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TBPN is a live tech talk show hosted by John Coogan and Jordi Hays, streaming weekdays from 11–2 PT on X and YouTube, with full episodes posted to Spotify immediately after airing.
Described by The New York Times as “Silicon Valley’s newest obsession,” TBPN has interviewed Mark Zuckerberg, Sam Altman, Mark Cuban, and Satya Nadella. Diet TBPN delivers the best moments from each episode in under 30 minutes.
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Speaker 2:TBPN. Is Monday, 05/18/2026. We are live from the TBPN ultra high on the technical technology, the fortress of finance, the capital of capital. Massive day to day. Tons of big stories.
Speaker 2:Five big stories I wanna go through. Obviously, the first one is that The US jury finds OpenAI CEO Sam Allman 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 OpenAI. 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
Speaker 3:I see multiple journalists on the horizon. Is Ben Bock?
Speaker 2:Joining around 11:45 today. That'll be fun to hear That's right. About the story from the ground because he went to the
Speaker 1:Apparently, they deliberated for about ninety minutes.
Speaker 2:Ninety minutes.
Speaker 1:And they didn't really make any type of statement other than Yeah. A statute of limitations.
Speaker 2: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 OpenAI.
Speaker 2:And Mike Isaac, the rat king, says unanimous verdict in Musk versus OpenAI is in after only ninety minutes of deliberation. So did they deliberate today? They showed up at nine and went from nine to 10:30 and then delivered the verdict? Is that what we think happened? Because Friday's off in the jury.
Speaker 2:Right? No Fridays.
Speaker 1:Yeah. Jury showed up this morning. Okay. Talked. Talked for ninety minutes.
Speaker 2:But they get to think about it all weekend and Friday? Interesting. Of course.
Speaker 1:Yeah. It's a full time job.
Speaker 2:I guess. It's just an interesting interesting dynamic because, you you know, you 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.
Speaker 2: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.
Speaker 4:W's in the shot. Shot. W's in the shot.
Speaker 2:Is that the opening eye Slack right now?
Speaker 1:I think that's when he is gambling in front of
Speaker 2:Oh, 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's Aschenbrenner's situational awareness hedge fund to drop the 13 f. It was supposed to go out Friday night, 5PM. Everyone was saying, oh, if he if he's not releasing
Speaker 1:throughout the entire day. Yeah. They were very excited. There was some speculation that you've been able to petition to not have to release
Speaker 2:it. That was one theory.
Speaker 1:That was one theory. The other theory is that he was just entirely in cash.
Speaker 2:Yeah. Don't need to report Just wind it down.
Speaker 1:Said it was a good run. Yeah. It's over.
Speaker 2:Yeah. Yeah. He's like, counted the oooms and there's none left to count. We're done. Pack it up.
Speaker 2:No. Quite the opposite. Leopold Aschenbrenner, 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, point five ooms per year, which translates into a thesis that AI will create unprecedented demand for compute and its associated bottlenecks.
Speaker 1:John, they're saying it is blindingly light. It is brighter in Ultra Down.
Speaker 2: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.
Speaker 2:There's been a number of times where we've gone and watched videos, and we've been very dark in the front. So we're bringing some light around. We'll see. Maybe maybe we overdid it. Maybe we'll dial it back.
Speaker 2:I need to brush my hair. My hair's a little a little scruffy today. I also need a haircut, but we'll get to that
Speaker 1:somewhat later in the show. John will be getting a haircut live Live. On the program.
Speaker 2:Potentially.
Speaker 1:But But before we go any further
Speaker 2:Yeah.
Speaker 1:Nick over the weekend Oh, yeah. Picked up a little gift for our very own Tyler. Tyler, let's open it on
Speaker 5:the video on the video Nick.
Speaker 2:Hold up.
Speaker 1:We waited in line.
Speaker 2:Look at this.
Speaker 1:We waited in line.
Speaker 2:Hey. What do we got for Tyler? We have
Speaker 1:a very long line just for you, Tyler, because
Speaker 2:What is it? Oh,
Speaker 5:I'm trying to open it.
Speaker 2:Okay. Little anti clock.
Speaker 5:Wow. It's a what is I don't know how to pronounce this. Am I reading upside down? It's a little little watch. Let's go.
Speaker 2:Watch for Tyler. Is not
Speaker 1:I don't know if you thought it might be might have been something else like the Swatch AP collaboration
Speaker 2: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?
Speaker 1:Yeah. Nick, what is it?
Speaker 6:It has a rotating bezel.
Speaker 5:That's rotating bezel. Okay. But just to
Speaker 1:be clear, it's not the It's not the it's not the
Speaker 2:pop. 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 you were mentioning that you thought it was maybe an aura loss for both companies because of the Yeah.
Speaker 1:I just Like, the the Your brand
Speaker 2:is now associated with chaos. Yeah. That's not good.
Speaker 1:Yeah. Yeah.
Speaker 2:Right? And AP, although it's exclusive, like you have to sort of wait in line. The waiting in line is like, here, have a 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 AEP
Speaker 1:Come back soon.
Speaker 2:Royal Oak or whatever. Right? Come back soon. And it's a and it's a it's a very high brow waiting in line. Yeah.
Speaker 2:And this was Yeah. They had
Speaker 1:to come out over time Just ask and for say violent. These are not gonna be limited. We're Yeah. Selling them a lot. Yeah.
Speaker 1:And so the the people that wait in line just to sell on the secondary market, think have done pretty well. Oh, really? At least in the short term. Okay. But I would expect that over time
Speaker 2:Bringing
Speaker 1:the list. Prices will will sort of retrace toward retail. I did see a funny graphic of somebody that was like Uh-huh. 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 instead of getting in the
Speaker 2:Yeah. I saw that.
Speaker 1:And then over time, you know, your your earnings really ramp out. Yep. But anyways, sorry, Tyler, if you thought that was a royal pop. I don't know why you would. Kevin
Speaker 2:O'Leary, mister wonderful two watches on one on each wrist. Looking good.
Speaker 1:There you go.
Speaker 2:I think that could be a good good daily for you. Who knows? It's got a little character to it. You make it your own.
Speaker 5:Yeah. It looks good.
Speaker 2:Sometimes a man makes a watch.
Speaker 1:Somebody should make a string that you can turn it into a royal like a royal pop. You know? Like Oh,
Speaker 2:like a lanyard?
Speaker 5:Yeah.
Speaker 2:Thing? Okay. Okay. Yeah. That's possible.
Speaker 2:Three d printing. Plenty of plenty of opportunities. Well, let's go back to Leopold Aschenbrenner and his 13 f, the infamous 13 f. 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.
Speaker 2:It's it's because it's breaking out of fin twit. It's breaking into tech teapot and techx and all of that, mostly because the a lot of the discussion centers around. The filing shows he's made some massive puts across the semiconductor sector, 2,000,000,000 on SMH, the Van X Semiconductor ETF. And so there's there's and it feels like maybe more more of a pointed thesis, less broad, hey, semiconductors are gonna 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 build out. And a lot of stuff has been priced very hotly.
Speaker 2: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 NVIDIA, which is interesting in the backdrop of is NVIDIA a car? Do they still have a moat? Well, there might still be something else going on there.
Speaker 2:You have to dig in through this and understand what's going on. But the filing is hard to interpret cleanly because an a 13 f is only a snapshot of holdings as of 03/31/2026. 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 Copy trading senators
Speaker 1:Yeah. Tends to work
Speaker 2:Mhmm.
Speaker 1:Pretty well. Right? They tend to be, you know, maybe they they 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.
Speaker 1:Sure. Whereas Leopold, he's operating a hedge fund. Right? Yeah. You don't really know.
Speaker 1:Yeah. His his holdings could could be wildly different just Yeah. You know, just just weeks or days after the end date of
Speaker 2:his
Speaker 1:The
Speaker 2:the team behind the Nancy Pelosi stock tracker, stock ticker, I forgot what it's They have one for Leopold now. Although, of course, it's based on the 13 f. It's a loose it's 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
Speaker 1:very accurate for three months ago.
Speaker 2:Maybe. Yeah. So reminder, 13 f's do disclose put and call options. They don't disclose the strike prices, expirations, premiums paid, hedge ratio, short position swaps, and 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.
Speaker 2:You're you're you know, you can only take away so much from these. So Fei Zhao, I don't know how to pronounce that, says unfathomably bad takes around this morning and a good reminder of why 13 digging is mostly a waste of time. March 31, we were in the heat of the Iran war. Makes sense to put on hedges at the time. Options exposure on 13 gets quoted notionally.
Speaker 2:So as if it were 100 delta, I. E, all 100 shares per contract. So when you see something like, oh, he owns $1,000,000,000 of Intel, it's usually he owns the right to purchase $1,000,000,000 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 convexity hedges and represent 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.
Speaker 2:Too much noise associated with the things that happened back in March that aren't relevant now. We have no idea about his turnover 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.
Speaker 2:So don't idolize people 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. Leopold's Aschenbrenner's portfolio where he sold all his holdings and went full cash. That certainly would roil the market.
Speaker 2:I do wonder, is the market actually moving on the 13 f? 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.
Speaker 1:Do you know one energy is
Speaker 2:t one energy is up on the new Seven today. And we talked to the CEO of that company. Right? T one energy is building solar panels in America. That is
Speaker 1:very exciting. Chinese company
Speaker 2:Yep.
Speaker 1:That had to Yep. Divest and turned into t one energy. Yep. And yeah, I think we first talked about t one in q four Mhmm. Of last year and done very well since then.
Speaker 2:I'm I'm excited about it. We can bridge into that in just a second. But investor Nick says, did that leprosy fella tank the market with his 13 f? 47 likes, but very, funny to just massively mispronounce Leopold's name. Anyway, where should we go from here?
Speaker 2:Options on 13 f. Everyone repeat after me. Satrini is reminding everyone that options are reported with notional value. So be careful out there. The interesting bridge is just around the AI backlash.
Speaker 2:And the fact that a lot
Speaker 1:of Situation in the chat says Bloom Energy is actually down.
Speaker 2:Well, he's on Bloom Energy for years. Or maybe not years, but like three or four thirteen f's have disclosed Bloom Energy and that one has been fully digested by the by the 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 concerned at least that this would all be fossil fuel based build out because everything else was too slow. They might be in they may be fans of nuclear.
Speaker 2:They might be fans of of 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 build out, 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 build out, but it's still 2032, you know, when we talk to folks, even even the optimistic ones. So there has been big pushback on AI data centers across the board. We've talked about this a bunch.
Speaker 2:And it's both a left and a right wing issue now. Sagar and Jetty predicted this, I think, last year when he joined our 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.
Speaker 2:That's the latest term is that they're they're they're used to spy on people. So that's an anti libertarian, anti right wing position. 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
Speaker 1:people have flagged too that both sides are using AI to create graphics to oppose data centers.
Speaker 2:That's true. Right? 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.
Speaker 1:Gabe says data centers need to be rebranded to data ranch.
Speaker 2:Data ranch. I like a data ranch. That's a good one. Anyway
Speaker 1:We got aux powered.
Speaker 2:Oh, interesting. Salty says that Leopold sold Blue Energy in the latest thirteen f. So Trimmed. Or Trimmed. So if that's if that's the case, then there you go.
Speaker 2:And, yes, Lulu does have a good breakdown of the narrative mishap, which we can go through. But the the the latest debate that I saw was over this huge data center in Utah that's being championed by Shark Tank's mister 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.
Speaker 2:I'm like, it's not gonna look like that. There's just no way. There's no point. Like, why would they ever build that? But someone dug into
Speaker 1:Or the render economy.
Speaker 2:Yeah. Someone dug into, like, the plan, and the plan actually seems pretty reasonable. But mister Wonderful, he is a he's sort of an over the top caricature of a businessman. Like, he plays one on TV. He is a real businessman, but he also plays a businessman on TV.
Speaker 2: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. 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?
Speaker 2:Yeah. 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, Kevin O'Leary is gonna do a lot of that. A lot of
Speaker 1:the heavy Mr. Wonderful in the context of developing large scale infrastructure that people are afraid of. Yeah. Sounds like a super villain too.
Speaker 2:Yes. And also, like, we can put this in contrast to, Eric Schmidt or Tim Cook, where, the the previous generation, like the major hyperscalers, like the big tech companies, they've done a pretty good job building a lot of infrastructure, making really, really bold climate pledges saying we're gonna 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 fifteen years or ten years of building AWS.
Speaker 2:And and and so now Neither
Speaker 1:of them were rocking dual iced out.
Speaker 2:You're making the case for quiet luxury. The quiet luxury of a of a Tim Cooker and Eric Schmidt, potentially.
Speaker 1:Definitely.
Speaker 2:Yeah. I mean, in this case No.
Speaker 1:I think mister mister wonderful is not not the guy to be the face of.
Speaker 2:Potentially not. But apparently, his his actual data center plans are reasonable. 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.
Speaker 2:We can pull up this video from Quick Thoughts that has a little bit of a breakdown and and goes through I think it's called I think Quick Thoughts calls it 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 we can watch this and break down
Speaker 1:Do have a link?
Speaker 2:Quick thoughts he was thinking. It's in the timeline. Because there was TikToker that was reacting to how bad it was and he is saying it's actually not that bad. So let's play this clip.
Speaker 3: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.
Speaker 7:I've been following really closely what's happening in Box Elder County, Utah, where Canadian billionaire Kevin O'Leary With
Speaker 2:Canadian. That's a big largest
Speaker 7:data center. A $100,000,000,000 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.
Speaker 2:Gigabytes. You saw that?
Speaker 7:Double the entire amount of electricity used by
Speaker 1:Well, said she said it correctly.
Speaker 3:The data center is built
Speaker 2:She's Yeah. Yeah. But but but the transcript said gigabytes, which is funny. AI fails again. We need another data center to fix that.
Speaker 1:Steve in the X Chat says TBPN Studio uses the equivalent of 23 atomic bombs of energy beams acres. Let's produce niche technology content.
Speaker 3:Is 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
Speaker 2:huge So he makes this sound good, but then it's like, wait. Are we gonna have less food? That doesn't seem that good. But the point is is that it's not taking it from, like, someone who is going to be paying water or some local community. It's like there's there's there's already water rights there that are staying in that valley.
Speaker 3: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.
Speaker 3:Texas is like 10 times more than 10 times what Utah uses. So if over the course of this project, 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 plant.
Speaker 7: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 seven to eight gigawatts of waste heat energy, meaning that the project in total will be 23 gigawatts of total thermal
Speaker 8:energy, which is the equivalent of
Speaker 7:dropping 23 atom bombs in Utah every single day.
Speaker 3:No. Also, let's Okay. Electricity generation across every state is going to have that same thermal load property. Not every generator is perfectly efficient, so they're going to generate waste heat as well. So if you say, okay.
Speaker 3:We're gonna have 23 atom bombs a day worth of electricity going off in Utah. Well, then currently, we have 230 atom bombs a day going off in Texas.
Speaker 2:You gotta put everything in the atom bomb comparison. Like, your car is, like, the size of, like, five atom bombs. Estimated that Utah's total An atom bomb is, like, maybe this big, maybe a little bit bigger.
Speaker 1:Yeah.
Speaker 2:Your car weighs as much as seven atom bombs. That's it sound so much more, like, weighty when you're, like, just comparing everything to atom bombs.
Speaker 7:By 28 degrees.
Speaker 6:This is
Speaker 2:actually pretty crazy. 28 degrees feels like a lot.
Speaker 3:Daytime temperature could increase two to five degrees throughout Hansel Valley, not the state of Utah, the valley where the data center is being built. Same with nighttime temperature, it 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.
Speaker 3: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. Worried about power cost? They're building
Speaker 1:their power. Not helping. Worrying about wasting? Helping you.
Speaker 2:But I like vegetables.
Speaker 3:Inhabited 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 uninhabited desert valley. I think the concerns in her video are just fearmongering for reasons that I hope I've explained here.
