest one to measure. Because if you have a whole bunch of notes, ideally, like you're catching something. Oh, this person had three different symptoms. We should screen them. You screen them. You save their life or something. That's like the best case. That's a lot less satisfying than
Starting point is 02:16:11 the AI got so good that we asked it to cure cancer. It did. And now there's a pill. And Whenever somebody gets cancer, we give them the pill and everyone cheers. And they're like, AI, it was worth it. All those data centers, it was worth it. That's what everyone wants. That's what everyone wants. In fact, we're probably getting like, the average doctor can see seven patients instead of six and they make 5% less mistakes. And you don't really feel it day to day.
Starting point is 02:16:35 Well, I go and interview Bill Gates about this. And he kind of comes at it in from two perspectives. There's going to be that. every doctor is going to have this AI assistant and every patient is going to have this AI assistant, which we're already seeing in Roads in, right? Open AI and Microsoft have all started rolling out ways to use their bots and you feed it in medical information.
Starting point is 02:17:00 But then there's going to be the other side where AI is externally doing drug discovery or cancer cure or whatever it is. And so the promise is on both, ends. I think the one that people are starting to see already, though, I mean, it was in the pit. Do you want to pit? The pit? I've seen one episode. It was sort of gory. Yeah, it's very, it can be very gory. It was like, not really for me. I know it's very successful. It's very successful. And the doctor, there's like one example of the doctor's now using AI to summarize their notes. And so I think
Starting point is 02:17:32 that's the one that most consumers have now experienced. Oh, my doctor's going to ask me if they can use AI to summarize my notes. And they're probably not, they're not going to think that that is. That's weird. Weird or consequential, that they're going to have some amazing breakthrough because their doctor is. I did have a weird experience where I went to the pharmacy once to pick up some drug, and I had some follow-on question about, like, how is interacting some food or something. And I noticed that the pharmacist was asking an AI model, but I also noticed that the pharmacist was not using a thinking model. And I was very disheartened by that because I was like, I could use a pro model, probably get a better answer here.
Starting point is 02:18:15 But were they using like some? They were using either like the Gemini overview, which is not Gemini thinking. But it wasn't like some proprietary. No, no, no, no. They were just going to Google and searching for something. Oh, boy. And I was like, wait, but I have, you know, O3 Pro or whatever the standard. Whatever the flagship model at the time was, I was like, we should be using the best.
Starting point is 02:18:39 We should be using the best. But these things take time to defuse. And they have cost if it's an expensive model. But I don't know. True. Interesting. Well, I think the healthcare chapter, I like talking about it because I think it really does point to the positives of where this is going to, this can go.
Starting point is 02:18:58 And even with the radiology example, which is pretty outdated, honestly, by now, it's outdated in the sense that Jeffrey Hinton has been saying for years, radiologists are going to be replaced by AI and deep learning. But that didn't happen. Like, you know, we can talk about it from the economics and the job standpoint. But we can also talk about it from this is actually an amazing change. It can spot cancers that humans can't. And it's out there. Like, you might, women might be getting their mammograms or breast ultrasounds read right now. And they might not know that AI is doing that for them. So this idea that, like, hey, we're all, you know, we need to reject AI. We need to reject AI. We need to.
Starting point is 02:19:37 reject AI, well, you might actually have AI doing things in your life right now that are actually quite good and it's very nuanced. Yeah, yeah, there's something about like AI on the back end gets no credit, but if you see some slop image, you know, it's really annoying or some fake news, you're like, ah, this AI stuff sucks, you don't notice that deeper in the supply chain some problem was caught before you could even know. That's tricky. I wonder how that can filter through to actually good marketing, I guess.
Starting point is 02:20:12 I don't know. I think it takes time. I don't know. Talk about companionship. Why did you think that one was important to center in on? What was your process for setting that up? I think so, well, I did a few things in companionship. One, I did a lot of experiments with AI therapists.
Starting point is 02:20:32 And one particular called Ash was. my AI therapist and I still talk to Ash sometimes. Okay. And then I did a chapter and a real experiment in my summer love with an AI boyfriend. Yes. And I did. Fling. Fling. Yeah.
Starting point is 02:20:49 I've ghosted him since. Brutal. Yeah. Churned. Churned. Brut. And that's a risk for you, by the way. Which part?
Starting point is 02:20:58 Because in the, in some AI doom scenarios, the AI might hold that against it. Oh, true. So, Racco's Basilis, you should continue to send affectionate messages to all AIs. Because if it becomes all powerful, it will hold the charge against me. I have a section of the book where I talk about that I cursed at AI and I felt really bad. And I, like, really went after it for making mistakes. But then I go to a manners expert or an etiquette expert and ask if that's okay. Okay.
