Vision Pro：用还是弃？Meta 对 Snap、SaaS 复苏、AI 投资与房地产危机

Jason Calacanis (00:00:00): Alright. Friedberg is back. Welcome back to the all in podcast. Episode, a 160 something. Your favorite podcast in the one old yadayadayada. With me again, the chairman dictator from off Palihapitiya. Loma Raymond. Yeah. Definitely David Sacks is here. And Sacks from his time, in the metaverse, we found him somewhere out in space in the solar system, in his apple goggles, Your favorite Sultan of Science David Friedberg is back from the metaverse. David Friedberg (00:00:29): I missed you guys. Jason Calacanis (00:00:29): Welcome home. David Friedberg (00:00:31): Sacks for having me. Jason Calacanis (00:00:32): What what did you discover when you went to your reign in in Google class? Sorry. Apple, did you actually use David Friedberg (00:00:39): the Apple Vision Pro Takeout? Jason Calacanis (00:00:40): I ordered them. I ordered them. And I walked by the Apple store, and I was gonna go in and try them. And there were so many lunatics in there. I was like, yeah, I'm not doing it. But I ordered them. You use you actually used them. David Friedberg (00:00:50): I ordered 1 online to be delivered, and it was, like, delayed by a month. So I went down to the Apple store and picked one up. Okay. And my kids cannot stop using it. David Sacks (00:01:00): Oh, really? I went down to the Apple store, but I got cleaned out by the thief all everything. So Jason Calacanis (00:01:05): Imagine the Oakland 1. Speaker 4 (00:01:09): The weather's wide. Rainman Jason Calacanis (00:01:13): David Sachs. Speaker 4 (00:01:16): And I said We open source it to the fans and they discard the reason. David Friedberg (00:01:24): That was crazy. That was crazy. Jason Calacanis (00:01:26): Well, put the video in here to the idiots who are robbing AppleSource. All the devices get bricked when you steal them. And they all have GPS in them. Have you tried it, Chima? Chamath Palihapitiya (00:01:34): No. It's too busy working out, making love, and winning. Oh, okay. Got it. Jason Calacanis (00:01:39): So you were you were making sweet love. You were watching your portfolio go up, and, you were just generally winning. Got it. Got it. Yeah. Yeah. So, Friedberg, the rest of us were being men. In the world accomplishing stuff. But but do tell us about your time in the metaverse. Chamath Palihapitiya (00:01:54): Do those goggles come with, a lifetime prescription of SSRIs? David Friedberg (00:01:59): You guys sound like one of these, like, tech journalists that are actually anti tech people. You guys are Jason Calacanis (00:02:04): Actually, tech journalists like it. David Friedberg (00:02:06): Talk about the last gen computing platform. I remember when the iPad came out and everyone pooped with the iPad. I thought it was stupid. I tried to use it. I couldn't get any value out of it. And in 2010 or 2011, when did it come out 2010, 2011, we started using it with our sales team selling to farmers And we gave every sales guy an iPad, and they went out in the field with 3 g, and they were able to close sales in the field meeting with farmers, which never been done before. Usually, I have to get a farmer to come into an office. How many iPads on the product? Oh, so we we have, like, 9. Jason Calacanis (00:02:36): We're selling Climbing.com software. David Friedberg (00:02:38): Yeah. We we dozens of these sales guys, we gave them out to our sales agents as well. The independent agents, they started using them, and it was like a real game changer in how sales was done in agriculture. I had never even contemplated that when I first used the other package. Jason Calacanis (00:02:50): It's like Let's let's get to brass Sacks here. What is the killer app? What do you think in the Sacks 5 years, people are gonna be doing with this thing on a daily basis. Is there a daily use case? David Friedberg (00:02:59): I'll say a couple things. One is, like, I feel the same way I did the iPad, which is I don't know what it is today, but I can tell that there's something there. And I'll give you an example of something I thought about. First of all, the AR is game changing. Jason Calacanis (00:03:10): Okay. David Friedberg (00:03:10): If you've used, like, the meta Yeah. The Oculus Quest. It, like, makes me super dizzy, makes my head hurt, makes my eyes hurt, like, you're super disoriented. What Apple solved is that you're, like, still in reality, but then you get to interact with these three-dimensional kind of objects in reality, and it's, like, really well done. It's definitely V 1, and there's gonna be incredible changes in the next couple generations, but it gets rid of all that dizziness disconnected kind of stuff that happens with the, the full VR experience, which I thought was really incredible. Then last week and I'm sorry I missed this show. We have a facility with my company in North Carolina. We have this giant greenhouse facility, and I was doing meetings with farmers and stuff. I go to the greenhouse facility and there's so much work that the greenhouse texts and lab texts are doing where they're using an iPhone and a barcode scanner and a printer, and they're holding all these pieces of equipment, scanning the QR codes on flowers, taking the pollen out, putting it in the next flower, training each other how to do it. And I was like, I put this Apple Vision Pro on, and I was like, man, all the the apps and all tools that we had all these different pieces for that was taking people tons of time, image collection, data collection, could all just be done streamlined while you're working. You could have a Jason Calacanis (00:04:23): task list. Yeah. Yeah. You have a task list to write. David Friedberg (00:04:26): Cameras are taking images in the middle. QR codes are automatically scanned. Data is being ingested. The task list is kind of, you know, giving folks next steps. They can listen to music while they're working. And I realize for that job. And I met with all the the team out there and spent time with them, and I actually did the work that they do to get a better sense for the workflow. And I was like, man, literally every aspect of this job will be massively improved, and productivity will go up by 10 x with these goggles. Will it happen in the next couple of weeks or months? I don't know. But my engineering team looking into it. Can we take it? Can we use some software? Can we build some software? And can we put this on folks to give them a better work experience, increase our productivity, do automated data capture. So I don't know exactly where it goes, but I could start to see how this can become a more ubiquitous part of a workforce setting and not just be a video game, and movie tool for consumers. So I'm I'm reasonably optimistic about where this goes. It's definitely V1. I feel like it's the iPad days where no one's really sure where the applications are, but yeah. Jason Calacanis (00:05:21): Enterprise applications. Unbelievable Sacks total sense. And also training training. Right? David Friedberg (00:05:25): Assembly line workforce, warehouse workers, where you're getting real time kind of task updates, data's being ingested all in real time. And and by the way, the other thing I'll say is training is incredible. There's spatial video recording on it. So it looks like you're living through the experience that someone else had. So you can train someone how to do a difficult task. And rather than have a human go spend hours training a workforce. The workforce can be trained by the goggles in a way that you cannot do a two dimensional video today. So I don't know. I'm I'm I'm pretty optimistic. Very strange days. Right? Jason Calacanis (00:05:55): I don't know. You're you're a fan of sci fi. I remember strange days. David Friedberg (00:05:58): Totally. Jason Calacanis (00:05:58): So come off. What's gonna happen 1st year? Are humans gonna become more like robots by putting these on and do this factory work? Or is Elon with optimists and some Chamath Palihapitiya (00:06:08): of humane, I think, is the Jason Calacanis (00:06:09): other one. There's a couple of other people building a general use robots. Chamath Palihapitiya (00:06:12): Figures the other one. Jason Calacanis (00:06:13): Figuring out which one wins the day? Is it gonna be humans having eyes and, you know, data collection like robots or robots having appendages like humans? Well, let me let me put two ideas together and Chamath Palihapitiya (00:06:28): see what you think of this argument. Jason Calacanis (00:06:30): If you think about the generation of human Chamath Palihapitiya (00:06:30): beings that have as close to any other generation before it lived in a totally immersive world. I would say the best representation of that are current teenagers and twenty year old people, and maybe at the upper edge, the early thirties people. And why is that? You know, they've lived inside of social media their entire lives. They've lived inside of immersive video games their entire lives. But the question is, Are they better off and happier as far as we know from an evolutionary perspective? And I would tell you that the answer is a is a huge gaping no. So if you believe that the rise in depression the rise in suicide, the dependency on drugs, the dependency on SSRIs, the sexual promiscuity, the lack of marriage, the lack of kids, If all of those things are, in some ways, a correlated by product, let's not say it's causal, right? Let's just say it's got correlated by product, of this entire immersive, almost exclusionary detached world that these Jason Calacanis (00:07:37): folks have grown up in, taking Chamath Palihapitiya (00:07:38): that to the limit I'm just gonna put out there. It may not be the solution to our problems. And so I guess the more directed answer to your question is I would hope that the latter winds so that we take these goggles off and actually learn how to talk to each other and look each other in the eyes, get married and have children because I think that's actually better for the world. And I would probably say that it's almost better for the world than a 10xing of productivity. Interesting. And then you see the correlation to cancer and disease that is disproportionately higher amongst these young people. So I think it's at some point to ask ourselves What is structurally happening in the lives of these Sacks, you know, fifteen to thirty one year olds that is just so poor in terms of outcomes? And if you look at some of the environmental variables that they live in and then take some of those and take them to the limit, I think that there's a reasonable argument that their lives get worse before that gets better. Jason Calacanis (00:08:42): Yeah. I mean, the amount of time you spend on social media is correlated, with depression. Not on Chamath Palihapitiya (00:08:46): social media. I'm just saying just this immersive, like, from the world and live through a microphone and glasses taken to the limit, I'm not sure is the solution to these kids feeling detached, lonely, isolated, Jason Calacanis (00:09:00): isolated. Yeah. I mean, it it correlates all of these things that we're seeing in this younger generation correlates the introduction. So could it be a Chamath Palihapitiya (00:09:07): good productivity device? Yes. Of course. Jason Calacanis (00:09:09): Do I Chamath Palihapitiya (00:09:09): hope it's a good productivity device? Yes. But if we try to make it the panacea for anything and everything, I think we're we're going to compound the systemic issues that these young people have. And I suspect on the margin, if you were gonna bet, All of these things that we see in these young people today will get worse as a byproduct of technology, not necessarily get better. So if you can take a different path like optimists or the figure AI robots where that work is done. At least we have a different problem probably maybe even more existential. Abundance. But a different problem, which is now how do you find purpose, but maybe you can find purpose through connection and that types of things that humans have been bred over 1,000,000,000 of years to actually optimize for. Jason Calacanis (00:09:53): Okay. Sacks, I remember when You were starting crap. You fired up like a group for VR, and and you got pretty heavy into it. You made a couple of small bets. I remember. They I don't think it any of it worked out. Really, you could tell me if I'm wrong