Speaker 3:Thanks for your time.
Speaker 2: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 seems like a desert. So it doesn't seem like they're growing food there. So, like, where is that water actually going? Because it's just getting piped to some other farm, like, far away? Or was it like they were
Speaker 1:way way way back in the day. Way back in the day. Yeah. You could just have a piece of land Yep. You could drill a well and you could pull up as much water as you wanted.
Speaker 1:Yeah. And then people realized that you might if if you have a property here
Speaker 2:Yeah.
Speaker 1:And there's property here here here here, they're oftentimes all pulling from the same aquifer. Yep. So you all of a sudden, if you come in, you move in next to me and you start pumping Yeah. You know, billions of Your milkshake. Yeah.
Speaker 1:You're drinking my milkshake. Right? And so it's very possible that all these parcels of land which they collectively bought, they all have their own water rights. That doesn't mean they're being used. Right?
Speaker 1:So because people will sell their water rights to like a neighboring property that is Yeah. And
Speaker 2:so 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?
Speaker 1:Well, agricultural could mean you have some like, have some cattle. Like, there's a there's a bunch of different
Speaker 2:Yeah.
Speaker 1:Potential meanings for that. It doesn't mean you're Yeah. Growing fresh produce.
Speaker 2:But were they actively using it or were they just like No.
Speaker 1:That's the other thing. I don't know. That's the other thing too. It could have been agricultural land. Yeah.
Speaker 1:But not
Speaker 2:It could have been like a failed farm. It's not farming anymore. Like a former livestock like a farm, something like that. But I don't know. I feel like people are gonna wanna go click deeper on that.
Speaker 2:Like he rebuts a lot of the good the the good rhetoric like but there's still like another another layer there
Speaker 1:Gabe the water could be used at Amonghiri. Yes. Influencers are protesting in the flats outside of Amangiri.
Speaker 2:If you drain the pool at Amangiri, it's it's going to be it's going to be a big protest
Speaker 1:Well, for
Speaker 2: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. And we should watch the video of Eric Schmidt getting booed on stage at University of Arizona. Alex Kantrowitz played a video here. I don't know if we need to watch all of this, but he says, This is incredible.
Speaker 2:Artificial intelligence 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.
Speaker 6:The architects of artificial intelligence. The question is whether you will help shape artificial intelligence. We do not know we do not know the precise contours of what this if you'd if you'd let me make this point, please.
Speaker 2:Step one. Get if you're giving a commencement speech, gotta bring a soundboard. Yeah. It'd be like, AI yeah. It's not that bad, but also, I hear you.
Speaker 2:Including
Speaker 6:the perspective of the immigrant who has so often been the person who came to this country
Speaker 2:They're really going crazy.
Speaker 6:We thought that we were adding stones to a cathedral of knowledge. And neither humanity had been constructing for centuries.
Speaker 1:There's just a low level boo the whole time.
Speaker 2:It's so rowdy. Like, normally, you think there'd be, like, a little bit of boo, and then they just, like, get quiet down. Okay? This is about to turn into a riot. This is crazy.
Speaker 2:Did he just bail on this thing? Rendering
Speaker 6:your agency. We have only
Speaker 2:seen At this point, I mean, you gotta go off script. You could you you could can't stay in a script.
Speaker 1:Funny that if you cut it up in the right way
Speaker 2:Yeah.
Speaker 1:You could make it seem sound like the most Also, could be Like, you will surrender your agency.
Speaker 2:Yeah. Okay. Now, we need to take this clip, do that thing where we
Speaker 5:Where he says he's lucky
Speaker 1:they didn't flash bang it.
Speaker 2:True. 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 it's just the same exact speech but everyone's just like, yes. This is amazing.
Speaker 5:I I could 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. Really? They were not fans.
Speaker 9:Wow. This
Speaker 2:is rough. Rough. Rough. Yeah. Not not good.
Speaker 2:I mean, the the the big thing is like I don't know that that is it it like everyone is booing for a slightly different reason, but it's like this ensemble of of problems and and grievances with AI generally. Like everyone is one thing that I'm that I've been like frustrated about is everyone is vibe coding like twenty four seven 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. Maybe like or Groupon.
Speaker 2:Like Groupon was like not a great business ultimately, but like for the first couple months of Groupon, 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 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
Speaker 6:these Do things
Speaker 1:think they were I'm just I'm just thinking that. Do you think they were do you think they were like angry at usage nano banana usage limit?
Speaker 2:Probably. Probably.
Speaker 1:Yeah. Is this whole thing just a misunderstanding?
Speaker 2:They they 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. I I need these models.
Speaker 1:I can clock it.
Speaker 2:Yeah. It's still clockable.
Speaker 5:Yeah. Yeah. At first, I I thought they were mad that like at Google, Eric Schmidt was he he was doing too too many, you know, stock buybacks instead investing
Speaker 2:Too much cash balance in addition. Yeah. Yeah. Having a 100,000,000,000 on the balance sheet in cash is just unacceptable. Yes, you get Waymo.
Speaker 2:Yes, you get DeepMind.
Speaker 1:Yeah. Because it just
Speaker 5:shows they don't know what to do with the money.
Speaker 2:Yeah. Yeah. They weren't innovating for a long time and and then that makes a lot of sense why you would boo them. Sort of the tealian.
Speaker 5:The tealian boo. Boo.
Speaker 2:No. And then and then also like yeah, the the jobs thing is super real like whether or not AI is affecting the jobs
Speaker 1:It's also so we should pull up Lulu's Yeah. 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 Mhmm. Contribute
Speaker 2:Mhmm.
Speaker 1:To AI? That's what I was like sort of that's that's what was standing out to me. Yeah. Being like, don't be afraid of this thing like jump in
Speaker 2:Yeah.
Speaker 1:And help shape it. Yeah. If you're maybe someone at in Stanford
Speaker 2:Yeah.
Speaker 1:And you have the opportunity to go actually be involved 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 Yeah. Hey, all the different career paths that I'm
Speaker 2:I would prefer terms of commencement speaker, I would prefer someone like a Sam Sulek to give the commencement speech. That would be that would be like my like Eric Schmidt is is like he's kind of like a meh. Sam Sulek, that's an inspirational speaker. That's gonna fire me.
Speaker 1:He's on the come up.
Speaker 2:Right? Exactly. Yeah. Do you have a question?
Speaker 5:Derek, more plates, more dates.
Speaker 2:That would be fantastic. Yeah.
Speaker 1:I was trying to Gabe's asking about the why would he give a speech there? I was trying to find a connection. I I think I think he's just a big name.
Speaker 2:Okay.
Speaker 1:And it's very obviously experience is very relevant in this moment.
Speaker 2:Show up to mock and none of you are getting any jobs. It's just terrible. Yeah. No. No.
Speaker 2:I mean, there there is this thing where, like, AI needs to create jobs because, like, even if AI isn't destroying the jobs, if we have a weak economy, there won't be good jobs and then like you're still hold you're still held accountable for that. And so you gotta you gotta 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 fossil fuels you're gonna have negative externalities. Diesel generators, these things are smoky.
Speaker 2:The the air quality, all of this stuff is 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 happen magically. Ben Thompson had a wild wild proposal.
Speaker 2:He he had a great great piece which I wish we had time to read through the whole thing but we we we can sort of run through it. So he starts with a with an anecdote from Politico. Texas County Southwest Of Dallas this week passed what may be the state's first county level moratorium on data centers. Not what everyone was expecting in the free state of Texas. Everything's bigger in Texas except for the data centers, 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
Speaker 1:rural rural
Speaker 2:Hill County's commissioner Hill County's commissioner vote court voted three to two Tuesday to put a year long moratorium on data center and power plant construction in unincorporated areas, citing an influx of as many as eight data centers planned there many of which could have have their own power plants. Operation opposition to data centers is spreading in regions led by both Democrats and Republicans as politicians try to balance economic development.
Speaker 1:Yes. Apparently according to
Speaker 2:Claim it over.
Speaker 1:According to AI, there's no official public count of operating data centers in Elk County, but there's eight proposed
Speaker 2:Eight proposed.
Speaker 1:Or planned data centers. So this is a place that
Speaker 2:They're gonna be delayed. Yeah. In Missouri, one small town unhappy over its city council's approval of data centers voted last month to oust all four incumbents running for reelection. In North Carolina, governor Josh Stein has made a point of saying that sales tax exemptions for data centers cost the state up to 57,000,000 per year. Texas has hundreds of data center locations operating or in development, second only to Virginia among US states.
Speaker 2: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 thoughts 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 sentiments.
Speaker 2:Seven in 10 Americans oppose constructing data centers for artificial intelligence in their local areas including nearly half, 48%, who are strongly opposed. Barely a quarter favored 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 I do think that there's an element of like Americans don't want to build anything.
Speaker 2:Like, the whole I was reflecting on the whole reindustrialization meme this weekend. I got a I got a version of that sweater mailed to me that I picked up. And and I was thinking about the actual knock on effects of reindustrialization. Like most people don't want a car factory in their town.
Speaker 1:But we do want new roads. Well, not necessarily new roads.
Speaker 2: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. Like, I don't know. Hospital? You want a bunch of people dying next to you?
Speaker 2:I don't think people want hospitals. I I literally golf courses, they have poisons. They're bad for your health. Like, I I actually think people just don't really want change necessarily. They don't want things built broadly.
Speaker 2: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.
Speaker 2: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.
Speaker 2:We're kind of good on building in America. I don't know. I I just think we're good. Like, we're just like, we're fine. It's good.
Speaker 2:Don't change anything. No new self interest.
Speaker 1:Right? When people want to build their house. Yeah. Right?
Speaker 2:When people want to create their data center. Their data center for sure. But people don't want other stuff built generally. Like there's very there are very very few things that people are like, yeah, I'd be down for that to be built. People people like the status quo.
Speaker 2:They're they're happy with things as they are and they don't like change. Like any anything new is going to be like somewhat somewhat unpopular as nuclear power was. Not building out nuclear power fifty 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, we have to do this another time, but the you know, did we run out of nuclear scientists? Was that what stopped the build out?
Speaker 2:Did we not have enough geniuses? I don't know. Maybe. We'll dig into it. But Ben Thompson has an interesting solution.
Speaker 2:He points out a bunch of ways to fix the problems of data center construction and opposition. He says, first Yes.
Speaker 1:People are saying homes in the chat, but then again People don't want really want more homes in their area once they already own a home?
Speaker 2:They block them all the time. They block they block home construction all the time. And and also permitting and also expansion of existing homes. Like these things I'm I'm not saying I'm not saying that they're like as unpopular as data centers. No way.
Speaker 2:Data centers are are are at the bottom. But but homes are something maybe in the abstract, but like new housing in communities is like razor's edge, fifty fifty, sixty forty. Like it's there is a lot of opposition to building just in America, probably. Like that's just the nature of our society. So Ben Thompson has some solutions though.
Speaker 2:What do you got to do to build a data center properly? He says, 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 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.
Speaker 2:Second, it is extremely hard to describe the benefits of inventions not yet made, cures not yet discovered, economic activity not yet engaged in, etcetera. This is always the burden of those arguing in favor of progress and the sheer potential of AI actually makes the problem even harder. Fifty years ago, everyone was like, Electricity isn't that expensive. Why do we need to build nuclear power plants? They're scary.
Speaker 2:And now electricity is expensive and we're like, Oh, we should have built those. That's the way these things always go. 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.
Speaker 2: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 of this. 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.
Speaker 2:And so it's like this weird thing. And then third, this was a wild card, which I didn't think of, but X is the 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 but it's an interesting, like, okay. And and he says, to be clear, he hasn't seen any evidence of thumb on the scale or not.
Speaker 2:I certainly haven't. 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.
Speaker 2:Simply start giving people money. 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, and 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 and he says for the data center up the road, it was expected to be 1.6 gigawatts which could generate around $3,000,000,000 in annual operator revenue.
Speaker 2: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 grossed by the data center. And 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 use users. It also highlights how relatively pathetic the original commitment that I think the data center said, hey, we'll give you 50,000,000, which is like nowhere near what that math works out to. So data center is coming to town.
Speaker 2: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 Yeah.
Speaker 1:And 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 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 And like a direct payment like that, I think, I'm sure that will happen more.
Speaker 2: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 gonna use this to build roads and hospitals and all the different things that we do. Like, I think that on net, the average American is a little bit skeptical about dollars going to the government actually benefiting them at a one to one ratio. They 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.
Speaker 1:The data center's already gonna generate a bunch of local tax revenues for that local government.
Speaker 2:Show me the money. Show me the money. That's what the the locals should potentially be saying.
Speaker 1:Well, that's that's what I'm saying. Yeah. It's like they don't, you know, the I I think it's totally fair for the local population to to think, okay, like this big infrastructure project is happening in my town even if I'm not gonna work there. Yeah. It's gonna generate some taxes Yeah.
Speaker 1:For to help improve our community. Mhmm. But show them give me the money, basically. Give me
Speaker 2:the money.
Speaker 1:Give the direct.
Speaker 2:Yeah. Go go go direct with the money. I like it. Well, we we have Mike Isaac from New York Times in the waiting room. We can come back to our data center today after we check-in with Mike.
Speaker 2:And I think he's on location. Is this correct? Mike, where are you? Welcome to the show. How are you doing?
Speaker 2:Fine.
Speaker 6:I'm good. Can you hear me? I'm sorry. I'm literally outside of the courtroom.
Speaker 2:Amazing. No. We can hear and see you. It's and clear. It's amazing.
Speaker 2:Well, take
Speaker 6:us My through actual.
Speaker 2:How has today been going? What's happened?
Speaker 6:It was crazy. Basically, today was supposed to be the first day of jury deliberations, and we were a few reporters in 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 interrupts the judge and says, hey, something's happening basically. They're scurrying, and everyone's like, oh my god, what's happening? And this is like less than two hours into it, they reach a verdict.
Speaker 6:And so the jury comes back in and and delivers the verdict.
Speaker 2:Interesting.
Speaker 1:What was your expectation going into today? Did you think you'd be hanging out at the courthouse all week?
Speaker 6:I'm hired, Bill. Yeah. Yeah. I'll see you soon. Sorry.
Speaker 6:That's lead opening eye council walking by that I should go run after, but he's doing his thing.
Speaker 1:I mean, we're just hanging it.
Speaker 2:We're just hanging chase out. Gotta him down. No. You want.
Speaker 6:I'll bug him later. Very, literally, was just chilling and walking out. I Sorry. I can't see that. Forgot what you I'm so tired.
Speaker 6:What did you ask me?
Speaker 1:Yeah. I I was I was What was your expectation for your week? Were you expecting to be at the courthouse every day?
Speaker 6:Yeah. We were Like, I got here again at 6AM 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 ten minutes notice from when the judge gets the jury verdict to get down here. I live ten minutes away, but still like no reassurances. So we had Vinny, my colleague Kate Metz, and then Natalie Rocha, another colleague of mine, just like ready. And I was just like, thank God when they when they came back because I didn't want to sleep out here.
Speaker 2: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 the core discussion or the or the talking point was Elon Musk, you can't steal a charity, very pithy phrase, easily memorizable, could stick with you or could bounce right off you, but you know what his grievance is. And then OpenAI 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.
Speaker 2:And was that like as you think back to the last three weeks, do you think that there were that there were actually good seeds planted around the Statue Of Limitations and when the case should be filed? Because it feels like from the reporting and from the viral, you know, the the screenshots and the emails and the and the quotes, like, there was never like, oh, yeah. We all remember the smoking gun of statute of limitations. No. I don't.
Speaker 2:I remember the you can't steal a charity or the Brockman diary. Right? And and it feels like we got a different outcome here.
Speaker 1:I think I I think I remember at different points
Speaker 2:Okay.
Speaker 1:Like they this this only this whole debacle only became a thing after the launch of ChadGBT and you know, the the company was showing, you know Traction. Massive traction and growth. But I never heard specifically, like you said, this statute of limitations.
