作弊丑闻、推特进展、AI 突进、拜登特赦与 230 条款

David Sacks (00:00:00): We're 7 minutes in, and we've produced absolutely nothing that will go in the show. David Friedberg (00:00:04): Here comes Saxx waking up with Chamath Palihapitiya (00:00:06): this with Jason Calacanis (00:00:07): his commentary When Friedberg is criticizing you for being too negative, you're in a dark place, sex. David Friedberg (00:00:12): I'm actually angry at sex for not publishing my AMA for the other night while -- -- publishes. -- publishes an evolved crashed. David Sacks (00:00:18): We had such a crowded room. Speaker 5 (00:00:20): We had David Sacks (00:00:20): -- That was impressive. -- people in the room for, like, 4 hours. Jason Calacanis (00:00:23): It was crazy. It was, like, the original days of clubhouse. David Friedberg (00:00:26): one I know that was trying to get in was texting saying they couldn't get in, so it definitely capped out. Right? David Sacks (00:00:31): I know. Well, we hit we hit some scalability level. Jason Calacanis (00:00:33): You may wanna buy an extra server, Saxx. cheap. Yeah. Weren't you the same guy who was responsible for scaring PayPal? Chamath Palihapitiya (00:00:38): No. That was somebody else. That was eBay. They sold it before it killed. David Sacks (00:00:42): No. No. That's not true. We we had huge scalability challenges at PayPal too. Jason Calacanis (00:00:47): Seems like a theme. David Sacks (00:00:48): Yeah. The theme is when you have an app that's breaking out, you hit scalability challenges. It's called a high class problem. Jason Calacanis (00:00:54): Two thousand people is not a high class problem. It's a trickle. It's David Sacks (00:00:58): 2022. Two thousand people who participate in the conversation is That's all. Jason Calacanis (00:01:02): I haven't written code in 20 years. Here's what you do. When you get to a thousand people coming to the room -- That's Chamath Palihapitiya (00:01:07): a lie. Jason Calacanis (00:01:07): That's why. -- else is in passive -- You've Chamath Palihapitiya (00:01:09): never written code ever. Jason Calacanis (00:01:10): Of course, I Of course, Speaker 5 (00:01:11): a dime. Chamath Palihapitiya (00:01:12): That's a lie. Come on, be honest. Jason Calacanis (00:01:13): Oh, yeah. It's actually been 25. The last time I wrote co was Lotus Notes. It's always true. Speaker 5 (00:01:19): to let your winners ride. Rainman, David Sanders. And it Jason Calacanis (00:01:22): said -- We Speaker 6 (00:01:27): open So there have Speaker 7 (00:01:30): been 3 cheating scandals Jason Calacanis (00:01:34): across poker chess and even competitive fishing. I don't know if you guys saw the fishing 1, but they found weights and fillets during a fish way in, and then everybody wants us to check-in on the chess and the poker scandals. chess.com just released their report. that this grand master has been suspended. They have evidence he cheated basically in a bunch of tournaments that were, in fact, for money he denied that he had done that. But he had previously cheated as a kid. They now have the statistical proof that he was playing essentially perfect chess, and they have outlined this in, like, hundreds of pages in a report. Saks you. What what are your thoughts on this scandal in chess? David Sacks (00:02:20): Magnus Carlson finally came out and explained why he thought Hans Neman was cheating. Basically, he got the strong perception during the game that Hans wasn't really putting in a lot of effort that he wasn't under a lot of stress. And he's he it's his experience that when he's playing, you know, the top players, they are intensely concentrating. And the Hans demon didn't seem to be exerting himself at all. So his you know, hackles were raised and got suspicious. And then, you know, he has had this meteoric rise, the fastest rise in classical chess rating ever. And I guess there were he had gotten suspended from chess.com in the past for cheating. So on this basis and maybe other things that Magnus is telling Magnus basically said that this guy is cheating. I think that maybe the interesting part of this is that there's been a lot of analysis now of Hans Neiman's games. And I just think the methodology is kind of interesting. So what they do is they run all of his games through a computer and they compare his moves to the best computer move. And they basically assign a percentage that hogs -- Correlation. -- matches the correlation matches the computer move. And what they found is there were a handful of games where it was literally a 100%. That's basically impossible without cheating. I mean, you look at the top players, who, through an entire career, have never had a 100% game. You know, chess is so subtle that the computer can now see so many moves into the future that nailing the best move every single time for 40, 50, a100 moves is just -- Chamath Palihapitiya (00:03:45): And and and in chess, which a human really can't do that well, is that there are positional sacrifices that you will make in short lines that pay off much, much later in the future, which is impossible for a human to calculate. And so, you know, And and you saw this, by the way, when I think it was the it was the Google AI, the DeepMind AI David Friedberg (00:04:04): that that Chamath Palihapitiya (00:04:05): also played chess. So The idea that this guy could play could play absolutely perfectly according to those lines is only possible if you're cheating. David Sacks (00:04:13): Right. Exact so so there were a handful of games at a 100%, and then there were tournaments where his percentages were in the seventies something plus. And so just to give you some basic comparison, Bobby Fisher during his legendary 20 game winning streak was at 72%. So he only matched the computer set for best move 72% of the time. Magnus Carlson playing at his best is 70%. Gary Kasparov in his career was 69%. And then the, you know, the the Super GMs category are typically in the 64 to 68% range. So I think it's really interesting actually how you can now quantify by comparing the human move to the best computer move. Jason Calacanis (00:04:54): And it's multiple computers doing David Sacks (00:04:55): that. -- best. Jason Calacanis (00:04:56): They actually have David Sacks (00:04:57): -- It provides a way to assess who the greatest player ever is. I actually thought that it was Magnus, but now maybe there's a basis for believing it was Bobby Fisher. because he was at 72, and Max was only at 70. However, look, the idea that Hans Neman is in the seventies, eighties, or nineties during tournaments would be you know, just an off the charts level of play. And if if he's not cheating, then we should expect over the next couple of years that he should rapidly become the world's number one player over the board, you know, now that they have all this anti cheating stuff. Right? So It'll be interesting to see what happens in his career now that they've really cracked down on, you know, on with with anti anti chain technology. Chamath Palihapitiya (00:05:39): I have a general observation which is these people are complete fucking losers. The people that cheat in any of these games don't understand this basic simple idea which is that trying is a huge part of the human experience. The whole point is to be out there in the field of play trying. And it's basically taking the wins and the losses and getting better that is the path. That's what's fun. Yeah. Once you actually win, it actually not that much fun because then you have this pressure of maintaining excellence. That's a lot less enjoyable than the path to getting there. And so the fact that these people don't understand that makes them slightly broken, in my opinion, And then the other thing is like, why is it that we have this strain of people now that are just so devoid of any personal responsibility? that they'll just so brazenly take advantage of this stuff. It's really ridiculous to be. They don't it's really sad. These people are pathetic. Jason Calacanis (00:06:35): It's really pathetic. I this but it it is really interesting how they caught him and running this against a computer. Here's a a chart of his scores in these tournaments. Oh, oh, there here is this first chart is how quickly he advanced, which was off the charts. And then the second chart that's really interesting is, his chest.com strength. So if you don't know chest.com, it has become like a juggernaut in the chest world, especially after that HBO series. came out. A lot of people subscribed to it. I subscribed to it. I like to play chess there. And, man, you look at the chess strengths were there. He was just, like, perfect. And then the number of games he likely cheated in, you can see the the last two columns. He's basically cheating in every game. You know, Queen's Gambit. Yeah. Great show on Netflix. And he he said he didn't cheat in any of the games where they were live streaming, but they've proven that wrong. Sachs, how does he cheat in person then? David Sacks (00:07:29): That's the thing. No one really knows. And I don't wanna overly judge it until they have hard proof that he was cheating. I mean, look, here's the thing. He was never caught in the act. It's just that the computer evidence, you know, seems pretty damning. And I don't know how they prove I don't know how they prove that he was cheating over the board without actually catching him doing it. And I don't still don't think anyone really has a good theory in terms of how he was able to do that. Jason Calacanis (00:07:54): Well, it's not just him to look. Look. Chamath Palihapitiya (00:07:56): The the fishing thing, Jason, which was crazy, I think Free Percher had the video. The sky was in a fishing competition, and they basically caught these fish. And then they put these big weighted pellets inside the fish's body. They even put, like, a you know, chicken breast and chicken fillets inside of the the thing so that Jason Calacanis (00:08:11): they would drink more. Yeah. Chamath Palihapitiya (00:08:13): They put chicken fillets in. You know? Then there's Jason Calacanis (00:08:16): in -- No. David Friedberg (00:08:16): Across Hopper now. Chamath Palihapitiya (00:08:17): The Hopper Poker, everybody's afraid that there are ways in which you can read the RFID and some of the cards and some of these, you know, tele situations and and front run what the what the playing situation is so that you know whether you're winning or losing. And again, I just asked the question like, Is it is it is this are things that bad that this is what it gets to? Like, we all play poker. The idea that we would play against somebody that would take that edge Jason Calacanis (00:08:43): Yeah. It's gross. Yeah. Chamath Palihapitiya (00:08:44): It really makes me really it's sad. So disappointing. Jason Calacanis (00:08:47): Yeah. And it's it's horrible. David Friedberg (00:08:49): One one observation might be that across all three because I'm trying to find some common thread across these, but it could be that there is a lot of cheating going on for a long time. and maybe the fact that we do have so much digital imagery that's live on these things now, and so much coverage and everyone's got a cell phone that suddenly our perception of the cheating in competitive events is becoming more tuned, whereas maybe there's been a lot of cheating for a long time, and it's just kinda coming to light. I mean, we didn't have a lot of live streaming in poker, who knows? I mean, we could probably ask Phil this or Keating, but, like, for how many years? Chamath Palihapitiya (00:09:31): Oh, they're suspicious. Jason Calacanis (00:09:32): Yeah. Well, there were some sort David Sacks (00:09:33): of cheating an online poker. And hi, Oscar. Remember, like, people are using these, like, software programs that would track the hand hit history -- Yeah. -- of your opponent. Hands up display. Yeah. Yeah. Exactly. So so it help you assess whether the person might be bluffing in that particular situation. Like, it has superhuman memory. David Friedberg (00:09:52): So I don't know if you guys I don't know if you guys watch Twitch like video games like Fortnite or whatever, but there are, like, players that have been accused of using these screen overlay systems that basically more accurately show you and drive the mouse to where an individual is on the screen so you can more accurately shoot them. And so there's software overlays that make you a better you know, competitive video based player. -- the Jason Calacanis (00:10:14): through line is. David Friedberg (00:10:15): And then this the stuff basically became, like, So now what's interesting is now there's eye tracking software that people are using on Twitch screens to see if the individual is actually spotting the target when they