here, but you you got in a little bit earlier there. Maybe you could talk about the business case for this and has that changed? Because you you believed. I believed a lot of folks thought, hey. Maybe this is the time when zuck really start, you know, had bought Hawk and and they started putting out some good product. Seemed like it was a false start. Is this the actual starting pistol? And is this the start of the VRAR Adoption race? David Sacks (00:10:31): I don't think we're quite there yet. Jason Calacanis (00:10:33): Okay. David Sacks (00:10:34): We've been talking about VR being a thing for over a decade. Jason Calacanis (00:10:39): Yeah. No. More like 30. You remember the Nintendo VR stuff? It's David Sacks (00:10:43): like always on the verge of happening. I think the the big complaint about the Apple devices has a lot of capability, but it's still a pretty huge device to wear on your forehead. This is not really gonna be comfortable enough to be something that people want to use all the time. I mean, there's also a question of use cases, but they're getting there with the use cases. In any event, I I do think that Apple Vision Pro is it's, like I said, last week, it's a useful prototype or proof of concept and it will get better. So I'm glad they did it because I think you need to start somewhere and then just keep iterating. But eventually for this to, I think, really take off, you need to shrink the form factor, miniaturize the technology. Does every version of it make it simpler lighter, easier to use. Jason Calacanis (00:11:33): Yeah. I mean, eventually, it'll feel like sunglasses. And so that is, I guess, If they become, like, regular glasses, I think we all agree. David Friedberg (00:11:41): Right. It becomes I don't know. I gotta tell you. I feel like it's pretty damn comfortable. I don't know if you guys you guys haven't really used Jason Calacanis (00:11:46): That's what I've heard. That's a surprise. It was totally people online saying it's unlike any David Friedberg (00:11:50): other headset I've ever worn, they did an incredible job designing air conditioning. Jason Calacanis (00:11:54): Does it feel ski goo goggles. David Friedberg (00:11:57): It doesn't feel heavy. It doesn't feel press pressure compared to Jason Calacanis (00:12:00): ski goggles. How if you were wearing ski goggles, David Friedberg (00:12:03): it's less stricting than ski goggles. It's more comfortable. It like floats on you a little bit. They did a great job with this cushioning device they built and the band you put on. It feels very It's Apple design. Right? It's like a really well designed product that's unlike anything else you've ever tried. Jason Calacanis (00:12:17): I've always felt like when Apple comes into the race, that's the starter's pistol. And I think this is it because I've I've heard the same thing from everybody. You you have to try it. It feels like different than Oculus and some of those versions that came out previously. And they have the app ecosystem, and I I would not discount that when know, the ability to monetize the app ecosystem and have all the people who are already building the comm app, the Uber app, whatever, notion. Well, you know, all the stuff that people use and love, Spotify, YouTube, and then poured it over here, Fortnite, whatever, I think that's gonna be the magic. And the statistics are not lying here. I mean, this is unbelievable. They've sold already 200,000 units. Which doesn't seem like a lot, but for a v 1, that is a lot, and they're gonna sell a half 1,000,000 this year. It's gonna be close to, like David Sacks (00:13:05): That's not that many. Well, Jason Calacanis (00:13:05): it's a couple of bills. David Sacks (00:13:06): I mean, Meta sells more. Jason Calacanis (00:13:08): They do. Yeah. But, you know, this is $4000. This isn't 500. So to sell that many of a $4000 device is incredible. David Sacks (00:13:17): It's not like a regular Apple product that is a mass market device that tens or 100 of millions of people are gonna buy, but it puts them on a path Yeah. Where they can iterate and keep making it better. David Friedberg (00:13:30): See, I think Jason Calacanis (00:13:31): and this is, I guess, what I'd Friedberg. Do you compare this to buying a MacBook Pro? Buying an iPhone or buying the Oculus, you know, whatever, the, you know, $500 unit, because everybody I see talking about online is comparing it to the purchase of a laptop because of the desktop, and you can kinda do your coding or surf the web and do all that. Do you put this? Is it buying a TV? Is it buying a laptop? Is it buying a smartphone? David Friedberg (00:13:55): Well, you have to have a keyboard to be really productive on it. If you're gonna use it for writing purposes or coding purposes. So it doesn't really work with just the the headset, but you could do that. Yeah. It's definitely like buying a new computing device, but people felt the same way about the iPad. It get go back to 2010 when the iPad came out. And everyone was like, Jason Calacanis (00:14:15): Who is it for? David Friedberg (00:14:16): It's a whole new computer. Who's it for? You already have a phone. You already have a computer. Why do you need an iPad? And then they sell tens of 1,000,000 a quarter now. Yeah. So I really I as I do the math on this, I was just kinda doing some back of the envelope stuff. I think they're gonna sell a $100,000,000,000 of Apple Vision Pros not this version, but this version plus the next version probably over the next I would guess for them to get to a 100,000,000,000 in sales, it'll take them less than 5 years. Jason Calacanis (00:14:42): I think they're gonna run the table on everybody. I think they're gonna own the entire space. David Friedberg (00:14:45): Everyone's underestimating this as a new computing platform and once these applications, particularly in the enterprise setting, start to kick in. And I will say that the movie watching experience is way better than watching on a TV in your living room. My kids cannot stop asking me to use the goggles to watch instead of an iPad or TV. The cause you see three d like, all Pixar movies are natively three d, And so you've got the Disney plus app on there. You watch a Pixar movie, and you're watching in 3 d. The kids are blown away. So I I think we're all gonna be surprised by how this Jason Calacanis (00:15:14): stuff Disney's all in on it. David Sacks (00:15:15): Remember when our parents close not to sit too close to the TV. Now we're just strapping the thing to our face. Jason Calacanis (00:15:21): Yeah. I had the most Silicon Valley moment ever. I go to buy a cup of coffee. I was going for my little walk. I go I see blue bottle. I'm like, oh, you know what? I get myself a mocha. You know, I lost a little bit of weight on treat myself. $9 for a mocha. Number 1, not Chamath Palihapitiya (00:15:35): In the city. Tilted me. $9 from Well, it Jason Calacanis (00:15:39): was $8. And then I gave a dollar tip then I felt cheap giving a dollar. Chamath Palihapitiya (00:15:43): So it's 8.99 for a carton of clover of milk, all organic. Jason Calacanis (00:15:47): I mean, do we that's you Chamath Palihapitiya (00:15:48): can make infinite lattes at home. Jason Calacanis (00:15:50): Anyway, where David Friedberg (00:15:51): did you go for your $9 MOCA? Jason Calacanis (00:15:53): I was I'm in Palo Alto right now because we lost bottle? Yeah. And I I posted this, I'm like $9. What am I doing? You know, I just, I, I felt like buying a chocolate bar. Chamath Palihapitiya (00:16:03): Look at the stain of dirty lips left on the cup. Jason Calacanis (00:16:08): Know what? You're a little obsessed with my lip. Take it easy there. Yeah. Yeah. So anyway, then there's a kid in the place wearing the goggles with David Friedberg (00:16:16): the keyboard. No. Stop. Jason Calacanis (00:16:18): He's getting work done. This kid was doing work. And I tell you the truth. Chamath Palihapitiya (00:16:23): In the hours. He is putting in the hours. David Friedberg (00:16:25): No one looks at your laptop. No one Jason Calacanis (00:16:26): looks at your screen. That's what I love about it. David Friedberg (00:16:28): -You can do all your work without anyone seeing what you're doing. Jason Calacanis (00:16:30): This can have 4 desktop stuff. This guy was probably on porn hub, Spotify, writing code. Chamath Palihapitiya (00:16:36): How many words did this person say to another human being while you were there? Jason Calacanis (00:16:39): No. 0. And you know what? But when they're on a laptop, they're the same. David Friedberg (00:16:42): What's the difference? He's he's coding. Jason Calacanis (00:16:44): Nobody batted and I. I think this is gonna they're gonna run the table on this. David Friedberg (00:16:50): 100,000,000. 100, 5 years. Yeah. I take the over. Jason Calacanis (00:16:53): I take the over. What do you you got the over, the under? Because even if they keep David Friedberg (00:16:56): it at $3,000,000,000 units to get to a 100,000,000,000 They're gonna make up a lot of money on this App Store too, Jason Calacanis (00:17:02): by the way. Chamath Palihapitiya (00:17:02): I think that you guys are right that it's gonna be successful in terms of revenue. Mhmm. What I'm asking is a more societal question is do you guys actually think it's better? Jason Calacanis (00:17:10): No. I don't want my kids in this all day. No. Chamath Palihapitiya (00:17:11): And I could see this becoming super addicting. Hey, Friedberg, I I can I buy 3 for your kids? Just have them walk around with them. David Friedberg (00:17:17): Oh, I have a no one. Rule as well. Chamath Palihapitiya (00:17:19): But wait a minute. Jason Calacanis (00:17:19): Hold on. Chamath Palihapitiya (00:17:20): What about productivity free brew? David Friedberg (00:17:22): My kids aren't trying to be productive. They're using a Chamath Palihapitiya (00:17:24): different only have a productive childhood. Jason Calacanis (00:17:27): It's supposed to be not productive. Chamath Palihapitiya (00:17:29): You guys understand that at some point, you guys will be the only 6 kids whose parents haven't given them the stupid thing to put on their face? Jason Calacanis (00:17:37): No. This is gonna be time restricted. David Friedberg (00:17:39): I even know I've had no phone. No. Like, I let them use the headset Chamath Palihapitiya (00:17:43): because we got it for them. No. No. David Friedberg (00:17:45): It burns their burns their brain away. Burns their brains turn away. Jason Calacanis (00:17:48): I, man. David Friedberg (00:17:50): I I totally agree with you. Social interaction, the loss of our ability to communicate as humans is critical, and it's a fail point. I do think that there are applications where these things create great unlocks. Jason Calacanis (00:18:00): I think this is an enterprise device. Chamath Palihapitiya (00:18:01): Can you imagine giving field sales team on the farms to go there? They can take off their sweaty headset when the sun is shining and then give it to the farmer to put on. And then he can put it on and feel the sweat and the the headband will be wet. David Friedberg (00:18:15): It doesn't by the way, it's it's a very personal device. In order to log in, you know, it does like a I scan, or you have to have, like, a login, like, log in like you do with your phone, but then you gotta reset eye because it automatically sets the eye position. So when you put on someone else's headset, you gotta reset the eye. It's a whole thing. So it's not a transferable device. It's a very personal computing you know, kind of thing. Jason Calacanis (00:18:36): So I don't think David Friedberg (00:18:36): it's gonna be the same as, like, an iPad or a phone. It's a very different kind of thing. I don't know what it's gonna look like yet. Jason Calacanis (00:18:41): I don't know. I I I say week, we do the show inside of these, or at least me and you, Friedberg, we'll be we'll be does is is there a David Friedberg (00:18:48): there's a There's an there's an avatar thing. Jason Calacanis (00:18:51): And so David Friedberg (00:18:51): what it does, it scans your face while you're talking, and it'll all can see each other as the avatar. Yeah. Jason Calacanis (00:18:56): Alright. Let's do it. It'll be hilarious. Chamath Palihapitiya (00:18:58): I had a moment this week in parenting. I had a moment this week where I told one of my children that when I send a text