Speaker 2:Mhmm. Yeah. But how do you process that?
Speaker 6:Well, that's a wonkier point too. Right? Like, it's it's very easy to And that's what I think, like, really the strategy on the must side was, 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, alright.
Speaker 6: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 OpenAI's 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 gonna be because certainly what I was thinking about, is this gonna be a battle of, the billionaires, who do you trust? Who do Yeah. You This like a character thing that is this a referendum on that?
Speaker 6:And exactly what you said, it's super surprising when they came back and essentially I would say statute of limitations was like, if that was that was the ballgame. Right? Yeah. And if they had blown 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 the whole thing, you know?
Speaker 1:Yeah. What so so last week, I was surprised that that Elon jumped on the China trip with with Trump.
Speaker 2:Oh, yeah. Was better
Speaker 1:addressed? Yeah. That that something I mean, a lot of the people online were just like, he's a billionaire. He can do whatever he wants. That was like
Speaker 2:president supersedes the federal judge. Yeah. I don't know if that's actually the case
Speaker 1:dialogue around like, hey, you're in the middle of this historic trial like Yeah. You should be present Yeah. Or at least able to be present. Mhmm. 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?
Speaker 6:I think he So so I I Yeah. NBC wrote a good story on that. Like, he was not excused. He could have been recalled and asked to testify again. Mhmm.
Speaker 6:And it's typically bad form when you leave the country to do when that happened. 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 were on a clock, so you only have so much time to to present your evidence. And the early testimony was running long, so OpenAI still needed to get through a lot of the testimony of their expert witnesses towards the end. So they decided Musk's side also decided they weren't gonna recall Musk.
Speaker 6:So, like, there was that part of it that probably made it okay. That said, like, it's probably about look, when you make it the first three days of the trial and Sam and Greg make it basically most of the time. But but at the same time, like, it didn't come down to character who pissed off the judge necessarily. It came down to, like, a legal technical argument, which seems to have. This jury was pretty sophisticated in at least in, like, focusing on something that I didn't know if it was gonna land or not.
Speaker 6:Mhmm.
Speaker 2:Yeah. Did I mean, it really makes all of the, like, the the the AI 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 are we expecting any sort of like closing statement from the judge or is this what we get here?
Speaker 6:We so by the way, sorry. There's still like people protesting in the background if you want to see that. But I'm a very terrible laptop camera.
Speaker 2:Are they protesting the statute of limitations because they're on
Speaker 6:side Protest or there's actually this has been the best part of this because like there's many different protest camps and it's kind of hard to define who is is against what.
Speaker 1:Are any of the protesting other protesters?
Speaker 6:I mean, genuinely, yes. Yes. Probably. There's the Yeah. Yeah.
Speaker 2:Out there protesting the diesel.
Speaker 6:Genuinely, there there were supporters. No, a 100%.
Speaker 2:Yeah.
Speaker 6:So, actually, usually post trial, you people like me go and try to find the jury and chase them down, which is what we were doing. Mhmm. I think they probably are already out of the building. Yeah. I ran around the back and saw a van that was like all blacked out and this marshal that I had known the whole trial and like they were just like getting the hell out of here.
Speaker 6:So I'm guessing they didn't want to get mobbed by Yeah. Us. But the judge I'm gonna try to get the notes out. The judge left the jury with like a pretty good summation, not of the trial, but just like appreciating a jury
Speaker 2:Sure.
Speaker 6:And like respecting a jury finding like finding parties liable or not 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 your verdict out or whatever, but she respected the jury. Was 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.
Speaker 2:Yeah. Yeah. That seemed also a little bit unexpected because when the jury verdict became popularized or publicized 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.
Speaker 6:And I think that's really important as far as appeals go because you could argue
Speaker 2:By his charge.
Speaker 6:Cut out. Like, you could argue like, oh, the the judge yeah, exactly. The judge didn't care, the so jury I think there's real incentive to be in line.
Speaker 2:Yeah. Yeah. That's very interesting. What was the snack set up today? Are you gonna get a proper lunch now?
Speaker 2:I feel like
Speaker 6:Oh my god.
Speaker 2:I I feel like that was one of the most disappointing arcs if I'm gonna be completely honest with you. The lunch game just didn't seem to evolve.
Speaker 1:You were saying that You were learning from your lessons know the Nathan all you've got you know the Nathan for you episode where he's got the chili We
Speaker 2:were gonna do that for you. Because it just felt like they day okay. Day three, you show up with an apple and a banana. It's like, okay. He's still learning his lesson, but, like, fool me seven times.
Speaker 2:I was expecting a Chipotle burrito or something with a little more substance. Get get into the four digits of calories, please.
Speaker 6: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. And then today, I forgot.
Speaker 6:I was out last night until way late at a show and I'm hungover and I forgot to bring food. So it's just this is basically my you get to see my slow descent into madness. But thank God we're done.
Speaker 2:Okay. So we I mean, we asked you earlier, is this the stuff of movies? Is there gonna be a movie about this or was this anticlimactic?
Speaker 6:I think like I think this that the movie is still going, man. Like Yeah. This thing is still there's so much. I feel like this is an exciting time in AI because OpenAI 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.
Speaker 6:So like, if anything, it's a brief reprieve, you know? But I wouldn't I wouldn't make the movie now. I'd wait a wait a couple of years.
Speaker 2:Okay. Okay. Anything else, Jordy?
Speaker 1:The story continues.
Speaker 2:Story continues.
Speaker 1:I'm expecting to see model wise around San Francisco that say, bought this after Elon lost his landmark trial against OpenAI. Yes. The sticker. New bumper sticker.
Speaker 6:Right on.
Speaker 2:Well, have a great rest of your day. Thank you so much for taking the time.
Speaker 1:We'll talk to you, Great
Speaker 2:to see you Flint forecast.
Speaker 6:Next time.
Speaker 2:We'll talk to you soon. 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 less robust answers potentially. But this is not a lot, less than 50¢ per million tokens.
Speaker 2:It will accomplish four things at least. It will push the big AI players to optimize tokenization, caching, 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 $10,000,000,000 a year to start. But over the next ten years could grow 30 x to a 100 year a 100 x. So he's he's thinking two orders of magnitude in a decade in terms of growth for AI. That's low end of what a lot of people think.
Speaker 2: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. Consumers will have the ability to choose between providers or do or to do everything using open source models locally, which I guess wouldn't be taxed.
Speaker 5:What do you This is kind of like the opposite of what we were saying before of like going direct, right? Yeah. Because we were saying, okay, you know, the actual data centers are gonna make so much revenue. You can just tax the data centers and then the the money goes to the local community and then that that's where you see the benefits. But isn't this going like up the chain even more so you're taxing the companies?
Speaker 5:Yeah. So then people in the community like definitely won't it like the the money will be like so abstract if it's at the federal level. Yep. I feel like this is the wrong way.
Speaker 1:Here's something here's something else.
Speaker 2:You should
Speaker 1:be giving people You should get
Speaker 5:a check from Open Anthropic every month maybe. That's I think the better version of his. Sure. If you wanna
Speaker 2:tax the company.
Speaker 1:Sure. What if we tax companies, you know, what what if what if we had something like like a sales tax or, you know, what profit. What if when when Income tax. Yeah. Like if someone when someone paid, what if some of that money went to the government to help pay for public, you know, services.
Speaker 1:And maybe even if your company is doing really really well, then you could take a percentage of their profits. Yeah. Because that
Speaker 2:company And has investors sold their stakes, they would
Speaker 1:They would also
Speaker 2:a tax.
Speaker 1:Pay on
Speaker 2:whatever gain
Speaker 1:And then every single what about every single underlying vendor that the company you had the same sort of like structure for every underlying
Speaker 2:company if NVIDIA serves sells a bunch of GPUs and they make a bunch of money, they don't
Speaker 1:to pay
Speaker 2:tax on the profits on that.
Speaker 1:Yeah. Or even somebody like a contractor that, you know, manages a building. Sure. Right? So they have a, you know Yeah.
Speaker 1:Maybe it's a small local business. Yep. They manage an office
Speaker 2:million dollars.
Speaker 1:Some of that.
Speaker 2:Are only half 1,000,000. Yeah. That that half 1,000,000 profit, that gets taxed.
Speaker 1:Has anyone thought of that?
Speaker 2:That might work. Anyway.
Speaker 1:And then you could use that money to sort of, you know, cover the costs of operating the government and
Speaker 2:then Potentially.
Speaker 1:Even potentially use some of the extra to pay down the debt.
Speaker 2:Potentially. Well, Palmer is going back and forth with Mark Cuban about this. Palmer Lucky says there are already massive economic incentives to optimize. This is just a tax on American companies that makes foreign models and products more attractive along with creating the infrastructure for government to track all AI usage and punish anyone who doesn't report. Mark Cuban says those incentives change over time.
Speaker 2:Right now, the incentive is to grow and spend market share over optimization. You know this. Don't 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 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 you referring to?
Speaker 2: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.
Speaker 2:And my business is one of the ones that would benefit because he's not token heavy. That is an interesting
Speaker 1:Semi analysis says 50¢ per mTalk is a lot of money. Mark, are you considering considered cash hit on prefill or just output tokens?
Speaker 2:These are the hard questions.
Speaker 1:Thank you. Steven says imagine a bit tax in 1995.
Speaker 2:Yes. Flops tax. I don't know. What what else is going on in the AI slop world? The bot farms
Speaker 1:What about every time you how about this? What about every time you move your cursor, it's just 1¢?
Speaker 2:Right? Yeah. I don't know. Tax on something.
Speaker 1:It's pretty funny. Was saying last week when I was saying like you're basically reinventing the US postal service. Yeah. 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
Speaker 2:break through.
Speaker 1:Doesn't break through.
Speaker 2:Well, the bot farms have figured out anti a a I anti data center posts on Facebook engagement. But ironically, they're using AI Slop to do it. You don't know this is AI Slop. This might be the most perfectly designed set of stones ever visited upon a beach. It's not worth thing it's not worth giving up an inch of this to a data center Indiana.
Speaker 2:Breaking an Indiana resident of reportedly arranged stones to make an anti data center message. This is 99% slop. And this one is really sloppy. Wow. Wisconsin's forest farms, lakes, rivers, small towns, not a single square inch of Wisconsin is worth giving up for an AI data center.
Speaker 2:Interesting that the I in is is capitalized makes me think that that was added after the fact, but the rest is pretty sloppy, but kinda beautiful. I kinda like the perspective on this image with the big farm and the the barn in the background and the
Speaker 1:This makes me wanna visit Wisconsin.
Speaker 2:Yeah. Does Wisconsin actually look like this? If it does, perfect place to build a data center. Yeah. That's the only thing that's missing.
Speaker 1:Well No. I want yeah. We we have to go and find we have to go find the the the the the ugliest 10,000 acres in Wisconsin. New challenge, Tyler.
Speaker 2:We gotta we we gotta cover Everlane. We gotta cover Everlane. There was a big Well,
Speaker 1:just to close out, should we should we should we cover this post from Ken Griffin that was going
Speaker 2:I wanna 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. The reports that we get from AI models are not actually 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.
Speaker 2: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 is being done by AI agents over the course of hours or days. And it's seen as sort of a black pilling moment because he says like I got home and I was sort of I gotta tell you I went home one Friday. Barely depressed by this because you could see how this was gonna have such a dramatic impact on society. And it is like a weird moment and it's sort of like, oh okay, he's he's waking up.
Speaker 2:But then if you actually watch the full interview, this is one minute from a forty minute interview or something. And he goes on to enumerate a whole bunch of different benefits and where he is allocating his workforce. And also Citadel's in a very interesting game theoretic dynamic where it's not they are not a monopolist. So by definition, like they are in competition with all their other funds. And so there's there there is a world where like, even if they're getting incredible value out of AI, they wind up using AI and humans in conjunction to compete because we are in like the centaur era, which is sort of what he enumerates.
Speaker 2:But anyway, what
Speaker 1:did you one one thought I had is that, you know, Citadel has, you know
Speaker 2:AI psychosis. That's what you're saying.
Speaker 1:No. They they have a team of thousands of, you know, PhD level talent
Speaker 2:Yeah.
Speaker 1:That are doing things that AI can do pretty well now. And him driving home on a Friday Yeah. Being depressed, part of me was thinking, is he depressed because he realizes everyone will soon have access to a thousand That's people with PhD level talent that they can turn on. Yeah. And maybe they can't cover the whole market.
Speaker 1:Yeah. Obviously, you know, he talked a lot about, you know, how much software there is to build. He's like, we'll never build enough software. Yeah. But at the same time, he was thinking like, wow, this this like resource that I've accumulated, this like capability, this like this this team, when AI can do what they do and everyone can access AI, like, how is my business gonna change?
Speaker 2: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 and I and I wound up being able to do a lot of other stuff.
Speaker 2: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 some research in the front office. But Citadel's Edge is more than just it's more than just research.
Speaker 2: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.
Speaker 1:Yeah. Scale.
Speaker 2:Yeah. There's a lot
Speaker 1:of So
Speaker 2:I don't know. It's interesting.
Speaker 1:Yeah. Let's let's talk about Everlane.
Speaker 2: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 12:45.
Speaker 1:Let's do that.
Speaker 2:Okay. Well, let's bring in Rowan from Redis because he's waiting in the waiting room. Rowan, welcome to the show. How are you doing?
Speaker 4:I'm doing great.
Speaker 2:Thanks for being here. Since it's your first time in the show, I've I've actually Are been you
Speaker 1:getting an act is this an active sauna session for you?
Speaker 2:It does look like a sauna background. No.
Speaker 4:I'm in Tenerife today actually. So
Speaker 1:Oh, amazing. Yeah.
Speaker 2:The wood paneling behind you really does look like a
Speaker 1:I like it. I like it. Well, yeah. We'll see in in in a few minutes if you start sweating.
Speaker 4:I'll be doing the cold plunge next.
Speaker 2:We'll see how
Speaker 1:that goes. Well, some people in tech do combine cold plunges with
Speaker 2:That's happening.
Speaker 1:Talking about their companies. Yeah. Yeah. Yeah.
Speaker 2:For sure.
Speaker 1:That's great to great to meet you.
Speaker 2:Great to meet you. I've I've used Redis a ton about a decade ago. 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.
Speaker 4:Yeah. Absolutely. Thanks, guys, for having me on. It's an honor to be on. I loved your show.
Speaker 4:Amazing. Big fan and watch all the time.
Speaker 2:Great.
Speaker 4:Yeah. So I'm the I I head up Redis, and we're one of the sort of core infrastructure components that, you know, has been around. We're one of the one of the bigger open source projects Yeah. Over the last fifteen years and sort of helped build out a lot of the Internet infrastructure and got a great team. We have just about we're three 1,500 people now, and we're starting to see a lot of there you go.
Speaker 2:That's great.
Speaker 4:We're starting to see a lot of traction in the AI world as people are starting to really build out agents.
Speaker 2:So as you
Speaker 4:guys were just talking about, you know, lots of opportunity there and and Is sort of being pulled in on agent data.
Speaker 2:Yeah. I wanna get to that. Is is is the is the correct framing for Redis for people who might not have actually used the product in memory key value storage, like nonrelational databases, think like MySQL, but less structured and also held in memory, therefore faster?
Speaker 4:Totally. You nailed it. It it the history of of it, it's a it's an in memory data structure server. It's not really a database. Yeah.
Speaker 4:But it's been treated as a database, and the killer app that kinda took off and made Redis a part of kinda got the tendrils into all the applications on the whole Internet was was key value Yep. Used being used for caching. Yep. So it originally started as an in memory database. The big thing that's changed, though, and this is just coming live now is over the last few years, we've re architected Redis and launched a new product
Speaker 2:Mhmm.
Speaker 4:That uses Flash as the back end storage. And so now we have the fastest, the world's fastest Flash object store. And and so that's that's a new thing. And that was really driven by AI because we were seeing huge demand for way bigger way way more data. And just and also RAM prices have gotten crazy.