shoot, or if the software is spotting the target David Sacks (00:10:31): -- Speaker 5 (00:10:31): Yeah. Jason Calacanis (00:10:31): They're called reverse -- -- bots. They're aim Speaker 7 (00:10:33): bots. Yeah. Yeah. David Friedberg (00:10:34): And, like, reverse cheap the whole thing. I think what's interesting is just that there's so much, you know, insight now, so much more video streams, so much more I mean, think about all those guys at that -- Well Jason Calacanis (00:10:43): and the tools. -- edition. David Friedberg (00:10:44): And there's had cell phones, and they all video this thing happening. Jason Calacanis (00:10:47): Yeah. Yeah. Think of David Sacks (00:10:48): 10 David Friedberg (00:10:48): years ago, that wouldn't have been the case, and there wouldn't have been a big story about it. And so David Sacks (00:10:52): -- Jason Calacanis (00:10:52): Shamati said there was a theme you wanted to there's a Chamath Palihapitiya (00:10:54): I I think the theme is is pretty obvious, which is that there's been an absolute decay of personal responsibility. People don't feel like there's any Downside to cheating anymore. And they're not willing to take it upon themselves to take a journey of wins and losses to get better at something. They want the easy solution the easy solve, the quick answer, you know, that gets them to some sort of finish line that they have imagined for themselves will solve all their problems. The problem is it doesn't solve any problems, and it just makes them a holy corrupt individual. Jason Calacanis (00:11:29): Yeah. Okay. So let's talk about this hustler casino live cash game play. There's this woman, Robbie, who is a a new player. Apparently, she's being staked in a very high stakes game. She's playing. This guy Garrett, who is a very very known winning cash game player. And it was a very strange hand on the turn all the money gets in. She says she has a bluff catcher. Then she claims that she had a thought she misread her hand. Now people are saying that The poker world seems to be 70:30 that she cheated, but people keep vacillating back and forth. There was a lot of weird words salad that she said that she had a bluff catcher, which would normally be an ace. Then she'd said she thought she had a pair of threes and then she immediately said afterwards that he was giving her too much credit. They confronted her in the hallway. She gave the money back because she supposedly loves production, so all of this stuff sounds very weird. One side says, okay. Well, this is happening because she's a noob player. The other side is saying somebody was signaling her, that she was good and giving her just a binary, you're good. Because if you weren't gonna cheat, cheating with Jack High in a situation where you just put all in for a a quarter $1,000,000 pot seems very suspect. I don't know if you guys watched the hand breakdown. Where does everybody stand on a percentage basis, I guess, of if they think she was cheating or not? because we we this is not definitive, obviously. It's not like Chamath Palihapitiya (00:12:53): they cut it open Jason Calacanis (00:12:54): and found the ball bearings. Chamath Palihapitiya (00:12:55): It's not it's not so obvious in that situation, but I think the way that that line played made no sense. Jason Calacanis (00:13:01): Did not make David Sacks (00:13:02): any changes. She was holding a jack 4, and I guess in her previous hand, she had a jack 3, and there was a 3 on the board. So if she misread her hand -- Speaker 5 (00:13:11): The board David Friedberg (00:13:12): was 1013. No. But you would've but you would've you would've had to Chamath Palihapitiya (00:13:15): call the flop thinking what? David Sacks (00:13:17): Yeah. No. I get it. The hand makes no sense, but I'm just trying to find a logical explanation. Jason Calacanis (00:13:22): And that jack 3 explanation, somebody kinda fed that to her, and then she changed her story to that. So this changing of the story is the thing I was sort of keyed on, Friedberg, is why does she keep changing her story? Is it because she's embarrassed? maybe she's had a couple of beverages or whatever. Or she's just a new player and she's embarrassed by her play and can't explain it. She can't explain the hand history? David Friedberg (00:13:43): All of the things you're saying are probable. I don't think that yeah. I don't think there's any data for us to have a strongly help point of view on this. I'm just looking forward to us all playing live, the HCL poker live, October 21st minus David Sachs, unfortunately. Tomop Jacob Gertzner, Stanley Tang, Phil Helmut's -- We're gonna Jason Calacanis (00:14:04): be playing on the same stream. David Friedberg (00:14:05): We're gonna be playing on the same stream, same table. I figured out how to hack into the video stream for Jason Calacanis (00:14:10): the -- I just got my RFID sunglasses as well. Speaker 5 (00:14:13): I'm gonna David Friedberg (00:14:13): read all your shitty hands, Jakell. I'm gonna take your money. And I'm gonna fuck my kids in my school. Chamath Palihapitiya (00:14:18): My and my for my fortieth birthday, Sky Organized poker in Tahoe. Okay? And And we we brought in the team from CBS. That was the present where David Sacks (00:14:27): -- Jason Calacanis (00:14:27): That's fun. Yeah. Chamath Palihapitiya (00:14:27): And they they taped it as if it was being broadcast with whole cards and commentators, and we edited it into a 2 day show. It was an incredible birthday present. I I it was it's one of the greatest things that that anybody's ever given me. I appreciate it. there is a 1 hour block where somebody at the table said, okay, guys. How about we do a cheating free for all? Yes. Where you could look at each other's cards and, you know, you could sort of help somebody else, switch cards, whatever. In that one hour, our beautiful home game of friendship became lord of the flies. I have never seen so much hatred angling mean behavior. Oh my god. It was incredible. David Friedberg (00:15:10): All all that humans are capable of. Chamath Palihapitiya (00:15:11): So I I hope that we never we never we never see cheating in our game. Speaker 5 (00:15:16): Yeah. David Friedberg (00:15:16): Well, we'll see how it goes on October 21st at HCL poker live. Chamath Palihapitiya (00:15:20): I'm excited. I can't wait. It should Speaker 5 (00:15:22): be a Jason Calacanis (00:15:22): lot of fun. It should be a lot of fun. Oh, and we're not having any official 100 stuff, but the fans some of the fans who were at the all in summit David Sacks (00:15:32): 2022 Jason Calacanis (00:15:33): are doing their own 100 episode 100 meetups on October 15th, I think. all in meetups.i0. So there are fan meetups happening in Zurich and a bunch of other places. I'm gonna FaceTime into some of them and just say hi to the fans. You know, it might be like ten people in a bar somewhere. I think the largest one is, like, Miami or San Francisco are gonna be, like, fifty people or something. Speaker 5 (00:15:54): We Jason Calacanis (00:15:54): should all FaceTime it. David Friedberg (00:15:55): That actually would be kind of fun. Jason Calacanis (00:15:57): I I'm basically I told them to send me an invite, and I'll FaceTime in any any time. This is David Friedberg (00:16:02): next week. When is this? Jason Calacanis (00:16:03): The 15th, I think, is it's occurring. Yeah. October 15th. It's a Saturday. The Saturday after the 100th episode, people are doing these, all in meetups.i0. Chamath Palihapitiya (00:16:12): What's next? David Friedberg (00:16:13): Earlier this week, it was reported that Elon contacted Twitter's board and suggested that they move forward with closing the transaction at the original terms and the original purchase price of $54.20 a share. In the couple of days since then, and even as of right now with some news reports coming out here on Thursday morning, It's it appears that there are still some question marks around whether or not the deal is actually gonna move forward at 54.20 a share because Elon As of right now, the the report said is still asking for a financing contingency in order to close, and there's a lot of back and forth on what the terms are. Meanwhile, the court case in Delaware is continuing forward on whether or not Elon breached his terms of the original agreement to close and buy Twitter at 54.20. As we know, leading up to the signed deal or or a post signing a deal, Elon put together a financing syndicate, a combination of debt investors as well as equity co investors with him to do the purchase of Twitter at $54.20 a share. So the 40 some odd $1,000,000,000 of capital that's needed was committed by a set of investors that were gonna invest debt and equity. And there's a big question mark now on whether or not those investors want to or would still consummate the transaction with Elon given how the markets have turned and given how debt markets are trading and equity markets are trading. So, Shamab, I'd love to hear your point of view on what hurdles does Elon still have in front of him? Does he still wanna get this done? And is there still a financing syndicate that's standing behind him at the original purchase price to get it done? Chamath Palihapitiya (00:17:47): That's a great question. Maybe the best way to start is, Nick, do you wanna queue up what I said in August 25th. The lawsuit really boils down to one very specific clause, which is the pinnacle Question at hand which is there is a specific performance clause that Elon signed up to. Right? Which, you know, his lawyers could have struck out and either chose not to or, you know, couldn't get the deal done without. And that specific performance clause says that Twitter can't force him to close at 54.20 a share. And I think the the issue at hand at the Delaware Business Court is going to be that because Twitter's gonna point to all of these you know, gotchas and disclaimers that they have around this spot issue as their cover story. And I think that really this kind of, again, builds more and more momentum in my mind that the most likely outcome here is a settlement where you have to pay the economic difference between where the stock is now and 54.20 which is more than a 1,000,000,000 dollars, or you close at some number below David Sacks (00:19:03): $54.20 Chamath Palihapitiya (00:19:05): a share. And I think that that is like, you know, if you had to be a betting person, that's probably And if you look at the the way the stock is traded and if you also look at the way the options market trades, that's what people are assuming, that there's a 7 to $10,000,000,000 swing. And if you impute that into the stock price, you kind of get into the David Sacks (00:19:24): $51 Chamath Palihapitiya (00:19:25): a share kind of a an acquisition price. Again, I'm not saying that that is right or should be right. That's just sort of what the market says. Yeah. So So I I it turns out that, you know, sort of like that kind of guesstimate turned out to be pretty accurate because the stock today is at $51 a share. So I think that the specific performance thing is exactly what this thing has always hinged on. And I think that there is a realization that there were very few outs around how that contractual term was written and agreed to. So there is an out in the contract. And that out says that I think it's by April. If if the deal doesn't get done by April, then the banks can walk away from their commitment to fund the debt. And if the banks walk away then Elon does have a financing contingency that allows him to walk away. So the actual set of events that have to happen is those 2 things specifically. Get to April so the banks can pass and say, we've changed our mind. Market conditions are different. And then Elon is able to say, Paul, you know, the banks just walked away. Right now, the banks if you look at all of the debt that they've committed to, well they committed at a point in time when the debt markets were much better than they are today. In the last, you know, 6 or 7 months since they agreed to do this, the debt markets have been clobbered. And specifically, junk bonds and a bunch of junk bond debt the yields that you have to pay. So the price to get that kind of debt has skyrocketed. So roughly back of the envelope math, would tell me that right now the banks are offside between $1,200,000,000 because they're not gonna be able to sell this debt to anybody else. So I think the banks obviously wanna weigh out. The problem is their only way out is to run the shot clock off until April. So I think that's the dance that they're in right now. Elon's trying to find a way to solve, you know, for the merger. I think Twitter's gonna say, we're not gonna give you a financing contingency. Chamath