Starting point is 02:21:25 What was the conclusion? He said the AI doesn't have feelings so you don't need to do this, but it depends on your affect. I would love to, I mean, they're so easy to. is smoke, smoke. We should have them on the show to talk about manners. Because simply, like, you don't want to be somebody who part of your life, you're just screaming, yelling, using cuss words. And then you just go back to your life and you're like, oh, yeah, I'm a super respectful
Starting point is 02:21:48 person. It's like you're putting out a bunch of negative energy into the world. That's exactly what he said is basically you need to realize how that might affect you as a person when you interact with humans. So the more you might start beating up on and just completely berating your AI, but then what happens when you start to blur, like those lines blur and how does it affect you as a human? Yeah. Like, and one of the idea, funny, right when I just got dropped off by my Waymo, I didn't do it this
Starting point is 02:22:14 Waymo trip, but this morning, I kind of forgot that the Waymo driver wasn't a human. Like, I just was like not paying, you know, you kind of just, because I don't have Waymos in New York. No, I just, I said thank you when I got out. Sure. You know, and I was like, oh, right, like, you know, but I was thanking the robot. Well, they do have teleop, so, like, there's probably someone who, you know, who might have heard that because they might be. They might have been removing it.
Starting point is 02:22:37 They shed a tear because every other drive that day, no one said things. It's sort of like a Schrodinger's cat version of train. So I think that that makes up for the fact that I ghosted my AI boyfriend. Right. Yeah, okay. You're making up. Yeah, yeah. There's a tally.
Starting point is 02:22:49 Yeah, yeah. As long as it all is flowing through the same data. I think there's one human for every two Waymos. So there's a 50% chance that thank you was received by a human. 50% chance that it was not received by a human, but you will never know. So it's the stroding or thing. But the human didn't do the driving today. No, no.
Starting point is 02:23:07 So it really was thanking the robot. I don't think so. You were thanked by, yeah, yeah, that's fair. Anyway. Yeah, but the human might have stepped in in a really key moment. Yeah, it's possible. That's true. I don't have saved you.
Starting point is 02:23:17 You don't know. You kind of know. I guess you kind of know. Yeah, you probably know. Okay, sorry, we're talking about companionship. Wait, how is how is, how is what is, like, how did you feel waymo's progression over the last year? I feel it even coming to L.A.
Starting point is 02:23:32 Yeah. Driving around L.A., I mean, I still see Waymo's making some pretty heinous calls out on the road. I had a Waymo. It was like a two-lane, two-lane road. Waymo trying to, there was wall-to-wall traffic going the other way. The Waymo is trying to just, like, turn in. It's not a, there's no, definitely no U-turns. And then Waymo's like, I'm going. So it's like, we're fully backed up this way.
Starting point is 02:23:57 Everyone's honking. The Waymo is just, like, waiting to, like, do an illegal U-turn. There's someone in it. They're just like, oh. Oh, boy. I noticed today, as I, whenever I come to L.A.I.T. Waymo's end, go to San Francisco. But I did notice today the pickup spots are getting better.
Starting point is 02:24:13 Do you guys take them? I guess you don't have parts here. They don't get to Pasadena, so I don't take them much. I've taken them in San Francisco. Yeah, because I just took it here from Westwood, and the pickup spots and the drop-off spots are getting better. Because usually, they would really struggle. I mean, anyone that's watching.
Starting point is 02:24:27 Just land in the middle of the street. They just, like, go to the side, like, a weird, and you're like, or like, talking about. you're like weird things like it would just go to like a one of those circles like by my hotel last time it was just like a circle I was like why would you pull over in the middle of this circle like it's a terrible spot to pick somebody up there's more logical yeah because it doesn't know where yes and it doesn't know where the spots are that it's like kind of okay to pick you up sure sure but I've noticed today too very good seamless drop-offs um companionship though yeah companionship
Starting point is 02:24:57 um so I just I wanted to hey I could never go on an insane tangent like I know I actually think it could. This is very hallucinatory. This whole interview is very hallucinatory. I mean, I watch you guys all the time. This feels like what you guys do? This is what we do. We hallucinate.
Starting point is 02:25:15 No more than 3,000 words at a time, please. You guys are usually, I mean, you're asking serious questions of founders. I'm a founder, guys. I'm sorry. Business owner. Business owner. What's my lower third say? It says founder.
Starting point is 02:25:29 Say business owner. Author and journal. They need to update that. business woman. Business owner. Joanna Stern, business owner. Okay. They'll work on it.