Speaker 2:Sure.
Speaker 4:And NVMe cost performance has improved dramatically.
Speaker 2:Sure. Okay. So then take me through some of the history of the business. I know you joined as CEO like in the modern era. But in terms of that transition, what is the shape of the business?
Speaker 2: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 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 what how would you describe it, shape of the business around the product right now?
Speaker 4:Yeah. So we're it's a great question. We're still a open core company. So we have an open source base, which is Redis. Anyone can download it and use it for free.
Speaker 4:It's used all over the place for free. And then we have a paid version. So for example, the the recent innovation I mentioned, the rewrite using Flash Sure. Story, that's that's not for free. That's something that you would pay for.
Speaker 4:We have a lot of performance advantages in the paid version. We have a hosted version. It runs on all three of the the major clouds. So if you get Redis on Amazon or Google or or Microsoft Azure, right, we we provide we we have our own version essentially that runs on those clouds. Mhmm.
Speaker 4:And so that's that's that's the 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.
Speaker 2:I like it. That's a good that's a good salesmanship. So the the relationship with the hyperscalers, is that, like, consumption revenue that's coming to you? I set up an an AWS instance. I pull Redis off the shelf from the dashboard of a million different tools.
Speaker 2:And then Yeah. As I'm using it every month, stuffing more and more data into it, that money is flowing to you from the hyperscalers?
Speaker 4:Exactly. So Yeah. It's a little different depending on which hyperscaler you're talking about. For Microsoft, they're hosted red if you buy the first party service from Microsoft. Right?
Speaker 4:So on on the on the hyperscalers, you have first party services that are offered by Mhmm. The hyperscaler themselves, 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 and exactly you're exactly right. We get a revenue share of that.
Speaker 4:So that's called Azure Cash for Redis. And then there's 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 Mhmm. To kinda get them off of our tail, frankly. So Amazon and Google now have their own code bases that they have to maintain that that that that have really diverged from what is now core Redis.
Speaker 4: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 their NeoClouds or, like, Vercel, for example. So if you ask an agent, you're building on Vercel and you say, hey. Please deploy Redis cloud. Boom.
Speaker 4:You'll get our product, And it it seamlessly is integrated into their platform as well.
Speaker 2:Yeah. That makes a lot of sense. So, I mean, I remember when I was using Redis, was using it a lot for, actually, like, business intelligence and, like, data analysis. 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's caching, but I'm interested in the shape of the agent business, like Yeah.
Speaker 2: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 Cerebrus founder last week. Like, there's an incredible amount of work being done really, really deep in the in in the in the AI supply chain. And then there's everything out to the hard drives and tape storage on the other side.
Speaker 2:Yeah. And so what is the sweet spot that Redis is filling right now?
Speaker 4:Yeah. So if if in the past, as you just talked about, sort of the the the kill use case in the cloud mobile era was caching your your database Mhmm. Basically. Yeah. Okay.
Speaker 4: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've we had to actually build a new product to do that. So what what developers have been using Redis for in the agent this, like, called the next era that we're heading into is storing agent data Mhmm.
Speaker 4: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. Mhmm. 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, kinda you went from, like, guys that were sitting at at green screens, like like bank tellers, for example.
Speaker 4:And and the load factor on your back end DB two might have been, like, 10,000 to one or something. Okay? Then you added mobile, and you added a million customers or 10,000,000 customers. So so two, three, four orders of magnitude more load, and Redis came in there as a scaling layer.
Speaker 2:Yeah.
Speaker 4:Okay? And you didn't you didn't have to go and scale d p two or your mainframe or whatever. It doesn't make any sense. You oracle that back end. Similarly, in the agent era, a similar transition is happening.
Speaker 4:So as that load increases, you can't have like, my company has a thousand employees. I can't have a 100,000 or a million agents
Speaker 2:Mhmm.
Speaker 4:And we're going crazy with agents right now internally
Speaker 2:Yeah.
Speaker 4:Hitting my back end data systems because I'm gonna 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
Speaker 2:Yeah.
Speaker 4:And we cache 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.
Speaker 2:Yeah.
Speaker 4: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 Pydantic models. So you define Pydantic models on top of your data, and you do the transformations underneath. And then what the agent sees is a manifestation or a view of the underlying data. And the difference it it it's not just a scale issue. It's also providing the data in the way the agent expects to get it.
Speaker 4: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.
Speaker 4:It's in that filing cabinet. And I gotta go rummage around as an agent calling a whole bunch of MCP tools and doing queries and figuring out relationships, etcetera, etcetera, versus I say to you, I need some data, and you just pull the exact file out of the cabinet and say, here it is. And hand it to me. Yeah. And that's the difference.
Speaker 4:So it's a huge reduction in token costs. Yep. And also agent speed and then a 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.
Speaker 4:So agent memory is the other big thing we've invested in. We have a state of the art memory server that we've just launched as well.
Speaker 2:Yeah. So I I mean, what 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's not one to one. I wanna do some 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?
Speaker 2:I imagine that there's some sort of cost relative trade off there. But how how how are how are companies even thinking about that?
Speaker 4:Yeah. It's interesting. You know, I haven't really talked to any customers who are thinking about it in that way.
Speaker 2:Okay.
Speaker 4: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
Speaker 2:Sure.
Speaker 4:You know, Salesforce or this or that other thing, whatever that whatever that underlying asset is. And so but I would say so so it's good question. I actually don't know the answer to that. Yeah. But they do think about it in terms of accuracy.
Speaker 2:Yeah.
Speaker 4: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 is you can put semantic descriptions on each thing. So so 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.
Speaker 2:Yeah.
Speaker 4:And we have a super fast search underneath the covers. We have a great vector search and then a b m 25 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 Mhmm. Is a much faster and much more token efficient agent experience.
Speaker 2:Yeah.
Speaker 4:And 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 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.
Speaker 4:When I when I checked the shipping status for this particular customer, like, that system was wrong, but this system was right. Mhmm. 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 sorta, like, get all that stuff in order in advance, it's just too tall of an order. And so we need to also remember things that the agent has learned over time and then store those.
Speaker 4:And that 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.
Speaker 2:Yeah. Yeah. How are you interacting with benchmarks these days? Because most of the benchmarks are centered around performance, like meters, like how how advanced of a software engineering task can the frontier models crank on and they're up to like twenty four hour it would take a software engineer twenty four hours to do something, but 4.7 or 5.5 can can can do it, period, and can achieve it with 50% accuracy. They're not really talking about the time to return that result.
Speaker 2:And Right. We've sort of settled into this equilibrium where if it's a big query, ten minutes is acceptable for most people. Maybe twenty. And then for, you know, a knowledge retrieval, I want to know an answer. It's got to come back in like thirty seconds, but it's not we're not in the Amazon e commerce era where a hundred milliseconds means losing dollars, which is sort of where you're where where the Redis DNA comes from in caching.
Speaker 2: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 turn out on, you know, a big cluster somewhere. But we're gonna keep the GPUs fed so much more effectively by keeping this in memory. How are you thinking about quantifying that for customers?
Speaker 4:Yeah. Well, so the first point you made about agent run time, certainly that we're we're witnessing what everyone else is witnessing, you know, the the the length of time an agent can run unattended. And the 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.
Speaker 4:Sure. 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 kinda engineers thinking, hey. We'll just preload the context window with all this stuff.
Speaker 2:Yeah.
Speaker 4:And and then the agent can go and 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 window. Your whole code base
Speaker 2:Yep.
Speaker 4:You know, all of your enter but but the truth is that really doesn't work. To stick everything into the context window, number one, is expensive.
Speaker 2:Mhmm.
Speaker 4:And number two, just really, it's overloading. You're just getting way too much rot in 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 what can I access and and that kind of stuff? So what we see is the longer the agent can run, the the better the context has to be Yeah. To make it effective.
Speaker 4:Otherwise, it just starts to go haywire.
Speaker 2:Yeah. Yeah. That makes a lot of sense. Switching over to just your philosophy as a CEO, you said 1,500 people, something like that, over a thousand work for Redis. You're obviously using these tools.
Speaker 2:How do you see the shape of the organization changing over the next few years?
Speaker 4:Well, dramatically. I mean, so I 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 thirty years ago is just not relevant anymore.
Speaker 2:Mhmm.
Speaker 4:And so, you know, I'm not gonna rely on a bunch of other people telling me, you know, and and, like, watching, you know, Twitter people breathlessly telling me how the world is changing. I'm gonna go learn it myself. So I've gone back to basics over the last year and a half. I mean, really, since we started using ChatGPT for coding
Speaker 2:Yeah.
Speaker 4:And OpenAI, and then really have been diving in myself personally. So I 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.
Speaker 4:But but that 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. 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.
Speaker 4: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 lines with the teams and build code ourselves as we reinvent the software development life cycle. And frankly, we're finding that a lot of folks have to make a big jump in terms of how they do work. Like a developer with 10 agents is more like a development manager of old. And the development manager does a different job.
Speaker 4: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. And it's really fundamentally different than what the developer of, let's say, three years used to do before these agents showed up. So I and by the way, I'm having a blast.
Speaker 4:Like, I love coding. I've always loved coding. I I love everything about it, and I love it even more now. I mean, it's like the I've taken out the gnarly part in the middle, which was the, you know, typing everything in and finding missing semicolons, and now I just go right from expressing intent to getting the result, and that's awesome. I mean,
Speaker 2:it's This is super cool. Are you seeing it instantiated more in like new greenfield projects, new internal tools or actual product velocity on the core product?
Speaker 4:Both, but it more on greenfield. On the brownfield, what we've and first of all, like, we use it differently. So for, like, front end stuff
Speaker 2:Yeah.
Speaker 4:You know, we can, like, pretty much vibe code everything.
Speaker 2:Sure.
Speaker 4:You know, on core Redis system software
Speaker 2:Yeah.
Speaker 4:I'll give you a good example. We just launched a new data type. Salvatore Sanfilippo, who's the original author of Redis Yeah. Launched a new data type called arrays. Yeah.
Speaker 4:It's 4,000 lines of c code. It took him four months, and he was deeply using codex
Speaker 2:Interesting.
Speaker 4:And and Anthropic. Okay, Claude. Yeah. The whole time. Yeah.
Speaker 4:And it was it's but the difference so it it took it was faster to do. Yeah. Okay? So that same idea, that that array data type would have taken probably a lot longer. Yeah.
Speaker 4:But more important, like, eight 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 your bet with your best design for this, and then we'll throw it at the other AI to say, what do you think?
Speaker 4:And back and forth. So so at that level, you really are still crafting the code at the systems level, which is kinda where the world that I come from. But at the higher end and and kind of for greenfield projects, you know, JavaScript and, you know, Next. Js applications, you're just, like, five coding and just going crazy. Yeah.
Speaker 2:Yeah.
Speaker 4:I would say if we have one project, what is a good example in a greenfield, it would have taken a typical like, we're building this big management infrastructure for for the Iris project. It would have taken us probably a year for, like, 10 devs to do something, like, big like that with LDAP support and all the different things you need for enterprise software. It took five guys one month. That's awesome. Guys and girls, actually.
Speaker 2:So Yeah.
Speaker 4:Of so that's a big acceleration on that front, but it's different at the systems level software side and brownfield.
Speaker 2:Yeah. Yeah. It makes a lot of sense. Well, thank you so much for coming on the show, breaking down for us. Hope you have a great week.
Speaker 2:We'll talk to you soon.
Speaker 4:Huge fan. Thank you so much for having me on.
Speaker 2:Yeah. We'll talk to you soon. Goodbye. Everlane was sold to Shein for just one hundred million dollars. It was a VC darling when it launched, says Sheil Monat, raising from Kleiner Perkin.
Speaker 2:I didn't realize how many big It was a who's who. It was a who's who. Kleiner Perkins, Kosla, Maveron, and others, a $145,000,000 raised. I think the bet was that consumers would pay more for ethical, sustainable basics and that consumers may not really exist at venture scale, that consumer. The low end consumer consumer wants price.
Speaker 2:The high end consumer wants brand, taste, and status. Everlane is kind of stuck in the middle. It sells smart basics at a premium, but I'm not sure people who are willing to pay a significant premium for simple clothes over Quince, Uniqlo, and Amazon. Maybe the radical the real radical transparency was showing everyone how brutal fashion economics can be. Wonder when what Shein does with it.
Speaker 2:Will they just make the same clothes and sweat shops now? And so people were very upset about this.
Speaker 1:Rachel Yeah. Have so so yeah. I think you one, pretty pretty shocking, right? Companies have had very different approaches to building their business.
Speaker 2:Yeah.
Speaker 1:And it's hard to see how Everlane can fit into Shein in a way that maintains their historical ethos. Who knows? Right? Shein Shein
Speaker 2:Is it that hard? I mean, doesn't like like Volkswagen Group owns Lamborghini or something? Like
Speaker 1:Yeah. But neither Volkswagen or Lamborghini were ever they were both saying we're making cars. Lamborghini says we make faster cars.
Speaker 2:Volkswagen They both make clothes. Everlane saying we make clothes in the system.
Speaker 1:Yeah. But Shein is a company She like, Everlane Everlane created as a response to people's concerns with sweatshops. Right?
Speaker 2:Was the Rivelto not a response to the Passat? I believe it was.
Speaker 1:No. So so Everlane came out
Speaker 2:as It is different because it's moral. It's not it's not purely stat.
Speaker 1:It's not like we're making Everlane wasn't like we're gonna make a better t shirt. That was maybe part of it, but it was more like we're gonna make we're gonna make good.
Speaker 2:At the same time, a lot of the car makers, they went EV directly to counteract the gas to look
Speaker 1:at when these car companies were founded at the time. There there wasn't yes. Speed if speed is is morality, horsepower is morality, maybe you're right, John. But but look at the when was Everlane founded? '20
Speaker 2:think like like like some car brands were founded with safety and
Speaker 1:Founded in 2011. Yeah. Two things top of mind at that point.
Speaker 2:When was she impounded?
Speaker 1:Sweat shops. Apparel sweat shops. Right? Then the entire, you know, sort of like eco sustainability
Speaker 2:Mhmm.
Speaker 1:Movement. Right? So Everlane was a response from that. They met the moment. The business absolutely ripped.
Speaker 1:I think the other thing at that time is like a lot of the big legacy brands Yeah. I'm thinking like Gap and and Old Navy and brands like that. They were just totally asleep at the wheel. Right? So they're they're I think they weren't keeping up with just weren't keeping up with the times.
Speaker 1: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?
Speaker 2:Like I've never I've I've actually never
Speaker 1:navigated But just imagine it. Right? Like one is extremely funky. The other one is like very easy to operate. Everlane was a pioneer of an entire style of, you know, photography, product photography.
Speaker 1:Okay. 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
Speaker 2:Shein's a little bus busier. I'm looking at Everlane. It's like a single model just showing like a few items of clothing and you open up the Shein website and it's just huge except all cookies and then 30% off if you sign up and save and then, like, a huge registration thing. Then another pop up? So many pop ups here.
Speaker 2:Yeah. Wildly different brands. Another pop up.
Speaker 1:Yeah. Yeah. So Everlane It's got created in the perfect moment, a response to consumer concerns and preferences. Yep. They ride that wave to couple 100,000,000 of annualized revenue.
Speaker 1:They've got owned retail in a bunch of different places. They're D to C darling. Mhmm. Michael, CEO, who's a friend of mine. I'm an I'm an investor in one of his new companies.
Speaker 2:Oh, cool.
Speaker 1:He Yeah. I mean, incredible execution by the team. They built a brand that effectively became a household name. He stepped away Mhmm. After basically ten years.
Speaker 1:And and a woman named Andrea took over.
Speaker 2:Mhmm.
Speaker 1:But but, yeah, I think ultimately when you look at there's 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, trying to scale, right, competing over the coastal millennial who's like on Instagram all day long shopping, right?
Speaker 2:Mhmm.