Palihapitiya (00:21:19): You have to bring the banks in and close right now. and then we will not go to court. Otherwise, we're going to court. And so I think it's a very delicate predicament that they're all in, but my estimate is that The equity is probably 20 percent off-site. So it's not a huge thing. He can make that up because he can create equity value like nobody's business. The debt is way offside by a couple $1,000,000,000, which is hard to make back, but I think in the end, you know, given enough time they could probably make that back. The best off in all of this are the Twitter shareholders. They're getting an enormous premium to what that company is worth today in the open market. And so I think this deal is gonna close. It's probably gonna close in the next few weeks. And had you bought Twitter when we were talking about it in August, you would have made 25% in 6 weeks and, you know, if the deal closed at 54, you you would have made, you know, a third of your money in 8 weeks, which is, you know, very hard to do. in a marketplace. David Friedberg (00:22:14): If you're a GP at one of the funds like Andreessen or Sequoia and you had made this commitment to Elon or even Larry Ellison, couple months ago, do you fight against closing at 54.20? Do you stick with the deal? And support him? I mean, what do you do given that the premium is so much higher than where the market would trade it at today? Some people are saying the stock should be at, like, 20 bucks a share Chamath Palihapitiya (00:22:38): The average premium in an m and a transaction in the public markets is about 30%. So and I think the fair value of Twitter is around 32 to 35 bucks a share. So, you know, it's not like he is massively, massively overpaying. And so, you know, I would just sort of keep that in the realm of the possible. So, like, if you take $35 as the midpoint, Fair value is really 45.50. So, yeah, he paid 20% more than he should have, but he didn't pay a 100% more. So it's not as if you can't make that equity back as a private company, particularly because there's probably $10 of fat in the stock if you think about just OpEx Speaker 5 (00:23:19): Right. Chamath Palihapitiya (00:23:19): In terms of all the buildings they have, maybe they don't need as many employees, maybe they revisit salaries. You know, one thing is when I looked at doing an activist play, at Twitter. I think I mentioned this 5 or 6 years ago, one of the things that I found was at that time, Twitter was running their own data centers. And, you know, the most obvious thing for me at that time was, like, we're gonna move everything to AWS. Now I don't know if that happened, but I'm sure that if it hasn't, Just bidding that out to Azure, GCP and AWS can raise, you know, 3 or $4,000,000,000 because I'm sure those companies would want this kind of an app on their cloud. So there's all kinds of things that I think Elon can do as a private company to make back maybe the small bit that he overpaid, and then he can get to the core job of rebuilding company to be usable or this product to be usable. Because I look, I'll just speak as a user right now. It has been decaying at a very, very rapid clip and I think that his trepidation in closing the merger in part also, even though he hasn't said it, has to do with the quality of the experience. It's just degraded. It's not as fun to use as it was during the pandemic or even before the pandemic. So something is happening inside that app that needs to get fixed. And if he does it, he'll make a ton of money. David Friedberg (00:24:33): Sort of like what happened with Friendster and Myspace and any social networking app over time, the quality degrades. If it's not growing, it's shrinking. And it gets David Sacks (00:24:41): -- Jason Calacanis (00:24:42): If it Chamath Palihapitiya (00:24:42): if it's if it's not growing and also if the product hygiene isn't enforced in code and product hygiene, in this case, or this, you know, spam, bots, you know, the trolling. It can really take away from the experience. David Friedberg (00:24:56): Yeah. I mean, interestingly, like, if you think back to the orig the the the starting days, the original days of Twitter, I don't know if you guys remember you would s send in an SMS to do your tweet, and then it would post up, and other people would get the SMS notification. And it would crash all the time, and the app were the app was notoriously crashing. It was poorly architected at the beginning, and some people have argued that Twitter has had a cultural technical incompetence from the earliest days. Chamath Palihapitiya (00:25:25): I think that's a little harsh. So I do think look. Twitter was known for what's called the fail whale. You know, they used to have these fail whales constantly, and they did hire people that attempted to try to fix it. I remember the the the funniest part of when I went in there and said, hey. Here's my plan, and here's what I wanna do, is literally a day or 2 later the head of engineering quit. I can't remember who his name was, but he was just out the door. but it is a I think it is a team that has tried its best. that probably at the edges definitely made some technical miscalculations, like I said, at that time. The idea that any app of that scale would use their own data centers made note. Technical sense whatsoever. It made the app laggy. It made it hard to use. It made it more prone to downtime, to your point. But that being said, I would be shocked if they haven't made meaningful improvements because the stack of the Internet has gotten so much better over the last 7 years. And so to your point, David, if they didn't take advantage of all these new abstractions and mechanisms to rebuild the app or to rebuild search or to rebuild you know, how, you know, all these infrastructure elements of the app work, I would be really surprised because then what are they doing over there? David Friedberg (00:26:40): Yeah. Well, look, I mean, to the point earlier, besides the product points, there was a a really good tweet I liked. That said, for what it's worth, I think Elon will show us just how lean the Silicon Valley advertising companies can be run. At the very least, it'll be an interesting thought experiment. for spectators. Because if he does go in and actually does significantly reduce OpEx and headcount and the company does turn profitable and he can grow it, Chamath Palihapitiya (00:27:07): Well, look, David Friedberg (00:27:07): it'll it'll really by the way, it'll really be a beacon from a financial David Sacks (00:27:11): -- David Friedberg (00:27:11): Big companies. Chamath Palihapitiya (00:27:12): Yeah. From a financial perspective, there is $10 a share in op cuts that he should make right away just so that he is economically breakeven, and he looks like every other M and A transaction. You know, you paid a 30% premium and you bought a company. There's a lot of margin of safety there if Elon does that. So to your point, there probably is, and there probably needs to be a meaningful riff at Twitter. I'm not saying it's right. I'm not saying it's you know, and I feel for the people that may go through it. But from a financial perspective, the math makes sense for him to do that because then he is a breakeven proposition on a go in m and a transaction. And I think that there's there's a lot of intelligent financial sense so that all the debt holders feel like he's doing the right thing and all the equity holders particularly see a chance for them to make a decent return here. David Friedberg (00:27:58): Alright. Well, let's move on. This is Jake. Great conversation. A great conversation between Chamath Palihapitiya and Dave Friedberg about the Twitter transaction, and now we're being rejoined by our besties who are David Sacks (00:28:11): -- Jason Calacanis (00:28:12): Yeah. By other besties. Yeah. David Friedberg (00:28:14): How was your cappuccino, Jay Cal? Jason Calacanis (00:28:16): Oh, it's great. I have a Speaker 5 (00:28:17): I have Jason Calacanis (00:28:17): a I have an iced cold brew here, a nice iced cold brew, and a a nice drip coffee. I'm I'm working both. Speaker 5 (00:28:22): I'd David Sacks (00:28:22): love to talk about topics that I'm being subpoenaed or depositioned about. Jason Calacanis (00:28:26): We will have a lot to say in the coming weeks. David Sacks (00:28:28): I love to talk about topics that my lawyers have advised me to talk about. How -- Chamath Palihapitiya (00:28:32): Ronald? -- how eerie was our prediction? 51 bucks a share. It is exactly where the stock is right now. That's eerie. Jason Calacanis (00:28:38): Yeah. Alright. Lots of advances. David Friedberg (00:28:40): Let's keep going. Jason Calacanis (00:28:41): Yeah. Speaking of Elon, Tesla AI Day was last week. I actually went. It was great. This is a recruiting event where David Sacks (00:28:50): -- David Friedberg (00:28:50): What did you do after? Jason Calacanis (00:28:51): Phil Helmuth and I went and I drove Phil Helmuth home. the end. No. It's a great event, and it is essentially a giant recruiting event. David Sacks (00:29:02): 100 Jason Calacanis (00:29:02): of AI team -- Sorry. David Friedberg (00:29:04): I'm sorry. Chamath Palihapitiya (00:29:04): I'm sorry. I'm sorry. Can we just talk about Phil Huntly? It's nonsecretary in the group chat about Ken Griffin. Jason Calacanis (00:29:11): I mean -- Oh, yeah. Where he's just, like, I made a joke about his net worth, and he responded. Chamath Palihapitiya (00:29:17): What is going on? We were talking about the most series of topics, and he just comes David Sacks (00:29:21): -- 7 Jason Calacanis (00:29:21): seconds to fill. It's what's going on. 7 seconds to fill. Chamath Palihapitiya (00:29:25): By the way, I I I was texting with Daniel McGranue. He did an incredible podcast. If you guys with Lex Friedberg, if you haven't listened to it, the Daniel Negronu pod with Lex is incredible. Jason Calacanis (00:29:37): Oh, I'm gonna let David Sacks (00:29:38): -- Chamath Palihapitiya (00:29:38): But I I I was joking with Daniel that there's a section where he's talking about the greatest poker players of all time. And if you look in the bar, of YouTube, it shows where the most viewership was. And it was exactly the 30 seconds he talks about Helmut, and I said to Daniel, this must have been Phil rewatching it Jason Calacanis (00:29:54): over and I put it on loop. He went to bed with it like it was ASMR to put him to bed. Just -- So -- -- the crowd are talking about him. Sorry, Jacob. Chamath Palihapitiya (00:30:04): Sorry about that. No. Jason Calacanis (00:30:04): No. It's it's all good. So anyway, the event was super impressive. Elon only spoke when he showed the optimist, the new robot. He's building a a general purpose robot that will work in the factories. It's very early days, but they showed 2 versions of it. And he said he thinks they could get it down to $20,000 work in the factory, so it's actually got a purpose. And, obviously, the factories already have a ton of robots, but this is more of a robot that will benefit from the general or the the computer vision and the AI, the narrow AI being pursued by the self driving team. This is like 2 a half hours of really intense presentations. The most interesting part for me was they're building their own supercomputer. and their chips and the Dojo Supercomputer was really impressive at how much they can get through scenarios. So they're building every scenario, a very self driving. I actually have the full self driving bait on my car. I've been using it. It's pretty impressive, I have to say. If you haven't used it yet, I feel like AI is moving at a a pretty advanced clip the past year if you haven't also seen Meta announced a text to video generator. So this is even more of than Dolly. Dolly, you put in a couple of words, Friedberg, and and you get a painting or whatever. This is put in a couple of words, and you get a short video. So they had one of a teddy bear painting a teddy bear. So it looks like you're going to be able to essentially create a whole movie by just talking to a computer. Really impressive. Where do you think we are, Friedberg, in terms of the compounding nature of these narrow AI efforts, you know, obviously we saw poker, chess, go, Dolly, GPT 3, self driving. It feels like this is all compounding at a faster rate. Or am I just reimagining that? David Friedberg (00:32:02): Yeah. Look. I mean, it's interesting when when people saw the first computer playing chess, they said the same thing. I think anytime that you see progress with a computer that starts to mimic the predictive capabilities of a human. It it it's it's impressive, but I will argue I just I'll say a few words on this. It's I I think this is part of a a 60 year cycle that