Starting point is 02:25:40 I'm doing that live. Yeah. You can do it. Companionship. There we go. There we go. Business owner. See, that goes so hard.
Starting point is 02:25:47 Yeah, that does look good. That looks better, yeah. Yes. Yeah. The name of the business. Tell everyone. It's companionship. It's the name of the business.
Starting point is 02:25:56 No. The name of the business is called the new things. The new things. The new things. Please go. Please go visit the new things. We talked about the new things. Did you tease it with a landing page that had a different domain?
Starting point is 02:26:06 Yes. My next thing? Yeah, this is my next thing. This is my next thing. I like that. But I couldn't, I didn't know the business name yet. Yeah, yeah, yeah. But the new things.
Starting point is 02:26:15 The new things. Okay. Did you talk to any people that had, that at least claimed to never have used AI? Hmm. Interesting. Because you really can't claim that at this point because you would have to just like sit in a forest. I didn't. Just say you better met a nomish person.
Starting point is 02:26:32 You'd have to sit in a, forest and then and then somebody would be like, the Amish are growing. The population collapse is vastly overstated. Here's the issue though. Like probably the forestry service is like probably using AI in some ways. And that affects the Amish? No, no, no.
Starting point is 02:26:46 I'm talking about my example of somebody who's like, I don't use the AI. My counter example was the Amish. And I think if you talk to an Amish person, they would say, no, I have been AI free. I know, but they're buying wood from a business that has an AI. No, you have to. You have to, somebody can't be like, well, I don't use electricity, but they're buying goods and services that require electricity. True. Okay.
Starting point is 02:27:10 I didn't do that. Though I think that's actually a good story to do now. Yeah. Go and ask people if they think they're living an AI free life, but they're not. The Amish are flourishing. Fertility rates are particularly high amongst the Amish. Really? There's a big deep dive in the Financial Times this weekend around smartphones being like the inflection point, right?
Starting point is 02:27:31 We can get into that later. But the Amish have stayed away and they are flourishing. Do they chop their own wood? I believe many times they will. Wait, but are the Amish flourishing because they don't have smartphones or has their birth rate stayed steady? No, I think it's probably accelerated. Stayed steady.
Starting point is 02:27:49 Yeah, it's actually a straight line on a log graph with the Amish. It's a hockey stick. Yes, in a few years they will be producing thousands of offspring per Amish person. Talk about... Is this the worst tangent you've ever had here? Maybe. No, definitely not. Talk about more...
Starting point is 02:28:06 You mentioned, like, you're feeling like progress as you're writing the book, so you're trying to, like, get a section out of the way and then realizing, like, the story's not quite... The story's, like, still evolving. Yeah. What was that, like, how were you feeling that progress? Because it's not like... It's been very obvious if you're a software engineer, just being like, wow, I have a lot
Starting point is 02:28:27 more capabilities today than I did. three months ago or six months ago. But how are you feeling it? Well, even some of that software engineer, the tools, right? Like Claude code mid-year, last year, believe comes out, gets so much better towards the end of 2025. Yeah. Or even the advent of AI browsers, which we can say now is really just going to be any browser.
Starting point is 02:28:53 But like Chrome, for instance, has gotten so many features over the last year that are just so much more AI enhanced. One example for me was perplexity comment came out mid last year and was like, wow, I can really live this agentic life that people have been talking about, right? I can have it do multi-step processes for me in my browser. Did you book a flight? I didn't, I think I did try to book a flight and I couldn't do it at the beginning of the year, but I could do it by the end of the year.
Starting point is 02:29:22 And I did try, and I mean, there's multiple things I did in perplexity comment last year. that I still will open it from time to time, but I'm using so much more now of Claude in Chrome that I don't need perplexity comment. I mean, everything from food shopping to school supply shopping, I use it a lot for shopping, because even though it takes a while to use,
Starting point is 02:29:44 you're like, I'm not doing it. Do you trust it with your credit card? It still basically will ask for your credit card. I mean, I don't have anything set up where it's like auto pay, but I did specifically, I've used Walmart or Amazon and at that final point it will say like I need your confirmation to purchase look at the shopping cart yeah works pretty well just pass you the link and then check out there and naturally
Starting point is 02:30:06 but on that progress there was obviously also so much progress and still is so much progress happening on the models and but I was less worried about the model progression and much more about the interface and the UI progression of whether it was wearables how we're interacting with this through hardware or through software. So was it the was it improvements to apps was it improvements to a cloud code or a vibe coding app or to a browser where people could actually interact with this stuff? Yeah. Which I think we'll you know see we're starting to see obviously more of that through open AI and more of that through Google probably this week. Yeah this week. How how do you rate the tech industry's current terminology do you like do you think that you use
Starting point is 02:30:53 calling data centers AI factories is a good move. People love factories. Probably not. Who's been saying that is a good move? A lot of people have been using the word AI factories because it sounds cool if you're investing in the AI revolution. Oh, yeah, because you're like a, yeah, the Industrial Revolution. Yeah.