message, I expect an immediate response Otherwise, I am going to cancel that child's phone and take it away. And then separately, when they respond, it has to be instructed, well thought out perfectly formatted English. And then then third, I said every single email I see from you interacting with your teachers or Anybody else that's there to help you needs to be incredibly well written and formatted. And if I see garbage English, I'm gonna take your phone away. Jason Calacanis (00:19:34): Oh, okay. So you don't want them on their phones, but they have David Friedberg (00:19:37): to respond right away. Well, they have very strict rules on what Chamath Palihapitiya (00:19:40): they can use. They're there for literally the all they can do is communicate. Like, they can use IMessage. But it is shocking to me that despite the lack of games that they have or whatever, how poor they are in being able to communicate. And what little access to devices they have have already made them orders of magnitude less able to communicate than frankly I was able to when I was their age. And so I can just imagine what happens when you become even more ensconced in something that you can cocoon yourself with and not have to interact with the rest David Friedberg (00:20:14): of the world. Agree with don't disagree with you. Chamath Palihapitiya (00:20:16): Not to say that it's not gonna be a revenue generator, but I think that you could just as easily, frankly, Instead of impacting Apple's revenues, you can probably go along the makers of SSRIs. Jason Calacanis (00:20:29): POT. Here comes the spread trade. POT. Chamath Palihapitiya (00:20:32): Bumble and Tinder, and you'll get to the same place economically. Alright. Alright. Here we go. We've got a lot on the what a great leap forward for humanity. I can't wait. Side. Jason Calacanis (00:20:42): I I I just see this as a laptop replacement. Okay. I wanted to talk a little bit about what apparently is gonna be the the spread trade of the last year. Meta is continued their unbelievable run and snap dropped, like, 30 percent. Here's a chart for y'all of Snap versus Meta. You can take a quick look at it here. And just for context, both companies David great during COVID and zerp hit all time highs in 2021, but they both got crushed due to the ad spend pullback, obviously, but then Meta started to get less focused on their headsets and more focused on AI, started doing their reduction in headcount, 22% year over year from 86,000 to 67,000 in the last quarter for Meta. And their quarterly profits have increased to an all time high of $14,000,000,000. That's profits Sacks in q 4 for meta, all time high for the stock price $470 a share, $1,200,000,000,000 market cap, snap down Sacks% from its closing price on its IPO day in 20 17. Let me just jump to Chamath before I get into more charts and everything. You pointed out Chamath, and maybe you could explain to the audience just how ridiculous The voting rights were and the massive, dependence that the snap team and the executives had on stock based comp 2 issues for each Chamath. Well, I mean, I think Chamath Palihapitiya (00:22:08): I said it before. I think that case studies have been written about how tilted the governance is in snap. I think the point is that they basically have infinite to 0 voting power over common shareholders. So there's no real feedback loop, and I think that that has probably adversely affected the types of people that traffic in their stock. Now, look, activists Jason short sellers sometimes have a very bad reputation, but if you steal man their side of it, what they are there to do is to shine a light on inefficiency and in the short seller case sometimes in propriety, but it should all lead to companies being better run. Right? I think Meta had this example where they had a really big hiccup and everybody, including us, sort of pointed out, the levels of spend that they were making really didn't make any sense. I think we had a chart that compared the level of spend of meta, second only to, like, the spaceship program, right? Just like bonkers, an enormous amount of money. And look, Mark got the message He heard it loud and clear. I think he got fed up with whatever was going on there, and he fixed it. And It's in the numbers. Now I don't know Snap because to be honest with you, I've never taken more than one second to look at that company. And the reason is there's just zero ability for me to have any useful say, so I've never honestly looked at its performance I've never studied a single characteristic. Folks who could give a reasoned opinion on how to make it better, land, and part of the reason is because there is no feedback loop that matters. Yeah. And when you know that, why would you waste your time? At least in the meta options, right? There are other options and and meta was another one. You know, you can write a letter. It gets picked up on CMBC and Bloomberg and whatever. And all of a sudden, they kind of pay attention. And I think and you look at Disney, Nelson Peltz goes in gets hike Pearl motor shares, buy some more, takes a large Yeah. We'll see whether that fix itself. The point is that in all of these other cases, people are investing the time because they think that there's even a small shred of a chance that the company listens. Chamath Palihapitiya (00:24:36): But if you literally have no say, you couldn't even do a proxy. You couldn't vote the shares. Why would you bother? And I think that that's more of an example where maybe there is a, so I don't even know why snapped it poorly. And again, I'm not gonna really take the time because it's like, why bother taking the time? Jason Calacanis (00:24:54): Sacks, should, should they unwind this, like, no voting, common shares, super voting shares, nonsense. And and should this go away as a concept in the stock market? David Sacks (00:25:03): Well, I mean, Facebook Hermeta has a pretty similar concept. I mean, I guess Zuckerberg has 60% of voting control, whereas Evan Spiegel is 99%. So Snap is more egregious. The difference is that Zuckerberg is listening, and Spiegel is not. The the reason why Snap is doing poorly is not because its revenue has deteriorated. So I looked up let's put it this way. I asked Chamath GPT. For their key metrics. So assuming GPT is not hallucinating, if you compare 2021 to 2023, their total revenue went up from 4.1to4.5000000000, and gross profit went from, call it, 2.4to2.5 1,000,000,000. So not a huge increase, but revenue and gross profit were slightly up. But if you look at operating expenses, they went from 3000000000 to 4000000000 a year. And that is why their operating income or operating loss went from a $700,000,000 loss to $1,400,000,000 loss in 2 year. So that's the source of the problem is that they increase their operating expense by a $1,000,000,000 a year from 2021 to 2023. David Friedberg (00:26:21): It's pretty simple. Jason Calacanis (00:26:22): They seem like they're the last ones to get the memo. David Sacks (00:26:24): Yeah. They they were the last ones to get the memo and just to finish the point. So you saw that a few days ahead of this quarterly announcement where their stock got crushed, they put out a press release saying they're gonna cut their headcount 10%. It's too little it's too little too late. Yeah. They knew. Right? They Jason Calacanis (00:26:40): knew that a problem. David Sacks (00:26:42): So they released the the press release saying, oh, we're gonna cut. Well, you should have done what Zuckerberg did. You know, Zuckerberg did a 20% cut last year. He got serious. He got lean and fit. And instead, these guys held out did nothing. Then when they know that the market's gonna crush them, they put out this lame announcement 10%. No. Not 10%. Really, if you just wanna get back to where you were 2 years ago in terms of operating expense, you need a 25% reduction. David Friedberg (00:27:12): Yeah. Yeah. But it's more than that. If you look at the numbers, let's see if the operating cash flow was a 165,000,000 for Snap for the quarter. Their operations generated a $165,000,000 of profit. But for the entire year, because they lost money in the quarters prior, They generated free cash flow of only $35,000,000. So the business net produced $35,000,000 of incremental cash. You know how stock based comp accounting Sacks. The charge happens when it vests. So this is what employees are vesting during the year of 2023 in employees invested $1,300,000,000 of stock based comp. So that means new shares or options were issued that on an accounting basis, the options are valued using Sacks Scholes, and the shares are valued based on the share price. So they issued 1,300,000,000 of stock based comp. So they generated 35,000,000 of free cash and they used $1,300,000,000 to compensate employees beyond their OpEx. So that means that they paid employees forty times the free cash flow that was generated for shareholders during the year, which is also equivalent to 10% of the enterprise market value of this company. So the enterprise value of the company is $15,000,000,000, 10% of that was issued to employees to compensate them. Now let me give you the the the story of another city. Meta. And by the way, Snap's share count, because they issued all the stock, the number of shares outstanding increased by 4% during the year. During the year, Meta's number of shares outstanding decreased by half a percent because they used cash to go and buy back stock, so they were able to reduce the shares outstanding. Now as you guys talked about, medic cut employee count by 22% and snap cut employee head counts by 3% during the year. But here's the crazy difference in performance. The stock based comp expense for Meta during that year was about $14,000,000,000 that vested that year that company generated 71,000,000,000 of operating cash flow. So while while Snap gave employees forty times, the free cash flow, Meta gave employees, you know, about a 20% of the, of the free cash flow. And then and then Meta went around and they used some of that extra cash to buy back $20,000,000,000 of stock. So they bought back more shares than what the employees were issued that that year were. David Friedberg (00:29:25): So it showed such a difference in looking out for shareholders. So if I'm an investor and by the way, Meta is creating at, like, 25 times free cash flow, which is not a crazy multiple, given all the new businesses that they have in Lama 2 and the progression to cloud and other things that they might do, if I'm looking at those 2 businesses as a shareholder, you got this guy that controls the whole stock, He's giving employees a 1,300,003 of shares a year when he's only making $30,000,000 of free cash flow a year. And then the other guy is issuing $14,000,000,000 of shares, buying them all back, and he's making 70,000,000,000 of free cash flow a year. I don't know. It's very hard to decide which way to go out. Jason Calacanis (00:30:01): Speaker Spiegel brought it up. In an interview I saw in in a lot of the layoffs were top heavy. So he got rid of a lot of the top people who had these huge comp packages. And then what I'm hearing from a lot of executives is cutting these highly stock comped executives who are, you know, also have big cash comp, cutting them, putting lieutenants in charge, and then moving more jobs to other locations where people don't expect stop stock based comp, you know, if you're in India or you're in South America, whatever, you know, stock based comp is not like the obsession it is here. So as everybody optimizes these businesses, I mean, Facebook even David Sacks (00:30:40): did that. 5000 employees. So they announced roughly 500 job cuts out of, what, 55100 employees. That's that's crazy. I mean, Should that company be operating with 2000 employees? Jason Calacanis (00:30:54): It's a good question. David Sacks (00:30:55): Yeah. Cut the number of Twitter employees from 8000 to 1500. David Friedberg (00:30:59): When you look at the number of apps that they're running and the number of products that they're running compared to Meta, right? Meta has far more apps, far more infrastructure. Meta is serving 3,200,000,000. Daily active users snap is about 400,000,000. So meta is 8x the users with many more applications and much more infrastructure. Jason I think it's a it's it's another great kind of ratio to look at the performance of these 