we've been going through. Fundamentally, what humans and human brains do is we can sense our external environment, then we generate knowledge from that sensing and then our brains build a model that predicts an outcome. And then that that predicted outcome is what drives our actions and our behavior. We observe the sun rides every morning, then we observe that it sets. And you see that enough times, and you build a predictive model from that data that's been generated in your brain that I predict that the sun has risen, it will therefore set. It has set it will therefore rise. And I think that the computing approach is very similar. It's all about sensing or generating data, and then creating a predictive model, and then you can drive action. And initially, the first approach was just basic algorithms. And these are deterministic models that are built. It's a piece of code that says, here's an input, here's an output, and that that model is really built by a human and a human designed it designed that algorithmic model and said, this is what the the predictive potential of this software is. Then there was this term called data science. So as data generation began to proliferate, meaning there was far more sensors in the world. It was really cheap. to to to create digital data from the physical world, really cheap to transmit it, really cheap to store it, and really cheap to compute with it. Data science became the hot term in Silicon Valley for a while, And these models were not just a basic algorithm written by a human, but it became an algorithm that was a similar deterministic model that had parameters, and the parameters were ultimately resolved by the data that was being generated. And so these models became much more complex and much more predictive finer granularity, finer range. Then we use this term machine learning. And and in the data science era, it was still like, hey. There's a model, and You would solve it statically. David Friedberg (00:34:12): You would get a bunch of data. You would statically solve for the parameters, and that would be your model, and it would run. Machine learning then allowed those parameters to become dynamic. So the model was static, but generally speaking, the parameters that drove the model became dynamic as more data came into the system and they were dynamically updated. And then this era of AI became, and that's the new catch word. And what AI is realizing is that the there's so much data that rather than just resolve the parameters of the model, you can actually resolve a model itself. The algorithm can be written by the data. The algorithm can be written by the software. And so with a with AI -- Example. David Sacks (00:34:50): -- David Friedberg (00:34:50): so poker playing, an adaptive model. So people so you're playing poker and the software begins to recognize behavior, and it builds a predictive model that says here's how you're playing. And then over time, it actually changes not just the parameters of the model, but the model itself, the algorithm itself. And so AI, and then it eventually gets to a point where the algorithm is so much more complex that a human would have never written it. And suddenly, the AI has built its own intelligence its own ability to be predictive in a way that a human algorithmic programmer would have never done. And and this is all driven by statistics. So none of this is new science per se. There's new techniques that all on their underlying use statistics as their basis, and then there's these techniques that allow us to build these new systems of model development like neural nets and so on. And those statistics build those neural nets, they they they solve those parameters and so on. But fundamentally, there is an geometric increase in data and a geometric decline in the cost to generate data from sensors because the cost of sensors is coming down with Moore's Law. transmit that data because the cost of moving data has come down with broadband communications. The cost of storing data because the cost of DRAM and and and Solid state hard drives has come down with Moore's Law. And now the ability to actually have enough data to do this AI driven where people are calling AI, but it really is the same part of a spectrum of things that have been going on for 60 years to actually drive predictions in the in the world is really being realized in a bunch of areas that we would have historically been really challenge and surprise to see. And so my argument is -- At this point -- Big Jason Calacanis (00:36:22): data played a big role. Yeah. David Friedberg (00:36:24): Yeah. We've over the last decade, we've reached this tipping point in terms of data generation storage and computation that's allowed these statistical models to resolve dynamically. And as a result, they are far more predictive And as a result, we see far more human like behavior in the predictive systems, both physic both those that are, you know, like a like a robot, is the same as one that existed 20 years ago. But the way that it's run is using the software that is driven by this dynamic model. And that data allows Jason Calacanis (00:36:52): for a better answer, Chamath. Chamath Palihapitiya (00:36:55): Okay. I have 2 things to say, but one the first one is a total nonsecretary to use the term data scientist. Do you know where the term data scientist came from? Has has classically used in Silicon Valley? It came from Facebook. And -- Uh-huh. David Sacks (00:37:08): -- Chamath Palihapitiya (00:37:08): it came from my team in a critical moment. If this was in David Sacks (00:37:11): 2007, Chamath Palihapitiya (00:37:12): I was trying to 2008. I was trying to build the growth team. This is the team that became very famous for getting to a billion users and, you know, building a lot of these algorithmic insights. And I was trying to recruit a person from Google. And he was like a PhD and some crazy thing like astrophysics or particle or something. And we gave him an offer as a data analyst because this is what I needed. At the time, this is what I thought I needed an analyst, you know, to analyze data. And he said absolutely not, I'm offended by the job title. And I remember talking to my my HR, you know, business process partner And I asked her, like, I don't understand what is this where is this coming from? And she said, he fashions himself a scientist. And I said, well, then call him a data scientist. So we wrote in the offer for the first time data scientists. And at the time, people internally were like, this is a dumb title. What does this mean? Anyways, we hired the guy. He was a star, and David Friedberg (00:38:09): and that title just took off internally. Shamal, it's funny because, parallel, we started Chamath Corp in 2006. and the original the first guy I hired with a buddy of mine who was a 4.0 for in applied math from Cal. And then everyone we hired on with him, we called them the math team. And they were all applied math and statistics, PhDs, and we called them the map team, and it was really cool to be part of the map team. But then we switched the team name to data scientist, and then it obviously created this much more kind of impressive role, impressive title, central function to the organization that was more than just a math person or data analyst as I think it may have been classically treated because they really were building the algorithms that drove the models that made the product work. Right? Chamath Palihapitiya (00:38:50): Peter Till is a very funny observation. Not funny, but, you know, observation, which is you should always be wary of any science that actually has science in the name. political science, social science. I guess maybe data scientists, you know, because the real scientists don't need to qualify themselves, physics chemistry biology. Anyways, that's so here here's what I wanted to talk about with respect to AI. 2 very important observations that I think is useful for people to know The first one, Nick, if you throw it up here is just a a base lining of, you know, when we have thought about intelligence and compute capability, we've always talked about Moore's Law. And Moore's law essentially this idea that there is a fixed amount of time where the density of transistors inside of ship would double and roughly that period for many, many years was around 2 years. And it was largely led by Intel. And we used to equate this to intelligence. meaning the more density there was in a chip, the more things could be learned and understood. And we used to think about that as the progression of how Computing intelligence would grow and eventually AI and artificial intelligence would would get to mass market. Well, what we are now at is a place where many people have said Moore's Law has broken. Why? It's because we cannot cram any more transistors into a fixed amount of area. We are at the boundaries of physics. And so people think, well, does that mean that that our ability to compute will essentially come to an end and stop, and the answer is no. And that's what's demonstrated on this next chart just to make it simple, which is that what you really see is that if you think about, you know, supercomputing power so the ability to get to an answer. That has actually continued unabated. And if you look at this chart, the reason why this is possible is entirely because we've shifted from CPUs to these things called GPUs. So you may have heard companies like NVIDIA. Chamath Palihapitiya (00:40:48): Why is companies like NVIDIA done so well? it's because they said they raised their hand and said we can take on the work. And by taking on the work away from a traditional CPU you are able to do a lot of what Friedberg said is get into these very complicated models. So this is just an observation that I think that we are continuing to compound knowledge and intelligence effectively at the same rate as Moore's Law. And we will continue to be able to do that because this makes it a problem of power and a problem of money. So as long as you can buy enough GPUs from NVIDIA or build your own, And as long as you can get access to enough power to run those computers, there really isn't many problems you can't solve. And that's what's so fascinating and interesting. And this is what companies like OpenAI are really proving. You know, when they raise the $1,000,000,000 What they did was they attacked this problem because they realized that by shifting the problem to GPUs, it left all these amazing opportunities for them to uncover, and that's effectively what they have. The second thing that I'll say very quickly is that it's been really hard for us as a society to build intelligence in a multimodal way like our brain works. So think about how our brain works. Our brain works in a multimodal way. We can process imagery. We can process words and sounds. We can process all of these different modes, text, into one system and then intuit some intelligence from it and make a decision. Right? So you know, we could be watching this YouTube video. There's going to be transcription. There's video voice audio. Everything all at once. And we are moving to a place very quickly where computers will have that same ability as well. Today, we go to very specific models and kind of balkanize silos Jason Calacanis (00:42:45): Correct. David Sacks (00:42:46): -- Chamath Palihapitiya (00:42:46): solve different kinds of problems. But those are now quickly merging, again, because of what I just said about GPUs. So I think what's really important about AI for everybody to understand is the marginal cost of intelligence is gonna go to 0. And this is where I'm just gonna put out another prediction of my own. When that happens, it's gonna be incredibly important for humans to differentiate themselves from computers. And I think the best way for humans to differentiate ourselves is to be more human. It's to be less compute intensive. It's to be more empathetic. It's to be more emotional, lot less emotional. because those differentiators are very difficult for brute force compute to solve. Jason Calacanis (00:43:31): Be careful. The replicants on this call are getting a little nervous here. Chamath Palihapitiya (00:43:34): They're not processing that. That was an emotional statement. Jason Calacanis (00:43:37): Do not wanna process that one. Well, to your point, during this AI day, they were showing in self driving as you're talking about this balkanization and trying to make decisions across many different decision trees, you know, they're looking at lane changes. They're looking at other cars and pedestrians. They're looking at road conditions like fog and rain. And then they're using all this big data to your point, Friedberg, to run tons of different simulations. So