Speaker 1:They're excited about newness, right? Mhmm. 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 Which white we might we might have to make. We might have
Speaker 2:to make.
Speaker 1:TBPN perfect white tee.
Speaker 2:Yes.
Speaker 1:And and so you have this customer base who Yeah. You met them at this amazing moment
Speaker 2:Mhmm.
Speaker 1:And their their revenue ramp reflects that. But then over time, it was in some ways like the 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.
Speaker 2:Mhmm.
Speaker 1:Right? And and so, yeah. Shifting consumer preferences. Also, when you look at a lot of the greatest apparel brands in history, they they didn't raise venture capital. Right?
Speaker 1:When you have venture capital, it's like we need to grow as much as possible year over year forever. Like that is what you sign up for. Right? And when you look at apparel brands, oftentimes like they're it's more of a kind of like winding road.
Speaker 2:Like Crum Hearts. Exactly. Up and down.
Speaker 1:Yeah. Exactly. Up and down but tightly Held. Held Yep. Right by by one family Yep.
Speaker 1:And they're okay. They're like, hey, if revenue dips one year or we wanna pull back
Speaker 2:Yep.
Speaker 1:On supply, that's great.
Speaker 2:Yep.
Speaker 1:Right? And so when you're venture backed, you don't have that luxury
Speaker 9:Yeah.
Speaker 1:And I think that venture is at odds with building it's just at odds with building
Speaker 2:It generates
Speaker 1:a super durable apparel brand
Speaker 2:Apparel brand.
Speaker 1:Simply because there's there's no network effects at all. Right? Yeah. Especially if your customer if if your customer base is excited about newness. Right?
Speaker 1: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.
Speaker 1:Mhmm. And the dollars that I'm putting towards his brand are are like effectively dollars that could have gone to Everlane. Right?
Speaker 2:Yeah. Amy Lyon Dor founded in 2014 seems to be doing well.
Speaker 1:Venture back though.
Speaker 2:Is it?
Speaker 1:I don't think so.
Speaker 2:I think so. No. And then I think it's very tightly held, very tightly controlled, very limited. Well, the deal was a $100,000,000. We don't know too much about the deal other than they'd raised over a $100,000,000 in equity.
Speaker 2:El Caterton invested $85,000,000 in Everlane in 2020 when the brand was doing $200,000,000 in revenue. Now revenue is down to 170,000,000 but there's $90,000,000 in debt. Sort of unclear, did Shein acquire the debt and then pay that $100,000,000 to the preferred equity holders? It feels like Common was probably wiped out, but unclear exactly the structure of this deal. They say this one post, FanB, says the $100,000,000 sale price essentially covers the debt, but it's possible that the $100,000,000 went to the preferred equity holders and Shein assumed the debt with the deal.
Speaker 2: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. Groons sold for 1,200,000,000. That's a good outcome. Huell Yeah.
Speaker 1:That's a brand that can go in every Target, every Walmart, every Whole Foods, every major retailer and sell billions of dollars worth of product. Yeah. Everlane, I'm not sure if they ever were selling in in other retails or was entirely their own their own stores.
Speaker 2:Yeah. Well
Speaker 1: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, something like that. Yeah. And it's still in their closet.
Speaker 4:And
Speaker 1:so unfortunate outcome for the Everlane team, but they their execution across that decade was pretty impressive.
Speaker 2: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? Normal mode.
Speaker 2:Back to the
Speaker 1:show. Full throw full throw a filter on.
Speaker 2:I haven't seen anyone nail that as well as you have. Well, you've been nailing lots of things. Give us the news. Tell us what's going on.
Speaker 10:So fun to be back here on TBPN. Last time we did this, we had some crazy filters.
Speaker 2:It was very psychedelic. I loved it.
Speaker 10:It was it was very psychedelic. If you're interested in newer ones, you should go on our site and try them out.
Speaker 2:It's
Speaker 10:been mind blowing.
Speaker 2:Amazing.
Speaker 10:But but today, you know, we announced a round. We had a big funding round.
Speaker 2:We raised
Speaker 10:a million dollars.
Speaker 1:Woo hoo. There we go. Nice. It's great to have you back.
Speaker 2:See? It's great to have you back.
Speaker 10:We missed It was worth doing the round just for that. We should we should do more and more rounds to get that going.
Speaker 1:Raise a dollar tomorrow. We'll have you back. No. Tell us tell us what you've been up to since the last conversation.
Speaker 10:So so, you know, today, the really exciting stuff is that we have announcements on all three of our product lines.
Speaker 1:Wow.
Speaker 10:So so we have we have three product lines at the cart. The the first two are world models. We have Lucy, that's a world model to real time video model that is used for immersive experiences. So gaming, live streaming, e commerce, ads, and then we have the new version of Lucy coming out soon, which which has been growing dramatically over the past three yeah.
Speaker 1:That's generally what you were demoing the last time you were on where you have this real time video Exactly. Of you in these sort of exotic settings. Right.
Speaker 10:Exactly. We have Lucy. Lucy can take any video stream, edit 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. And at the same time, it can also be used for for beneficial experiences.
Speaker 10: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 ecommerce providers. So that's 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 just interact with the real world.
Speaker 10:It stops being just in the virtual world and text space and actually is real time pixels lets the AI see the real the real world in real time and interact with it. And then we have our our third product line which is DOS, the the cart optimized stack. It's our inference engine. It's basically what powers both Lucy and Oasis, and it lets us run models, all types of models, a LLM models, agentic 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 two point o.
Speaker 10:That's already being used by some
Speaker 1:Hit it again.
Speaker 2:I think I think I caught Ben Lachin. Over there. He gave him a heart attack.
Speaker 1:When did when did you release DOS one point o? You you you've realized at some point, hey, we're cooking pretty hard over here. Maybe we should let other people use it. Feels, you know, pretty aligned with with the other products. But but yeah.
Speaker 1:How did you get into it?
Speaker 10:So I think that's a great question. Actually, you we we don't talk about DOS too much, but DOS was actually the first product we commercialized.
Speaker 1:Mhmm. When the
Speaker 10:company was just three months old, we closed the first multimillion dollar license deal for DOS.
Speaker 9:Overnight success.
Speaker 10:Literally. Literally three months in. Was less than a hundred days. Fantastic.
Speaker 1:Why did it take why did it take you
Speaker 2:so long?
Speaker 1:Days. Why did it take you so long?
Speaker 10: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.
Speaker 2:Mhmm.
Speaker 10:Okay? But but yeah. We you know, to go back to your question, DOS one point o was the first product we ever had at the card. We licensed it back to the Neo Clouds back then and to some of the younger AI labs. Now DOS two point o is being used by by all the players including the tier one players as well and the hyperscalers to to really use compute much more efficiently.
Speaker 10:And for the models that we support, really focus on very fast models. So either agentic models or live video models. For those models, we're anywhere between five to eight x more performance than anything on the market.
Speaker 2:Okay.
Speaker 1:Is focus overrated? No. It's either you're doing a lot. You're competing. You're fighting.
Speaker 1:You're fighting, you know, fighting on, you know, three different fronts, but clearly doing doing a great job at it. How do you how do you make it how do you make it work? I could imagine any one of these opportunities being, you know, big enough at some point to warrant kind of going all in on it.
Speaker 10:Well, we're all in on them, on all three of them. Now, the nice thing is that it really I think 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, on taking this to market, on talking to customers, on building the product itself.
Speaker 10:And and we inside the car, we 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 the reason we we accelerated DOS two point o 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 it's just become we're hearing this from all our customers that there's no capacity left basically till 2028.
Speaker 2:And
Speaker 10:and so getting more performance out of chips is the only way to actually grow your revenue and and to 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.
Speaker 2:Yeah. How how tightly linked are the different products? Because when I think of Lucy real time interactive video world models, I think like optimization there is what you're, a, good at, but also incredibly important because even the demos that we've seen, they're not four ks. They're not 60 FPS. There's clearly room to run there.
Speaker 2: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 world models. Like, we're not there yet and so optimization feels really important. How how tightly linked are those two projects?
Speaker 10:Yes. They're very tightly linked through DOS. Yeah. And DOS two point o today, it can run real time video models
Speaker 6:Sure.
Speaker 10:At full HD for the first time Okay. Up to a 100 frames per second.
Speaker 2:Wow.
Speaker 10:Okay? Yeah. So that's huge breakthroughs there. Yeah. And on the tech side, what DOS can do so DOS runs on all the three major chips.
Speaker 10:It runs on NVIDIA, on Google TPU, and Amazon Trainium.
Speaker 2:Yeah.
Speaker 10:It's it's it's the only the only stack that really supports all three for all the different types of models. And on the AgenSys side
Speaker 2:So for AMD?
Speaker 10:It's So over. The the the chips the chip space is incredibly incredibly interesting. We will support the force eventually. We we we will we will support everyone. We will support everyone.
Speaker 2:Yeah. Yeah.
Speaker 10:But to your question about fast text models Yeah. Where you really need them is agentic workloads. You really need it if if you want to be able to run, for example, coding models very, very quickly.
Speaker 2:Yeah. Yeah.
Speaker 10:And DOS two point o can, for the first time, run at above a 500 tokens per second
Speaker 6:Okay.
Speaker 10:Which is more than 10 times the industry.
Speaker 2:Interesting. What at 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 journey era, the DALL E era, the diffusion. You start with a bunch of noise versus token based, next token prediction. Like, have these converged?
Speaker 2:Have they diverged? Are there different requirements? Like, we're seeing with agents, we need more CPUs now. We might need more more context in cache. We might need RAG or or vector databases.
Speaker 2:Like like, what are different if you're to build out, the ultimate data center for generative interactive world models? Like, are you looking for Cerebras like chips? Are you going all in on NVL 70 twos? Like, what what is the how is there is there a difference to the shape of the of the architecture that lends itself to, different hardware constraints?
Speaker 10:Yes. I think that that's that's probably one of the best questions in this field right now because AI is moving so quickly
Speaker 2:Yeah.
Speaker 10:That it's very, hard to predict what the right infrastructure will be three months from now.
Speaker 2:Yeah.
Speaker 10:You you brought up, you know, the 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 hyperscalers today.
Speaker 2:Yeah.
Speaker 10:And and that's and that's happening overnight.
Speaker 2:Mhmm.
Speaker 10:And now it's becoming it's becoming very hard. What we're seeing, what we hear 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 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.
Speaker 10:And then each one has its own unique strengths and weaknesses. Mhmm. And and that lets us really we developed a very, very deep expertise in knowing how to map the model to the chip itself.
Speaker 2:Mhmm.
Speaker 10:I think that it 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
Speaker 2:Mhmm.
Speaker 10:Then there's software, for example, Kudo, and then there's the hardware layer.
Speaker 1:I call it a five layer cake, but
Speaker 10:It's I wonder if someone else will will adopt your five layer cake terminology.
Speaker 1:Is that Johnson? Yeah.
Speaker 2:You have
Speaker 10:the two layers above and below. You have the data center and you have the application layer.
Speaker 2:Lots of ingredients.
Speaker 10: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. Yeah. We literally write assembly for all these three chips.
Speaker 2:Sure.
Speaker 10:We know how to write v l I w for TPUs. We know how to write assembly for Trainiums. We know how to write at SAS and PTX for NVIDIA chips. Sure. And so we have all these different layers, and they really enable us to very quickly move between these workloads that constantly change.
Speaker 2:Okay. Are you seeing glimpses of consumer product opportunities in video world models? When I see your your technology, when I see Genie 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. And maybe the hardware is not there.
Speaker 2:But I think just as, you know, lots of folks who are interacting with LLMs during like the GPT-three 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 what other dominoes need to fall for that to actually happen?
Speaker 10:So over the past month actually, we've seen huge usage for using Lucy in live streaming.
Speaker 2:Mhmm.
Speaker 10:You can go to delulu.ai.
Speaker 2:Sure. Yeah.
Speaker 10:Delulu.ai. Delulu. And you can delulu, come on. Of course. Yeah.
Speaker 10:It's good. It's good. It's good.
Speaker 1:It's good.
Speaker 10:Yeah. And it just plugs right into your OBS. Mhmm. 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 nonstop.
Speaker 9:Mhmm.
Speaker 10:So so we've seen that we've seen that pop really over the past month, month and a half. Have a new subscription service there that people just subscribe to and they can turn it on forever long they want, and that's just been growing exponentially fast.
Speaker 2:K. Well, thank you, sir, coming on. We actually have some videos that we're gonna play because we've been demoing it or the team has.
Speaker 1:No way. Only while we've been Can we while we've been talking.
Speaker 2: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. This is Tyler
Speaker 10:You guys are doing the live demo instead of me this time? That
Speaker 1:is insane. Yeah.
Speaker 2:Yeah. Yeah. So we have a video here. We recorded it of I believe it's Tyler as Albert Einstein. Is that correct?
Speaker 2:Let's see it. Let's see. And pull this up. Pulling it up might be the harder part Yeah.
Speaker 1:Real time video models but pulling It looks up good. A on stream
Speaker 2:The shadow and the lighting
Speaker 1:Still a challenge.
Speaker 2:Did you prompt this?
Speaker 5:So it started as Einstein and then I I went through a couple different
Speaker 2:You wanted pink tuxedo on as well? That's very funny. What a funny prompt. And the yeah. The the visual fidelity on Einstein's face.
Speaker 2:That is weird. Okay. We go. You got that. It's a very humanoid Oh, that's a jacked horse.
Speaker 2:That's that is odd. That's very odd. But the horse had oh, there you go. Okay. That's interesting.
Speaker 2: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 that was impressive. It wasn't purely
Speaker 1:Last question. Last question before you jump. Is is there a certain milestone that if achieved you will cut your hair? Like is it in Oh, yeah.
Speaker 2:Oh, yeah?
Speaker 10:Oh, yeah.
Speaker 2:Really?
Speaker 10:It's the the the milestone is that we need to hit 1,000,000,000 ARR. Milestone. It's a it's a bet from early on in the company. Now, this this is a year and a half long. Okay?
Speaker 10:This is just one and a half years. We have to get rid of it now with with DOS and the way that's scaling. That's that's at some point, I'm gonna get a haircut.
Speaker 1:Fantastic. Amazing.
Speaker 2:Well Amazing. We'll be here when you hit that milestone.
Speaker 1:Selfishly, I kind of cut your hair
Speaker 2:on the stream.
Speaker 1:Your waist. Right? Oh,
Speaker 10:we should we should do a haircut on stream. We're gonna do a haircut on stream. Love that.
Speaker 2:Come to the El Trio. We'll shave your head.
Speaker 1:Dean, you're the man.
Speaker 2:This is great.
Speaker 1:The the chat loves you. Thanks guys
Speaker 10:so much.
Speaker 2:Say hello to everyone at Radical. We're big fans. We'll talk to you soon.
Speaker 10:Cheers. Love them.
Speaker 2:Goodbye.
Speaker 4:Another one.
Speaker 2:We have Joanna Stern, author of I am not a robot coming in person today. Alright. About before. What do you
Speaker 1:want to talk about? What are going to talk about? The protein shortage is coming.
Speaker 2:What's going on with the protein shortage?
Speaker 1:Ellen Cushing in the Atlantic says making all that whey is complicated.
Speaker 2:Okay.
Speaker 1:She says, in retrospect, maybe the protein pop tarts were a bit much. Americans, broadly speaking, are in a state of protein mania.
Speaker 2:Mania.
Speaker 1:We are eating it at breakfast, lunch, dinner, Mania. 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. Whey protein prices are surging and a shortage may be imminent.
Speaker 1:If you're not investing in the protein bottlenecks, I don't know what to do. Yeah.
Speaker 2:Where's the situational awareness for the protein shortage?
Speaker 1:We really
Speaker 2:need that.
Speaker 1:Demand is strengthening. The USDA warned in a recent report inventories remain tight. Some manufacturers have already sold their supplies for the full year.
Speaker 2:No way. Getting PTSD.
