E99: Cheating scandals, Twitter updates, rapid AI advancements, Biden's pardon, Section 230 & more

00:00:00.000 | We're seven minutes in and we've produced absolutely nothing that will go in the show.

00:00:03.520 | Here comes Sax waking up with his commentary.

00:00:08.000 | When Freberg is criticizing you for being too negative, you're in a dark place, Sax.

00:00:12.320 | I'm actually angry at Sax for not publishing my AMA from the other night.

00:00:15.440 | It's coming, it's coming.

00:00:16.320 | While he publishes an Evolved content.

00:00:17.440 | His app crashed.

00:00:18.800 | We had such a crowded room.

00:00:20.320 | We had over 2000 people in the room for like four hours.

00:00:23.760 | It was crazy.

00:00:24.480 | It was like the original days of Clubhouse.

00:00:26.560 | Everyone I know that was trying to get in was texting saying they couldn't get in.

00:00:29.120 | So it definitely capped out, right?

00:00:30.880 | I know.

00:00:31.360 | Well, we hit, we hit some scalability.

00:00:33.280 | You may want to buy an extra server, Sax.

00:00:35.360 | Cheap f***.

00:00:36.160 | Weren't you the same guy who was responsible for scaring PayPal?

00:00:38.800 | No, that was somebody else.

00:00:39.920 | That was eBay.

00:00:40.640 | They sold it before it scaled.

00:00:42.880 | No, that's not true.

00:00:43.760 | We had huge scalability challenges at PayPal too.

00:00:46.000 | It seems like a theme.

00:00:48.240 | Yeah, the theme is when you have an app that's breaking out, you hit scalability challenges.

00:00:52.320 | It's called a high class problem.

00:00:53.920 | 2000 people is not a high class problem.

00:00:56.640 | It's a trickle.

00:00:57.600 | It's 2022.

00:00:59.280 | 2000 people participating in the conversation is a challenge.

00:01:02.560 | I haven't written code in 20 years.

00:01:04.400 | Here's what you do.

00:01:05.280 | When you get to 1000 people coming to the room, everybody else is in passive mode.

00:01:09.120 | You've never written code ever.

00:01:10.480 | Of course I have.

00:01:11.200 | Of course I have.

00:01:12.000 | That's a lie.

00:01:12.480 | Come on, be honest.

00:01:13.280 | Oh yeah, it's actually been 25.

00:01:14.800 | The last time I wrote code was Lotus Notes.

00:01:16.640 | It's true.

00:01:17.840 | [Music]

00:01:34.640 | So there have been three cheating scandals across poker, chess, and even competitive fishing.

00:01:40.320 | I don't know if you guys saw the fishing one, but they found weights and fillets during a fish

00:01:45.200 | weigh in and then everybody wants us to check in on the chess and the poker scandals.

00:01:49.360 | Chess.com just released their report that this grandmaster has been suspended.

00:01:55.840 | They have evidence he cheated basically in a bunch of

00:02:01.040 | tournaments that were in fact for money.

00:02:04.960 | He denied that he had done that, but he had previously cheated as a kid.

00:02:07.840 | They now have the statistical proof that he was playing essentially perfect chess.

00:02:13.520 | And they've outlined this in like hundreds of pages in a report.

00:02:18.000 | Sax, what are your thoughts on this scandal in chess?

00:02:20.960 | Magnus Carlsen finally came out and explained why he thought Hans Niemann was cheating.

00:02:26.000 | Basically, he got the strong perception during the game that Hans wasn't really

00:02:30.080 | putting in a lot of effort, that he wasn't under a lot of stress.

00:02:32.880 | It's his experience that when he's playing the top players, they're intensely concentrating.

00:02:39.520 | And Hans Niemann didn't seem to be exerting himself at all.

00:02:42.320 | So his hackles were raised and got suspicious.

00:02:45.760 | And then he has had this meteoric rise, the fastest rise in classical chess rating ever.

00:02:51.920 | And I guess he had gotten suspended from chess.com in the past for cheating.

00:02:57.600 | So on this basis, and maybe other things that Magnus is telling us,

00:03:00.560 | Magnus basically said that this guy is cheating.

00:03:02.880 | I think that maybe the interesting part of this is that there's been a lot of analysis now of Hans

00:03:06.880 | Niemann's games.

00:03:07.680 | And I just think the methodology is kind of interesting.

00:03:10.480 | So what they do is they run all of his games through a computer, and they compare his moves

00:03:16.960 | to the best computer move.

00:03:19.120 | And they basically assign a percentage that Hans matches, a correlation matches the computer

00:03:25.280 | move.

00:03:25.520 | And what they found is there were a handful of games where it was literally 100%.

00:03:28.640 | That's basically impossible without cheating.

00:03:31.280 | I mean, you look at the top players who, through an entire career, have never had 100% game.

00:03:37.280 | Chess is so subtle that the computer can now see so many moves into the future,

00:03:41.760 | that nailing the best move every single time for 40, 50, 100 moves is just...

00:03:45.600 | And in chess, which a human really can't do that well, is that there are positional sacrifices

00:03:52.160 | that you will make in short lines that pay off much, much later in the future,

00:03:56.000 | which is impossible for a human to calculate.

00:03:58.000 | And so, you know, and you saw this, by the way, when I think it was the Google AI,

00:04:03.680 | the DeepMind AI that also played chess.

00:04:05.920 | So the idea that this guy could play absolutely perfectly according to those lines

00:04:12.000 | is only possible if you're cheating.

00:04:13.840 | Right, exactly.

00:04:14.720 | So there were a handful of games at 100%, and then there were tournaments where his percentages

00:04:19.120 | were in the 70-something plus.

00:04:21.680 | And so just to give you some basic comparison, Bobby Fischer during his legendary 20-game

00:04:26.640 | winning streak was at 72%.

00:04:28.720 | So he only matched the computer for best move 72% of the time.

00:04:33.760 | Magnus Carlsen playing at his best is 70%.

00:04:37.040 | Garry Kasparov in his career was 69%.

00:04:40.480 | And then the, you know, the super GMs category are typically in the 64% to 68% range.

00:04:47.920 | So I think it's really interesting, actually, how you can now quantify by comparing the

00:04:52.480 | human move to the best computer move.

00:04:54.480 | And it's multiple computers, right, Sax?

00:04:56.800 | They actually have...

00:04:57.520 | It provides a way to assess who the greatest player ever is.

00:05:00.880 | I actually thought that it was Magnus, but now maybe there's a basis for believing it

00:05:04.800 | was Bobby Fischer, because he was at 72% and Magnus was only at 70%.

00:05:08.320 | However, look, the idea that Hans Niemann is in the 70s, 80s, or 90s during tournaments

00:05:13.920 | would be, you know, just an off-the-charts level of play.

00:05:17.280 | And if he's not cheating, then we should expect over the next couple of years that

00:05:24.000 | he should rapidly become the world's number one player over the board, you know, now that

00:05:29.280 | they have all this anti-cheating stuff, right?

00:05:30.960 | So it'll be interesting to see what happens in his career now that they've really cracked

00:05:35.280 | down on, you know, on with anti-cheating technology.

00:05:39.600 | I have a general observation, which is these people are complete fucking losers.

00:05:44.480 | The people that cheat in any of these games don't understand this basic, simple idea,

00:05:51.040 | which is that trying is a huge part of the human experience.

00:05:54.480 | The whole point is to be out there in the field of play, trying.

00:05:58.720 | And it's basically taking the wins and the losses and getting better that is the path.

00:06:03.280 | That's what's fun.

00:06:04.240 | Once you actually win, it's actually not that much fun because then you have this pressure

00:06:09.520 | of maintaining excellence.

00:06:10.880 | That's a lot less enjoyable than the path to getting there.

00:06:14.560 | And so the fact that these people don't understand that makes them slightly broken, in my opinion.

00:06:20.240 | And then the other thing is, like, why is it that we have this strain of people now that

00:06:25.280 | are just so devoid of any personal responsibility that they'll just so brazenly take advantage

00:06:31.120 | of this stuff?

00:06:31.600 | It's really ridiculous to me.

00:06:32.880 | They don't.

00:06:33.280 | It's really sad.

00:06:34.480 | These people are pathetic.

00:06:35.680 | They're losers.

00:06:36.080 | Really pathetic.

00:06:36.960 | This is really interesting how they caught him and running this against the computer.

00:06:41.040 | Here's a chart of his scores in these tournaments.

00:06:45.120 | Oh, here is this first chart is how quickly he advanced, which was off the charts.

00:06:50.640 | And then the second chart that's really interesting is his chess.com strengths.

00:06:55.040 | If you don't know chess.com, it has become like a juggernaut in the chess world, especially

00:06:58.320 | after that HBO series came out.

00:07:01.600 | A lot of people subscribe to it.

00:07:02.640 | I subscribe to it.

00:07:03.200 | I like to play chess there.

00:07:04.160 | And man, you look at the chess strength score there.

00:07:07.200 | He was just like perfect.