2. Jason Calacanis (00:31:23): I think David Friedberg (00:31:24): you're exactly right. Yeah. Sacks. Yeah. David Sacks (00:31:26): The other advantage that Meta has is because they're so profitable, they have the resources to go big in AI. Jason Calacanis (00:31:33): Big time. David Sacks (00:31:33): Which is very expensive. So, yeah, so they are the leader. David Friedberg (00:31:36): You get all this option value at meta, which you don't get at Snap. There's all this infrastructure that they can leverage much like Amazon with AWS into things like cloud, AI tools for 3rd party developers, 3rd party applications, And then obviously the, you know, Meta is the biggest advertising platform next to Google and the world now. And there's much more that they can start to do to to to extend further into the Jason Calacanis (00:32:00): the platforms. They did get an awesome save. Remember Apple screwed them and was like, you can't track devices now. And, like, that just took a massive hit in the ad network and with all those headwinds, they were like, okay. We're just gonna use AI to optimize ads. And supposedly, the AI optimization of ads I was talking to somebody on the inside, They David, like, yeah, we got it all back. We gained it back. We've got massive AI advertising optimization going on. David Friedberg (00:32:25): So Totally. Jason Calacanis (00:32:25): Yeah. That's great. That Tim Cook you know, kicked us in the nuts, but we don't care. David Friedberg (00:32:30): By the way, that's a great point, Jtail. It really says a lot about how Meta was able to respond to that change, which A lot of people speculated would destroy the advertising business. And the fact that they were able to engineer solutions to drive advertising revenue up to $40,000,000,000 is just mind blowing. It's a really kind of impressive outcome for the team, and I think it speaks a lot to the quality of the engineers there. Yeah. I think it's a great point. Jason Calacanis (00:32:53): Yeah. Sacks, you tweeted that you're seeing a little Sacks bounced back all of a sudden. That's interesting. I am seeing something similar last year or last 2 years. You had a ton of people cutting their SaaS spend may be removing the number of SaaS vendors. They had consolidating vendors. You tweeted many public and private software companies are experiencing accelerating growth. After 6 to 7 quarters of deceleration, Sacks recession appears to be over according to the SaaS Master, David Sachs. You wanna pack this for us? What are you saying? David Sacks (00:33:24): Well, it's still pretty early because not everyone's reported, but if you looked at the big tech cloud performance. In q 4, you could see that there's a bounce back in here. This is net new ARR. Added for AWS, Azure, and Google Cloud. So you see here in q 4 that there's a huge increase in net new ARR for the the big cloud computing platforms. And then I think another bellwether is Atlassian. We're still waiting to hear from HubSpot, Salesforce, zoom, Adobe, companies like that. They were reported yet, but if you look at it Jason Calacanis (00:33:59): Sacks Jira amongst other products, their base in Australia. Yeah. The major. David Sacks (00:34:03): Yeah. Exactly. Jason Calacanis (00:34:04): Collection of Sacks Companies. Right? It's a collection of SAS products. David Sacks (00:34:08): Yeah. So net new ARR would be the amount of growth in that quarter. And this is a on a year over year basis. So you can kinda see Whoop. Q 4 of 21 was the absolute peak. And then plummeted, and then it actually went negative for about a year. Jason Calacanis (00:34:26): That's that's tough to be in a company with net new ARR going negative. Yeah. David Sacks (00:34:30): Yeah. That doesn't mean, by the way, the company is shrinking. It just means that the amount of net new ARR, which is the amount of growth is actually smaller than that same quarter a year before. Jason Calacanis (00:34:43): Yeah. David Sacks (00:34:44): And then in q 4, you could see there's some acceleration here. That they're starting to add more. They added more net new ARR, I guess, 33% more in q 4 than they did over the previous year. Jason Calacanis (00:34:55): And part of that Sacks because the comps are lower and they kinda bottomed out. Yeah. David Sacks (00:35:00): They bottomed out. Now they're reaccelerating. Jason Calacanis (00:35:02): So we're, you David Sacks (00:35:02): know, we're starting to see this in some of my board meetings as well. Where in 2022, everybody was missing their numbers and reforecasting down, and then they would miss the reforecast. So by 2023, the forecasts were very, very conservative. And I would say, and now I'm seeing companies beat the the sort of the lower forecasts in q 4. This wasn't happening earlier in the year, but finally, I think people are starting to beat their sort of their lower forecast for q 4. Chamath Palihapitiya (00:35:33): That's the question that I was curious about. What do you, what do you actually think is happening? Is that we've rebaseline these businesses So now what would have looked like just a massive miss over the last 2 years now looked like a beat because we've just completely reset expectations Is it that, or is it that the economy is actually expanding and we can count on some reasonable growth rates? Is it a combo of the 2? What do you think it actually is? David Sacks (00:36:01): Yeah. I mean, it's definitely a new baseline in the sense that and if you go back to 2020 or 2021, we considered good growth to be, you know, 2 to 3 x year over year. And now if it's going from 60 to 80% growth year over year, you're happy. So there's definitely been a a lowering of expectations. That being said, you still see in these numbers, there has been a bottoming out, and we're starting to now grow from this new baseline. So, for example, I think with Atlassian here, we are seeing an increase in spend, basically, in in growth. Right? So the way our recessions typically defined is, 2 quarters of negative growth. Right? We had 6 to 7 quarters of decelerating or negative growth? Jason Calacanis (00:36:48): In Sacks. In David Sacks (00:36:49): SaaS, which is why I called it the SaaSstra session or or b that It was actually kind of a depression. You're right. But now we're seeing quarter over quarter growth. So growth is reaccelerating. Growth is higher than it was. So is he gonna get to where it was? That probably will take some time, but it feels like the problems in the ecosystem work themselves out, and now we're back to growth again. Jason Calacanis (00:37:11): That, yeah, I can add psychologically, because I'm on a couple of Sacks boards as well. And psychologically, it felt like you tell me if I'm right Sacks, if you saw the same thing. There were 2 years of calling up customers, and they were like, we're we're consolidating vendors. And by the way, we did a rift. And so we need 20% less seats. So we're gonna have 20% less SaaS companies that we're buying from, and we're gonna have 20% less seats. So you you start putting that all together. Man, everybody was just in psychological triage mode. We cannot spend money. I don't wanna lose my job. So you're if you're a procurement person, you're the CTS don't wanna lose your job. You don't wanna have more cuts. So you're like, well, I can cut some software costs. Do I get points for that? And the points you would score the last 2 years was cutting costs. With the market ripping and you, you know, you now got a really, you know, efficient company. You're like, hey. Can we spend a little bit on Sacks to make the remaining employees even more? You know, productive? Okay. Maybe that's a reasonable discussion. And then people are playing ball in terms of negotiating prices. That's the other thing I see. It's like people are like, well, we'll take your software, but here's what we wanna pay. And then they're coming to the board and saying, can we do this deal have been a $1,000,000 deal, but it's a $200,000. So, yeah, take the money. Take the money. Let's let's bear hug that customer. David Sacks (00:38:25): The market is generally an escalator on the way up and elevator on the way So the recovery is gonna take a long time, but at least we bottomed out and we're in recovery as opposed to continuing declines. Yeah. By the same token, if you're a startup and you're not seeing improvement in your Q Four sales, then you no longer have a macro excuse. For why you're not doing well. Jason Calacanis (00:38:48): Interesting. And then Friedberg, you added, you know, you're like, I'll I'll make my own software. You said, you know, some software software is too expensive. I'll put a developer on it. And so how's that working out for you? Are you still in that mindset of like, yeah, maybe we just build our own software? David Friedberg (00:39:03): Yeah. I mean, I it's not just us. I think we're seeing a lot of companies pursuing this path. A couple engineers can rebuild the functionality of core applications, particularly because I think if you think about the business model that makes SAS so great, is they could value share rather than charge the cost of an engineer plus some margin, the great business model, the equity value that comes in software, if you can build something once that creates a $100 of value, you could probably charge your customer 30, $40 for that because it's saving them 60 bucks, 70 bucks, and they'll make that switch to software. So, you know, the ROI driven value share model in Sacks has made it incredibly valuable. The problem now is that an engineer can be hired to build the replacement, and so it creates price compression. So the SaaS company can no longer capture that much value because the savings is actually less than that. Because the enterprise might say, hey. I'm gonna hire someone and instead of spending $60 a year on your software, I'm gonna allocate a quarter of an engineer's time to build that software. And it's gonna replace that That cost. So I think that that's still the case. So while there might be bookings, there's still w which are driven largely by a search for efficiency gains, a search for more profitability, for more productivity within an enterprise, there are other options for that enterprise to realize that productivity gain today, And that's what's gonna cause perhaps price compression and more competition than has been the case. But I don't think that the adoption of software is gonna slow down. It certainly seems to be reaccelerating, which is more competitive. Jason Calacanis (00:40:41): Right? We're we're moving into a hyper competitive market, right, especially with AI. David Friedberg (00:40:46): It's a mix of internal it's a mix of internal software analysis. As you guys know, there are very few traditional non tech enterprises now that don't have a software team that can write code. So now that so many companies have software teams that write code, they're all gonna be asking the question. Should we be buying the software, or should we be building something internal? Jason Calacanis (00:41:03): Yep. It's a classic buyer build situation. Alright. Let's talk a little bit about VCs and how they're investing in AI. There there seems to be 3 camps shaping up here to month. You know, one group is like and the incumbents are gonna win. You know, Microsoft Google Amazon, everybody, they're gonna win the day. So they're gonna wait and see. Then there's another group who's sitting it out because they're like, hey. Open source is gonna win. Meta's committed to open source and collaborative platforms I've been playing with, hugging face with Sandeep as well, a a huge month, and it's pretty amazing what's happening over there. Men a bunch are obviously placing bets right now. The valuations are absurd. Founders fund and Andreessen Horowitz, 2 notable firms are approaching it differently. Founders fund bought into OpenAI at a $29,000,000,000 valuation. But aside from that investment, they're generally avoiding the AIDL my other hand, Andreessen, is betting heavily. Character AI, replet, Eleven Labs. Mishro, you're also in replet, Sacks. So what do you think? Is open source gonna win the day? You've been Sacks and shovels the whole way. You've been talking about compression. Maybe this isn't actually a good market. What what what's your thinking as a capital Calacanis of? Chamath Palihapitiya (00:42:14): I think foundational models will have no economic