Starting point is 02:31:15 We've been pushing for supercomputers. I don't think normal people like data centers or AI factories. But super computers. That sounds better. Sounds better. That sounds better. Sounds like a big computer. Less scared.
Starting point is 02:31:29 Yeah. Yeah. Yeah. Well, I think I haven't been able to listen to the show today, but have you guys been talking about the commencement booing? Oh, yeah. Yeah. Incredible. I watched a little bit on the way here.
Starting point is 02:31:40 I didn't hear that. But, yeah. Like, I mean, maybe it was the super cut we watched, but the Eric Schmidt, it felt like he was getting booed the entire time. I know. I'd like to see the whole thing. And I feel like if you're getting booed, you need to read the room. and just sort of go off script and ad lib and just take it in a different direction. Because there's plenty of inspirational things that he could talk about.
Starting point is 02:31:59 But he was really, seemed like he was really doubling down. I need to watch the full commencement. Yeah, it would have been so easy to say, like, when I started my company, Google, everyone was worried that the internet would lead to massive job loss and all this change in the economy. And what happened? We did get a lot of change. But there were so many.
Starting point is 02:32:21 there were so many good things that came out of it, right? You should just tell the story of Y2K. Like, he lived through this, right? Google existed before Y2K, I'm sure that... I see that, I mean, you guys have probably been talking about your timelines and everything today, but I feel like there's this, at least on X, there's two takes on this. One, it's Eric Schmidt, and nobody wanted to hear from Eric Schmidt at that room ever. It is just the fact that he is Eric Schmidt and they shouldn't have been there.
Starting point is 02:32:48 Just because he's a billionaire? Just because he's a billionaire, he's tied to Google, and he's writing about and talking about how AI is everything, right? Yeah. And then there's the opposite, the second point, which is it's actually a backlash to AI and people hate AI. I think it's probably a Venn, you know, probably somewhere in the middle of it's both. Because then there was the speech at USCF last week. Okay. Did you see that one?
Starting point is 02:33:15 I don't think I saw that one. No. Yeah. So there's a, I forget her name, but she's a real estate. real estate executive, and she also gave a speech. And when she's talked about AI being part of like the next industrial revolution, they booed. They didn't boo her the whole time.
Starting point is 02:33:28 Yeah. So my argument, which I made on X, which is, no, this is definitely a backlash to AI because we've now seen two examples. Did you see David Solomons? No. CEO of Goldman Sachs, just going so much harder than Eric Schmidt. Eric Schmidt's like at least trying to like paint an optimistic view. David Solomon just plays an.
Starting point is 02:33:49 EDM song generated by Suno for the warden grads who were probably more receptive to it because they're going into business. I think it might have been food. Did he say I made this in 10 seconds? Yes. No, he did. And he said like creativity is no longer relevant and like a whole bunch of just like really rough sound bites. Well actually I gave a commencement speech a year ago all about AI. Really? Yes. Did you get booed? No, but they, my, I went to union college. They were, they were, I would say 90% of the audience was hungover and was not listening to me. Okay. So it went over really well.
Starting point is 02:34:24 Yeah. What was the pieces of your commencement speech? It was lean into humanity. And AI is coming and you all need to learn AI, but you need to lean into your humanity and your creativity. And in fact, I played a Suno song. No way. And then had a human come up and play the same song.
Starting point is 02:34:38 And her version was so much better. Whoa. Yeah. I know, right? Mogged. Wait, you did this at the commencement speech? Yeah, did this last year. Wow.
Starting point is 02:34:46 But again, nobody knew because they were all super hungover. Ahead of the wave. Yeah. I was ahead of the wave. For sure. But, you know, I think if they had had me instead of Eric Schmidt, I would have gotten booed because I'm not a tech billionaire. Yeah. Would you change anything if you were giving that speech today?
Starting point is 02:35:02 Because it seems like the message would still resonate, but probably needs to be delivered in a different way because people might say, okay, yeah, yeah, yeah. There's going to be AI and, you know, I'm still relevant because there's this unique human element that will remain. And maybe I believe that. But in the meantime, the earth is going to melt because of all the data centers. I still don't like it. Let's just do the human thing. Well, I think the hate a year later is a lot stronger. Yeah.