00:07:09.040 | And then the number of games he likely cheated in, you can see the last two columns.

00:07:12.640 | He's basically cheating in every game.

00:07:13.920 | Queen's Gambit.

00:07:15.760 | Yeah, great show on Netflix.

00:07:19.120 | And he said he didn't cheat in any of the games where they were live streaming, but

00:07:25.680 | they've proven that wrong.

00:07:26.880 | Sax, how does he cheat in person then?

00:07:28.960 | That's the thing.

00:07:29.600 | No one really knows.

00:07:30.800 | And I don't want to overly judge until they have hard proof that he was cheating.

00:07:35.760 | I mean, look, here's the thing.

00:07:36.640 | He was never caught in the act.

00:07:38.080 | It's just that the computer evidence, you know, seems pretty damning.

00:07:42.080 | And I don't know how they prove.

00:07:45.520 | I don't know how they prove that he was cheating over the board without actually

00:07:48.720 | catching him doing it.

00:07:49.520 | And I don't, I still don't think anyone really has a good theory in terms of how he

00:07:53.360 | was able to do that.

00:07:54.400 | Well, it's not just him though.

00:07:55.440 | Look, look, the fishing thing, Jason, which was crazy.

00:07:58.400 | I think Friedberg shared the video.

00:08:00.000 | This guy was in a fishing competition and they basically caught these fish and then

00:08:03.360 | they put these big weighted pellets inside the fish's body.

00:08:07.120 | They even put like a, you know, chicken breast and chicken fillets inside of the thing.

00:08:11.280 | So that they would drink more.

00:08:12.400 | Yeah, they put fish fillets in.

00:08:14.560 | You know, then there's in poker now.

00:08:17.520 | And in poker, in poker, everybody's afraid that there are ways in which you can read

00:08:21.680 | the RFID and some of the cards and some of these, you know, televised situations and

00:08:26.320 | front run what the, what the playing situation is so that, you know, whether you're winning

00:08:29.600 | or losing.

00:08:30.640 | And again, I just asked the question, like, is it, is it, is this, are things that bad

00:08:35.680 | that this is what it gets to?

00:08:36.880 | Like we all play poker.

00:08:38.480 | The idea that we would play against somebody that would take that edge.

00:08:41.760 | That's gross.

00:08:44.080 | It's really makes me really sad.

00:08:46.560 | So disappointing.

00:08:47.680 | Yeah.

00:08:48.180 | One observation might be that across all three, because I'm trying to find some common thread

00:08:53.840 | across these, but it could be that there was a lot of cheating going on for a long time.

00:08:58.240 | And maybe the fact that we do have so much digital imagery that's live on these things

00:09:06.400 | now and so much coverage and everyone's got a cell phone that suddenly our perception

00:09:13.200 | of the cheating in competitive events is becoming more tuned.

00:09:18.080 | Whereas maybe there's been a lot of cheating for a long time and it's just kind of coming

00:09:22.400 | to light.

00:09:23.120 | I mean, we didn't have a lot of live streaming in poker.

00:09:26.560 | Who knows?

00:09:27.200 | I mean, we could probably ask Phil this or Keating, but like for how many years was there

00:09:31.520 | was fish?

00:09:32.020 | Yeah.

00:09:33.060 | Remember like people are using these like software programs that would track the hand

00:09:42.240 | history of your opponents.

00:09:44.080 | Heads up display.

00:09:45.200 | Exactly.

00:09:45.760 | So.

00:09:46.260 | So it help you assess whether the person might be bluffing in that particular situation.

00:09:50.480 | Yeah.

00:09:50.980 | Like he has superhuman memory.

00:09:52.480 | So I don't know if you guys, I don't know if you guys watch Twitch, like video games,

00:09:55.440 | like Fortnite or whatever, but there are like players that have been accused of using the

00:10:00.640 | screen overlay systems that basically more accurately show you and drive the mouse to

00:10:05.600 | where an individual is on the screen.

00:10:06.960 | So you can more accurately shoot them.

00:10:09.040 | And so there's software overlays that make you a better.

00:10:11.680 | You know, competitive video, tell you what the through line is.

00:10:15.520 | And then the stuff basically became like.

00:10:19.200 | So now what's interesting is now there's eye tracking software that people are using on

00:10:24.160 | Twitch screens to see if the individual is actually spotting the target when they shoot

00:10:29.600 | or if the software is spotting the target.

00:10:31.440 | Yeah, they're called reverse aim bots.

00:10:33.440 | They're a box.

00:10:34.400 | Yeah.

00:10:34.640 | And like reverse cheap, the whole thing.

00:10:36.240 | And I think what's interesting is just that there's so much, you know, insight now so

00:10:39.360 | much more video streams, so much more.

00:10:42.160 | I mean, think about all those guys at that competition.

00:10:44.720 | Yeah.

00:10:44.960 | And their cell phones, and they all video this thing happening.

00:10:47.440 | Yeah, I think 10 years ago, that wouldn't have been the case.

00:10:49.520 | And there wouldn't have been a big story about it.

00:10:51.520 | And so she said there was a theme you wanted to there was a I think the theme is is pretty

00:10:56.720 | obvious, which is that there's been an absolute decay of personal responsibility.

00:11:02.160 | People don't feel like there's any downside to cheating anymore.

00:11:07.120 | And they're not willing to take it upon themselves to take a journey of wins and losses to get

00:11:11.440 | better at something they want the easy solution.

00:11:14.000 | The easy solve the quick answer, you know, that gets them to some sort of finish line

00:11:20.480 | that they have imagined for themselves will solve all their problems.

00:11:24.240 | The problem is, it doesn't solve any problems.

00:11:26.560 | And it just makes them a wholly corrupt individual.

00:11:29.600 | Yeah, so let's talk about this Hustler Casino live cash game play.

00:11:34.640 | There's this woman, Robbie, who is a new player.

00:11:38.000 | Apparently, she's being staked in a very high stakes game.

00:11:40.560 | She's playing as a guy Garrett, who is a very, very known winning cash game player.

00:11:46.080 | And it was a very strange hand on the turn, all the money gets in.

00:11:52.320 | She says she has a bluff catcher, then she claims that she had a thought she misread

00:11:55.840 | her hand. Now people are saying that the poker world seems to be 7030 that she cheated.

00:12:02.160 | But people keep vacillating back and forth.

00:12:04.800 | There was a lot of weird words salad that she said that she had a bluff catcher, which

00:12:10.400 | would normally be an ace.

00:12:12.000 | Then she said she thought she had a pair of threes.

00:12:14.000 | And then she immediately said afterwards that he was giving her too much credit.

00:12:17.760 | They confronted her in the hallway, she gave the money back because she supposedly loves

00:12:22.400 | production.

00:12:22.800 | So all this stuff sounds very weird.

00:12:24.880 | One side says, Okay, well, this is happening because she's a new player.

00:12:28.240 | The other side is saying somebody was signaling her that she was good and giving her just

00:12:33.360 | a binary.

00:12:34.240 | You're good.

00:12:34.880 | Because if you are going to cheat, cheating with Jack high in a situation where you just

00:12:38.640 | put all in for a two and a quarter million dollar pot seems very suspect.

00:12:42.480 | I don't know if you guys watch the hand breakdown.

00:12:45.920 | Where does everybody stand on a percentage basis, I guess, if they think she was cheating

00:12:50.640 | or not, because we this is not definitive, obviously, it's not like they cut it open

00:12:54.160 | and found the ball bearings.

00:12:55.200 | It's not it's not so obvious in that situation.

00:12:57.600 | But I think the way that that line played made no sense.

00:13:01.040 | Did not mean she was holding a jack four.

00:13:04.480 | And I guess in her previous hand, she had a jack three, and there was a three on the

00:13:09.200 | board.

00:13:10.080 | So if she misread her hand for 10 1093 No, but you would have but you would have you

00:13:14.640 | would have had to call the flop.

00:13:16.160 | I'm thinking what?

00:13:17.040 | Yeah, no, I get it.

00:13:18.800 | The hand makes no sense.

00:13:19.920 | But I'm just trying to find a logical explanation.

00:13:22.320 | And that jack three explanation, somebody kind of fed that to her, and then she changed

00:13:27.360 | her story to that.

00:13:28.240 | So this changing of the story is the thing I was sort of keyed on Friedberg is why does

00:13:32.400 | she keep changing her story?

00:13:33.280 | Is it because she's embarrassed?

00:13:34.720 | Maybe she's had a couple of beverages or whatever.

00:13:37.280 | She's just a new player and she's embarrassed by her play and can't explain it.

00:13:42.240 | She can't explain the hand history.

00:13:43.840 | All of the things you're saying are probable.

00:13:45.520 | I don't think that Yeah, I don't think there's any data for us to have a strongly held point

00:13:50.720 | of view on this.

00:13:51.840 | I'm just looking forward to us all playing live.

00:13:55.120 | Yeah, HCL poker live October 21 minus David Sachs, unfortunately, to mop J.

00:14:00.480 | Cal Gerstner, Stanley Tang, Phil Helmuth.