value. I think that they will be an incredibly powerful part of the substrate, and they will be broadly available and entirely free. Wow. So if you think about that, any closed model, especially a closed model that operates on open on the open internet is not very valuable. And any open source model that operates on the o that trains on the open internet will make that so. So in that world, things like misrule and llama will essentially decay the market to 0. So if you if you're looking at any economic value that has been captured up until today, if it has been captured by having a proprietary closed model, trained on open data, that economic value will go away. And I think Google and Microsoft and Facebook and Amazon and all these startups have a deep economic incentive actually to make that so. So now you can evaluate what that means. So if you get an open model from hugging face that's just kick ass, where do you spend money? Well, you're gonna have to spend money to actually train it, to fine tune it, maybe to have some pretty zippy inference And all of that means that there's a new kind of substrate that has to be built, which is all around the way that the tokens per second are provisioned to the apps that sit on top of the model. What that means is you need to go back to 2000 and 6 and 7 and say, okay, when we first created the cloud, Who made money? And fast forward 18 years later, it's the same people that are still making money. So the people that made money in 2006 Sacks 7 were Amazon, principally, because of e C2NS3. The perfect analogy of EC 2NS 3 in 2024 is the token per second provider. Now There, you have to double click and say, okay. Well, what does a token per second provider need to do to make a lot of money? And I think the ultimate answer is you need your own proprietary hardware. Who is in a position to do that? Amazon has announced that they have an inference and training solution for training, Cyprus has announced a pretty compelling solution. Chamath Palihapitiya (00:44:38): Google obviously has TPU, then there's a handful of startups, including one that I'd helped get off the ground in 2016 that I funded called Grock. All of those companies are in a position to build a tokens per second service. Then you have companies like Together AI, which basically just go and take venture money and wrap NVIDIA GPUs. And you can debate what the advantage will be there. One could say, well, it's not really a huge advantage over time. So my refined thoughts today are sort of what my initial guess was when we started talking about AI a year ago. Which is the picks and shovels providers can make a ton of money. And the people that own proprietary data can make a ton of money, But I think open source models will basically crush the value of models to 0 economically, even though the utility will go to infinity. Economic value will go to 0. David Friedberg (00:45:34): David any of you guys see Shamoff's interview with Jonathan Ross? Jason Calacanis (00:45:37): Nope. Not yet. David Friedberg (00:45:38): Keep you put it out right, Shamoff. Chamath Palihapitiya (00:45:40): You you made it public. You know, I did it just for my subscribers, but Jonathan is the founder and CEO of Grok, the company that I just mentioned. And The quick version of that story is I was I would I would pour over the Google earnings results in the mid teens of 2000 because I was pretty actively investing in a bunch of different public equities. And Sundar said in a press release, he mentioned that they had rolled their own silicon for machine learning. Called TPU. And I was like, what is going on that Google thinks that they can actually roll their own silicon? What must they know that the rest of us don't know. And so it took me about 6 or 9 months, but through Sunny, I got introduced to Jonathan, and then We were able to get Jonathan to leave Google, and he started. And he Jonathan was the founder of TPU at Google, and then he started rock, which I was able to lead that funding round in in Sacks, so 8 years ago. Anyways, I did a a Sacks with Jonathan, talking about the entire AI landscape and AI acceleration to my subscribers, but it was so good. I gotta say he is he was so impressive that we kind of like figured out a way to just play the space and tape it, and then we published it to everybody. So it's it's on it's on my Twitter for anybody that wants to listen Jason Calacanis (00:47:01): to it. Chamath Palihapitiya (00:47:01): It is amazing. He is really impressive. David Friedberg (00:47:05): I was sitting on the 17 going to Santa Cruz not moving for an hour a half, and I listened to it. So I kept me alive. Jason Calacanis (00:47:14): But, David Friedberg (00:47:14): I thought it was really great. Yeah. He's great. No? He's great. He had some great insights, and I think he's very compelling in arguing why some of the big cloud providers today that are offering infrastructure for AI model training and inference are gonna be challenged if someone can build full stack and be sick and do it successfully. So It was a really good interview. I actually think it's really worth listening to. But I enjoyed it. Yeah. Thanks for putting it out there. I was, like, literally just sitting in I was sitting in the car, browsing porter, saw your thing and I clicked on it, Jason Calacanis (00:47:50): and then Chamath Palihapitiya (00:47:51): I just ended up listening. Well, yeah, it was a little it's Jason Calacanis (00:47:53): a it's Chamath Palihapitiya (00:47:53): a little hard actually. When you do a space for your subs, you can't actually just flip a switch and then release it to all of your followers. So we actually had to, like, literally play it and then just capture the audio out and then republish it. But, anyways, despite that inconvenience, if anybody's interested in learning about AI hardware, he is very compelling, and he's very educational. Jason Calacanis (00:48:16): So, Sacks, your thoughts on just how you're approaching, investing in AI, if you're specifically investing in the underpinnings of ai, picks and shovels, yadayada, or if you're just looking on the application level and it's, you know, you know, that kind of approach. David Sacks (00:48:30): Well, we we divided it the space into 3 categories. One is the the the models themselves, the foundation models, which can be either open source or closed source. There's infrastructure. So like Shamal saying, it could be like model training. It could be vector databases. Tools that developers use to create the AI stack typically inside their enterprise. And then the third would be applications. Which can be things like copilots or it could be a pre AI app that's using AI to kind of turbo charge its capabilities. Yeah. Most SaaS would be in the application bucket, and so that's principally where we're focused. Although we do look at infrastructure plays and models. However, I do think there is an argument for, I mean, really with the question of commoditization, well, like, all the model companies just get totally commoditized. Well, really, we're talking about OpenAI, right, because we're the leader. So the question is can they maintain their lead? I do think there is an argument that OpenAI will stay in the lead and actually do quite well. And I think there's a few points there. One is that if you're a consumer, you just wanna use the best GPT. Jason Calacanis (00:49:45): You wanna use Google. David Sacks (00:49:46): That's what it's just like search. Right? If Google is a little better or the perception is, it's a little better than Bing or the other search engines. You don't win a plurality of search traffic. You actually end up winning it all because consumers just want the very best one. So most of the tests show that OpenAI is still ahead of the open source models, and I think even people in the open source movement will tell you that OpenAI is call it 6 months ahead. They have no doubt that open source will get to where open AI is now in 6 months. Nonetheless, if OpenAI just maintains a little bit of a lead over open source, then it could Jason Calacanis (00:50:25): Compound Yeah. David Sacks (00:50:26): It can basically win the vast, vast majority of the, call it, consumer search or consumer GPT market. So that's point number 1. Point number 2 is Now that OpenAI has these 100 of millions of consumers using it, that's a pretty attractive audience for developers to 1 to reach. And OpenAI has done a really good job creating a platform for developers to create, you know, what I call custom GPTs. So most developers don't wanna go through the hassle of training a model, fine tuning a model, doing all of that work that you would have to do in the open source ecosystem, they just want to point chatgpt at a repository of data or documents information Jason it learn what it needs to learn, fine tune it in that way, maybe add some lightweight functionality using OpenAI's platform, to create a custom GPT. That's what I think most developers want is they just want a simple stack to work with, and they're gonna prize again, simplicity and the power of the developer tools over the theoretical control they get by rolling their own models training and functioning their own models and open source. And so I think what you're seeing now is, I mean, how many custom GPTs have already been created on the overhead platform? Jason Calacanis (00:51:46): It might be tens of hours. I mean, Yeah. It's so easy to create them. Yeah. David Sacks (00:51:50): So you have a classic developer network effect where you've got open AI aggregating 100 of millions of consumers. Because they perceive that chat GPT is the best. Then you've got developers wanting to reach that audience. So they build custom GPTs on the OpenAI platform, that actually gives chat GBT more capability. Jason Calacanis (00:52:09): Yeah. David Sacks (00:52:09): And that's something that open source can't easily catch up with. Jason Calacanis (00:52:12): Well, actually, actually, David Sacks (00:52:14): it's a flywheel. The point. So, yeah, so it is a flywheel where, you know, classic operating system developer network effect where you wanna use the operating system that is the most programs written for it. Jason Calacanis (00:52:27): Yeah. And interestingly hugging face has realized this in hugging face re released this week their own version of GPTs, which is really interesting. And you can pick Sacks which open source project you wanna use to make it. So unlike GPTs on chat GPT, we have to pick theirs. On the hugging phase 1, you could pick you know, llama or whichever one you want. Chamath Palihapitiya (00:52:49): There's an account called artificial analysis that you can follow. The thing to keep in mind, Sacks, is that for any of this to be true, these APIs need to be usable. Right? I mean, I don't know if you remember, but when we were building apps, even as back as the late 2000s early 2010s, one of the things was there was a pretty important paper that was published by Google about attention span. And it would look at page load times in a cold cash environment, right? And it basically said you have to be at like 150 milliseconds, right? That's like best in class performance or faster. And I remember when we read that at Facebook, we went crazy so much so that at one point, a small team and I kind of actually launched a stripped down version of Facebook to compete with Facebook. If there's a, Nick, you can probably find this article on tech crunch, and we did it without telling everybody it was called like Facebook 0. Anyways, the point is speed matters. Because in the absence of having very snappy response, you could have the best model in the world But if it takes 10, 20, 30 seconds to basically initiate and get back data from a fetch request, it's an impossible thing to do. So I think one of the things that you have to keep in mind is that there are these 2 things that need to move at the same time. One is the quality of how the model is, but 2 is the speed and its responsiveness, which is a function of, again, hardware and your ability to basically tokenize tokens per second very, very quickly. So that developers are incentivized to not just play around in a sandbox, but to actually build production code. And I don't think we seen that second thing happen because nobody is delivering it, and that's the big thing that nobody talks about. For example, like AWS, if you look