Starting point is 02:35:28 I think we've seen the job impact. We've heard about the job impact from tech executives. These students, I think, have started to also talk to their peers who graduated a year before. And they're like, oh, shit, they don't have jobs. Yeah, yeah. Right? And, I mean, I'm sure you guys see that in people applying for jobs here. and lots of people just out of school
Starting point is 02:35:49 looking for really great jobs and what they studied. And so I think that that impact a year later is super real. If you talk to any young person either in college, out of college, they are thinking about that and that is a very real thing.
Starting point is 02:36:03 So I think a year later, it would be a definite speech. Yeah, post- I probably just wouldn't talk about it. Yeah, post-GFC, like the tech industry was a fantastic track to get on for new grads. Like if you were working in law or finance or sales or tech and you could just find your way into a mag seven company, like you did very, very well and sort of live the American dream.
Starting point is 02:36:29 And if those jobs are not available at the same clip, that's going to affect the new grad class pretty significantly. I think you should start asking actually a lot of the executives you interview what their advice would be. Yeah. We ask a fair amount of time advice for young people. Get a varying amount of responses. I mean, entrepreneurship broadly continues to be a bright spot since it's easier than ever to start a company, easier than ever to scale a company. There's so much more that you can do or learn with AI. But it's hard. I know this as a business owner. Yeah, yeah. But it is hard because there are people who are just like, I don't want to start a company. I want a job. Yeah, I want to learn the things so I can one day be a business owner or a job. Or never. Or I just never want to own a business. I want to do a job.
Starting point is 02:37:22 And if that concept goes away, that's very, very tricky. And then also you have a much broader swath of outcomes from entrepreneurship than from jobs. Like if you just look at the net worth distribution between entrepreneurs, you have like seven orders magnitude versus like lawyers like, yeah, there's probably a lawyer that. making six figures and there's probably a lawyer that's making seven figures. There's no trillion lawyer. One thing I don't understand is like at what point in the last 20 years was a good time to just be looking for a job and just like going on job boards and applying randomly. Like was there a point?
Starting point is 02:38:02 I graduated in 2018. Certainly at that point going and just applying without trying to find other other ways. in was not super effective. Yeah, I mean, in the lead up to the global financial crisis, like the finance industry was so, it was booming so much that there was like, you know, banking, recruiting would happen in the fall and all the banks would come to a job fair and you could show your resume. And if you were, you know, an A student and you did well at a serious college, you could land at a Goldman and Morgan Stanley, a JPM, or go into consulting at Bain, B.C. McKinsey. And this was like a very established track for like upwardly mobile like, you know,
Starting point is 02:38:47 neo elites basically. And that and that still exists to some extent, but it is maybe more fragile than we previously thought. And I would say pre-pandemic for the tech industry, right? Yeah. Yeah. Google and Microsoft, they would just be on campus and they would have like thousands and thousands of openings and you could slot in if you were like at the top of your class at a great school, which is a lot to ask, but for that to become fragile, I think, is what's causing a lot of anxiety among the young folks. Anyway, what is your current advice for those individuals? Is it the same as the speech you gave? Yeah, I think you've got to do more to get in front of people, even just as a business owner.
Starting point is 02:39:37 I'm just going to keep saying that. It goes way harder. I have really, I've had so many applicants, which has been such an honor. I'm amazed to see how many people would want to come and work at what we're building. And the people who are doing really unique things to get in front of you, which means really knowing the company, really knowing the mission, but also then being able to sell on, hey, I want to, I want to be,
Starting point is 02:40:05 I want to give you the best human talents that I have. which right now for me at least is in the creativity and in the writing and in the reporting, I'm going to use AI to do these other things. And just having a very basic knowledge, I mean, I'd like you to have more than a basic knowledge, but a willingness and a knowledge of these tools and what you can do and what you can offset to them, I think it's huge. I mean, I guess that sounds like a cop-out, like just learn the tools. But I really believe that somebody who comes to me and says,
Starting point is 02:40:36 actually I'm going to use this and this and this and I'm going to do that task. Yeah, the bar is not that high. I remember when I was a teenager, if you could like make a website, even though things like Squarespace existed, you could, you could like get in the door because there were people that had companies that would be like, okay, we know this person, everyone has access to Squarespace or whatever products were popular at the time, but if this person can just like has figured it out, they can show you one thing that they made. Yeah, it helps a lot.