00:14:03.840 | We're going to be playing on the same stream.

00:14:05.840 | We're going to be playing on the same screen, same table.

00:14:08.000 | I figured out how to hack into the video stream for the car.

00:14:10.560 | I just got my RFID sunglasses as well.

00:14:13.440 | I'm going to read all your shitty hands, J.

00:14:15.200 | Cal.

00:14:15.520 | I'm going to take your money and I'm going to buy my kids some nice clothes.

00:14:19.440 | For my 40th birthday, Sky organized poker in Tahoe.

00:14:22.560 | Okay.

00:14:23.280 | And we brought in the team from CBS.

00:14:25.920 | That was the present.

00:14:26.800 | Where fun.

00:14:27.600 | And they taped it as if it was being broadcast with whole cards and commentators.

00:14:31.920 | And we edited it into a two day show.

00:14:33.760 | It was an incredible birthday present.

00:14:36.240 | I it was it's one of the greatest things that anybody's ever given me.

00:14:39.840 | I appreciate it.

00:14:40.320 | There was a one hour block where somebody at the table said, okay, guys, how about we

00:14:46.400 | do a cheating free for all?

00:14:48.400 | Yes, where you could look at each other's cards and, you know, you could sort of help

00:14:52.560 | somebody else switch cards, whatever.

00:14:55.120 | In that one hour, our beautiful home game of friendship became Lord of the Flies.

00:15:00.480 | I have never seen so much hatred, angling, me, behavior.

00:15:07.600 | Oh, my God.

00:15:08.240 | It was incredible.

00:15:09.120 | All that humans are capable of.

00:15:11.600 | So I hope that we never, we never we never see cheating in our game.

00:15:16.160 | Yeah, we'll see how it goes on October 21 at HCL poker live.

00:15:19.200 | I'm excited.

00:15:20.960 | I can't wait.

00:15:21.680 | It should be a lot of fun.

00:15:22.560 | It should be a lot of fun.

00:15:23.280 | Oh, and we're not having any official 100 stuff.

00:15:27.120 | But the fans, some of the fans who were at the all in summit 2022 are doing their own

00:15:35.360 | 100 episode 100 meetups on October 15, I think, all in meetups.io.

00:15:42.000 | So there are fan meetups happening in Zurich and a bunch of other places.

00:15:45.120 | I'm going to FaceTime into some of them and just say hi to the fans.

00:15:47.360 | You know, it might be like 10 people in a bar somewhere.

00:15:49.600 | I think the largest one is like Miami or San Francisco are going to be like 50 people or

00:15:54.160 | something.

00:15:54.480 | We should all think that actually be kind of fun.

00:15:56.960 | I'm basically I told them to send me an invite and I'll FaceTime in any any time.

00:16:01.200 | This is next week.

00:16:02.720 | When is this?

00:16:03.440 | The 15th, I think this is occurring.

00:16:05.040 | Yeah, October 15.

00:16:07.440 | It's a Saturday, the Saturday after the 100th episode, people are doing these all in meetups.io.

00:16:12.160 | What's next?

00:16:12.880 | Earlier this week, it was reported that Elon contacted Twitter's board and suggested that

00:16:19.200 | they move forward with closing the transaction at the original terms and the original purchase

00:16:23.280 | price of $54.20 a share in the couple of days since then.

00:16:27.840 | And even as of right now, with some news reports coming out here on Thursday morning, it appears

00:16:33.520 | that there are still some question marks around whether or not the deal is actually going to

00:16:37.760 | move forward at $54.20 a share because Elon, as of right now, the report said is still

00:16:42.800 | asking for a financing contingency in order to close.

00:16:46.000 | And there's a lot of back and forth on what the terms are.

00:16:48.400 | Meanwhile, the court case in Delaware is continuing forward on whether or not Elon breached his

00:16:54.560 | terms of the original agreement to close and buy Twitter at $54.20.

00:16:58.400 | As we know, leading up to the signed deal or post signing the deal, Elon put together

00:17:05.600 | a financing syndicate, a combination of debt investors as well as equity co investors with

00:17:10.640 | him to do the purchase of Twitter at $54.20 a share.

00:17:15.280 | So the 40 some odd billion dollars of capital that's needed was committed by a set of investors

00:17:21.040 | that were going to invest debt and equity.

00:17:22.960 | And there's a big question mark now on whether or not those investors want to or would still

00:17:27.760 | consummate the transaction with Elon given how the markets have turned and given how

00:17:32.000 | debt markets are trading and equity markets are trading.

00:17:34.320 | So tomorrow, I'd love to hear your point of view on what hurdles does Elon still have

00:17:39.280 | in front of him?

00:17:40.480 | Does he still want to get this done?

00:17:41.760 | And is there still a financing syndicate that's standing behind him at the original purchase

00:17:45.920 | price to get it done?

00:17:46.720 | It's a great question.

00:17:48.560 | Maybe the best way to start is Nick, do you want to queue up what I said in August 25?

00:17:54.000 | The lawsuit really boils down to one very specific clause, which is the pinnacle

00:18:00.240 | question at hand, which is there is a specific performance clause that Elon signed up to,

00:18:07.680 | right, which, you know, his lawyers could have struck out and either chose not to or,

00:18:14.240 | you know, couldn't get the deal done without.

00:18:16.480 | And that specific performance clause says that Twitter can force him to close at $5420 a share.

00:18:23.200 | And I think the issue at hand at the Delaware Business Court is going to be that because

00:18:29.360 | Twitter is going to point to all of these, you know, gotchas and disclaimers that they

00:18:34.240 | have around this bought issue as their cover story.

00:18:39.200 | And I think that really, you know, this kind of again, builds more and more momentum in

00:18:45.840 | my mind that the most likely outcome here is a settlement where you have to pay the

00:18:53.680 | economic difference between where the stock is now and 5420, which is more than a billion

00:18:59.040 | dollars, or you close at some number below $54 and 20 cents a share.

00:19:05.360 | And I think that that is like, you know, if you had to be a betting person, that's probably

00:19:10.160 | and if you look at the way the stock is traded, and if you also look at the way the options

00:19:15.280 | market trades, that's what people are assuming that there's a seven to $10 billion swing.

00:19:21.200 | And if you impute that into the stock price, you kind of get into the $51 a share kind

00:19:26.000 | of an acquisition price.

00:19:27.280 | Again, I'm not saying that that is right or should be right.

00:19:30.320 | That's just sort of what the market says.

00:19:31.760 | Yeah, so so it turns out that, you know, sort of like that kind of guesstimate turned out

00:19:37.600 | to be pretty accurate, because the stock today is at $51 a share.

00:19:40.800 | So I think that the specific performance thing is exactly what this thing has always hinged

00:19:46.000 | on.

00:19:46.720 | And I think that there was a realization that there were very few outs around how that

00:19:51.360 | contractual term was written and agreed to.

00:19:53.920 | So there is an out in the contract.

00:19:56.880 | And that out says that I think it's by April.

00:19:59.440 | If if the deal doesn't get done by April, then the banks can walk away from their commitment

00:20:06.080 | to fund the debt.

00:20:06.880 | And if the banks walk away, then Elon does have a financing contingency that allows him

00:20:12.320 | to walk away.

00:20:13.440 | So the actual set of events that have to happen is those two things specifically get to April

00:20:19.040 | so the banks can pass and say, we've changed our mind market conditions are different.

00:20:23.520 | And then Elon is able to say, oh, you know, the banks just walked away.

00:20:27.200 | Right now, the banks, if you look at all of the debt that they've committed to, what they

00:20:32.320 | committed at a point in time when the debt markets were much better than they are today.

00:20:37.120 | In the last, you know, six or seven months since they agreed to do this, the debt markets

00:20:42.640 | have been clobbered and specifically junk bonds and a bunch of junk bond debt, the yields

00:20:48.080 | that you have to pay.

00:20:48.640 | So the price to get that kind of debt has skyrocketed.

00:20:51.440 | So roughly back of the envelope math would tell me that right now the banks are offside

00:20:57.120 | between one and two billion dollars because they're not going to be able to sell this

00:21:00.880 | debt to anybody else.

00:21:02.240 | So I think the banks obviously want to weigh out.

00:21:04.000 | The problem is their only way out is to run the shot clock off until April.

00:21:08.560 | So I think that's the dance that they're in right now.

00:21:12.160 | Elon's trying to find a way to solve, you know, for the merger.

00:21:15.280 | I think Twitter is going to say, we're not going to give you a financing contingency.

00:21:19.280 | You have to bring the banks in and close right now and then we will not go to court.

00:21:24.000 | Otherwise, we're going to court.

00:21:25.280 | And so I think it's a very delicate predicament that they're all in.

00:21:29.360 | But my estimate is that the equity is probably 20 percent offside.

00:21:34.640 | So it's not a huge thing.

00:21:35.760 | He can make that up because he can create equity value like nobody's business.

00:21:39.600 | The debt is way offside by a couple billion dollars, which is hard to make back.

00:21:44.160 | But I think in the end, you know, given enough time, they can probably make that back.