they're building like this virtual world on Market Street, and then they will throw people, dogs, cars, people who have their behavior -- -- about that -- -- into the simulation. It's such Chamath Palihapitiya (00:44:16): a wonderful example. Imagine that system hears a horn. Jason Calacanis (00:44:20): Yeah. Chamath Palihapitiya (00:44:21): Well, you hear a horn. So clearly, there's some auditory expression of risk. Right? There's something risky And now you have to scan your visual field. You have to probabilistically decide what it could be, what the evasive maneuver if anything should be, So that's a multimodal set of intelligence that today isn't really available. Yeah. But we have to get there if we're gonna have real full self drive So that's a perfect example, Jason, a real world example of how hard the problem is, but it'll get solved because we can brute force it now with with chips and with compute. I think that's gonna be Jason Calacanis (00:44:55): the very interesting thing with the robots as well as all of these decisions they're making moving cars through roads, all of a sudden, we're gonna see that with Vitol's, vertical takeoff and landing, you know, aircraft, and we're gonna see it with this general robot. And everybody wanted to ask you a lot about general AI, you know, the Terminator kind of stuff. And his position is, I think if we solve enough of these problems, Friedberg, it'll be an emergent behavior or an emergent phenomenon, I guess, would be a better word. based on each of these cities crumbling, you know, each of these tasks getting solved by groups of people. You have any thoughts as we wrap up here on the discussion about general AI and the timeline for that because, obviously, we're gonna solve every vertical AI problem in short order. David Friedberg (00:45:41): I spoke about this a little bit on the ask AMA on Colin on Tuesday night. Once Saks gets it out, you can listen to it. But I I really have this strong belief Jason Calacanis (00:45:53): that -- The servers crash is David Sacks (00:45:54): -- Jason Calacanis (00:45:54): There's no way out there no way out by the David Sacks (00:45:56): time this episode drops. Jason Calacanis (00:45:57): Oh my god. Yeah. You guys can try to download the app, but it might crash. So just be careful. David Friedberg (00:46:01): So so here's here's my my Speaker 5 (00:46:04): core thesis David Sacks (00:46:04): on there. -- Friedberg that you are ten times more popular than Jake Cal. it's unacceptable. You have also traffic. Jason Calacanis (00:46:10): Well, you had you did have an account with 11,000 followers. I mean, it's -- Yeah. David Sacks (00:46:14): -- Chamath Palihapitiya (00:46:14): it might David Sacks (00:46:14): look like -- Right, Jacob. We'll put you on that account next time. Jason Calacanis (00:46:17): Yeah. Please. Yeah. I'm starting from David Sacks (00:46:19): 0. Yeah. That's fair. That's fair. David Friedberg (00:46:21): Yeah. Look, my my core thesis is, I think, humans transition from being let's call it, you know, passive in this system on earth to being laborers. And then we transition from being laborers to being creators And I think our next transition with AI is to transition from being creators to being narrators. And what I mean by that is as as we started to do work on earth and engineer the world around us. We did labor to do that. We literally plowed the fields. We walked distances. We built things. And over time, we built machines that automated a lot of that labor. You know, everything from a plow to a tractor, to a caterpillar, equipment to a microwave that cooks for us, labor became less we became less dependent on our labor abilities, and then we got to switch our time and spend our time as creators as knowledge workers. And a vast majority of the developed world now primarily spends their time as knowledge workers creating. and we create stuff on computers. We do stuff on computers, but we're not doing physical labor anymore. As a lot of the knowledge work gets supplanted by or as as it's being termed now, but really gets supplanted by software. The role of the human, I think, transitions to being one of a narrator. where instead of having to create the the the blueprint for a house, you narrate the house you want, and the software creates the blueprint for you. David Sacks (00:47:43): -- David Friedberg (00:47:43): dictate. And instead of, yeah, and instead of creating the movie and not spending a $100,000,000 producing a movie, you dictate or you narrate the movie you wanna see and you iterate with the computer, and the computer renders the entire film for you because those films are shown digitally anyway, so you can have a computer render it. instead of creating a new piece of content, you narrate the content you want to experience. You create your own video game. You create your own movie experience. And I think that there's a whole evolution that happens. And if you look, Steve Pinker's book, enlightenment now has a great statistic a set of statistics on this, but the amount of time that humans are spending on leisure activities per week has climbed extraordinarily over the past couple of decades. we spend more time enjoying ourselves and exploring our creative interests than we ever did in the in the past in human history because we were burdened by all the labor and all the creative and knowledge work we had to do. And now things are much more accessible to us, and I think that AI allows us to transition into an era that we never really thought possible to realize where the limits are really our imagination of what we can do with the world around us, and the software resolves to the and and the automation resolves to make those things possible. And I was thinking David Sacks (00:48:50): -- -- David Friedberg (00:48:51): a really exciting kind of vision for the future that I think AI enables. Jason Calacanis (00:48:54): Star Trek had this. Right? People didn't have to work, and they could pursue things in the Holodec or whatever that they felt was rewarding to them. But speaking of jobs, the job report for August came in. We we talked about this. We were we were trimming 300,000 jobs a month. We're wondering if the other shoe would drop, avoided a drop, a hon over a million jobs burned off in August. So without getting into the macro talk, it does feel like what the Fed is doing and companies doing hiring freezes and cuts is finally finally having an impact. If we start losing a 1,000,000 as we predicted could happen here, on the show, people might actually go back to work and Lyft and Uber are reporting that the driver shortages are over. They no longer have to pay people and stuff like that to get people to come back to work. So at least you're in America. Feels like we're turning a corner. Do we wanna go David Sacks (00:49:45): -- Jason Calacanis (00:49:45): Hey, Cal. Can we -- Yeah. David Friedberg (00:49:45): Let's talk about the marijuana Yeah. Breaking news. That Biden just did. Yeah. Yeah. Speaker 7 (00:49:50): I was Jason Calacanis (00:49:50): about to say we got a couple of things we really wanna get to here. Ukraine section 230 and then this breaking news, I will pull it up here on the screen. While we're recording the show, president Biden says, and I'm just gonna quote here, First, I'm pardoning all prior federal offenses of Simple marijuana possession. There are thousands of people who are were previously convicted of simple possession who may be denied employment housing or educational opportunities as a result. My pardon will remove this burden. That's big news. 2nd, I'm calling on governors to pardon simple state marijuana possession offenses just as no one should be federal prison solely for possessing marijuana, nobody should be in a local jail or stay prison for that reason either. Finally, this is happening. 3rd, and this is an important one, we classify the marijuana at the same level as heroin and even and more serious than fentanyl. Makes no sense. I'm asking secretary Barrackara and the attorney general to initiate the process of reviewing how marijuana is scheduled under federal law. I'd also like to note that as federal and state regulators change, we still need important limitations on traffic marketing and under a shells of marijuana. thoughts on this breaking news. David Friedberg (00:51:02): Zach, does is this mode do you think that the the timing on this is kind of midterm related? It it just seems is this is Jason Calacanis (00:51:09): this I guess, is this David Friedberg (00:51:10): a politically popular decision to do? David Sacks (00:51:13): I think so. I mean, look. I support it. So Biden finally did something I like. Jason Calacanis (00:51:18): Great. David Sacks (00:51:19): I mean, I thought that we should decriminalize Marijuana for a long time. Or specifically, you know, I agree with this idea of rescheduling it. It it does not make sense to treat marijuana the same as heroin as a schedule 1 narcotic. This doesn't make any sense. It should be regulated separately and differently. Obviously, you wanna keep it out of the hands of minors, but no one should be going to jail, I think, for simple possession. So I do agree with this. And I think the thing they need to do, I don't see it mentioned here, is they should pass a federal law that would allow for the normalization of, let's call it, legal, you know, cannabis companies. So so companies that are allowed to operate under state laws like in California should have access to the banking system, should have access to payment rails. Because right now, The reason why the legal cannabis industry isn't working at all in California is because they can't bank they can't take payments. So it's this weird all cash business that makes no sense. So so listen, if we're not gonna criminalize it as a drug like heroin, if we're gonna allow states to make it legal, then allow it to be a more normal business where the state can tax it and it can operate in a more above board way. So -- Federal mandate Jason Calacanis (00:52:39): is what you're saying, the federal mandate. David Sacks (00:52:40): I think it could still be regulated on a state by state basis, but I think you need the feds to bless the idea that banks and payment companies can take on those clients, which states have already said, are legally operating companies. And right now, they can't, and it's a huge gap in the law. So maybe that's the one thing I would add to this, but I don't have any complaints about this right now. based on what we know from this tweet storm and I would say Tech, what's up on the date of birth? This was an about face by Biden. David Friedberg (00:53:09): Do you know what the polling data says? I mean, is is there I'm assuming there's big support in kind of the independence in the middle for Speaker 5 (00:53:17): this move. Jason Calacanis (00:53:17): It was 70% at one point. David Sacks (00:53:18): Yeah. Yeah. So so look. This to me, this is the kind of thing that Biden should be doing with the 5050 senate, finding these sorts of by parts and compromises. David Friedberg (00:53:28): Right. David Sacks (00:53:29): So, yeah, I look. This is good news for us. I'm concerned. David Friedberg (00:53:31): Why hasn't this happened in the past? Like, what's been the political reason that other presidents Obama even didn't that that have Jason Calacanis (00:53:41): There was David Friedberg (00:53:42): rumors ideology. Like, why why but why does anyone know why this hasn't been done in the past? Jason Calacanis (00:53:46): There was rumors he was gonna do in the second term. They just didn't have the political capital -- Why? David Sacks (00:53:51): -- Jason Calacanis (00:53:51): to do it. David Friedberg (00:53:52): Why? I don't I don't well, the pardon doesn't require Chamath Palihapitiya (00:53:55): yeah. The pardon doesn't require political capital. I think it's probably the perception that this is soft on crime in some way or there wasn't enough broad based support as David said. I mean, I think the The United States population has moved pretty meaningfully in the last 20 years towards David Sacks (00:54:11): -- Jason Calacanis (00:54:11): Look at the chart here. You know, if we were talking about 2000, it was only 31%. And then you look at 2018, it's up at 60 plus percent. So when people saw the states doing it and they saw absolutely no problem, you know, in every state. And I think what people will see next David Friedberg (00:54:27): is -- That's a gallup poll. Jason Calacanis (00:54:28): That's a gallup poll you see in there. Yeah. Yeah. So, I mean, it's increased dramatically. MDMA, Celisibam, and some of these other plant based medicines, Ayahuasca are next, and they're doing studies on them now. Chamath Palihapitiya (00:54:38): I don't wanna take away from how important this is for all the people from this will positively impact. I just want to talk about the schedule change for marijuana. As a parent, One of the things that I'm really really concerned about is that through this process of legalization, getting access to marijuana has frankly become too easy particularly for kids. At the same time, I saw a lot of really alarming evidence that the the intensity of these marijuana based products have gone you know, I think it's, like, 5 or 6 times more intense than they David Sacks (00:55:12): -- Jason Calacanis (00:55:12): Oh, no. No. 50 or a100 a month. Much higher. Chamath Palihapitiya (00:55:15): Right. So so it's no longer you know this kind of like you know do no harm drug that it was 20 years ago. This is this could be actually David the way that it's productized today as bad as some of these other you know narcotics. So In June of this year, the Biden administration basically made this press release that said the FDA is gonna come out with regulations that would cap the amount of nicotine in cigarettes. And I think that was a really smart move because it basically set the stage to taper nicotine out of out of cigarettes, which would essentially, you know, decapitate it as an addictive product. And I think by thinking about how it's how it's dealt with. What I really hope the administration does is it empowers the FDA. If you're gonna legalize it, you need to have expectations around what the intensity of these drugs are because Speaker 5 (00:56:15): if you're delivering Chamath Palihapitiya (00:56:15): drugs OTC and now any kid can go in at eighteen years old and buy them, which means that eighteen year olds are gonna buy them for sixteen year olds, sixteen year olds are gonna get fake IDs to buy them for themselves. You need to do a better job so that parents you're helping parents do our job. Here's what you need. Speaker 7 (00:56:33): So David Sacks (00:56:33): shouldn't it shouldn't it be anyone like alcohol? If alcohol is 21, then -- Jason Calacanis (00:56:38): Of course. Yeah. Fine. Chamath Palihapitiya (00:56:39): But even alcohol, David, you know that there are there are we know what the intensity of these are. There are labels and there's warnings, and you know the difference between beer Speaker 5 (00:56:47): versus Chamath Palihapitiya (00:56:47): wine versus hard alcohol. Jason Calacanis (00:56:48): But let me just give you some statistics here, Shamoff. If you think about the the the cannabis in the nineties and prior to that, and there have been very you've been a ton of studies on this in Colorado. It was the THC content was less than 2%. And then in David Sacks (00:57:02): 2017, Jason Calacanis (00:57:04): we were talking about, you know, things going up to David Sacks (00:57:08): 17 Jason Calacanis (00:57:09): to 28% for a specific strain. So they have been building strains like girl scout cookies etcetera that have just increased and increased, and then there are things like shards and, obviously, edibles. You can create whatever intensity you want. So you have this incredible, you know, variation. You could have an edible that's, you know, got 1 milligram of THC. You got one that has 100, where you can have a pack of edibles and you see this happen in the news all the time. Some kid gets their parents pack or somebody gives 1, and the kids don't know. and this dabbing phenomenon combined with dabbing is, like, Bashards, like this really intense stuff. combined with the edibles is really the issue and the labeling of them. So you gotta be incredibly careful with this. It's not good for kids. It screws up their brains. And so Yeah. Be very careful. I have Chamath Palihapitiya (00:57:55): a I have a zero tolerance policy on this stuff. I don't care if it's legal or illegal. Like, I don't want my kids touching any of this stuff until David Sacks (00:58:00): -- David Friedberg (00:58:00): It's not Jason Calacanis (00:58:01): for kids. Yeah. David Friedberg (00:58:02): But you also should not do it until Chamath Palihapitiya (00:58:04): they're 35 or 40. And even then, I hope they never do it. But but I need some help. And I'm not sure I'm the only parent that's asking. You can't have this stuff be available effectively sold, like, in a convenience store David Sacks (00:58:15): -- Chamath Palihapitiya (00:58:15): No. No. -- or they're gonna happen. Where there isn't even labeling, at least like cigarettes are labeled. It's very clear how bad this stuff is for you. Jason Calacanis (00:58:22): Oh, do you guys have any feedback on the job report or anything? David Friedberg (00:58:25): They're all going when when the when the AI wins. Jason Calacanis (00:58:28): Well, that's why I brought it up is, like, we're now gonna see a potential, you know, a situation where jobs go away and a lot of stuff. Like, even developers. Right? Don't you think Friedberg, developers are gonna start? Development tasks. David Friedberg (00:58:40): No. Design tasks are going to Jason Calacanis (00:58:42): be AI out of this. David Friedberg (00:58:43): Everyone assumes a static lump of work. I think what happens particularly in things like developer tools is the developer can do so much more. and then we generate so much more output. And so the overall productivity goes up, not down. So it's pretty exciting as these And and remember, like like, we were talking on the AMA the other night, Adobe Photoshop was a tool for photographers, so you didn't have to take the perfect photograph and then print it you could, you know, you could use the software to improve the quality of your photograph. And I think that that's what we see happening with all software in the creative processes. It helps people do more than they realize they could do before, and and that's pretty powerful. And it opens up all these new avenues of interest and things we're not even imagining today. Jason Calacanis (00:59:24): Alright. So Skotis is gonna hear 2 cases for section David Sacks (00:59:27): 2:30. Jason Calacanis (00:59:28): The family of Nohima Gonzales, a twenty three year old American college student who was killed in an ISIS terrorist attack in Paris back in David Sacks (00:59:35): 2015. Jason Calacanis (00:59:37): You remember those horrible attacks is claiming that YouTube helped and aided and abetted ISIS The family's argument is YouTube's algorithm was recommending videos that make it that makes it a publisher of content. As you know, it's section 230, common carrier. If you make editorial decisions, if you promote certain content, you lose your 230 protections. In court papers filed in 2016, they said the company quote, knowingly permitted ICE to post on YouTube hundreds of radicalizing videos inciting violence, which helped the group recruit, including some who are actually involved in the terrorist attack. So they have made that connection. Chamath Palihapitiya (01:00:11): Well, look. Let's let's be honest. We can we can we can put a pin in this thing because I think it would be shocking to me if this current SCOTUS, all of a sudden, founded in the cockles of their heart to protect big tech. I mean, they've dismantled a lot of other stuff that I think is a lot more controversial than this. And so, you know, we've we've basically looked at gun laws. We've looked at affirmative action. We've looked at abortion rights. Sorry. Well, I mean, I think as as we've said, I think we all know where that die is unfortunately going to get cast. So to me it just seems like this could be an interesting case where it's actually 90 in favor for complete for completely different sets of reasons. I mean, if you think of the liberal left part of the court, they have their own reasons for saying that there are 230 protections for Big Tech. And if you look at the the the far right or the the right leaning parts members of this of Skotis, they have they have another set of So Jason Calacanis (01:01:12): you think they're gonna make a political decision, not illegal? Chamath Palihapitiya (01:01:14): No. But even even in their politics, they actually end up in the same place. They both don't want the protections but for different reasons. So there there is a reasonable outcome here where, you know, Roberts is gonna have a really interesting time trying to pick who writes a maturity opinion. David Sacks (01:01:28): There was a related case in the 5th Circuit in Texas where do you guys see this 5th Circuit decision where Texas passed a law imposing common carrier restrictions on social media companies. The idea being that social media companies need to operate like phone companies, and they can't just arbitrarily deny you service or deny you access to the platform. And the argument why previously that had been viewed actually is Speaker 5 (01:01:55): unconstitutional was this idea of compelled speech that you David Sacks (01:01:59): can't compel a corporation to support speech that they don't want to because that was a violation of their own first amendment rights. And what the first the 5th Circuit said is, no. That doesn't make any sense. Facebook or Twitter can still advocate whatever speech they want as a corporation. But as a platform, they if Texas requires them to not discriminate against people on the basis of their viewpoint, then Texas has the right to to impose that because that doesn't it their quote was. That does not chill speech if anything it chills censorship. So in Jason Calacanis (01:02:31): other words -- -- the right legal decision here? In your mind, putting aside politics, if you can, for a moment, putting on your legal hat. What is the right thing for society? What is the right legal issue around section 230? Specifically in the YouTube case and Just generally, should we look at YouTube? Should we look at a blogging platform like medium or blogger? Twitter? Should we look at those as common carrier? and they're not responsible for what you publish on them. Obviously, they have to take stuff down if it if it breaks our terms of service, etcetera, or if it's illegal. David Sacks (01:03:01): I've made the case for that I I do think that common carrier requirements should apply on sublevel of the stack to protect the rights of ordinary Americans to have their speech in the face of these giant monopolies, which could otherwise deplatform them for arbitrary reasons. Just to, you know, just explain this a little bit. So Historically, there was always a debate between so called positive rights and negative rights. So where the United States start off as a country, was with this idea of negative rights that what a right meant is that you be protected from the government taking some action against And if you look at the bill of rights, you know, the original rights are all about protecting the citizen against an intrusion on their liberty by by a state or by the federal government. In other words, Congress shall make no law. It was always a restriction. So the right was negative, and it wasn't sort of positively enforced. And then With the progressive era, you started seeing, you know, more progressive rights. Like, for example, American Systems have the right to health care. Right? That's not protecting you from the government. That's saying that the government can be used to give you a right that you didn't otherwise have. And so that was sort of the big progressive revolution. My take on it is I actually think that the problem we have in our society right now is that free speech is only a negative right. It's not a positive right. I think it actually needs to be a positive right. I'm embracing a more progressive version of rights but on behalf of sort of this original negative right. So and the reason is because the the town square got privatized. Right? I mean, you used to be able to go anywhere in this country. There'd be a see it town squares. Anyone could pull out their soapbox, draw, crowd. They could listen. That's not how speech occurs anymore. It's not on public land or public spaces. David Sacks (01:04:46): The way that speech political speech especially occurs today is in these giant social networks that are that have giant network effects and are basically monopolies. So if you don't protect the right to free speech in a positive way, it no longer exists. Jason Calacanis (01:05:00): So you not only believe wow. That YouTube keep its section 230, you believe they YouTube shouldn't be able to deplatform as a private company you know, Alex Jones, as about one example, they should have their free speech rights, and we should lean on that side of forcing YouTube to put Alex Jones or Twitter to put Trump back on the platform. Is that your position? David Sacks (01:05:23): I'm not saying that the constitution requires YouTube to do