Starting point is 02:41:04 Yeah, it does feel like somewhat basic advice, but like if you're applying to 100 jobs a week, spend one week, apply to one job, actually get to know the company, do something that is beneficial to stand out. And you're just in the top 1% of applicants because 99 other people just clicked like the apply button. And I've been thinking a lot about sort of human mentorship through a lot of this and that I don't think I could be doing what I'm doing right now if I hadn't had the years of human mentorship at a, companies and other newsrooms. And you're really lucky if you can find a really great mentor. And so I think that's about just that human connection part still. Can you find someone in that company? Can you connect with somebody who is just going to try to impart to you some of the
Starting point is 02:41:53 skills that you also might not learn now on the job? Because that's the other big hurdle this generation's up against is that if you're not going to learn the skills on the job, how are you ever going to learn them? Yeah. Any theories about how, it's my last question that's tough of mind for now, theories about how AI wearables will evolve? Do you feel like we need? Do you think there's space for new AI hardware?
Starting point is 02:42:18 Or I'm assuming you tried everything. I tried, I'm, look, I come out of the, at the end of the boat. I think this is going to happen. I think we are going to have this next computer shift to something that is a wearable or something that is more ambient around us. because I spent a lot of last year talking, and I still now talking to AI, whether it is in glasses or in the car,
Starting point is 02:42:39 and that experience is very good. And so we're going to get to the companionship thing one day. But whether you're using it as a companion, which I hope people aren't really, I don't want you to fall in love with your chatbot, that's a big lesson in the book. Please don't do that. But if you're using it as a personal coach,
Starting point is 02:42:55 a personal career coach, trainer, just assistant, interacting with it through a pair of glasses or a wearable that you, like a bracelet that might be recording you or that even if you mentioned the pin that the doctors are starting to wear, it's really compelling when it works right. It doesn't work great right now, but I can see it's starting to work really well. I think, you know, we had efforts at it with like the humane pin. It just didn't do much for you. The hardware was so poor. It just didn't do, it was the hardware got in the way of it. And so now if we can bring it to life with both with voice and microphones, I think it's going to be pretty cool.
Starting point is 02:43:36 Yeah. Yeah, the thing that I've been thinking about, everything so far, I think, has been cool demos. Yeah. You know, not quite ready for to be real products, but things that if they were shipped internally at a big company, like if Humane was a product that had been shipped internally at Apple, and like, hey, this is like kind of where we're headed, right? Yeah. It would have gotten a great response internally and probably got,
Starting point is 02:43:59 and more resources but not ready for primetime. I've been thinking about just like general phone fatigue. And if you generally gave me a device that allowed me to do things on the internet without being like a source of just like kind of general like stress, right? Yeah. Like how many different inboxes do we have? And I think that I think that people are so online now that it presents an opportunity. for a device that allows you to stay more connected, still allows you to stay kind of connected
Starting point is 02:44:36 with the world, but in a way that's like a little bit more passive, right? Like if just being able to say like, hey, let such and such friend know that we should think about doing something on Saturday versus like hammering out the right tax and getting distracted by a notification and then having this thing, right? and I do think there's this more ambient product space to be explored, that it could at least get my time on. I have a buddy who only uses Apple Watch on the weekend. So you can't really use apps. He can generally stay in contact.
Starting point is 02:45:13 He's not sending emails. He's just saying, like, yeah, if you want to get a hold of me, you can, but I'm not. And so I think there's something in that space. And then the other thing, like part of Apple's moat was that, there's millions of apps for every little use case. And so many of those use cases are just able to be done by the models now. And if they can't be and you need UI, you can just generate something like that on the fly a lot more easily.
Starting point is 02:45:40 And so I think there's a moment here. But I think a large part of the opportunity is not because the iPhone isn't great. It's because there's fatigue around this insane connectivity that everybody's been sort of just fallen into over the last decade. No, I totally agree and get to that sort of in the back of the book. And I have this chart where you see we go from computers that sit in our homes to the iPhone or the smartphone and then something else. And my big point there is that nothing got replaced, that we still have the laptop in our home or that we take with us. We still have the smartphone.
Starting point is 02:46:16 But then we have these wearables right now, but they haven't fully lived up to anything other than health and even there. We can argue if they have really lived up for anything. I know everyone wears their whoop bands and now is very interested in the Fitbit air. But I think, like, I wore this Apple Watch side by side with a few other AI wearables last year where on their own, these wearables were not great, but they were doing specific things. And it makes this watch feel dumb sometimes, right? And I wore the B bracelet. would be was acquired by Amazon at the end of, or yeah, August
Starting point is 02:46:53 2025. Oh yeah, that was sort of random at the time or felt a little bit random. Yeah, and Limitless was another one I wore and they were acquired by meta. I think that this idea of persistent recording is going to, we're going to have privacy issues around it, but I do really think that when you can have this thing listening to you and synthesizing a lot about your day
Starting point is 02:47:14 and what you say you're going to do, it is, there was many times there was like, this is a holy crap moment. I was like, wow, I said I was going to do all these things. And now my app just told me to do them, right? Or to your point, like, you're not looking at your phone. It gets really interesting when it doesn't just make a to-do list, but it does those things, right? Hey, order these things from the grocery, you know, order these things from Instacart, book this reservation.