00:21:48.240 | The best off in all of this are the Twitter shareholders.

00:21:51.200 | They're getting an enormous premium to what that company is worth today in the open market.

00:21:56.400 | And so I think this deal is going to close.

00:21:59.040 | It's probably going to close in the next few weeks.

00:22:00.560 | And had you bought Twitter when we were talking about it in August, you would have made 25

00:22:06.400 | percent in six weeks and you know, if the deal closes at 54, you would have made a third of

00:22:10.480 | your money in eight weeks, which is very hard to do in a market.

00:22:14.320 | You're a GP at one of the funds like Andreessen or Sequoia.

00:22:18.480 | And you had made this commitment to Elon or even Larry Ellison a couple of months ago.

00:22:24.240 | Do you fight against closing at 5420?

00:22:28.320 | Do you stick with the deal and support him?

00:22:31.120 | I mean, what do you do, given that the premium is so much higher than where the market would

00:22:34.800 | trade it at today?

00:22:35.520 | Some people are saying the stock should be at like 20 bucks a share or something.

00:22:38.000 | The average premium in an M&A transaction in the public markets is about 30 percent.

00:22:42.320 | So and I think the fair value of Twitter is around 32 to 35 bucks a share.

00:22:49.200 | So, you know, it's not like he is massively, massively overpaying.

00:22:53.600 | And so, you know, I would just sort of keep that in the realm of the possible.

00:22:58.880 | So like if you take thirty five dollars as the midpoint.

00:23:01.360 | Fair value is really forty five fifty.

00:23:04.880 | So, yeah, he paid 20 percent more than he should have, but he didn't pay 100 percent more.

00:23:08.640 | So it's not as if you can't make that equity back as a private company, particularly because

00:23:14.240 | there's probably ten dollars of fat in the stock if you think about just

00:23:18.240 | OPEX in terms of all the buildings they have.

00:23:21.040 | Maybe they don't need as many employees.

00:23:23.200 | Maybe they revisit salaries.

00:23:24.960 | You know, one thing is when I looked at doing an activist play at Twitter, I think I mentioned

00:23:28.880 | this five or six years ago.

00:23:30.720 | One of the things that I found was at that time Twitter was running their own data centers.

00:23:35.360 | And, you know, the most obvious thing for me at that time was like, we're going to move

00:23:38.240 | everything to AWS.

00:23:39.360 | Now, I don't know if that happened, but I'm sure that if it hasn't, just bidding that

00:23:44.320 | out to Azure, GCP and AWS can raise, you know, three or four billion dollars because I'm

00:23:48.960 | sure those companies would want this kind of an app on their cloud.

00:23:52.800 | So there's all kinds of things that I think Elon can do as a private company to make back

00:23:58.000 | maybe the small bit that he overpaid.

00:24:00.080 | And then he can get to the core job of rebuilding this company to be usable, this product to

00:24:04.640 | be usable.

00:24:05.440 | Look, I'll just speak as a user right now.

00:24:06.960 | It has been decaying at a very, very rapid clip.

00:24:11.840 | And I think that his trepidation in closing the merger in part also, even though he hasn't

00:24:17.680 | said it has to do with the quality of the experience.

00:24:20.400 | It's just degraded.

00:24:21.600 | It's not as fun to use as it was during the pandemic or even before the pandemic.

00:24:27.760 | So something is happening inside that app that needs to get fixed.

00:24:31.760 | And if he does it, he'll make a ton of money.

00:24:33.520 | Sort of like what happened with Friendster and MySpace and any social networking app

00:24:37.360 | over time, the quality degrades.

00:24:39.520 | If it's not growing, it's shrinking and it gets if it's if it's not growing.

00:24:43.520 | And also, if the product hygiene isn't enforced in code and product hygiene in this case are

00:24:49.600 | this, you know, spam bots, you know, the trolling, it can really take away from the experience.

00:24:56.720 | Yeah.

00:24:57.440 | I mean, interestingly, like if you think back to the the starting days, the original days

00:25:01.920 | of Twitter, I don't know if you guys remember, you would send in an SMS to do your tweet,

00:25:06.160 | and then it would post up and other people would get the SMS notification and it would

00:25:12.480 | crash all the time.

00:25:13.520 | And the apps were the app was notoriously crashing.

00:25:16.160 | It was poorly architected at the beginning.

00:25:18.880 | And some people have argued that Twitter has had a cultural technical incompetence from

00:25:24.880 | the earliest days.

00:25:25.840 | I think that's a little harsh.

00:25:27.200 | So I do think look, Twitter was known for what's called a fail whale.

00:25:30.320 | You know, they used to have these fail whales constantly.

00:25:32.640 | And they did hire people that attempted to try to fix it.

00:25:37.200 | I remember the the funniest part of when I went in there and said, Hey, here's my plan.

00:25:41.360 | And here's what I want to do is literally a day or two later, the head of engineering

00:25:45.680 | quit.

00:25:46.160 | I can't remember who his name was, but he was just out the door.

00:25:50.720 | But it is a I think it is a team that has tried its best.

00:25:56.720 | That probably at the edges definitely made some technical miscalculations.

00:26:02.000 | Like I said, at that time, the idea that any app of that scale would use their own

00:26:06.640 | data centers made no technical sense whatsoever.

00:26:09.520 | It made the app laggy.

00:26:11.280 | It made it hard to use.

00:26:12.240 | It made it more prone to downtime, to your point.

00:26:14.160 | But that being said, I would be shocked if they haven't made meaningful improvements,

00:26:18.960 | because the stack of the internet has gotten so much better over the last seven years.

00:26:23.440 | And so to your point, David, if they didn't take advantage of all these new abstractions

00:26:27.840 | and mechanisms to mechanisms to rebuild the app or to rebuild search or to rebuild, you

00:26:32.800 | know how you know, all these infrastructure elements of the app work, I would be really

00:26:37.520 | surprised because then what are they doing over there?

00:26:39.840 | Yeah, well, look, I mean, to the point earlier, besides the product points, there was a really

00:26:44.960 | good tweet I liked that said, for what it's worth, I think Elon will show us just how

00:26:52.480 | lean the Silicon Valley advertising companies can be run.

00:26:55.440 | At the very least, it'll be an interesting thought experiment for spectators.

00:26:59.120 | Because if he does go in and actually does significantly reduce op ex and headcount,

00:27:03.840 | and the company does turn profitable and he can grow it, well, it'll really, by the way,

00:27:09.120 | it'll really be a beacon for financial big companies.

00:27:12.160 | Yeah, from a financial perspective, there is $10 a share in op ex cuts that he should

00:27:16.560 | make right away just so that he is economically break even.

00:27:20.080 | And he looks like every other M&A transaction, you know, you paid a 30% premium and you bought

00:27:24.800 | a company.

00:27:25.680 | There's a lot of margin of safety there if Elon does that.

00:27:28.400 | So to your point, there probably is.

00:27:30.400 | And there probably needs to be a meaningful riff at Twitter.

00:27:32.720 | I'm not saying it's right.

00:27:33.520 | I'm not saying it's, you know, and I feel for the people that may go through it.

00:27:36.480 | But from a financial perspective, the math makes sense for him to do that, because then

00:27:41.600 | he is a break even proposition on a go in M&A transaction.

00:27:45.920 | And I think that there's, there's a lot of intelligent financial sense, so that all the

00:27:50.720 | debt holders feel like he's doing the right thing.

00:27:53.040 | And all the equity holders, particularly, see a chance for them to make a decent return

00:27:57.200 | here.

00:27:57.440 | All right, well, let's move on.

00:27:59.360 | This is a great conversation between Chamath Palihappitya and Dave Friedberg about the

00:28:07.360 | Twitter transaction.

00:28:08.880 | And now we're being rejoined by our besties.

00:28:11.120 | Yeah, by other besties.

00:28:13.920 | Yeah.

00:28:14.400 | How was your cappuccino, J. Cal?

00:28:16.000 | That was great.

00:28:16.640 | I have a nice cold brew here, a nice iced cold brew and a nice drip coffee.

00:28:21.600 | I'm working both.

00:28:22.400 | I'd love to talk about topics I'm not being subpoenaed or depositioned about.

00:28:26.480 | We will have a lot to say in the coming weeks.

00:28:28.480 | I love to talk about topics that my lawyers have advised me not to talk about.

00:28:31.680 | How eerie was our prediction?

00:28:34.240 | 51 bucks a share.

00:28:35.280 | It is exactly where the stock is right now.

00:28:36.960 | That's eerie.

00:28:37.520 | Yeah.

00:28:38.800 | All right.

00:28:39.920 | Lots of advances.

00:28:40.800 | Let's keep going.

00:28:41.760 | Yeah.

00:28:42.000 | Speaking of Elon, Tesla AI Day was last week.

00:28:46.400 | I actually went.

00:28:47.200 | It was great.

00:28:47.760 | This is a recruiting event where--

00:28:49.920 | What did you do after?

00:28:50.800 | Phil Helmuth and I went and I drove Phil Helmuth home.