anything What I'm saying is that if a state like Texas or if the federal government wants to pass a law saying that YouTube If you are, say, of a certain size you're a social network of a certain size. You have monopoly network effects. I wouldn't necessarily apply this to all the little guys. But for those big monopolies, we know who they are. If the if the federal government or state wanted to say that they are required to be a common carrier, and they cannot discriminate against certain viewpoints. I think the government should be allowed to do that because it furthers a positive right. Historically, They've not been able to do that because of this idea because this idea of compelled speech, meaning that it would infringe on YouTube speech rights Chamath Palihapitiya (01:06:08): I don't David Sacks (01:06:09): think it would. I mean, Google and YouTube can advocate for whatever positions they want. They can produce whatever content they want. Yeah. But but the point that and I think section 230 makes this point as well is that they are platforms. They're distribution platforms. They're not publishers. Speaker 5 (01:06:22): So if David Sacks (01:06:23): they want so especially if they want Section 230 protection, they should not be engaging a viewpoint discrimination. Okay. Jason Calacanis (01:06:28): So now there is a rub here. Chamath Palihapitiya (01:06:29): Can I just say can I just say go ahead. Your explanation, David, your explanation that you just gave before was so excellent. Thank you. That it allows me to understand it even more clearly. That was really David Sacks (01:06:40): -- Jason Calacanis (01:06:40): So, Shamoff, do you think the algorithm is an act of editorialization. Chamath Palihapitiya (01:06:46): Yes. Yes. Yes. Jason Calacanis (01:06:47): And so then should YouTube David Sacks (01:06:49): -- Chamath Palihapitiya (01:06:49): Look. At the end of the day, let me let me break down an algorithm for you. Okay. Effectively, it is a mathematical equation of variables and weights. An editor 50 years ago was somebody who had that equation of variables and weights in his or her mind. Okay? And so all we did was we translated Again, this multimodal model that was in somebody's brain into a model that's mathematical that sits in code. Jason Calacanis (01:07:20): You're talking about the front page editor of the New York Times. Chamath Palihapitiya (01:07:23): Yeah. And I think it's a fake leaf to say that because there is not an individual person who writes point David Sacks (01:07:28): 2 Chamath Palihapitiya (01:07:29): in front of this one variable and point 8 in front of the other, that all of a sudden that this isn't editorial decision making is wrong. We need to understand the current moment in which we live, which is that these computers are thinking actively for us. They're providing this computationally intensive decision making and reasoning. And I think it's It's pretty ridiculous to assume that that isn't true. That's why when you go to Google and you search for, you know, Michael Jordan, we know what the right Michael Jordan is because it's reasoned. There is an algorithm that is doing that making an editorial decision around what the right answer is. They have deemed it to be right. And that is just true. And so I think we need to acknowledge that because I think it allows us at least to be in a position to rewrite these laws through the lens of 21st century. And we need to -- Yeah. David Sacks (01:08:26): -- Chamath Palihapitiya (01:08:26): we need to update our understanding for how the world works today. Jason Calacanis (01:08:30): And, you know, Chamath, there's such an easy way to do this. If you're TikTok, if you're YouTube, if you want section 230, if you want to have common carrier and not be responsible if it's there, when a user signs up, it should give them the option. Would you like to turn on an algorithm? Here are a series of algorithms which you could turn on. You could bring your own algorithm. You could write your own algorithm with a bunch of sliders, or here are ones that other users and services provide like an App Store. so you chumov could pick 1 for your family, your kids. That would be I want one that's leaning towards education and takes out conspiracy there is, takes out cannabis shoes takes out this one. Chamath Palihapitiya (01:09:05): It's a wonderful what you're saying is so wonderful. Because, for example, like, you know this organization, Common Sense Media. Jason Calacanis (01:09:10): Yes. I love that website. Every time I put in a movie, I put Common Sense Media to decide if we should watch it. Chamath Palihapitiya (01:09:15): Or, like, an I use it a lot for apps because they're pretty good at just telling you which which apps are reasonable and unreasonable. But, you know, if common sense media could raise a little bit more money and and create an algorithm that would help filter stories in TikTok for my kids, I'd be more likely to give my kids TikTok when they turn 14. Right now, I know that they're gonna sneak it by going to YouTube and looking at YouTube shorts and all these other things because I cannot control that algorithm. And it does worry me what kind of content that they're getting access to. And you could do this, Jason Calacanis (01:09:48): by the way, Chymouth, on the operating system level or on the router level in your house, you could say, I want the Common Sense algorithm. I will pay $25 a month, a $100 a year for it. Chamath Palihapitiya (01:09:58): We are -- Put it on your router. David Sacks (01:09:59): -- Jason Calacanis (01:09:59): and then any IP that goes through? It would be programmed properly. I want less violence. I want less sex. Chamath Palihapitiya (01:10:04): You know, whatever. I think we are, as a society, sophisticated enough now Yes. To have these controls. And so I think we need them. And so I think we do need to have the right observation of the current state of play. Friedberg, where Jason Calacanis (01:10:21): do you sit on this? Do you think the algorithm should be David Sacks (01:10:23): -- Jason Calacanis (01:10:23): I don't I don't I don't agree with -- -- out David Friedberg (01:10:25): of 2:30? Yeah. I I don't fully agree with Saks on the monopolistic assumption. I I think that there are David Sacks (01:10:33): -- Jason Calacanis (01:10:34): Unpack it. David Friedberg (01:10:34): I think that there are other places to access content, and I think that there is still a free market to compete, and it is possible to compete. I think that we saw this happen with TikTok. We saw it happen with Instagram we saw it happen with YouTube competing against Google Video and Microsoft video prior to that. There has been a very significant battle for the attention of kind of being the next gen of media businesses, and we have seen Spotify compete, and we're seeing Spotify continue to be challenged by emerging competitors. So I don't buy the assumption that these are built in monopolies and therefore it allows some regulatory process to come in and say, hey. Free speech needs to be actively enforced because they're monopolies. This isn't like when utilities laid power lines and sewer lines and and trains across the country, and they had a physical monopoly on being able to access and move goods and services. the Internet is still thank god, knock on wood, open, and the ability for anyone to build a competing service is still possible. And there is a lot of money that would love to disrupt these businesses that is actively doing it. And I think every day, look at how big TikTok has gotten. It is bigger than YouTube almost or will be soon. And there is a competition that happens, and because of that competition, I think that the the market will ultimately choose where they wanna get their content from and how they wanna consume it. And I don't think that the government should play a role. Jason Calacanis (01:12:00): Saks rebuttal to that. Did you buy that? David Sacks (01:12:01): Well, So not all these companies are monopolies, but I think they act in a monopolistic way with respect to restricting free speech, which is they act as a cartel. They all share, like, best practice with each other on how to restrict speech. And we saw the the watershed here was when Trump was thrown off First, Twitter made the decision. You know, Jack I don't even know if it was Jack, but, basically, the company -- Jason Calacanis (01:12:25): Jack said it wasn't him, actually. He said it was the woman who was running it specifically. David Sacks (01:12:28): Jack laid a shot. except for that. Yeah. Yeah. Jack actually said it was a mistake. But any event, Twitter did it first, and then all the other companies followed suit. I mean, even like Pinterest and Octa and Snapchat, like, officially -- Facebook, Jason Calacanis (01:12:42): YouTube, everybody. David Sacks (01:12:43): Yeah. But Trump was actually on Facebook. He wasn't on all these other companies. They still threw him off. So they all copy each other. And Jack actually said that in his comments where he said it was a mistake, he said he didn't realize the way in which Twitter's action would actually cascade. He said that he thought originally that the action was okay because it was just Twitter decided to take away Trump's right to free speech. but he could still go to all these other companies. And then all these other companies, basically, you know, they they are all subject to the same political forces. The the leadership of these companies are all sort of they all drink from the same monocultural found. They all have the same political biases. The polls show this. So the problem of Friedberg is, yeah, I agree a bunch of these companies aren't white monopolies, but they all act the same way. Jason Calacanis (01:13:27): I hate to say it, but I agree with the collective David Sacks (01:13:29): -- Jason Calacanis (01:13:29): I'm agree with David Sacks (01:13:30): you. -- a fact is of a speech cartel. So the question is how do you protect the rights of Americans to free speech in the face of a speech cartel that wants to basically block them? Jason Calacanis (01:13:40): ahead, Friedberg. Respond. David Friedberg (01:13:41): Here's my argument. My argument is that these are not public service providers. They're private service providers, and the market is telling them what to do. the market is saying, and I think that David Sacks (01:13:50): the press David Friedberg (01:13:51): I think that the pressure that was felt by these folks was that so many consumers were pissed off that they were letting Trump rail on, or they were pissed off about Jan 6, or they were pissed off about whatever whatever the current fad is, the trend is, they respond to the market, and they say, you know what? This has crossed the line. And this was the case on public television when nudity came out, and they're like, okay. You know what? We need to take that off the TV. we need to because the the market is telling us they're gonna boycott us, and I think that there's a market pressure here that we're ignoring that is actually pretty pretty relevant that as a private service provider, if they're gonna lose half their audience because people are pissed about 1 or 2 pieces of content showing up, that they're acting in the best interests of their shareholders and in the best interests of their platform. They're not acting as a public service. David Sacks (01:14:36): Look, I love market forces as much as the next Liberitarian. But I just think that fundamentally, that's just not what's going on here. This has nothing to do with market forces. It has everything to do with political forces. That's what's driving this. Look. Do you think the average consumer, the average user of PayPal, is demanding that they engage in all of these restrictive policies throwing off all these accounts who have the wrong viewpoints? No. That has nothing to do with it. It has to do Jason Calacanis (01:14:59): with the vocal minority Speaker 5 (01:15:01): type -- Yeah. David Sacks (01:15:01): It's a small it's a small number of people who are political activists who work at these companies and create pressure from below. It's also the, you know, the the the people from outside, the actors who create these boycott campaigns and pressure from outside. And then it's basically people on Capitol Hill who have the same ideology who basically create threats from above. So these companies are under enormous pressure from above, below, and sideways that and it's a 100% political. Hold on. It's not it's not about maximizing profits. I think it's about maximizing, you know, political outcomes. Yeah. Jason Calacanis (01:15:33): I don't even David Sacks (01:15:33): think that -- That is what the American people need to be protected from. Now