Speaker 1:Since January, wholesale prices for food grade whey protein powder have risen by more than 50% to the highest level on record. Retail prices are going up. Two, six months ago, a two pound jug of Optimum Nutrition's delicious strawberry flavored whey protein went for about $40 on Amazon. Now it's $54. We've absolutely felt it, says Steven Zaminsky, CEO of the supplement company Nick Nutrition said of the shortage in an email.
Speaker 1: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 smoothies and those big tubs of protein powder comes from whey. Raw milk is treated with heat, acid or enzymes to coagulate it into two distinct substances, curds which become cheese and whey, which was at least until recently the cheese making processes unlovely byproduct. Almost as long as industrialized agriculture has existed, the problem with whey wasn't scarcity at all at all, but the opposite. Farmers did anything they could to do to get rid of it as cheaply as possible.
Speaker 1:Fed it to livestock, sprayed it onto fields, dumped it into rivers and sewers. Can you imagine swimming in a river that that was used as a dumping ground for whey, John? Weird. Especially combining that with a place like Switzerland where you can you know drink the the water and the lakes and the rivers and and you'll be totally fine. That could be a powerful combination.
Speaker 1:For much of our nation's history, any fish unlucky enough to be born in Wisconsin or Vermont had a good chance of being woah murdered by whey.
Speaker 2:Woah. I'll keep reading from here.
Speaker 9:Then environmental regulation limited whey dumping and technological developments made processing whey into powder much easier. Starting in the nineteen eighties, whey was the few industries go to source of supplemental protein. Cheap, a vegetarian, efficient, and already weighed down abundant supply and demand were more or less in alignment for a while.
Speaker 1:I'll I'll yeah. Keep reading.
Speaker 4:No. Another one.
Speaker 2:Then came the crib Pregnant. Pregnant. Pregnant. Pregnant. Pregnant.
Speaker 2:Certainly seems that there is not because the helium is flowing throughout The TBPN UltraDome.
Speaker 1:Influencers 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 latch on to protein 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. If the reality is obviously more complicated. And food manufacturers responded to this new demand.
Speaker 1: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 because it was just like too was too filling at times.
Speaker 2:Oh. Yeah. Not enough calories in protein. You need fat. Yeah.
Speaker 1:More dense, more caloric dense. Food manufacturers responded to this new demand enthusiastically cramming in America's new favorite macronutrient wherever they could, usually in the form of whey. Now the infrastructure can't keep up. The North American dairy industry has pumped about a decade of investment. Let's go.
Speaker 1:Wow. Heavy infrastructure.
Speaker 2:Build out.
Speaker 1:Ask for the build out. Out. Say that again.
Speaker 9:The build out. The
Speaker 2:build out. The protein.
Speaker 9:The protein powder build out of the late twenty tens.
Speaker 1: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.
Speaker 9:We got a capability overhang.
Speaker 1:Turning shelf stable, scoopable, tasty enough protein powder people want is a massively complicated process. One that requires space and time Yeah. And huge expensive machine.
Speaker 2:I didn't think the is
Speaker 1:AUV machine? What is the ASML of way? The UV machine? Sorry. Sorry.
Speaker 1:EUV.
Speaker 2:The advanced lithography machines. Yeah. What is the ASML? Probably I don't know. Maybe that company what's that collar?
Speaker 2:The cow collar company? They're right at the top.
Speaker 1:Yeah. Top of the
Speaker 2:Founders Fund's going long into what what's it called? Cattle holler? Collar? Something like that? Cowler?
Speaker 1:Whoops for cows.
Speaker 2:It's Whoop for cows and they're printing. Business is growing really really quickly.
Speaker 1:A full processing plant can cost up to $1,000,000,000 to build. Mhmm. 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.
Speaker 1:Higher the protein content, the more complex and expensive the processing. Whey protein isolate, the protein is protein available, The kind that makes it possible to stuff half a chicken's breath 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.
Speaker 1:Okay. Just say you're not protein pilled based on whatever new shiny consumer preference there is out there. Polzen grew up on a dairy farm. He remembers the cottage cheese craze of the past when fitness fixated when the fitness fixated country set its sight on a different milk based superfood that was supposed to make you healthier and thinner and more powerful. Trends come and go.
Speaker 1:What's his point? They move quickly. Our appetites change faster than the systems that satisfy them. North America is currently building out 12,000,000,000 of dairy processing capacity. Projections suggest that the current shortage will be short lived and that the dairy industry will catch up with demand in the near future.
Speaker 1:I just wonder what consumers will be demanding then. I'm I don't I don't see I don't see supply ever catching up with demand, John. I think I think we're in a fast take off.
Speaker 2:You think never.
Speaker 1:I think we're in a fast take off scenario. I think that the fitness influencers of the twenty thirties will be recommending five to 10 grams five to 10 grams.
Speaker 2:Per per pound of body weight. Yeah. Yeah. I wouldn't be surprised. I I did not know that the protein boom was going as well as to to drive up, you know, supply.
Speaker 2:CapEx? Yeah. Yeah. Because we I mean, we
Speaker 1:You ramp CapEx.
Speaker 2:We we we've talked on the show a few times about how like the they're putting protein and everything, protein and cereal. But I thought that was maybe like overhyped. It was gonna be like a temporary trend.
Speaker 1:They're calling it a way bubble.
Speaker 2:A way bubble. Potentially. Potentially. Well, we have our next guest, Joanna Stern, the author of I am not a robot in the TBPN UltraDumb. We'll bring her in in just a second.
Speaker 2:But I I don't know. What have you have you added anything to your diet recently that actually contains newly added protein? 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.
Speaker 2:But I don't know if there's something that jumps out to me as 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.
Speaker 1:Yeah. I think when you add protein to most things, it just tastes
Speaker 2:And then explain me with the David bar EPG. That
Speaker 1:is That's a fat.
Speaker 2:That's a fat. So he still has That
Speaker 1:is Yeah. From from how it's been explained.
Speaker 2:And so they still have to buy normal protein.
Speaker 1:It passes through Yes. Yes.
Speaker 2:Yes. So it doesn't count.
Speaker 1:So so I would expect that part of this whole thing is that Peter
Speaker 2:It's core of the market somehow. I wouldn't be surprised. Anyway, we have our next guest, Joanna Stern, author of I am not a robot live with us in the TBPN Ultra Dome. Let's bring her into the studio. Welcome to the show.
Speaker 2:Will you be enjoying a Diet Coke? Yes. Escort me No. No. Cameras want cameras.
Speaker 2:Grab a seat. You're welcome
Speaker 5:What's happening?
Speaker 2:To have a sit down. How are you doing?
Speaker 8:It's real.
Speaker 2:It's real. Today, official book launch day is today the day. Last week. Okay.
Speaker 8:Last week.
Speaker 2:But the tour continues. Right?
Speaker 8:This is the West Coast tour. This is my first stop on the West
Speaker 2:Coast tour. LA. We're having a conversation tonight then Yep. Up to San Francisco.
Speaker 8:Up to San Francisco, Mountain View.
Speaker 2:International? Fates yet?
Speaker 8:June is when it goes international,
Speaker 2:so
Speaker 8:we'll find out.
Speaker 2:They'll have me. The the bot replies come to Brazil. Come to Brazil. Come to Brazil. It's a very popular thing.
Speaker 8:Haven't knew
Speaker 2:about this. Right?
Speaker 8:I do know about that. But I think They're like huge
Speaker 2:fans, the fandoms.
Speaker 8:I think London.
Speaker 2:London would be great.
Speaker 8:London? Yes, I Well
Speaker 2:Alright. How are you introducing yourself these days?
Speaker 8:I know you guys had me as author. Author? Author. I think founder. Is a founder popular name.
Speaker 2:Founders. Correct?
Speaker 1:I think I I prefer business owner
Speaker 8:Okay.
Speaker 1:Or businesswoman. Yeah. Think founder is already sort of fading.
Speaker 2:Oh, okay.
Speaker 1:We hit peak founder.
Speaker 2:Oh, okay.
Speaker 1:Because because Yeah. Being anybody can be a founder but not everyone can be a businesswoman or a businessman.
Speaker 8:Was at LinkedIn last week and they said that they're seeing a big uptick in people putting founder in their profiles.
Speaker 2:Yeah. And Angel and Duster too became very trendy.
Speaker 1:It's over.
Speaker 2:It's over.
Speaker 8:It's surprising at
Speaker 1:LinkedIn. Put You business owner. Because you're selling subscriptions, you're selling books.
Speaker 2:Ads.
Speaker 1:You have sales. Ads.
Speaker 8:Yeah. Alright. Business
Speaker 2:the shape of the 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 of I don't if
Speaker 8:that's a great way.
Speaker 2:I I think it makes so much sense.
Speaker 8:It's a good marketing vehicle,
Speaker 2:Yeah. I Yeah.
Speaker 8: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
Speaker 2:Yeah?
Speaker 8:I thought, okay, I've got this book coming out, I've got to get out right away
Speaker 2:Yeah.
Speaker 8:Because I've got to start building this business so it's ready when the book is ready. And I probably should have, you know
Speaker 2: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
Speaker 8:It's a flywheel.
Speaker 2:It's a flywheel.
Speaker 8:It's It's the it's the value of that flywheel here. We need flywheel. Flywheel?
Speaker 2:Don't know what a flywheel looks like. Is that a water wheel?
Speaker 8:I think it's
Speaker 2:What is a flywheel?
Speaker 8:Well, if you the Amazon flywheel is like a
Speaker 2:I'm familiar with the metaphorical flywheel.
Speaker 8:It but
Speaker 1:heavy rotating mechanical device used to store rotational kinetic energy, so we're gonna need some proper machinery.
Speaker 8:Okay. We can get that made.
Speaker 2:And we
Speaker 8:can get a
Speaker 2:It's specifically not a windmill?
Speaker 8:No. I I think Okay. In my mind, it does look like a windmill.
Speaker 2:Okay. I think so. This is funny.
Speaker 8:Okay. Can we get one of those here?
Speaker 2:We definitely
Speaker 8:return to the studio. Yeah. Look at all these people, they already went out and started to get the flywheel. Yeah. They're already.
Speaker 8:Yeah. Is a
Speaker 2:flywheel flywheel creation. Yeah. Anyway, what was the flywheel for writing this book?
Speaker 8:You know, it wasn't the motivation for writing the book was not actually really a business reason at first. I mean, a little bit in the sense
Speaker 1:of Now it is. Now it is. Sales are rolling in.
Speaker 8:Now it's but as you know, I wrote a popular column for the Wall Street Journal for a long time, twelve years. Yeah. My biggest you know, one of the reasons I didn't want to
Speaker 9:leave Success.
Speaker 8:I thought you guys might not read me anymore. Because I know you read the Wall Street Journal. Love the Wall Street Journal.
Speaker 2:And we love your coverage.
Speaker 8:So I have been considering just making a newspaper of just my newsletters Yeah. And sending it to you guys.
Speaker 1:But Yeah. 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're a part of. And and I think for you it's certainly
Speaker 2:You had way more of a personal brand.
Speaker 1:Yeah. Yeah. You had a personal brand.
Speaker 2:With the video
Speaker 8:means like you guys won't pick that up and be like, oh yeah, Joanna wrote about robots today. Let's have her on the show.
Speaker 2:Well, print edition of the newsletter.
Speaker 8:I know. Printed but so just to kinda 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. Sure. And I was testing all of this AI stuff 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?
Speaker 8:Because when you're writing these, even whether it's newsletters or columns, getting the theme and like big picture is very hard to do. Some newsletter writers are really great at it. 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.
Speaker 8:And that was generative AI, but that was also self driving cars, and that was gonna be medical AI, and that was also gonna be humanoid robots, but it really just turned into robots.
Speaker 1:And describe your headspace going into that year. Are you, you know
Speaker 8:Insane.
Speaker 1:Print print are you reading situational awareness like at night, you know, before bed, like are you AGI pilled, are you are you skeptical?
Speaker 8:I guess I'm skeptical, but I'm thinking more we have all of these tech executives out there and this is 2024, just all the hyperbole in the world, right? AI is going to change everything, it's going to change the way we eat and educate ourselves and healthcare 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?
Speaker 2:Yeah.
Speaker 8:How is life going to be different, better or worse with AI? Mhmm. Which is kind of a perfect moment for this book to come out right now 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 be great. And so I think it's a pretty balanced look at all of these different things.
Speaker 8:But yeah, I mean my headspace was just I want to what's real? I want to find out what's real here.
Speaker 2: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 experiences, and then in a fugue state churn out the entire book in a couple of sleepless nights?
Speaker 8:It was a mix of both. So I try the book is structured seasonally. Yeah. So every season I try to figure out a theme, right?
Speaker 2:Yeah.
Speaker 8:So like the book starts in winter, beginning of the year and I'm very focused on health and so Yeah. I wrote that or I've lived that and then wrote that, and then started realizing, oh crap, this stuff is moving so quickly. Yeah. And so I started realizing, okay, 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.
Speaker 8: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. So it was a mix. I AI did not write the book.
Speaker 2:Yeah.
Speaker 8:I think it's very me. The writing is very me, but AI definitely helped make the book in so many ways. Yeah. It would not have been done by now if I did not have AI.
Speaker 4:Yeah.
Speaker 8:Just the back end systems I used to organize my notes and all of the timelines and the thing getting like things like the end notes done. All these little things AI did for sure.
Speaker 1:Have you seen the the chart of Amazon Kindle releases post ChatGPT? So basically after the release of ChatGPT, you just see this massive uptick in book releases on Amazon.
Speaker 2:A 100,000 a month prior to AI. Now it's up to 400,000.
Speaker 1:And and the funny thing the funny thing is people people are everyone is just saying like, oh, people are obviously just like prompt, you know, making just prompting out a prompt and prompt the whole book. But what you're saying is like there's actually just like a speed up
Speaker 2:what's driving that 300,000.
Speaker 1:Yeah. Yeah. I some of them some of them certainly are.
Speaker 8:Well And I
Speaker 1:think a lot of You have the perfect book to be able to like say like of course I used AI to in the process because it's like why would anyone trust anything else in the book if you were just going say like all this stuff is completely
Speaker 8:Hand
Speaker 1:know, fake and Yeah.
Speaker 8:One thing that's interesting and I do these generative AI experiments every season where I try to just one seasons I just listen to AI music, or one seasons I just read AI books. Yeah. And so I read a few AI generated books off Amazon. Sure. They're not terrible, guys.
Speaker 8:Yes. I mean, I hate saying it, but they're really not terrible. It's like
Speaker 1:Is this fiction, non fiction?
Speaker 8: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 it's about how AI has taken over all radiology, we don't have radiologists anymore, which I'm very clear in my chapter on radiology, that's just not gonna happen. And the AI has decided it's not gonna spot cancer anymore, and a human figures out that the AI has gone rogue, and so it's like an it's a novel, a thriller about this.
Speaker 1:How interesting.
Speaker 6:Is it
Speaker 8:really good story?
Speaker 1:AI writing an AI Exactly.
Speaker 8:I mean
Speaker 2:Seems like it would be
Speaker 8:I got in touch with the author and he said, I think it's only like 3,000 words that you can actually get
Speaker 2:At a time.
Speaker 8:At a time. So he had to keep prompting every chapter.
Speaker 2:Yeah.
Speaker 8:So that was basically all
Speaker 2:he Yeah. You need some sort of harness to Yeah. Work through. You can open the diet code by
Speaker 8:the way. I know. I'm worried
Speaker 2:about this I'll I'll burn some
Speaker 8:I want it I keep wanting fit in.
Speaker 2:Yeah, please. That's why I'm
Speaker 8:doing this.
Speaker 2:On on the medical question, I'm 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 Salesforce for radiologists and done very well. 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, a paper form. And I'm like, we're not even seeing a fast takeoff in, 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 in your interrogation of the medical questions in particular.