00:28:54.640 | The end.

00:28:56.800 | No, it's a great event.

00:28:57.920 | And it is essentially a giant recruiting event.

00:29:02.000 | Hundreds of AI--

00:29:03.600 | Sorry, but--

00:29:04.080 | Sorry, I'm sorry.

00:29:05.680 | Can we just talk about Phil Helmuth's non sequitur in the group chat about Ken Griffin?

00:29:09.120 | I mean--

00:29:11.120 | What?

00:29:11.200 | Oh, yeah, where he's just like, I made a joke about his net worth and he responded--

00:29:16.880 | What is going on?

00:29:18.080 | We were talking about the most serious of topics and he just comes--

00:29:21.040 | Seven seconds to fill.

00:29:22.000 | It's what's going on.

00:29:24.240 | Seven seconds to fill.

00:29:25.280 | By the way, I was texting with Daniel Negreanu.

00:29:29.520 | He did an incredible podcast.

00:29:31.840 | If you guys, with Lex Friedman, if you haven't listened to it, the Daniel Negreanu

00:29:35.920 | pod with Lex is incredible.

00:29:37.920 | Oh, I got to watch it.

00:29:38.640 | But I was joking with Daniel that there's a section where he's talking about the greatest

00:29:42.400 | poker players of all time.

00:29:43.760 | And if you look in the bar of YouTube, it shows where the most viewership was.

00:29:48.240 | And it was exactly the 30 seconds he talks about Helmuth.

00:29:51.920 | And I said to Daniel, this must have been Phil re-watching it over and over.

00:29:55.440 | I put it on loop.

00:29:56.480 | He went to bed with it like it was ASMR.

00:29:59.600 | To put him to bed.

00:30:00.480 | Just--

00:30:01.280 | Oh my god.

00:30:01.520 | Negrano talking about him.

00:30:03.280 | Sorry, Jacob.

00:30:04.000 | Sorry about that.

00:30:04.320 | No, no, it's all good.

00:30:05.600 | So anyway, the event was super impressive.

00:30:08.480 | Elon only spoke when he showed the Optimus, the new robot.

00:30:14.080 | He's building a general purpose robot that will work in the factories.

00:30:18.000 | It's very early days.

00:30:19.120 | But they showed two versions of it.

00:30:21.280 | And he said he thinks they could get it down to $20,000.

00:30:24.800 | It's going to work in the factory.

00:30:25.760 | So it's actually got a purpose.

00:30:26.880 | And obviously, the factories already have a ton of robots.

00:30:29.600 | But this is more of a robot that will benefit from the general--

00:30:35.520 | or the computer vision and the AI, the narrow AI being pursued by the self-driving team.

00:30:41.840 | This is like two and a half hours of really intense presentations.

00:30:44.480 | The most interesting part for me was they're building their own supercomputer and their chips.

00:30:50.160 | And the Dojo supercomputer was really impressive at how much they can get through scenarios.

00:30:58.400 | So they're building every scenario of every self-driving.

00:31:01.280 | I actually have the full self-driving beta on my car.

00:31:04.160 | I've been using it.

00:31:04.880 | It's pretty impressive, I have to say.

00:31:07.760 | If you haven't used it yet, I feel like AI is moving at a pretty advanced clip.

00:31:14.720 | The past year, if you haven't also seen, Meta announced a text-to-video generator.

00:31:19.280 | So this is even more impressive than DALI.

00:31:23.040 | DALI, you put in a couple of words, Friedberg, and you get a painting or whatever.

00:31:27.360 | This is put in a couple of words, and you get a short video.

00:31:29.920 | So they had one of a teddy bear painting a teddy bear.

00:31:32.560 | So it looks like you're going to be able to

00:31:35.520 | essentially create a whole movie by just talking to a computer.

00:31:40.640 | Really impressive.

00:31:42.480 | Where do you think we are, Friedberg, in terms of

00:31:44.880 | the compounding nature of these narrow AI efforts?

00:31:49.520 | Obviously, we saw poker, chess, Go, DALI, GPT-3, self-driving.

00:31:55.440 | It feels like this is all compounding at a faster rate.

00:31:58.320 | Or am I just imagining that?

00:32:02.000 | Yeah, look, I mean, it's interesting.

00:32:03.600 | When people saw the first computer playing chess, they said the same thing.

00:32:06.720 | I think any time that you see progress with a computer that

00:32:10.080 | starts to mimic the predictive capabilities of a human, it's impressive.

00:32:16.960 | But I will argue, and I'll say a few words on this.

00:32:20.000 | I think this is part of a 60-year cycle that we've been going through.

00:32:25.280 | Fundamentally, what humans and human brains do is we can sense our external environment,

00:32:32.560 | then we generate knowledge from that sensing, and then our brains build a model that predicts

00:32:37.840 | an outcome.

00:32:38.960 | And then that predicted outcome is what drives our actions and our behavior.

00:32:42.640 | We observe the sun rise every morning, then we observe that it sets.

00:32:45.920 | And you see that enough times, and you build a predictive model from that data that's been

00:32:49.280 | generated in your brain that I predict that the sun has risen.

00:32:53.280 | It will therefore set.

00:32:54.800 | It has set.

00:32:55.360 | It will therefore rise.

00:32:56.640 | And I think that the computing approach is very similar.

00:32:58.800 | It's all about sensing or generating data, and then creating a predictive model, and

00:33:03.520 | then you can drive action.

00:33:04.480 | And initially, the first approach was just basic algorithms.

00:33:08.960 | And these are deterministic models that are built.

00:33:11.520 | It's a piece of code that says, here's an input, here's an output.

00:33:14.480 | And that model is really built by a human.

00:33:17.200 | And a human designed that algorithmic model and said, this is what the predictive potential

00:33:23.200 | of this software is.

00:33:24.640 | Then there was this term called data science.

00:33:26.800 | So as data generation began to proliferate, meaning there was far more sensors in the

00:33:31.200 | world, it was really cheap to create digital data from the physical world, really cheap

00:33:35.840 | to transmit it really cheap to store it really cheap to compute with it.

00:33:38.960 | Data science became the hot term in Silicon Valley for a while.

00:33:42.080 | And these models were not just a basic algorithm written by a human, but it became an algorithm

00:33:47.760 | that was a similar deterministic model that had parameters, and the parameters were ultimately

00:33:54.320 | resolved by the data that was being generated.

00:33:56.960 | And so these models became much more complex and much more predictive, finer granularities,

00:34:02.000 | finer range, then we use this term machine learning.

00:34:04.960 | And in the data science era, it was still like, hey, there's a model.

00:34:09.040 | And you would solve it statically, you would get a bunch of data, you would statically

00:34:13.360 | solve for the parameters, and that would be your model, and it would run machine learning

00:34:16.960 | machine learning, then allowed those parameters to become dynamic.

00:34:20.480 | So the model was static.

00:34:22.160 | But generally speaking, the parameters that drove the model became dynamic as more data

00:34:27.840 | came into the system, and they were dynamically updated.

00:34:31.120 | And then this era of AI became and that's the new catchword.

00:34:34.240 | And what AI is realizing is that there's so much data that rather than just resolve the

00:34:39.200 | parameters of the model, you can actually resolve a model itself, the algorithm can

00:34:43.520 | be written by the data, the algorithm can be written by the software.

00:34:47.520 | And so with a with AI example, so poker playing an adaptive model, so people, so you're playing

00:34:54.240 | poker and the software begins to recognize behavior, and it builds a predictive model

00:34:58.640 | that says, here's how you're playing.

00:35:00.240 | And then over time, it actually changes not just the parameters of the model, but the

00:35:03.840 | model itself, the algorithm itself.

00:35:05.920 | And so AI and then it eventually gets to a point where the algorithm is so much more

00:35:09.880 | complex that a human would have never written it.

00:35:12.480 | And suddenly, the AI has built its own intelligence, its own ability to be predictive in a way

00:35:16.720 | that a human algorithmic programmer would have never done.

00:35:20.560 | And this is all driven by statistics.

00:35:22.760 | So none of this is new science per se, there's new techniques that all on their underlying

00:35:28.200 | use statistics as their basis.

00:35:30.320 | And then there's these techniques that allow us to build these new systems of model development,

00:35:33.920 | like neural nets, and so on.

00:35:35.680 | And those statistics build those neural nets, they solve those parameters, and so on.

00:35:39.600 | But fundamentally, there is a geometric increase in data and a geometric decline in the cost

00:35:46.520 | to generate data from sensors, because the cost of sensors is coming down with Moore's

00:35:50.000 | Law, transmit that data, because the cost of moving data has come down with broadband

00:35:54.280 | communications, the cost of storing data, because the cost of DRAM and solid state hard

00:35:59.640 | drives has come down with Moore's Law.

00:36:01.720 | And now the ability to actually have enough data to do this AI driven where people are

00:36:05.920 | calling AI, but it really is the same.