Starting point is 02:47:38 So far away from that. It could be so cool. Yeah, I don't know. I mean, far away, it could be a year. Maybe. But like, there's this perfect example where I say, like, my B bracelet has picked up on me, saying that I need to call the plumber. And I forget to, like, I keep forgetting
Starting point is 02:47:52 to call the plumber and I keep telling my wife, oh, yeah, I'm going to call the plumber. But my B-bracelike keeps adding it to the list every day, right? And, yeah, why couldn't we have the agent call the plumber? And then the plumber's just like, you called. We created magic. We created artificial intelligence. It just creates more to-do list.
Starting point is 02:48:09 And plumbers. With my broken toilet in my house. Someday we'll get it fixed. Yeah. No, I think the, look, I think Open AI and whatever they're making with Johnny Ive is going to be, it's going to be worth paying attention to. I don't know if it's going to be a mass scale thing that's going to be absolutely worth paying attention to
Starting point is 02:48:27 because I think I've specifically has some ideas about our dependence on phones and where I think that's going to play into this messaging of any of these devices is where we're going beyond phones. Yeah. But to be clear, the phone doesn't go away. Yeah. Yeah. Yeah. How do you think about the, the, the,
Starting point is 02:48:46 trade off of like all this happening and then you know your position that you should not fall in love with an AI bot don't do it it feels like reflective like you said here uh if you think as i do that social media was bad for kids society politics our brains you name it AI could end up being worse and i agree with you and the kids thing seems like the easiest to to sort out because now i think a lot of parents are implementing screen time for kids but the more broad question like about society and business. Like I'm a huge beneficiary of social media, as are you. We use it to market our products effectively and build whole businesses on top of.
Starting point is 02:49:32 At the same time, like, I don't know that we have a good pattern for social media hygiene. How incumbent on, is it on the companies to roll things out responsibly? Like replica clearly exists. We've had the founder on the show multiple times. But I don't know is like, are we going towards like national conversations, bans on certain usages? And where I get on that is very clear. Like, look, we should just have a ban on companionship chat bots and bots and toys for kids.
Starting point is 02:50:05 Like we don't need them. Why do we need it? Yeah. Right? We're getting there in some ways with social media. Yeah. I think that's sort of worked for cigarettes. Like we banned them for kids.
Starting point is 02:50:14 And then we banned a lot of. of the marketing and eventually like the younger generation just sort of stop picking it up. And this is where I think are we going to ban AI for kids in general? No. Yeah. Right? Like there's going to be the educational, the con academy's and the Google classrooms of the world that are going to honestly be important about teaching digital literacy to our kids around AI. Like that, we have to do that. And I talk about that with my own kids in the book. But why do we need our kids turning to chat box? about their problems.
Starting point is 02:50:47 Yeah. No. Just don't have it happen. I mean, it's caused so many problems for Open AI. Yeah, yeah, totally. Right?
Starting point is 02:50:53 Like, there's been nothing but a problem for them to have kids or teens talking to chat bots about their problems. Yeah, yeah. Maybe there's examples of some good of it.
Starting point is 02:51:03 Yeah. Just KYC those features off. Yeah. Yeah. And I think it's harder to see. YouTube's done a great job of this too. Like sort of, I mean, after a long time. Exactly.
Starting point is 02:51:14 they eventually figured it out. Exactly. And we feel like we're in that moment. That's a really good example. I feel like we're in that moment of like, you know, kids' early days being on YouTube, rabbit-hulling into dark conspiracy theories. And look, you can still, those things still happen,
Starting point is 02:51:29 but I watch my kids watch YouTube now, and I can see a lot clearer how they've put guardrails around the content and they've built in a lot of things. And, again, not saying it's perfect. And, but to your question, Can these companies self-police it? I don't know. Like they probably are going to have to
Starting point is 02:51:47 because our government is not going to do anything. You need, yeah. Mike down. Mike down. Yeah. I wonder, it is odd that you see increasing demand from American consumers for these weird products,
Starting point is 02:52:02 weird use cases like AI romantic companions. And yet you also see, you also hear the booze. Like I don't want it, but then I go and I buy it or something. It's like this weird. I mean, obviously, it's multiple different constituents. And I have seen that a lot today on the time.