Speaker 8:Well, I'm forgetting the name of the company. It's not my chart. Yeah. It's one of the companies that is doing the AI note taking in medical I right mean, there's a number of them. Yeah.
Speaker 8:That seems to be the biggest catch on right now. And it's I mean, would consider it in whatever back ends, they just get this tool now.
Speaker 2:Yeah.
Speaker 8:And have you been to a doctor where they ask you can they record?
Speaker 2:I haven't actually. But I did see a company that sells a wearable device for doctors that's doing hundreds of millions Yeah. Dollars in sales and has been very successful in rolling that out. Interesting. But
Speaker 1:Such a magical and useful feature. I can I can just remember trying to like understand like doctors Yeah? Every time. Even if it's just like a medication like Yeah. Get this at CVS and it's like, you get to CVS and you're like, sorry, buddy.
Speaker 8:Gotta So nobody knows what it's
Speaker 2:I mean, that feels like the hardest one to measure because if you have a whole bunch of notes, ideally, 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.
Speaker 2:That's a lot less satisfying than the AI got so good that we asked it to cure cancer. It did. And now there's a pill. And whenever somebody gives gets cancer, we give them the pill and everyone cheers. And they're like, AI, it was worth it.
Speaker 2:All those data centers, it was worth That's what everyone wants.
Speaker 8:That's what
Speaker 2:everyone 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.
Speaker 8: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, the every doctor is going to have this AI assistant and every patient is going to have this AI assistant Yeah. Which we're already seeing inroads in, right? OpenAI and Microsoft have all started rolling out ways to use their bots and you feed in medical information.
Speaker 8:But then there's going be the other side where AI is externally doing drug discovery or cancer cure or 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 watch The Pit?
Speaker 2:The Pit? I've seen one episode. It was sort of gory.
Speaker 8:Yeah. It's very it was was very
Speaker 2:like not really for me. They No. It's very successful.
Speaker 8:It's very successful. And the doctor there's like one example of the doctors now using AI to summarize their notes. And so I think that's the one that most consumers have now Oh, my doctor's gonna ask me if they can use AI to summarize my notes. And they're probably not they're not gonna think that that is
Speaker 2:That's weird. Yeah.
Speaker 8:Weird or consequential. Yeah. But they're gonna have some amazing breakthrough because their doctor is
Speaker 2: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 does interacting And with some food or 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 I could use a pro model, probably get a better answer here.
Speaker 8:But were they using like some
Speaker 2:They were using either either like, you know, the Gemini overview Okay. Which is not Gemini thinking.
Speaker 8:But it wasn't like some price. Very
Speaker 2:No. No. No. No.
Speaker 8:Farm and lawn.
Speaker 2:Just going to Google and searching for some
Speaker 8:Oh, boy.
Speaker 2:And I was like, wait. But I have, you know, o three pro or whatever the flagship model at the time was, I was like, I we should be using the vest.
Speaker 8:We should be using the best CVS
Speaker 2:or wherever they work. Yeah. These things take time to diffuse, and they have cost if it's an expensive model, but I don't know.
Speaker 8:True. Interesting. Well, I think I think the health care chapter, I I like talking about it because I think it really does point to the positives of where this is going to, this can go. And even with the radiology example, which is pretty outdated honestly by now, it's outdated in the sense that Geoffrey Hinton has been saying for years radiologists are going to be replaced by AI and deep learning, but that didn't happen. 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.
Speaker 8:It can spot cancers that humans can't, and it's out there. Women might be getting their mammograms or breast ultrasounds read right now, and they might not know that AI is doing that So for this idea that, hey, we need to reject AI, we need to 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.
Speaker 2:Yeah. Yeah. There's something about like AI on the back end gets no credit, but if you see some slop image, you know,
Speaker 4:like Right.
Speaker 2: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
Speaker 8:Marketing, I guess. Better.
Speaker 2:I don't know. Think it takes time. I don't know. Yeah. Talk about companionship.
Speaker 2:Like, why did you think that one was important to to center in on? How what was your process for setting that up?
Speaker 8:I I think so. Well, I did a few things in companionship. Yeah. One, I did a lot of experiments with AI therapists. Okay.
Speaker 8: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 in my summer love with a with an AI boyfriend.
Speaker 2:Yes.
Speaker 8:And I did Fling. Fling. Yeah. I've ghosted him since.
Speaker 1:Brutal.
Speaker 8:Yeah. Churned. Churned. And
Speaker 1:That's a risk for you, by the way.
Speaker 8:Which part?
Speaker 1:Because in the in in some AI doom scenarios, the AI might hold that against
Speaker 2:Oh, true. Rocco's Basilisk, you should continue to send affectionate messages to all AIs. Because if it becomes all powerful, it will
Speaker 8:I have a section of the book where I talk about that I cursed at AI and I felt really bad. Yeah. 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.
Speaker 2:Okay. What was the conclusion?
Speaker 8:He said the AI doesn't have feelings, so you don't need to to do this, but it depends on your I would love to.
Speaker 1:I would love I mean, they're so easy to smokes smoke. We should have them on the show to talk to our managers. Because simply, like, you don't wanna be somebody who
Speaker 8:Yeah. Yeah. That's right.
Speaker 1:Part of your life, you're just screaming That's right. Yelling, using cuss words Negative.
Speaker 2:And then
Speaker 1:you just go back to your life, and you're like, oh, yeah. I'm I'm a super respectful person. It's like you're putting out a bunch negative
Speaker 8:energy That's exactly what he said. It's basically you need to realize how that might affect you as a person when you interact with humans.
Speaker 2:Mhmm.
Speaker 8:Yeah. So the more you you might start beating up on and just completely berating your AI, but then what happens when you start to blur the 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 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 you kind of just because I don't have Waymos in New York.
Speaker 1:Oh, I'm the
Speaker 8:No. I just I said thank you when I got out.
Speaker 2:Sure.
Speaker 8:You know? And I was like, oh, right. Like, you know, but I was thanking the robot.
Speaker 2:Well, they do have teleop. So, like Yeah. There's probably someone who might have heard that because they might be. They might have And they shed a tear.
Speaker 1:They shed a tear because every other drive that day, no one said thank you.
Speaker 2:It's sort of like a Groeninger's cat.
Speaker 8:I think that that makes up for the fact that I ghosted my AI boyfriend. Right? Yeah. Yeah. Being There's a kept and so I think that's
Speaker 1:As long as it all is flowing through the same day of time.
Speaker 2:Human for every two Waymo's. 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 floating
Speaker 8:But the human didn't do the driving today. No. So it really was thanking the robot. Don't think so.
Speaker 2:You were spanked by yeah. Yeah. That's fair. Anyway Yeah.
Speaker 1:But the human might have stepped in in a really key moment.
Speaker 2:Yeah. It's possible.
Speaker 8:That's true.
Speaker 2:Could have saved you. You don't know.
Speaker 8:You kinda know. You kinda
Speaker 1:know. Yeah. You probably
Speaker 8:know. Okay. Sorry. We're talking about companionship. We got Wait.
Speaker 1:How is how is Waymo how is what it like, how how did you did you feel Waymo's progression over the last year?
Speaker 8:I feel like yeah.
Speaker 1:Driving around LA, 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. Yeah. Waymo trying to there was wall to wall traffic going the other way. The Waymo's trying to just like turn in.
Speaker 1:It's not a there's no u definitely no u turns. And the Waymo's like, I'm going. So it's like, we're fully backed up this way. Everyone's honking. The Waymo's just like waiting to like do an illegal u-turn.
Speaker 1:It's like, there's someone in it. They're just like, oh.
Speaker 2:Oh, boy. The name of the business, tell everyone.
Speaker 8:It's companionship is the name of the business. No. The name of the business called The New Things.
Speaker 2:The New Things.
Speaker 8:Thenewthings.com.
Speaker 2:The New Things.
Speaker 8:Please go. Please go visit thenewthings. We talked about
Speaker 2:the new withthings. A landing page that had a different domain? Yes. My next thing?
Speaker 8:Yeah. This is my next
Speaker 2:thing. This is my next thing? I
Speaker 8:like that. I I couldn't I I didn't know the business name
Speaker 2:yet. Yeah.
Speaker 8:But this the new things.
Speaker 2:The new things. Okay.
Speaker 1:Did you talk to any people that had that at least claimed to never have used AI?
Speaker 2:Interesting.
Speaker 1:Because you really can't claim that at this point because you would have to just like sit in a forest.
Speaker 2:I didn't say you've never met an Amish.
Speaker 1:You'd have to sit in a forest then somebody would be like
Speaker 2:The Amish are growing. Well The population collapse is vastly
Speaker 1:issue though. Like probably the forestry service is like probably using AI in some ways.
Speaker 2:And that affects the Amish?
Speaker 1:No no no. I'm talking about my example of somebody who's like, I don't use AI.
Speaker 2:Yeah. 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.
Speaker 1:I know. But they're buying wood from a business
Speaker 8:has a real need. Tell
Speaker 1:you that you have to. Somebody can't be like, well, I don't I don't use electricity but they're buying goods and services that require electricity.
Speaker 2:Okay.
Speaker 8:True.
Speaker 2:Okay.
Speaker 8: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
Speaker 2:they're not. They're flourishing. Yeah. Fertility rates are particularly high amongst the Amish.
Speaker 8:Really?
Speaker 2:There's a big deep dive in the in the Financial Times
Speaker 8:And there's
Speaker 2:this weekend around smartphones being like
Speaker 8:I saw
Speaker 2:inflection point. Right? We can get into that later. But but the Amish have stayed away and they are flourishing.
Speaker 1:Do they chop their own wood?
Speaker 2:I believe many times they will.
Speaker 8:Wait. But are the Amish flourishing because they don't have smartphones or has their birth rate stayed steady?
Speaker 1:No. I think it's probably
Speaker 2:accelerating. Yeah. For yeah. It's actually a straight line on a log graph with the Amish.
Speaker 8:It's a hockey stick.
Speaker 2:Yes. By by in a few years, they will be producing thousands of offspring Interesting. Per Amish person. Yeah.
Speaker 1:Talk about
Speaker 8:Is this the worst tangent you've
Speaker 2:ever had here? Maybe.
Speaker 1:No. Definitely not. This is yeah. Talk about more 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 is not quite the story is like still evolving.
Speaker 1:Yeah. What what was that like? How are 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'm I have a lot more capabilities today than I did three months ago or six months ago. But how are you feeling it?
Speaker 8:Well, even some of that software engineer, the tools, right? Like Claude Code mid year, last year, I believe comes out, gets so much better towards the 2025.
Speaker 1:Yeah.
Speaker 8:Or even the advent of AI browsers, which we can say now is really just going to be any browser, 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 Comet came out mid last year and was like, wow, I can really live this agentic life that people I have been talking about, can have it do multi step processes for me in my browser.
Speaker 1:Did you book a flight?
Speaker 8: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. And I did try, and I I mean there's multiple things I did in Perplexity Comet 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 Comet. Yeah. 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, you're like, I'm not doing it. Yeah.
Speaker 2:You trust it with your credit card?
Speaker 8:It still basically will ask for your credit card. I mean, don't have anything set up where it's like auto pay, but I had like 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.
Speaker 2:Yeah. Works pretty well. Pass you the link and
Speaker 4:then
Speaker 2:you can check out there naturally.
Speaker 8:But on that progress, there was obviously also so much progress and still is so much progress happening in 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 Claude 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 OpenAI and more of that through Google probably this week too at Bubble.
Speaker 8:Yeah.
Speaker 1:How how do you rate the tech industry's current terminology? Do you like do you think that you calling data centers AI factories is a good move? People love factories.
Speaker 8:Probably not. I don't Who's been saying that is a good move?
Speaker 1:A lot of people have been using the word AI factories because it sounds cool if you're, you know, investing in the
Speaker 8:Oh, yeah. Yeah.
Speaker 1:We've been pushing for supercomputers.
Speaker 8:I don't think normal people like data centers or AI factories.
Speaker 1:But supercomputers
Speaker 8:That sounds better.
Speaker 1:Sounds better.
Speaker 8:That sounds better.
Speaker 1:Sounds like a big computer. Less scary. Yeah.
Speaker 8:Yeah. Yeah. Well, I think I haven't I haven't been able to listen to the show today. But have you guys been talking about the commencement booing?
Speaker 2:Oh, yeah. Yeah. Incredible.
Speaker 8:I watched a little bit on the way here. I didn't hear that. But yeah.
Speaker 2:Like, I mean, maybe it was just the Supercut we watched, but the Eric Schmidt, it felt like he was getting booed the entire I know.
Speaker 8:I'd like to see the whole thing.
Speaker 2: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. But he was really seemed like he was really doubling down. I need to watch the full the full commencement
Speaker 1:Yeah. It would have been so it would have been so easy to say like when I started my company Google
Speaker 2:Yeah.
Speaker 1: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
Speaker 8:Yeah.
Speaker 1:There were so many good things that came out of it. Right? Did you
Speaker 2:just tell a story of y two k? Like he lived through this. Right? Well Like Google, it would existed before y two k. I'm sure that
Speaker 8: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 and nobody wanted to hear from Eric Schmidt at that
Speaker 2:At
Speaker 8:A. Room ever. Yeah. Yeah. Yeah.
Speaker 8:It is just the fact that he is Eric Schmidt and that he shouldn't have been
Speaker 2:Just because he's a billionaire or
Speaker 8:Just because he's a billionaire, he's tied to Google and he's writing about and talking about how AI is everything. 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 probably
Speaker 2:Interesting.
Speaker 8:A van der you know, probably somewhere in the middle of
Speaker 2:Yeah.
Speaker 8:It's both. Because then there was the speech at US UCF last week. Okay. Did you see that one?
Speaker 2:I don't think I saw that one. No.
Speaker 8:Yeah. So there's a I forget her name, she's a real estate Oh. Real estate Yeah. Executive. And she also gave a speech and when she's talked about AI being Yeah.
Speaker 2:Part of
Speaker 8:like the next industrial revolution
Speaker 2:Sure. They booed.
Speaker 1:For sure.
Speaker 2:Yeah.
Speaker 8:They didn't boo her the whole time.
Speaker 2:Yeah.
Speaker 8: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.
Speaker 9:Did you
Speaker 2:see David Solomons? No. So CEO of Goldman Sachs just going so much harder than than Eric Schmidt. Eric Schmidt's, like, at least trying to, like, paint an optimistic view. David Solomon just plays an EDM song generated by Suno for the Wharton grads who I were probably more receptive to it because you know They're going into business.
Speaker 2:I I think it might have
Speaker 1:been Did say I made this in ten seconds?
Speaker 2:Yes. He did. And he said like creativity is no longer relevant and like a whole bunch of just like really rough sound bites.
Speaker 8:Well, actually I gave a commencement speech a year ago Okay. All about AI.
Speaker 2:Really?
Speaker 8:Yes. Did you get booed? No. But they my I went to Union College. They were Okay.
Speaker 8:I would say 90% of the audience was hungover and was listening to me.
Speaker 2:Okay.
Speaker 8:It went over really well.
Speaker 2:Yeah. What was the thesis of your commencement speech?
Speaker 8: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
Speaker 2:No way.
Speaker 8:And then had a human come up and play the same song, and her song version was so much better.
Speaker 1:Woah. Wow. Interesting.
Speaker 8:I know. Right?
Speaker 1:Mogged.
Speaker 2:Wait. You did this at the commencement speech?
Speaker 8:Yeah. Did this.
Speaker 2:Wow.
Speaker 8:But but again, nobody nobody knew because they were all super hungover.
Speaker 2:I'm ahead of the wave. Yeah.
Speaker 8:Yeah. I was ahead of the wave.
Speaker 2:For sure.
Speaker 8:But, you know, I think if they had had me instead of Eric Schmidt, I wouldn't have gotten booed because I'm not a tech billionaire.
Speaker 2:Yeah. Would you would you change anything if you were giving that speech today? Cause it seems like the message would still resonate but probably needs to be delivered in a different way because people people might say, okay. Yeah. Yeah.