00:36:08.000 | It's part of a spectrum of things that have been going on for 60 years, to actually drive

00:36:11.480 | predictions in the in the world is really being realized in a bunch of areas that we

00:36:16.280 | would have historically been really challenged and surprised to see.

00:36:19.320 | And so my argument is, at this point, data played a big role.

00:36:23.600 | Yeah, yeah, we've over the last decade, we've reached this tipping point in terms of data

00:36:27.280 | generation, storage and computation, that's allowed these statistical models to resolve

00:36:31.920 | dynamically.

00:36:32.920 | And as a result, they are far more predictive.

00:36:36.280 | And as a result, we see far more human like behavior in the predictive systems, both physical

00:36:41.280 | both those that are, you know, like a like a robot is the same as one that existed 20

00:36:45.400 | years ago.

00:36:46.400 | But the way that it's run is using the software that is driven by this dynamic model.

00:36:51.160 | And that data allows for a better answer.

00:36:53.960 | Come on.

00:36:54.960 | Okay, I have two things to say.

00:36:56.800 | But one, the first one is a total non sequitur.

00:36:58.920 | So use the term data scientists.

00:37:00.360 | Do you know where the term data scientists came from?

00:37:04.520 | As classically used in Silicon Valley, it came from Facebook, and it came from my team

00:37:09.900 | in a critical moment.

00:37:11.220 | This was in 2007.

00:37:12.320 | I was trying to 2008 I was trying to build the growth team.

00:37:15.760 | This is the team that became very famous for getting to a billion users and, you know,

00:37:20.160 | building a lot of these algorithmic insights.

00:37:22.480 | And I was trying to recruit a person from Google.

00:37:27.080 | And he was like a PhD in some crazy thing like astrophysics or particle physics or something.

00:37:32.720 | And we gave him an offer as a data analyst, because this is what I needed at the time.

00:37:37.160 | This is what I thought I needed an analyst, you know, to analyze data.

00:37:41.520 | And he said, Absolutely not.

00:37:43.080 | I'm offended by the job title.

00:37:45.080 | And I remember talking to my my HR, you know, business process partner, and I asked her

00:37:49.840 | like, I don't understand what is this?

00:37:51.720 | Where's this coming from?

00:37:52.720 | And she said, he fashions himself a scientist.

00:37:55.700 | And I said, Well, then call him a data scientist.

00:37:57.880 | So we wrote in the offer for the first time, data scientist.

00:38:01.220 | And at the time, people internally were like, this is a dumb title.

00:38:04.640 | What does this mean?

00:38:05.640 | Anyways, we hired the guy, he was a star.

00:38:09.440 | And, and that title just took off internally.

00:38:11.520 | It's funny, because parallel, we started climate corp in 2006.

00:38:15.200 | And the original the first guy I hired was a buddy of mine, who was a 4.0 for in applied

00:38:18.900 | math from Cal.

00:38:20.160 | And then everyone we hired on with him, we called them the math team.

00:38:24.040 | And they were all applied math and statistics, PhDs.

00:38:27.360 | And we called them the math team.

00:38:28.360 | And it was really cool to be part of the math team.

00:38:30.320 | But then we switched the team name to data scientist.

00:38:33.160 | And then it obviously created this much more kind of impressive role, impressive title,

00:38:38.880 | central function to the organization.

00:38:41.000 | That was more than just a math person or data analyst, as I think it may have been classically

00:38:45.400 | treated because they really were building the algorithms that drove the models that

00:38:49.580 | made the product work, right?

00:38:50.880 | Peter Thiel has a very funny observation, not funny, but you know, observation, which

00:38:54.880 | is, you should always be wary of any science that actually has science in the name, political

00:39:00.160 | science, social science, I guess, maybe data scientists, you know, because the real sciences

00:39:04.920 | don't need to qualify themselves physics, chemistry, biology.

00:39:08.000 | Anyways, that's, so here's what I wanted to talk about with respect to AI.

00:39:12.640 | Two very important observations that I think is useful for people to know.

00:39:16.760 | The first one, Nick, if you throw it up here is just a baselining of, you know, when we

00:39:20.920 | have thought about intelligence and compute capability, we've always talked about Moore's

00:39:24.800 | law and Moore's law, essentially this idea that there is a fixed amount of time where

00:39:30.240 | the density of transistors inside of a chip would double.

00:39:34.140 | And roughly that period for many, many years was around two years.

00:39:37.880 | And it was largely led by Intel.

00:39:40.080 | And we used to equate this to intelligence, meaning the more density there was in a chip,

00:39:45.440 | the more things could be learned and understood.

00:39:48.840 | And we used to think about that as the progression of how computing intelligence would grow and

00:39:54.800 | eventually AI and artificial intelligence would get to mass market.

00:39:59.040 | Well, what we are now at is a place where many people have said Moore's law has broken.

00:40:05.600 | Why?

00:40:06.600 | It's because we cannot cram any more transistors into a fixed amount of area.

00:40:11.240 | We are at the boundaries of physics.

00:40:14.700 | And so people think, well, does that mean that our ability to compute will essentially

00:40:19.540 | come to an end and stop?

00:40:21.040 | And the answer is no.

00:40:22.160 | And that's what's demonstrated on this next chart, just to make it simple, which is that

00:40:26.840 | what you really see is that if you think about, you know, supercomputing power, so the ability

00:40:33.060 | to get to an answer that has actually continued unabated.

00:40:38.080 | And if you look at this chart, the reason why this is possible is entirely because we've

00:40:43.280 | shifted from CPUs to these things called GPUs.

00:40:46.920 | So you may have heard companies like Nvidia, why is companies like Nvidia done so well?

00:40:51.560 | It's because they said they raised their hand and said, we can take on the work.

00:40:56.100 | And by taking on the work away from a traditional CPU, you're able to do a lot of what Freeberg

00:41:01.460 | said is get into these very complicated models.

00:41:04.380 | So this is just an observation that I think that we are continuing to compound knowledge

00:41:11.520 | and intelligence effectively at the same rate as Moore's law.

00:41:16.520 | And we will continue to be able to do that because this makes it a problem of power and

00:41:22.840 | a problem of money.

00:41:24.880 | So as long as you can buy enough GPUs from Nvidia or build your own, and as long as you

00:41:30.280 | can get access to enough power to run those computers, there really isn't many problems

00:41:36.280 | you can't solve.

00:41:37.620 | And that's what's so fascinating and interesting.

00:41:39.760 | And this is what companies like OpenAI are really proving, you know, when they raised

00:41:44.160 | a billion dollars, what they did was they attacked this problem, because they realized

00:41:48.760 | that by shifting the problem to GPUs, it left all these amazing opportunities for them to

00:41:55.040 | uncover and that's effectively what they have.

00:41:57.600 | The second thing that I'll say very quickly is that it's been really hard for us as a

00:42:03.200 | society to build intelligence in a multimodal way like our brain works.

00:42:09.640 | So think about how our brain works.

00:42:10.920 | Our brain works in a multimodal way.

00:42:13.480 | We can process imagery, we can process words and sounds, we can process all of these different

00:42:20.000 | modes, text, into one system, and then intuit some intelligence from it and make a decision,

00:42:28.360 | right?

00:42:29.360 | So, you know, we could be watching this YouTube video, there's going to be transcription,

00:42:32.640 | there's video, voice, audio, everything all at once.

00:42:36.240 | And we are moving to a place very quickly where computers will have that same ability

00:42:40.800 | as well.

00:42:41.800 | Today, we go to very specific models and kind of balkanized silos to solve different kinds

00:42:47.220 | of problems.

00:42:48.220 | But those are now quickly merging, again, because of what I just said about GPUs.

00:42:53.040 | So I think what's really important about AI for everybody to understand is the marginal

00:42:59.000 | cost of intelligence is going to go to zero.

00:43:02.120 | And this is where I'm just going to put out another prediction of my own.

00:43:06.280 | When that happens, it's going to be incredibly important for humans to differentiate themselves

00:43:12.520 | from computers.

00:43:13.880 | And I think the best way for humans to differentiate ourselves is to be more human.

00:43:19.180 | It's to be less compute intensive.

00:43:21.720 | It's to be more empathetic, it's to be more emotional, not less emotional, because those

00:43:26.800 | differentiators are very difficult for brute force compute to solve.

00:43:31.000 | Be careful, the replicants on this call are getting a little nervous here.

00:43:34.520 | They're not processing that.

00:43:36.000 | That was an emotional statement.

00:43:37.000 | Do not want to process that one.

00:43:38.840 | Well, to your point, during this AI day, they were showing in self driving, as you're talking

00:43:45.320 | about this balkanization, and trying to make decisions across many different decision trees,

00:43:51.880 | you know, they're looking at lane changes, they're looking at other cars and pedestrians,

00:43:56.200 | they're looking at road conditions like fog and rain.

00:43:59.640 | And then they're using all this big data to your point, Friedberg to run tons of different

00:44:04.840 | simulations.

00:44:05.840 | So they're building like this virtual world on Market Street, and then they will throw

00:44:11.440 | people, dogs, cars, people who have weird behaviors into the simulation.