inside of how it's expensive it is to build an app there. I've Friedberg when they give you credits, the credits they give you aren't sufficient enough to even pay for half the power. And then the way that they apps unless you are willing to spend 1,000,000 and 1,000,000 of dollars for a very slow app, unfeasible. And so if you go back to a startup economy raising money here, The venture investor should start asking the question. Well, what is the speed and usability of these services that I'm funding? Chamath Palihapitiya (00:55:01): And the reason is because you could build the best experience in the world that runs on local host, but if all of a sudden you actually try to launch it as an app And the thing takes 35:40 seconds to generate something. It's DOA. And I don't think enough people ask those questions or understand that that's true. So this is why I think you have to sort of be looking at both of these two things at the same time, but this account is interesting because it kind of just strips things down to the bare Sacks, and they start to allow you as a third party to understand what you can do. Jason Calacanis (00:55:36): Yeah. Speed is just such a a critical, component of this. And what Google found was, as you know, free brokers, you were there. Every time they lowered a certain number of milliseconds, the usage went up. Right? People did more searches, which makes sense if you get your results back faster. David Friedberg (00:55:50): Yeah. It was a key metric from day one at Google, Marissa Mayer, she ran all the consumer facing product at Google during this, you know, earlier era, and she was like, beat it into the team. I mean, if you guys remember, one of the first the the the first kind of early feature of the Google results page was the amount of time it took to load the results. They'd show you how many milliseconds. Yeah. Jason Calacanis (00:56:11): Show you that. Yeah. They they literally put your north star metric exposed to the consumer, which coated. David Friedberg (00:56:16): That must have lit Jason Calacanis (00:56:17): a fire under the asses of all the developed person server people. Yeah. David Friedberg (00:56:19): Well, I mean, they were kinda showing off the quality of the infrastructure and the way they did indexing and everything, but the result really played out in usage. The the faster the results, the more frequently you would use the search engine, and the more likely you were to come back, And it's amazing how much consumer behavior drifts based on milliseconds. Like, you have a few milliseconds of the white dots Jason Calacanis (00:56:40): on this. Right? I mean, if you look if you ever see the movie, the founder, where they explained the McDonald's process, they learned it too. Chamath Palihapitiya (00:56:46): Guys, look at this. This is really interesting on this analysis. David Sacks (00:56:49): I mean, Chamath, are are you saying that you don't think OpenAI can achieve the necessary levels of performance? Chamath Palihapitiya (00:56:54): No. I'm saying 2 things. OpenAI is 3 different businesses. OpenAI has a closed model that's trained on the open internet. I think economically it's going to be very hard to sustain that unless they start buying all number of apps so that they can get some fine tunes that they control that are prietary to them. So for example, if OpenAI were to buy all of Reddit, that would be a really interesting development that would improve the quality of OpenAI in a unique and differentiated way, relative to where things like WAMA and Misra will get to at the same time, as well Sacks is grok. I think they're all gonna converge to the same quality in the next, probably 12 to 18 months. That's point number 1. Jason Calacanis (00:57:35): Your belief fair is there's enough data in those pools that everybody reaches parody. Chamath Palihapitiya (00:57:40): Oh, did you guys okay, Nick, did you so I I published this primer There is a slide in there, Nick, that you can pull out, but it just shows you that there is a converging In the quality of the results, as the number of the parameters of the model gets higher and higher, and what it effectively shows you is that we are already in the land of diminishing returns when models are trained on the same underlying data. So if you are using the open internet, LAMA Mistral OpenAI, they're all getting to the same quality code point, and they will be there within the next 6 to 9 months. So that's business number 1 on OpenAI. Business number 2 is a consumer facing app called chatgbt. That has a lot of legs because I think people are, you know, develop habits it'll be very sticky, and I think it'll get better and better. And then the 3rd business that they're in is selling enterprise services 2 large fortune 500. In fact, if you look at their open AI days, what they talk about is they sell they've sold already to, like, 94% of the fortune 500. What does that mean? I think what that actually means is they've sold a lot of test environments and sandboxing, but again, in order to translate that into Functional production code that's used by Bank of America, right, or Boeing in production, you have to have zippy, zippy, fast SLAs, and a level of performance that no cloud provider yet has delivered. None, nobody. So Nick, if you just go to that, please, the thing. I just wanted to show you this because it's a really interesting chart. This is not mine. This is theirs. If you look at quality versus price Sacks, it starts to starts to show you, like, where do you want to be? You want to be in the upper left quadrant in their analysis, right? And so the point is, what you can see is that a ton of different models are getting to the same place. And so, obviously, you'd wanna use the model that's the cheapest. David Sacks (00:59:37): For most convenient. Chamath Palihapitiya (00:59:40): Well, who's gonna pay for that? If you if you and your LPs wanna pay for that, The person that figures out the way that it's the cheapest to give you the same answer will actually end up winning because you will run out of money and they will not. David Sacks (00:59:53): I don't know. I mean, I think that there's a lot of business problems inside companies where people just wanna very quickly set up their own, again, custom GPT without having to go through the time, the cost, the hassle of trying to do model training or fine tuning. So let let's just back up. Here's the path that OpenAI is on. So step 1, get 100 of millions of consumers using it, and getting them to view OpenAI or chat GPT as the Google in this area. Right? Strong presumption. This is just the one you go to when you have a a question. Step 2, these same people, these same consumers now wanna use chatgbt at work because there's some research they wanna do. So OpenAI has just rolled out, both enterprise licenses and team workspaces. So you can work collaboratively on the same queries in a team workspace. Step 3 is they're rolling out a very easy use dev platform that allows developers to, again, create custom GPTs by just pointing open AI at repositories. Okay. And so let's say that you are the customer support team and you wanna create a a GPT to help customer support answer cases. You could basically then train try GPT on, let's say, every customer support ticket and email that the company has ever produced. Right? Now you could wait for the company's IT department to get us act together and figure out how to train an open source model on the same thing, but do you really wanna wait for that, or do you just wanna get going? You know, and now OpenAI has given you the enterprise license that you need to pacify the concerns about security and privacy and all that kind of things. To some degree, there's always gonna be those super paranoid fortune 500 companies that will insist on doing on owning everything and and doing it, doing it open source. Chamath Palihapitiya (01:02:00): Let me build on your example. So I run a small software company during the day called hustle. And we saw a lot of tickets related to this specific legislation that exists whenever you're texting or you're doing auto dialing stuff called 10 DLC. And so we wanted to eliminate those those tickets, right? So I actually went in I built a GPT, which was called the privacy policy generator because of these trouble tickets were because the privacy policies were bad. And we trained them using a handful of ones that were good and a handful of ones that are bad with a bunch of rules. And I train them up. And it's wonderful, except I can't run it in production because it's not the kind of thing that is usable in that way right now. It's still very difficult. And so all I'm saying is I'm happy to keep spending a few $100 a month, a few thousand bucks a month, whatever it is that I'm spending, I don't quite exactly know. And I agree with you. It was very easy. I think OpenAI doesn't excellent job of getting off the ground. But what I'm also saying is that when you actually translate that into a main line use case, right? Where I want to now give it to my support team and say this is now a tool you can rely on It's integrated into your workflow, into your other tools. It's integrated into how you pipe out data into Salesforce or what have you, It's just very hard. And I'm not saying it's not going to get fixed. I'm saying we're just not there yet. And one of the rays in which it's not there is that there is no place I can go, including OpenAI, that actually makes it fast enough to be usable in production. David Sacks (01:03:44): You wrote this on OpenAI Sacks. You wrote a custom GPT? Chamath Palihapitiya (01:03:48): Yeah. Built myself. David Friedberg (01:03:49): Yeah. And you can do Jason Calacanis (01:03:51): the monohugging phase now. It's gonna be a lot of option. David Sacks (01:03:53): In terms of integrating into your workflows. I think it's a really interesting point because I saw a demo somewhere where now actually, I think OpenAI announced this that you can at mention a custom GPT. Jason Calacanis (01:04:05): Yeah. Yeah. Sunny showed me that this week on the. David Sacks (01:04:09): Yeah. In in chat GPT, you can now at mention a custom GPT to kind of invoke it. Jason Calacanis (01:04:14): Yeah. So how it works is you'd say, hey. I'm heading to New York. What flights can I get at Expedia, at kayak, whatever, and then it gives you you know, the results here and you you you're kind of pulling that up? David Friedberg (01:04:26): Just to the point about about where data advantages lie, and that's ultimately gonna drive value. I cannot I've tried to think a lot about this. I cannot think about a better data advantage that is orders of magnitude better than anything else. Say YouTube. Jason Calacanis (01:04:45): Say YouTube. Yeah. It Chamath Palihapitiya (01:04:47): is tricky game. David Friedberg (01:04:48): So here's here's the numbers. I I pulled this up. You guys know, like, GPT 3 and 3a half were trained with a heavy weighting on common crawl, which is this open source We talked about this before. Gil Elba's runs it. Open source crawling of the web. The total amount of data in common crawl, which I think counted, and I could be off on this, something like 40 to 60 percent of the weighting in GPT 3 or 35. I'm off on this probably. So the total amount of data in that common crawl data set is about 10 petabytes. Okay. Based on YouTube's public statement, recently, they're seeing about 500 hours a minute of the video uploaded or 7 20000 hours a day. And if you assume somewhere between, you know, just under 10:80 p on that video, we're talking about probably 1 to 2 petabytes of data being uploaded to YouTube per day. So if you assume, like, over time, the definition of the video's gone gotten better and the amount of upload's gotten up, you could probably assume that there's roughly, I'm guessing, There's probably somewhere between 2003,000 petabytes of data in YouTube growing by 1 to 2 petabytes per day. Which makes YouTube's data repository 300 times larger than common crawl, which makes it bigger than anything that anyone else has, and here's the amazing thing about it. It has video. It has image. It has audio. It has text. It has everything. Multinate is growing. So if you were to take a a bet or build a thesis around this point that the data advantage is gonna drive value creation, If Google gets its act together and leverages the data repository