I I will I will add one nuance to to my theory though, which is I'm not sure what level of the stack we should declare it to be common carrier. So in other words, you may be right actually that at the level of YouTube or Twitter or Facebook, maybe We shouldn't make them common carrier, and I'll tell you why because just to take the other side of the argument for a second, which is, you know, if you don't because those companies do have legitimate reasons to take down some content. I don't like the way they do it, but I do not wanna see bots on there. I do not wanna see fake accounts. And I actually don't wanna see like truly hateful speech or harassment. And the problem is I do worry that if you subject them to common carrier, they won't actually be able to engage in, let's say, legitimate curation of their social networks. David Friedberg (01:16:25): Yeah. Right. David Sacks (01:16:26): However, So so there's a real debate to be had there, and it's gonna be messy. But I think there's one level of the stack below that, which is at the level of pipes. Like in EWS, like a Cloudflare, like a PayPal, like the the ISPs, like the banks, they are not doing any content moderation, or they have no legitimate reason to be doing content moderation. None of those companies should be allowed to engage in viewpoint discrimination. We have a problem right now where American citizens are being denied access to payment rails -- Jason Calacanis (01:16:53): So wait. Speaker 5 (01:16:53): Hold on. David Sacks (01:16:53): -- into the banking system -- You're saying -- -- because of their viewpoints. -- Jason Calacanis (01:16:56): AWS shouldn't be able to deny service to the Ku Klux Klan or some hate speech route? David Sacks (01:17:00): I think that they should be under the same requirements that the phone company is under. Jason Calacanis (01:17:05): Okay. So when when it's David Sacks (01:17:06): in break -- Confirmish that way, it it Yeah. You know, The question is, like, look, I could frame the same question to you. Should, you know, such and such horrible group? Should such and such horrible group be able to get a phone a phone account. Right? Yeah. Jason Calacanis (01:17:18): No. No. David Sacks (01:17:18): And you'd say, no. They shouldn't get anything, but they have that right. Chamath Palihapitiya (01:17:21): That has been litigated, and that's been pretty much protected by the Supreme Court. You know, even if it's a government conferred monopoly, the Supreme Court has said, okay, listen, like, it's not violating one's constitutional right. For example, your water bill gets terminated without you getting due process. And the the inverse is also true, so for what whether we like it or not, that Jason, that issue has been litigated, I think. I think I think for me, again, just like practically speaking for the functioning of civil society, I think it's very important for us to now introduce this idea of algorithmic choice. And I don't think that that will happen in the absence of us rewriting section 230 in a more intelligent way. Speaker 5 (01:18:06): I don't Chamath Palihapitiya (01:18:06): know I don't know whether this specific case creates enough standing for us to do all of that. But I think it's an important thing that we have to revisit as a society because Jason, what you described as having a breadth of algorithmic choices over time where there are purveyors and sellers. Could you imagine that's not a job or a company that The 4 of us would ever have imagined could be possible 5 years ago. But maybe there should be an economy of algorithms and there are these really great algorithms that one would wanna pay a subscription for because one believes in the quality of what it gives you. We should have that choice and I think it's an important set of choices that will allow actually YouTube as an example to operate more safely as a platform. Because it can say, listen. I've created this set of abstractions You can plug in all sorts of algorithms. There's a default algorithm that works, but then there's a marketplace of algorithms, just like there's a marketplace of ideas. I don't discriminate and let people choose. Jason Calacanis (01:19:06): This is the the key thing David Friedberg (01:19:08): is model. Like, if if it if it was on Chamath Palihapitiya (01:19:10): a blockchain, David Friedberg (01:19:10): if all the videos, all the content was uploaded to a public blockchain and then distributed on distributed computing system, then your ability to search and use that media would be a function of a service provider you're willing to pay for that provides the best service experience. And, by the way, this is also why I think over time to Saks to to kind of Sachs and I are both arguing both sides a Chamath Palihapitiya (01:19:31): little bit. But I I David Friedberg (01:19:32): think that what will happen, I don't think that the government should come in and regulate these guys and tell them that they can't take stuff down and whatnot. I I really don't like the precedent it sets, period. I also think that it's a terrible idea for YouTube and Twitter to take stuff down. And I think that there's an incredibly difficult balance that they're gonna have to find because if they do this, as we're seeing right now, the quality of the experience for a set of users declines, and they will find somewhere else any market will develop for something else to compete effectively against them. And so I that's why I don't like the government intervening because I wanna see a better product emerge when the big company makes some stupid mistake and does a bad job, and then the market will find a better outcome. And it it just it's messy in the middle. And as soon as you do government intervention on these things and tell them what they can and can't take down, I really do think that over time, you will limit the user experience to what is possible if you allow the free market -- And Jason Calacanis (01:20:27): this is where the the industry needs to police itself. If you look at the movie industry with the MPAA and Indiana Jones and the the Temple of Doom, they came out with the PG 13 rating specifically for things that were a little too edgy for PG. This is where our industry could get ahead of this. They could give algorithmic choice and algorithmic app store and if you look at the original sin, it was these lifetime bans. Like, Trump should not have been given a lifetime ban. They should have given him a 1 year ban. They should have had a process and I overreached, we wouldn't be in this position where David Sacks (01:20:58): it is so big drop. When you talk about, like, having a industry from, like, the MPAA, what you're doing is formalizing the communication that's already taking place is already happening between these companies. And what is the result of that communication? they all standardized on overly restrictive policies because they all share the same political bias. Chamath Palihapitiya (01:21:15): No. No. But they if they Jason Calacanis (01:21:16): did it correctly, it's all in the execution It has to be executed properly like the movie initiative. Speaker 5 (01:21:21): I know. David Friedberg (01:21:21): It doesn't matter. You'll end up with the same problem as having the government intervene. If you have the government intervene or a private body intervene, Any sort of set standard intervention that prevents the market from competing? I I disagree with you. Chamath Palihapitiya (01:21:32): I think you can create more competition if the government says, Okay, folks. You can have the standard algorithm, but you need to make a simple abstracted way for Speaker 8 (01:21:40): somebody else to write some other filtering Chamath Palihapitiya (01:21:44): mechanism basically, you so that users can victims. Jason Calacanis (01:21:47): Power users. Yes. David Friedberg (01:21:47): I don't like it. I don't like it. Jason Calacanis (01:21:49): The what the MPAA did was Well, I David Friedberg (01:21:50): don't know. Chamath Palihapitiya (01:21:51): I don't understand why you're saying why isn't that more choice? Jason Calacanis (01:21:53): because as a product David Friedberg (01:21:54): person as a product company, I don't wanna be told how to make my product, right, if I'm Chamath Palihapitiya (01:21:58): not on YouTube. You have you have an Algo. David Friedberg (01:22:00): You're now saying that there is this extinction of the algo from the UX, from the data, and my choice might be to create different content libraries. For example, YouTube has YouTube kids. and it's a different content library, and it's a different user interface, and it's a different algorithm, and you're trying to create an abstraction that may not necessarily be natural for the evolution of the product set of that company. would much rather see them figure it out. Chamath Palihapitiya (01:22:20): That's not a good argument. That again, if if you were not a monopoly, I I would be more sympathetic. But because, like, somebody somebody's feelings would get hurt, a product manager's feelings will get hurt inside Speaker 5 (01:22:30): of Google. Chamath Palihapitiya (01:22:30): feelings beginning her trap. -- the reason to not protect free speech. David Friedberg (01:22:33): I think you're unnaturally disrupting product evolution. And I love luck. Chamath Palihapitiya (01:22:37): That's what that's what happens when you're worth $2,000,000,000,000 down risk. David Sacks (01:22:40): -- Jason Calacanis (01:22:40): you impact a billion people on the planet. When you start having massive impact in society, you have to take some responsibility. David Friedberg (01:22:46): Guess what's kinda -- Those companies Jason Calacanis (01:22:48): are not taking responsibility of your power. David Sacks (01:22:49): -- Chamath Palihapitiya (01:22:50): super, super, super successful. this is not gonna affect you. So you don't have nothing to worry about. David Friedberg (01:22:54): You'll see you'll see apps offshore, and you'll see TikTok and other things compete because they'll have a better product experience. they don't know. Like, nobody's gonna no. David Sacks (01:23:01): No. No. Why it's gonna create a new Google because they're -- Exactly. -- downranking -- Yeah. I disagree. -- 1 to 10% of the search results for political reasons. David Friedberg (01:23:09): Agreed. Some accountability. Hold on. David Sacks (01:23:11): In an ideal world, companies like Google and so forth would not take sides in political debates to be politically neutral, but they're not. You look at all the data around the political leanings, the people running these companies. And then you look at the actual actions of these companies, and they have become fully political, and they've waited into all these political debates. with the result that the American people's rights to speech and to earn have been reduced. You have companies like PayPal, which are just engaging in retaliation, basic financial retaliation purely on based on what political viewpoints they have. Why? it's not like face it's not like PayPal needs to be in the business -- Let's continue -- -- content curation. David Friedberg (01:23:47): Let's continue this conversation. We're not gonna solid in it. Collin AMA. as well. Jason Calacanis (01:23:52): If if they get if they can get some servers over there, I don't know. Maybe so you gotta raise some money, Saxx, for this app, and get some more servers. Alright. Listen. For the dictator, who needs to hit the loo to do a number David Sacks (01:24:02): 2 -- Jason Calacanis (01:24:03): Yes. I am. The world's greatest moderator. Friedberg is the Sultan of Science And David Sacks is the prince of peace. See you all next week on the episode. Chamath Palihapitiya (01:24:17): Wait. Wait. Is this 98 or 99? No. It's 99. It's David Sacks (01:24:20): 99. Only one episode left. Chamath Palihapitiya (01:24:21): Wayne Gretzky. David Sacks (01:24:22): Get it while less. Jason Calacanis (01:24:23): Enjoy a while less. They were wrapping it up here. Alright. We'll see you all Next time. Have a great movement, Tremont. Bye bye. Speaker 5 (01:24:31): We'll let your winter ride. Rain Speaker 7 (01:24:34): David Sachs. Speaker 6 (01:24:38): And I said -- We open source it to the fans, and they've just gone crazy with it. Speaker 7 (01:24:42): US ID. -- queen of Kenwa. besties are gone. Speaker 8 (01:25:01): We should all just get a room and just have one big queue, George, because they're all just useless. It's like it's like sexual tension, but they just need to release it. Jason Calacanis (01:25:09): What? You're the Beat feet. What? You're David Sacks (01:25:12): every feet. Speaker 5 (01:25:13): Beat feet. What? Jason Calacanis (01:25:14): What? Good feet. Where did you get murky are back. in.