00:44:16.040 | It's such a wonderful example.

00:44:17.680 | Imagine that system.

00:44:19.560 | Here's a horn.

00:44:20.840 | Yeah.

00:44:21.840 | Well, you hear a horn.

00:44:23.360 | So clearly, there's some auditory expression of risk, right?

00:44:26.640 | There's something risky.

00:44:28.640 | And now you have to scan your visual field, you have to probabilistically decide what

00:44:33.400 | it could be, what the evasive maneuver, if anything, should be.

00:44:37.720 | So that's a multimodal set of intelligence that today isn't really available.

00:44:43.800 | But we have to get there if we're going to have real full self driving.

00:44:46.440 | So that's a perfect example, Jason, a real world example of how hard the problem is,

00:44:50.640 | but it'll get solved because we can brute force it now with, with chips and with compute.

00:44:54.760 | I think that's going to be the very interesting thing with the robots as well as all of, you

00:44:58.600 | know, these decisions they're making, moving cars through roads, all of a sudden, we're

00:45:04.160 | going to see that with VTOLs, vertical takeoff and landing, you know, aircraft, and we're

00:45:09.480 | going to see it with this general robot.

00:45:11.800 | And everybody wanted to ask you a lot about general AI, you know, the Terminator kind

00:45:15.320 | of stuff.

00:45:16.560 | And his position is, I think, if we solve enough of these problems, Friedberg, it'll

00:45:20.560 | be an emergent behavior or an emergent phenomenon, I guess would be a better word, based on each

00:45:27.520 | of these cities crumbling, you know, each of these tasks getting solved by groups of

00:45:31.440 | people.

00:45:32.440 | You have any thoughts as we wrap up here on the discussion about general AI and the timeline

00:45:36.920 | for that, because obviously, we're going to solve every vertical AI problem in short order.

00:45:41.320 | I spoke about this a little bit on the Ask AMA on Colin on Tuesday night, once Saks gets

00:45:48.360 | it out, you can listen to it.

00:45:50.360 | But I really have this strong belief that servers crash, there's no AI team at Colin.

00:45:56.520 | This episode drops.

00:45:57.520 | Oh, my God.

00:45:58.520 | Yeah, you guys can try to download the app, but it might crash.

00:46:01.000 | So just be careful.

00:46:02.000 | So here's, here's my, my court.

00:46:04.520 | The problem is, Friedberg, that you are 10 times more popular than J Cal.

00:46:07.640 | So it was unexpected levels of traffic.

00:46:10.240 | Well, you had you did have an account with 11,000 followers.

00:46:12.920 | I mean, you're right, Jake, I will put you on that account next time.

00:46:17.200 | Yeah, please.

00:46:18.200 | Yeah, I'm starting from zero.

00:46:20.000 | Yeah, that's fair.

00:46:21.000 | That's fair.

00:46:22.000 | So my, my core thesis is I think humans transition from being, let's call it, you know, passive

00:46:29.200 | in this system on Earth to being laborers.

00:46:33.040 | And then we transition from being laborers to being creators.

00:46:36.040 | And I think our next transition with AI is to transition from being creators to being

00:46:39.960 | narrators.

00:46:41.160 | And what I mean by that is, as as we started to do work on Earth and engineer the world

00:46:45.960 | around us, we did labor to do that we literally plowed the fields, we walk distances, we

00:46:51.920 | built things.

00:46:53.280 | And over time, we built machines that automated a lot of that labor.

00:46:58.120 | You know, everything from a plow to a tractor, to a caterpillar equipment to a microwave

00:47:03.280 | that cooks for us, labor became less, we became less dependent on our labor abilities.

00:47:08.840 | And then we got to switch our time and spend our time as creators, as knowledge workers.

00:47:13.120 | And a vast majority of the developed world now primarily spends their time as knowledge

00:47:18.120 | workers creating.

00:47:19.440 | And we create stuff on computers, we do stuff on computers, but we're not doing physical

00:47:23.160 | labor anymore.

00:47:24.160 | As a lot of the knowledge work gets supplanted by AI, or as it's being termed now, but really

00:47:30.080 | gets supplanted by software.

00:47:32.000 | The role of the human I think transitions to being one of a narrator, where instead

00:47:36.000 | of having to create the blueprint for a house, you narrate the house you want, and the software

00:47:41.920 | creates the blueprint for you.

00:47:43.120 | You dictate.

00:47:44.120 | And instead of yeah, and instead of creating the movie and not spending $100 million producing

00:47:48.080 | a movie, you dictate or you narrate the movie you want to see, and you iterate with the

00:47:52.720 | computer and the computer renders the entire film for you.

00:47:55.760 | Because those films are shown digitally anyway, so you can have a computer render it.

00:47:59.340 | Instead of creating a new piece of content, you narrate the content you want to experience,

00:48:04.920 | you create your own video game, you create your own movie experience.

00:48:08.080 | And I think that there's a whole evolution that happens.

00:48:10.040 | And if you look, Steve Pinker's book, Enlightenment Now has a great statistic, a set of statistics

00:48:14.520 | on this, but the amount of time that humans are spending on leisure activities per week

00:48:19.480 | has climbed extraordinarily over the past couple of decades.

00:48:22.900 | We spend more time enjoying ourselves and exploring our creative interests than we ever

00:48:27.000 | did in the past in human history, because we were burdened by all the labor and all

00:48:31.040 | the creative and knowledge work we have to do.

00:48:33.160 | And now things are much more accessible to us.

00:48:36.060 | And I think that AI allows us to transition into an era that we never really thought possible

00:48:39.880 | or realized, where the limits are really our imagination of what we can do with the world

00:48:44.360 | around us.

00:48:45.360 | And the software resolves to the automation resolves to make those things possible.

00:48:50.560 | And that's a really exciting kind of vision for the future that I think AI enables.

00:48:54.160 | Star Trek had this right, people didn't have to work and they could pursue things in the

00:48:57.800 | holodeck or whatever that they felt was rewarding to them.

00:49:02.160 | But speaking of jobs, the job reports for August came in, we talked about this, we were

00:49:07.520 | trimming 300,000 jobs a month.

00:49:10.080 | We're wondering if the other shoe would drop, oh boy, did it drop 100 over a million jobs

00:49:14.960 | burned off in August.

00:49:16.400 | So without getting into the macro talk, it does feel like what the Fed is doing, and

00:49:20.920 | companies doing hiring freezes and cuts is finally finally having an impact.

00:49:26.320 | If we start losing a million, as we predicted could happen here on the show.

00:49:32.360 | People might actually go back to work and Lyft and Uber are reporting that the driver

00:49:36.860 | shortages are over.

00:49:38.080 | They no longer have to pay people spiffs and stuff like that to get people to come back

00:49:41.080 | to work.

00:49:42.080 | So at least here in America feels like we're turning a corner.

00:49:44.760 | Do we want to go?

00:49:45.760 | Can we let's talk about the marijuana?

00:49:47.080 | Yeah, breaking news.

00:49:48.080 | Biden just did.

00:49:49.080 | Yeah, yeah.

00:49:50.080 | I was gonna say we got a couple of things we really want to get to here.

00:49:53.200 | Ukraine, section 230.

00:49:54.880 | And then this breaking news.

00:49:56.120 | We'll pull it up here on the screen.

00:49:58.760 | While we're recording the show, President Biden says, and I'm just going to quote here.

00:50:04.520 | First, I'm pardoning all prior federal offenses of simple marijuana possession.

00:50:10.480 | There are 1000s of people who are were previously convicted of simple possession, who may be

00:50:14.580 | denied employment, housing or educational opportunities.

00:50:16.600 | So my pardon will remove this burden as big news.

00:50:19.560 | Second, I'm calling on governors to pardon simple state marijuana possession offenses,

00:50:25.580 | just as no one should be in a federal prison solely for possessing marijuana.

00:50:28.880 | Nobody should be in a local jail or state prison for that reason, either.

00:50:33.080 | This is happening.

00:50:34.640 | Third, and this is an important one, we classify the marijuana at the same level as heroin

00:50:38.960 | and even and more serious than fentanyl.

00:50:41.040 | It makes no sense.

00:50:42.200 | I'm asking Secretary Burr, but Kara and the Attorney General to initiate the process of

00:50:48.080 | reviewing how marijuana is scheduled under federal law.

00:50:52.080 | I'd also like to note that as federal and state regulators change, we still need important

00:50:57.080 | limitations on trafficking, marketing and under a shells of marijuana.

00:51:00.960 | So I'm asking for your thoughts on this breaking news.

00:51:03.240 | Is this giving the timing on this is kind of midterm related.

00:51:09.280 | This seems is this is this I guess is this a politically popular decision to do?

00:51:13.320 | I think so.

00:51:14.320 | I mean, look, I support it.

00:51:15.800 | So I finally did something I like.

00:51:18.200 | Great.

00:51:19.200 | I mean, I thought that we should decriminalize marijuana for a long time, or specifically,

00:51:25.800 | you know, I agree with this idea of de scheduling it, it does not make sense to treat marijuana

00:51:30.920 | the same as heroin as a schedule one narcotic.