at YouTube, it is an insurmountable moat that will only continue to extend because the quality of the YouTube experience and the network effects continue to accumulate for them. So I think it's the most valuable asset in the world today based on this thesis that AI value is gonna accrue to the data owner. Chamath Palihapitiya (01:06:47): I think you're making such an important point, and this is why the counterfactual is is true, and it's actually showing up in the data. And Nick will show you this slide again from from the AI primer. But that is why we're seeing these diminishing returns Friedberg in all of these party benchmarks of these models. It's all using the same data set. David Friedberg (01:07:05): It's all Chamath Palihapitiya (01:07:05): using the same data set. So what we are proving is not that the underlying hardware can't scale, nor that transformers are only efficient to a point. That's not what all of this convergence is showing. It's that in the absence of proprietary data, you're just gonna get to the same model quality and we're seeing a bunch of different models get to a very early finish line, which, again, if people like Facebook are doing for free, that's much easier to underwrite because you you don't have to underwrite it, dealing a differentiator in 5 years. But if you have a if you have a startup with equity value tied to a model, I think it's very it's much more of a tenuous place to be in the absence of proprietary data. David Friedberg (01:07:46): And everyone in the world has a camera and a microphone in their pocket and high speed internet now from the phone in their pocket and more and more people are uploading that content, that, that data that's being generated, YouTube's got this free data vacuum. And they're just out in the world. And most of it's getting up. Jason Calacanis (01:08:04): Well, it is public facing, though. So it's Chamath Palihapitiya (01:08:07): not just true for Sacks it's also true for, you know, all of the image generation. So, like, if you look at David Friedberg (01:08:12): they could train more than just an LLM on it. Right? They can build All sorts of Jason Calacanis (01:08:17): yeah. Go ahead. No. No. Chamath Palihapitiya (01:08:18): No. I was just gonna say, like, the version of common crawl for training these image models also exist. And so to your point, It's like we are all operating from the same brittle, very fixed, small quantum of training information. And so that is why I think like Facebook and Google are doing a really important job by deciding that these models should be free. Right. And then being able to the so then the question that Jason Calacanis (01:08:46): just accentuates their data advantage. Chamath Palihapitiya (01:08:49): It does. And and I think that it allows them to decide how much to leak out. So for example, whenever like if you were using a lot of Google services like GFS, bigtable, bigquery, you know, TensorFlow, the versions that you had access to via GCP, was always 1 or 2 generations behind what the Google employees got to use, right? But it was still so much better than anything else that we could get anywhere else that you would still build to those endpoints. And I think there's a similar version of this where Facebook and Google probably realize like, look, we'll have version 5 running internally to optimize ads and all of this other stuff that makes our business that much better. And we'll expose version 3 to the public. But version 3 is still trained on so much proprietary data that it's so much better than version 10 and anything else that's just operating on the open internet. Jason Calacanis (01:09:42): And, you know, to your point, Friedberg, that's the outward facing stuff. YouTube is a collection of things people want to share. What Google also has is Google Docs and Gmail, things that people say privately. So they have another data resource there that they can tap, you know, and and there'll be regulations or privacy around that. David Friedberg (01:10:01): But Maybe there's a difference there, but I I honestly can't think of the quantum coming close to YouTube. Not even close. Chamath Palihapitiya (01:10:07): Well, the the thing to Jason's point, which is really interesting, is like, you know, there's a modality in AI called RAG where you can actually just augment with very specific training on a very specific subset of, of documents to improve. It's like a, it's like a hacked version of a fine tune. But the beautiful thing about that is like if you have a Google Workspace, my entire company runs on on Google Workspace. In fact, most of my companies do at this point. To click a button where all of a sudden now, all of that stuff in all of my g drives, all of a sudden, is trainable so that the N plus 1st employee comes in and has an agent that's tuned on every deck, every model, spreadsheet every document. That's a huge edge. Jason Calacanis (01:10:49): Yeah. Huge edge. David Friedberg (01:10:51): By the way, and Chamath Palihapitiya (01:10:52): as a CEO, if you gave me that choice, I don't think anybody underneath that reports to me has any right to make that decision, but as a CEO, I would click that button instantly, and I have that right as a CEO. And so, like, that's the CEO pitch. It's like, look, I can just give you these agents that are that are like the next version of a knowledge base that we've always wanted inside of a company. Right? Jason Calacanis (01:11:14): Notion has this. You know, they've basically you can start asking your entire Notion instance questions about Notion, which is incredible. And, Yeah. You can just and as a CEO, you can see across everything, Shamoff, because as you know, with Google Docs, if you're in a compliance based industry like finance, You can see everything. Every message, every email, every document, and you can search Chamath Palihapitiya (01:11:36): the security model and the data model becomes very complicated in all of that stuff. Like, for example, like, how do you know that this spreadsheet is actually you should learn on it, but who gets to actually then have that added to the subset of, of answers, right? All of a sudden, like, salaries. Jason Calacanis (01:11:55): Yeah. The HR information information gets put into the training model. Very dangerous. Chamath Palihapitiya (01:12:00): Or subset a of a company is working on a proprietary chip design that they actually like the way that Apple runs highly, highly segregated teams where nobody else can know. So there's all kinds of complicated security and and data model and usage questions there, but Jason Calacanis (01:12:14): Yeah. Revenue World. So there's been a lot of discussion real estate. You you shared a video with us. Why don't you kick it off for us here, pre bird? What's going on in commercial real estate in Sacks holdings and a lot of thoughts as well. So let's kick up the commercial real estate challenges of the moment. David Friedberg (01:12:28): Well, I mean, I think we're teeing off of Berry's comments at this event last week. He and I met backstage because I spoke right before him. And then He gave this talk, which is available on YouTube, where he talked about the state of the commercial real estate market, and particularly he talked about the office market. Just to take a step back to talk about the scale of commercial real estate as an asset class in the US, Nick, if you'll pull up this chart, The total estimated market value of commercial real estate in the US across different categories is about $20,000,000,000,000. With about $3,000,000,000,000 being in the office market, which is specifically what he was talking about. And he was saying that in the US, we're seeing people not coming back to work, and all these offices are empty. And we've talked a lot about these offices being written down. So how significant of a problem is this? So $20,000,000,000,000 asset class, obviously, the multifamily market is probably not as bad as office and retail, which are the most heavily affected, each of which are about $3,000,000,000,000. A piece, the rest of these categories seem relatively unscathed in comparison, industrial, hospitality, health care, you know, those those real estate sectors are probably pretty strong. Data center is obviously growing like crazy self storage. They're a great market. If you pull up the next image, So it turns out that of the $20,000,000,000,000 of market value, there's about $6,000,000,000,000 of debt So you can kinda think about that 20,000,000,000,000 being 6,000,000,000,000 owned by the debt holders. And the debt is owned roughly 50% by Sacks and thrifts. And this was this concern that we've been talking about with higher rates the debt on office actually gonna be able to pay? The debt on retail is gonna be able to pay when half of that debt is held by banks and thrifts that as we've talked about have such a close ratio to deposits that you could actually see many banks become technically insolvent if the debt starts to default, Berry's point Jason Calacanis (01:14:27): that he made was if you look at David Friedberg (01:14:29): the office market, which, you know, is marked on everyone's books as $3,000,000,000,000 of market value, He thinks it's probably worth closer to 1,800,000,000,000. So there's $1,200,000,000,000 of loss. In the office category. And if you assume 40 percent of that 3,000,000,000,000 that's held as debt, you're talking about $1,200,000,000,000 of office debt, A reduction from 3,000,000,000,000 to 1.8000000000000 means that the equity value has gone down from 1,800,000,000,000 to 600,000,000,000. So they've lost equity holders in office real estate have probably lost 2 thirds of their value. 2 thirds of their investment. And who owns all of that? Most of that, 60 plus percent, call it 2 thirds of that is likely owned by private equity funds and other institutions where the end beneficiary is actually pension funds and retirement funds. And so if 2 thirds of the value has to be written off in these books and it hasn't happened yet, what's gonna happen to all these retirement funds? This is where going back to my speculation a couple months ago kind of gets revisited. If you're actually talking about a 2 third write down on the value in these funds, most of that being pension funds, you're not gonna see governments let that happen. You're gonna see the federal government. It's gonna there's gonna be some action at some point And it's unlikely the office market is gonna mat suddenly rebound overnight. If this stays the way it is, who's gonna fill that hole for retirees and pensioners because we're not gonna let that all get written down. Someone is gonna step in and say, we've gotta do something about this. And there's gonna need to be some sort of structured solution to support retirees and pensioners because that's ultimately who ends up holding the bag in this massive write down. He didn't go all the way there in his statements. He was talking more about his estimate of 3,000,000,000,000 to 1,800,000,000,000. And then I tried to connect the dots and what that actually means. And, ultimately, there's gonna be some pain felt by retirement funds that's gonna need to be dealt with somehow. David Friedberg (01:16:27): So I so, I don't know if that if that Sacks right with you. David Sacks (01:16:29): I mean, I think the big picture is right. I think you're applying a lot of averages. Chamath Palihapitiya (01:16:33): Right. David Sacks (01:16:34): I think in the office market in particular, the typical office deal is more like 1 third equity and 2 thirds debt. There's just a lot more leverage. David Friedberg (01:16:42): Right. David Sacks (01:16:43): So that'd be point number 1, which makes the situation worse. David Friedberg (01:16:46): Even worse. Yeah. David Sacks (01:16:47): So I would say that there's a huge amount of equity that's been written off. But in addition to that, there's a lot of debt holders who are in trouble too. And that debt is is held by regional bank. So these commercial loan portfolios are significantly impaired. That's what we saw with Community Bank of New York is that their stock cratered when they reported higher than expected losses than their commercial real estate portfolio. So, Freeport, I think the point is just the the pain from this is not just gonna be on the equity holders, but also on these Sacks, which can't afford to lose. It's David