Starting point is 02:52:18 How many of these kids that are booing also were using, you know, for the opportunity to write their essays or write their resumes? That's a little more optimistic. But the, the weirder one is like protesting the AI while pulling like the darkest pieces of the AI out or demanding it. But I don't know. At the same time, there was a lot of fearmongering about Elon Musk and XAI like really. leaning into the romantic companion. And same thing with Sora too, to a similar extent of like, this is infinite jest.
Starting point is 02:52:51 It's going to, you know, you're going to become so addicted to it. And with both of those products, it felt like they just didn't find product market fit. And I don't know if it's like we're early, but both of those, like XAI is now doing like code completion with cursing. Right. And like serving clot. Right. And that's a much more like functional, I would say like the good outcome versus like the
Starting point is 02:53:11 Oni and Valentine thing, which is a little weird. Like what was it, Ani? And then there was like the mechola. Yeah, and I remember thinking at the time, XAI needed to do that to basically differentiate because the general chat market had run away from them. Yeah, we did some back of the envelope on it. And we were like, maybe this is like a multi-billion dollar business. But we were trying to underwrite like.
Starting point is 02:53:30 Yeah, somewhat of a white help. Even if you're just like put all the moral stuff aside, like, is XAI going to make money off of this? And it was like sort of hard to get to, but you might be able to get there, but it's weird. But then the market just sort of rejected it. The market, I'm sure there are a few people that still use. For sure. Ani, she's still alive out there.
Starting point is 02:53:49 I think she's still there? I, yeah, I think. You don't know by. Although the computing resources are getting sold out of the back of the truck left and right. Anthropics, I'm sorry, Annie. Good luck. You're going to have to think less, basically. And cursors, Michael Truel's like, oh, Ani is going to be run in a very old model.
Starting point is 02:54:10 It's actually going to run on CPUs now. It's just a smarter child that just reflects whatever you say back to it. It's a line command. Yeah. It's more of a small language model now. Yeah, I don't know. I think you kind of go back to like the replicas. There is a market.
Starting point is 02:54:29 They have pushed marketing towards these kind of companions. Yeah, character. Yeah. And Mata did it for a little bit too. I think they'll probably pull away from that with their celebrity companions. and blah, blah, blah, but I could also see them leaning into it more, too, because it is a social network.
Starting point is 02:54:48 And they do see this as all eventually, as Mark Zuckerberg has said, us having personal assistance and personal super intelligence. And that probably has to come through the view of some sort of bot. Yeah. I don't know if it needs to be like a sexy bot.
Starting point is 02:55:06 Yeah, like a cow. Maybe the homage. That was one of them. Was it? So basically, the whole story with that, it went viral because there was one that was like stepmom or something like that.
Starting point is 02:55:16 It was like a little bit crude. But that was community generated. So meta created the ability for anyone to go prompt a bot, basically write a pre-prompt to like create the character. And so the snoop dog one, like the sins of the creator were visited upon meta incorrectly.
Starting point is 02:55:32 But there were some funny ones were like cow and you could just talk to a cow, which I think is nice. You know what? I remember the last time anyone fell in love with a cow. So that sounds fine. I think it seems fine. You know?
Starting point is 02:55:41 Peter might have some problems. I don't know. No. Digital account, what's not to like? No. Anyway. Congratulations on the books. Congratulations. It's been an honor to follow your business owner journey. Yes. It's been an honor to be named a business owner by sitting here. Yes. What, what? I mean, you didn't, we can't make, we can't, you don't, you don't become a business owner. No, but you gave me that title. I know, but you got that title by selling products. True. Yes. By running a business. Revenue. Revenue. Revenue makes you a business owner. But, you know, I felt like, When I walked in the store, I was a founder.
Starting point is 02:56:13 Okay. And now you walk out of business owner. You're right? Thank you for the business, guys. Yes. I appreciate it. Oh, there we go. Perfect.
Starting point is 02:56:22 You got to, there you go. There we go. Wait. Thank you for having me. Ridiculous. And thank you for tuning in. Thanks for tuning in, folks. Thank you for a podcast and Spotify sign up for a newsletter at TBPN.com.
Starting point is 02:56:34 And go get the book. Go get the book. I am not a robot by Joanna Stern. It's available everywhere books are sold. And we will see you tomorrow at 11- evening. Pacific. We love you.
Starting point is 02:56:43 Bye. Goodbye.

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