Speaker 2: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 gonna melt because of all the data centers. I still don't like it. Let's just let's just do the human thing.
Speaker 8:Well, think the hate a year later is a lot stronger. Yeah. I think we've seen the job impact. We've heard about the job impact from tech executives. Yeah.
Speaker 8: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.
Speaker 2:Yeah. Yeah.
Speaker 8:Right? And I mean, I'm sure you guys see that in people applying for jobs here, and lots of people out of out of just out of school looking for really great jobs and what they studied.
Speaker 2:Yeah.
Speaker 8: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. Yeah. So I think a year later, it would be a definite difference.
Speaker 2:Post post
Speaker 8:I probably just wouldn't talk about it.
Speaker 2: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. And if those jobs are not available at the same clip, that's going to affect the new grad classic pretty significantly.
Speaker 8:I think you should start asking actually a lot of the executives you
Speaker 2:Yeah.
Speaker 8:You interview what how what their advice would be.
Speaker 2:Yeah. We we we ask a fair amount of time advice for for young people. Get a varying amount of amount of responses. I mean entrepreneurship broadly is Yep. Continues to be a bright spot since it's easier than ever to start a company, easier than ever to scale a company.
Speaker 2:There's so much more that you can do or learn with AI. It's I hard
Speaker 8:know this as a business owner.
Speaker 2:Yeah. Yeah. But but it is hard because like there are people who are just like, I don't wanna start a company. Yeah. I want a job.
Speaker 2:Yeah. And and I
Speaker 8:wanna learn the things so I can one day be a business owner or job.
Speaker 1:Or I just Or never.
Speaker 2:I just never want to Yeah. Own a business. I want to do a job. And Yeah. If that concept goes away, that's very very tricky.
Speaker 2:And then also you have a much broader swath of of outcomes from entrepreneurship than from jobs. Like, if you just look at the the net worth distribution between entrepreneurs, you have like seven orders magnitude versus like lawyers like, yeah, there's probably a lawyer that's making 6 figures and there's probably a lawyer that's making 7 figures.
Speaker 1:Yeah. There's no trillionaire I don't understand is like at what point in the last twenty years was a good time to just be looking for a job and just like going on job boards and applying randomly? Like Mhmm. Was was there a point? I I graduated in 2018.
Speaker 1:Certainly at that point, going and just applying without Any trying to find other other ways in was not super effective.
Speaker 2:Yeah. I mean, in in in the lead up to the global financial crisis, like, finance industry was so 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 a Goldman, a Morgan Stanley, JPM, or go into consulting at Bain, BCG, McKinsey. And this was like a very established track for, like, upwardly mobile, like, you know, neo elites, basically. Basically.
Speaker 2:And and and that and that still exists to some extent, but it is maybe more fragile than we previously thought.
Speaker 8:And I would say pre pandemic for the tech industry. Right?
Speaker 2:Yeah. Yeah. Google, Microsoft, they would just be on campus. They would have like
Speaker 8:used to be known as
Speaker 2: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, think is what's causing a lot of anxiety among the young folks. Anyway.
Speaker 1:What is your current advice for for those individuals? Is it the same as the speech you gave?
Speaker 8:Yeah. I think you've got to do more to get in front of people. I mean, even just as a business owner. Yeah. I'm just gonna keep saying that.
Speaker 8:It
Speaker 1:goes way harder.
Speaker 8:It goes way I have really I've had so many applicants, it's just been such an honor. I'm like 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 I wanna 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 gonna use AI to do these other things. Mhmm.
Speaker 8: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 and a knowledge of these tools and what you can do and what you can offset to them, I think is
Speaker 2:huge. Yeah.
Speaker 8:I mean, don't I I guess that sounds like a cop out, like just learn the tools, but I I really believe that someone who comes to me and says, actually I'm gonna use this and this and this and I'm gonna do that task
Speaker 1:Yeah. The bar is not that high. I remember when I when I was a teenager, if you could like make a website even 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.
Speaker 2:I mean, they like yeah. It does feel like somewhat basic advice, but, like, applying to a 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 Yep. And you're just in the top 1% of applicants because 99 other people just clicked like the apply button and
Speaker 8: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 other 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 is just gonna try to impart to you some of the 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 gonna learn the skills on the job, how are you ever gonna learn them?
Speaker 1:Yeah. Any theories about how it's my last question that's top of mind for now. Theories about how AI wearables will evolve. Do we do you feel like we need do you think there's space for new AI hardware? Or I'm assuming you tried everything
Speaker 8:tried I I'm I'm look. I come out of the at end of the book. I think this is gonna 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, and that experience is very good. Mhmm.
Speaker 8:And so we we're gonna get to the companionship thing one day, but whether you're using it as a companion, which I hope people aren't really, you know, 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, a personal career coach, trainer, just assistant, interacting with it through a pair of glasses or a wearable that you 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 we had efforts at it with like the humane pin. It just didn't do much for you.
Speaker 8: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 in both with voice and microphones, I think it's gonna be pretty cool.
Speaker 2:Yeah.
Speaker 1:Yeah. The the thing that I've been thinking about, everything so far, I think has been cool demos. Yeah. You know, not not quite ready for to be real products, but things that if they were shipped internally at a big company, like if Humane was was a product that had been shipped internally at Apple, like, this is like kinda where we're we're headed. Right?
Speaker 1:Yep. It would have been gotten a great response internally and probably gotten more resources but not ready for prime time. 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?
Speaker 1:Yeah. Inbox 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 still allows you to stay kind of connected 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
Speaker 8:The right text and getting distracted by a notification next thing.
Speaker 1:Right? And 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 I have a buddy who like only uses his Apple Watch on the weekend.
Speaker 2:Mhmm.
Speaker 1:So he can't really use apps. He can like generally stay in contact. He's not sending emails. Yeah. He's just saying like, yeah, if you wanna get ahold of me, you can, but I'm not.
Speaker 1: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.
Speaker 8:Yep.
Speaker 1:And if they can't be and you need UI, you can just generate something like that on the fly a little bit more, a lot more easily. And so I think there's a moment here and 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, like, insane connectivity that everybody's been sort of just fallen into over the last decade.
Speaker 8:No. I 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, 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.
Speaker 2:Oh
Speaker 8:yeah. 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 I was like, wow. This it makes this watch feel dumb sometimes.
Speaker 1:Yeah.
Speaker 8:Right? And like I wore the the the Bee bracelet with Bee was acquired by Amazon at the August 2025.
Speaker 1:Oh, yeah. That was sort of random at the time or felt a little bit random.
Speaker 8:Yeah. And Limitless was another one I wore and they were acquired by Meta. I think that
Speaker 1:Oh, yeah.
Speaker 8: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 and what you say you're going to do, it is there was many times where I was like, this is a holy crap moment. I was like, wow. I said I was gonna do all these things and now my app just told me to do them. Right? Or to your point like
Speaker 1:Well, it really interesting when that it doesn't just make a to do list, but it does those things. Right? Hey, order these things from the you know, order these things from Instacart. Book this reservation.
Speaker 8:It's far away from that, but it could be so cool.
Speaker 1:Yeah. Don't know. I mean, far away could be a year. Maybe. Yeah.
Speaker 1:We'll see.
Speaker 8:But like there's this perfect example where I say, like, my Bee bracelet has picked up on me saying that I need to call the plumber, and I forget to like I keep forgetting to call the plumber, and keep telling my wife, oh yeah, I'm going call the plumber, but my Bee bracelet keeps adding it to the list every day. Right? And yeah. Why couldn't the why couldn't we have the agent call the plumber, and then the plumber's just like We
Speaker 1:created magic. We created artificial intelligence that just creates more to do lists. And plumbers. Yeah.
Speaker 8:With my broken toilet in my house.
Speaker 1:Well someday we'll get it fixed.
Speaker 8:Yeah. No, I think the look, I think OpenAI and whatever they're making with Johnny Ive is gonna be it's gonna be worth paying attention to. I don't know if it's gonna be a mass scale thing that's gonna be absolutely worth paying attention to because I think I've specifically had some ideas about our dependence on phones and where I think that's gonna play into this messaging of any of these devices is where we're going beyond phones. Yep. But to be clear, the phone doesn't go away.
Speaker 2:Yeah. Yeah. Yeah. How do you think about the the trade off of like all this happening and then, you know, your position that you should not fall in love with an AI bot?
Speaker 8:Don't do it.
Speaker 2:It feels like reflective like you said here. 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 questions like about society and business, like I'm a huge beneficiary of social media as are you.
Speaker 2:We use it to market our products effectively build whole businesses on top of. 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, like, the companies to roll things out responsibly? Like, Replica clearly exists. We've had the founder on the show multiple times.
Speaker 2:And but I don't know. It's like, are we going towards, like, national conversations, bans on certain usages, like
Speaker 8:And where I get on that is very like, look, we we should just have a ban on companionship chatbots and bots and toys for kids. Like we don't need it. Why do we need it? Yeah. Right?
Speaker 8:We're getting there in some ways with social media. Yeah. I think
Speaker 2:That sort of worked for cigarettes. Like we banned them for kids, and then we banned a lot of the marketing, and eventually like the younger generation just sort of stopped picking it up.
Speaker 8:And this is where I think are we going to ban AI for kids in general? No.
Speaker 2:Yeah.
Speaker 8:Right? Yeah. There's going to be the educational, the the con academies Yeah. 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's we have to do that and I I talk about that with my own kids in the book.
Speaker 8:But why do we need our kids turning to chatbots about their problems?
Speaker 1:Yeah.
Speaker 8:No. Just don't have it happen. I mean it's caused so many problems for OpenAI.
Speaker 2:Yeah. Totally.
Speaker 8:Right? Yeah. Like there's been nothing but a problem for them to
Speaker 2:have kids
Speaker 8:or teens talking to chatbots about their problems. Yeah. Maybe there's examples of some good of it. Yeah. You
Speaker 2:know. Just KYC those features off like it's Yeah. Another thing.
Speaker 8:Yeah. Yeah. And like I think it's harder
Speaker 2:YouTube's done a great job of this too. Like sort of I mean after a
Speaker 8:long time. Exactly. But
Speaker 2:they eventually figured it out
Speaker 8:next Exactly. And that we feel like we're in that moment. That's a really good example. Feel like we're in that moment of like, you know, kids' early days being on YouTube rabbit holing into dark conspiracy theories. And look, you can still, those things still happen, but I 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.
Speaker 8:And again, not saying it's perfect. And Mhmm. But to your question, can these companies self police it? I don't know. Like they they probably are gonna have to because their government is not gonna do anything.
Speaker 8:Need yeah.
Speaker 2:Is it.
Speaker 1:Mic down.
Speaker 8:Mic down. Mic Yeah. Non effector.
Speaker 2:I wonder, it is odd that you see increasing demand from American consumers for these weird products, weird use cases like AI romantic companions. And yet you also see you also hear the booze like I don't but want then I go and I buy it or something. It's like this weird I mean, obviously it's multiple different
Speaker 8:And I have seen that a lot today on the timeline. How many of these kids that are booing also were using, you know, to write their essays or write their resumes?
Speaker 2:That's a little more optimistic. But the the the weirder one is like, yeah, protesting the AI while while pulling like the darkest pieces of the AI out or demanding it. But I don't know. At the same time, there there was a lot of fear mongering about Elon Musk and x AI like really leaning into the romantic companion. And same thing with Sora too Yeah.
Speaker 2:To a similar extent of like this is Infinite Jest. It's gonna, you know, you're gonna 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 x AI is now doing like code completion with cursor Right. And like serving clot.
Speaker 2:Right? Right. And that's a much more like functional, I would say like the good outcome versus like the Ani and Valentine thing which
Speaker 8:is Right. A little And then
Speaker 2:there was like the mecca ones.
Speaker 1:I remember thinking at the time XAI needed to do that to basically differentiate because the Right. General chatbot market had run away from them.
Speaker 2:Yeah. We did some back of the envelope on it. We were like, maybe this is like a multi billion dollar business, we were trying to underwrite like
Speaker 1:Yeah. Somewhat of a white somewhat of a white belt.
Speaker 2:Even if you're just like, put all the moral stuff aside like, is x AI gonna 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 was weird. But then the market just sort of rejected it.
Speaker 8:The market I'm sure there are a few people that still
Speaker 2:For sure.
Speaker 8:Use
Speaker 2:this. For sure.
Speaker 8:Annie, if she's still alive out there.
Speaker 2:I think she is.
Speaker 8:She's still there? She's there?
Speaker 2:I I yeah. I I think
Speaker 8:You don't know by
Speaker 2:Although although the the computing resources are getting sold out of the back of the truck left and right. That's Anthropic was I'm sorry, Annie.
Speaker 8:Right. Annie had
Speaker 2:to Good luck. You're gonna
Speaker 8:have to is actually improving.
Speaker 2:You have to think less, basically. And cursors, Michael Trullo's like, look. Annie is gonna be running a very old model. It's actually gonna run on CPUs now. Just a smarter child that just reflects whatever you
Speaker 8:Yeah.
Speaker 2:Say back to it. That's it.
Speaker 8:It's a line command.
Speaker 2:Yeah. It's more of a small language model now.
Speaker 8:Yeah. I don't know. I think you kind of go back to like the replicas. Yeah. There there is a mark they have pushed marketing these these kind of companions.
Speaker 1:Yeah. Character AI.
Speaker 8:Yeah. Yeah. And Meta did it for a little bit too. Think they'll probably pull away from that with their
Speaker 2:Yep.
Speaker 8:Celebrity Yeah. Companion blah blah blah. But I could also see them leaning into it more too because it is a social network. And they do see this as all eventually as Mark Zuckerberg has said, us having personal assistance and personal super intelligence. Yeah.
Speaker 8:And that probably has to come through the view of a some sort of bot. Yeah.
Speaker 2:I don't know if
Speaker 8:it needs to be like a sexy bot. Yeah. Like a cow.
Speaker 2:They they Maybe that's of them. That that was one of them. Was it? So basically, the the whole story with that, they it went viral because there was one that was like stepmom or something like that. It was like a little bit crude.
Speaker 1:But that was
Speaker 2: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
Speaker 8:The Snoop Dogg.
Speaker 2:The visit the like, the sins of the creator revisited upon Meta incorrectly, but there were some funny ones like cow, and you could just talk to a cow, which I think is nice.
Speaker 8:You know what? I don't remember the last time anyone fell in love with a cow. So that sounds fine.
Speaker 2:I think it sounds fine. You know? Anyway.
Speaker 8:Pita might have some problems. I don't know.
Speaker 2:No. Digital cow, what's not to like? No. Anyway, congratulations on the both It's been honor
Speaker 1:to follow your business owner journey.
Speaker 8:Yes. It's been an honor to be named a business owner by sitting here.
Speaker 2:Yes. What what
Speaker 1:I mean, you didn't we can't make we can't you you don't become a business owner.
Speaker 8:Yeah. No. But you gave me that title.
Speaker 1:I know. But you got that title by selling products.
Speaker 2:By running a business.
Speaker 1:Revenue. Revenue. Revenue makes you a business owner.
Speaker 8:But, you know, I felt like when I walked in the store, was a founder.
Speaker 2:Okay. And you walked of owner.
Speaker 8:You're right? Thank you for the business, guys.
Speaker 2:Yes. I appreciate Oh, there we go. Perfect.
Speaker 1:You got it. There you go.
Speaker 2:There we go. Wait. Nailed
Speaker 8:it. Thank you for
Speaker 2:having me. Ridiculous. And thank you for tuning in.
Speaker 1:Thanks for tuning in.
Speaker 2:The latest Fox Taurus on Apple Podcast and Spotify. Sign up for a newsletter at tbpn.com, and go get the book.
Speaker 8:I am
Speaker 2:not a robot by Joanna Stern. It's available everywhere books are sold and we will see you tomorrow at
Speaker 1:Pacific. We love
Speaker 2:you. Goodbye. Goodbye.