Friedberg (01:17:27): not evenly distributed. David Sacks (01:17:28): Yeah. Yeah. David Friedberg (01:17:28): Right. Yeah. David Sacks (01:17:30): Right. David Friedberg (01:17:30): And we saw this in San Francisco where some of these buildings have 70% debt equity ratios and, you know, the the value puts them in the hole and the equities wiped out completely, and the debt holders have to take ahead. Normally, you know, that debt is not really written off very often. David Sacks (01:17:45): Well, this is why the the debt holders, the debt holders don't wanna foreclose. They don't wanna get these buildings back because when they do, they're gonna have to write down the loan. As long as the loan is still outstanding and they haven't foreclosed, they can pretend that the value of the building is not impaired. Jason Calacanis (01:18:00): Kick the can down the road is the best strategy for them. David Sacks (01:18:03): So it's it's called, pretend and extend. So what they'll do is they'll work out a deal with the the landlord, the equity holder, that the equity holder would say, Jason, I can't pay the interest. So they'll just tack on the interest basically as principal at the end of the loan, and they'll extend out the term of the loan. Jason Calacanis (01:18:20): Which would wipe out the equity at a certain point. Yeah. And all that. David Sacks (01:18:24): Well, then what it does is it allows the equity holders to stay in control on the building. Right? Because, yeah, the equity holder can't pay make their debt payments today, but they're gonna postpone those debt payments till the end of the of the loan. And, again, in the meantime, just kinda hope that the market comes up. Jason Calacanis (01:18:42): At some point since they have so little equity in these buildings, typically, just exceed the value of the of the property. And it's like, I'm just working for the bank now. And, you know, why am I even putting this work in David Sacks (01:18:53): because everyone kinda hopes that the market will recover the value their equity will go up, and they'll be able to make their debt payments David Friedberg (01:19:00): Yeah. David Sacks (01:19:00): So if you're the equity if you're the equity holder, you'd rather hold on and have a chance of your equity being worth something in recovery then definitely lose the building. And if you're a regional bank, you'd rather blend and extend or pretend and extend as opposed to having to realize the loss right now. David Friedberg (01:19:18): Yep. David Sacks (01:19:19): And showing the market that your solvency may not be as good as you thought. The same thing happened with government bonds. Remember that with SVB and these other banks, they had these huge held to maturity port bond portfolios. Yep. These are being mostly just, t bills that were worth, I don't know, 60¢ on the dollar when interest rates spiked from 0 to 5%. But they didn't have to recognize that loss as long as they weren't planning to sell them. Jason Calacanis (01:19:47): Right. And then when they had the bank run, they had to sell. David Sacks (01:19:50): Well, what yeah. That's right. So when depositors left because they needed their money or because there was a run or because they could get higher rates in a money market fund, all of a sudden, these banks had to sell their health maturity portfolios and to recognize that loss. And that's when everyone realizes, oh, wait a second. They're not actually solving Jason Calacanis (01:20:08): Okay. So Jaemap supplydemand matters in real estate. We have a tale of 2 cities here on one side in real estate for commercial real estate no demand for office space, which, is in way too much supply. Paradoxically, on the other side, We have this incredible market for developers, which is, gosh, there there's not enough homes. I think we need 7,000,000 more homes, and the demand is off the charts for homes. Yeah. Chamath Palihapitiya (01:20:33): Yeah. I mean, I think I think you're basically right. David Friedberg (01:20:35): It's David Sacks (01:20:35): not I keep trying to explain residential. It's not a great market either because interest rates have spiked up. So there's not a vacancy problem. Multifamily developers are still able to lease the units. They're still able able to rent. The problem is their financing costs have shot through the roof. So, again, let's say you were a developer who built multifamily in the last few years, you took out a construction loan. That construction loan might have been at 3, 4%. Now you wanna put long term financing on it. But if you can even find debt right now, because there's a credit crunch going on, you may have to pay 8, 9, 10%. David Friedberg (01:21:10): Yeah. But at least you Jason Calacanis (01:21:11): can find a renter. David Sacks (01:21:13): You could find a renter. That's true, but only at a certain price. And let's say you underwrote that property, to, I don't know, like a 5 cap, like a certain yield. Yep. But now your financing costs are much higher than you thought. You might be underwater. Jason Calacanis (01:21:26): Yeah. But that that situation isn't as bad as what's happening in. David Sacks (01:21:30): Why? I think it's, you know, I think it's worse in some ways. If you're fully if if you're fully rented and your building is underwater because now your debt payments are much higher than you expected, then there's no business model. Jason Calacanis (01:21:43): Are we seeing that? Are we seeing tons of multifamily go under? Can I Chamath Palihapitiya (01:21:46): make two points? 1, I think I think David is is right, which is that I don't know this market very well, but just as a, as a bystander, here's what I observe. It seems that the residential market has a feature And I don't know whether it's good or bad, but that feature is that you reprice to market demand every year. So to the extent that supply demand is changing and default rates are up or whatever, that's reflected in rents. And you see that because rents change very quickly and most human beings are signing 6 months to 1 year leases. So that reset happens very quickly so it can more dynamically adapt. So to the extent that A market segment is impaired, you see the impairment quickly. On the, on the office side, what I see is that there's been a structural behavior change in COVID that has reset in every other part of the world except for the United States where there are these, frankly, typically younger, typically more junior employees that have held many of these companies hostage in a bid to return back to office space. And so we know that there is this vacancy cliff that's going to hit commercial real estate We just don't know when because there are they're in long term leases. They're cancelling these leases over long periods of time. So the reset cycle is longer. That's just my observation as an cider. I don't know what that means for for prices or anything else, but it just seems that at least the residential market can find a bottoming sooner because you can reset prices every year, but Commercial just seems like a smelting ice direction, correct, to you, Sacks? That assessment? David Sacks (01:23:23): Commercial has both a demand problem and a financing problem. Multifamily just Sacks a financing problem, but it's important to understand. David Friedberg (01:23:31): We're talking about office David Sacks (01:23:32): because there's David Friedberg (01:23:32): retail, and then there's office. And then there's other industrial. David Sacks (01:23:35): Could you also see Jason Calacanis (01:23:36): in China? China has 50,000,000 homes ahead of schedule. 50,000,000 a additional supply that can house a 150,000,000 people. So as acute as our issues are, the China issue might be much, much Yeah. Seismic. David Friedberg (01:23:50): Can David Sacks (01:23:51): we just give you an example on the multifamily side? Okay. Let's say Jason Calacanis (01:23:54): that you David Sacks (01:23:54): buy a building. Okay. Let's say you bought a building in 2021, the absolute peak of the market and you could get debt at, say, 4%. Okay? And you penciled out, let's call it a 6% yield, that with the debt you're getting, so let's say you did 2 thirds debt at 4%, you could now lever up that 6% yield to 10%. K? That's like sort of the math. Right? Now all of a sudden and and to get there, you'd have to do some value added work on the property. You have to spruce it up. Okay? Now it's a few years later, and your short term financing is running out and you need to refi. And you've done your value added work, but here's the problem. The overall valuations in the market have come way down. So before the bank was willing to give you 2 thirds loan to value, Now the values come way down, you may not even be able to get 2 thirds loan to value. So you're gonna have to do what's called an equity in refinancing. You're gonna have to produce more equity. You're gonna have to pony up more money. So instead of taking equity out, like when the deal goes well, you're gonna have to put equity in. You may not have that equity if you're the the developer. The other thing is that your financing cost now might be 10%. So now you've got negative leverage. You're generating a 6% yield, but you're borrowing at 10% to generate that 6% yield. So the debt no longer makes sense. You're, again, you're not positively leveraged. You're negatively leveraged. So you're not gonna wanna take out that debt. And if you do take out that debt, the the buildings to be underwater. It's not gonna be generating net operating income. It's gonna be generating losses. So that's why even categories like multifamily, where you don't have a vacancy problem, there's strong demand. Yeah. Those properties still don't make sense. If you had long term debt on your multifamily, if you were able to lock in that 4% loan for 10 years, you're fine. But for all the people who are refinancing now, who are coming up this year, last year, next year, they're in deep trouble. David Sacks (01:25:56): And that's why there's a rolling crisis in real estate is because the debt rolls over time. It's not like everybody hits the wall and has to refinance at the same time. Jason Calacanis (01:26:05): Well, that god, right? I mean, this would be cataclysmic if it if it was. If ever and can you imagine if Silicon Valley and Sacks Francisco had to say, here's actually the reality anybody wanna actually pay for this office all in the same year. Oof. David Sacks (01:26:17): Right. Jason Calacanis (01:26:18): That would be insane. David Sacks (01:26:20): But the crisis is growing is as the leases roll and those old rents that were higher than market roll off and now you have to take on new leases if you can even get them. Jason Calacanis (01:26:30): It's gonna be balanced. David Friedberg (01:26:31): At a David Sacks (01:26:31): much lower rate. And as the old loans roll that were at a much lower interest rate, you have to get financing, even if you get it at a much higher interest rate, that's when all of a sudden these buildings go from being basically solvent to insolvent. Jason Calacanis (01:26:45): Yeah. I mean, Janet Yellow's just gonna bail these folks out. That means you won't bail out the banks themselves, but you'll bail out the creditors, obviously, that people holding. The bag. They'll get bailed. Yeah. That's everybody agrees. David Friedberg (01:26:56): Janet yelling? Chamath Palihapitiya (01:26:58): Don. Our treasury secretary. Jason Calacanis (01:27:00): I don't know if she's gonna be the one to do it. Chamath Palihapitiya (01:27:01): I think it's gonna there's gonna be congressional action on this stuff. Jason Calacanis (01:27:05): Yeah. I mean, they tend to lead it. So Alright. For the Sultan of Science, David Friedberg, and David Sacks, and Tremont probably have Tia, the chairman dictator. I am the world's greatest moderator. We'll see you next time, and we're all in pod. Bye bye. Speaker 4 (01:27:22): Your winter's ride. Jason Calacanis (01:27:25): Rainman, David Sacks. And it Speaker 4 (01:27:29): said we open sources to the fans, and they've just gone crazy with it. Uh-huh. David Friedberg (01:27:46): Driveway checks. Speaker 6 (01:27:52): We should all just get a room and just have a think you, Dorothy, because they're all just useless. It's like this, like, sexual tension, and they just need to release her mouth. Jason Calacanis (01:28:06): Where did you get Murphy's our