00:51:33.960 | This doesn't make any sense.

00:51:35.120 | It should be regulated separately and differently.

00:51:39.000 | Obviously you want to keep it out of the hands of minors, but no one should be going to jail,

00:51:42.600 | I think for simple possession.

00:51:44.360 | So I do agree with this.

00:51:45.520 | And I think the thing they need to do, I don't see it mentioned here is they should pass

00:51:50.040 | a federal law that would allow for the normalization of let's call it legal, legal, you know, cannabis

00:52:00.000 | companies.

00:52:01.000 | So so companies that are allowed to operate under state laws, like in California, should

00:52:06.840 | have access to the banking system should have access to payment rails.

00:52:09.960 | Because right now, the reason why the legal cannabis industry isn't working at all in

00:52:15.160 | California is because they can't bank, they can't take payments.

00:52:18.640 | So it's this weird all cash business that makes no sense.

00:52:21.400 | So So listen, if we're not going to criminalize it as a drug like heroin, if we're going to

00:52:26.480 | allow states to make it legal, then allow it to be a more normal business where the

00:52:32.480 | state can tax it, and it can operate in a more above board way.

00:52:37.680 | So federal mandate is what you're saying the federal mandate, I think it's could still

00:52:41.400 | be regulated on a state by state basis.

00:52:43.600 | But I think you need the feds to bless the idea that banks and payment companies can

00:52:49.200 | take on those clients, which states have already said, are legally operating companies.

00:52:54.880 | And right now, they can't.

00:52:56.440 | And it's a huge gap in the law.

00:52:57.600 | So maybe that's the one thing I would add to this.

00:52:59.880 | But I don't have any complaints about this right now, based on what we know from this

00:53:03.240 | tweet storm.

00:53:04.240 | And I would say, this, by the way, was about face.

00:53:07.400 | This was an about face by Biden.

00:53:08.760 | Yeah, do you know what the polling data says?

00:53:10.520 | I mean, is there?

00:53:11.520 | I'm assuming there's big support in kind of the independence in the middle.

00:53:17.160 | For this was 70%.

00:53:18.160 | At one point.

00:53:19.160 | Yeah, yeah.

00:53:20.160 | So So look, this, to me, this is the kind of thing that Biden should be doing with the

00:53:24.360 | 5050 Senate to finding these sorts of bipartisan compromises.

00:53:28.280 | Right?

00:53:29.640 | So yeah, I look, this is good news for us.

00:53:31.600 | I'm concerned.

00:53:32.600 | This happened in the past, like, what's been the political reason that other presidents

00:53:36.920 | Obama even didn't that have their similar ideology?

00:53:43.400 | Like, why?

00:53:44.400 | But why?

00:53:45.400 | Does anyone know why this hasn't been done in the past?

00:53:47.120 | There was rumors he was going to do in the second term.

00:53:49.760 | They just didn't have the political capital.

00:53:51.040 | Why to do it?

00:53:52.040 | Why?

00:53:53.040 | I don't want to fire.

00:53:54.040 | Yeah, the pardon doesn't require political capital.

00:53:57.280 | I think it's probably the perception that this is soft on crime in some way, or there

00:54:02.640 | wasn't enough broad based support.

00:54:04.160 | As David said, I mean, I think the the United States population has moved pretty meaningfully

00:54:09.280 | in the last 20 years.

00:54:10.720 | Look at the chart here.

00:54:12.800 | You know, we were talking about 2000.

00:54:14.400 | It was only 31%.

00:54:17.040 | And then you look at 2018.

00:54:19.120 | It's up at 60 plus percent.

00:54:20.800 | So when people saw the states doing it, and they saw absolutely no problem, you know,

00:54:25.360 | in every state, and I think what people will see next, that's a Gallup poll.

00:54:29.000 | That's a Gallup poll.

00:54:30.000 | You're seeing the so it's increased dramatically.

00:54:32.040 | MDMA, psilocybin, and some of these other plant based medicines, ayahuasca are next,

00:54:36.880 | and they're doing studies on them.

00:54:38.280 | Now, I don't want to take away from how important this is for all the people for whom this will

00:54:42.880 | positively impact.

00:54:44.520 | I just want to talk about the schedule change for marijuana.

00:54:48.200 | As a parent, one of the things that I'm really, really concerned about is that through this

00:54:53.000 | process of legalization, getting access to marijuana has frankly become too easy, particularly

00:54:59.400 | for kids.

00:55:01.120 | At the same time, I saw a lot of really alarming evidence that the the intensity of these marijuana

00:55:08.000 | based products have gone, you know, I think it's like five or six times more intense than

00:55:12.720 | the other 50 or 100 much higher, right?

00:55:16.000 | So so it's no longer, you know, this kind of like, you know, do no harm drug that it

00:55:22.160 | was 20 years ago, this is this could be actually David, the way that it's productized today,

00:55:29.280 | as bad as some of these other, you know, narcotics.

00:55:33.040 | So in June of this year, the Biden administration basically made this press release that said

00:55:40.320 | the FDA is going to come out with regulations that would cap the amount of nicotine in cigarettes.

00:55:46.120 | And I think that was a really smart move, because it basically set the stage to taper

00:55:50.300 | nicotine out of out of cigarettes, which would essentially, you know, decapitate it as a

00:55:57.560 | an addictive product.

00:55:59.540 | And I think by thinking about how it's how it's dealt with, what I really hope the administration

00:56:06.360 | does is it empowers the FDA, if you're going to legalize it, you need to have expectations

00:56:13.160 | around what the intensity of these drugs are.

00:56:16.280 | Because if you're delivering drugs, OTC, and now any kid can go in at 18 years old and

00:56:20.520 | buy them, which means that 18 year olds are going to buy them for 16 year olds, 16 year

00:56:25.400 | olds are going to get fake IDs to buy them for themselves.

00:56:28.280 | You need to do a better job.

00:56:29.280 | So the parents you're helping parents do our job.

00:56:32.480 | That's what you need.

00:56:33.480 | Shouldn't it shouldn't be 21 like alcohol?

00:56:37.040 | If alcohol is 21, then of course, yeah, fine.

00:56:39.400 | But even alcohol, David, you know that there are there are we know what the intensity of

00:56:42.720 | these are, there are labels, and there's warnings.

00:56:44.840 | And you know, the difference between beer, they're getting wine versus hard alcohol.

00:56:48.880 | But let me just give you some statistics here.

00:56:50.360 | Chamath, if you think about the the cannabis in the 90s, and prior to that, there was very,

00:56:56.480 | you've been a ton of studies on this in Colorado, it was the THC content was less than 2%.

00:57:02.400 | And then in 2017, we were talking about, you know, things going up to 17 to 28% for specific

00:57:11.440 | strains.

00:57:12.440 | So they have been building strains like Girl Scout cookies, etc, that have just increased

00:57:15.800 | and increased.

00:57:16.800 | And then there are things like shards and obviously edibles, you can create whatever

00:57:20.840 | intensity you want.

00:57:22.000 | So you have this incredible there, you know, variation, you could have an edible that's,

00:57:27.360 | you know, got one milligram of THC, you got one that has 100, or you could have a pack

00:57:31.440 | of edibles.

00:57:32.440 | And you see this happen in the news all the time, some kid gets their parents pack or

00:57:35.800 | somebody gives one, and the kids don't know.

00:57:38.280 | And this dabbing phenomenon combined with a dabbing is like the shards like this really

00:57:42.680 | intense stuff.

00:57:45.560 | Combined with the edibles is really the issue and the labeling of them.

00:57:48.340 | So you got to be incredibly careful with this.

00:57:50.640 | It's not good for kids, it screws up their brains.

00:57:52.760 | And so yeah, be very careful.

00:57:55.400 | I have a zero tolerance policy on this stuff.

00:57:57.080 | I don't care if it's legal or illegal.

00:57:58.800 | Like I don't want my kids touching any of this stuff until

00:58:00.800 | it's not for kids, obviously.

00:58:01.800 | Yeah, but you also should not be until they're until they're 35 or 40.

00:58:05.640 | And even then I hope they never do it.

00:58:07.000 | But but I need some help.

00:58:09.600 | And I'm not sure I'm the only parent that's asking you can't have this stuff be available

00:58:13.320 | effectively sold like in a convenience store.

00:58:15.720 | No, no, that's not going to happen where there isn't even labeling at least like cigarettes

00:58:19.440 | are labeled.

00:58:20.440 | It's very clear how bad this stuff is for you.

00:58:22.760 | Or do you guys have any feedback on the job report or anything?

00:58:25.360 | They're all going away when when the when the AI wins?

00:58:29.080 | Well, that's why I brought it up is like, we're now going to see a potential, you know,

00:58:33.200 | a situation where jobs go away.

00:58:35.200 | And a lot of the stuff like even developers, right?

00:58:37.480 | Don't you think freeberg developers are going to start development tasks?

00:58:40.760 | No, I designed tasks are going to be I added it.

00:58:43.720 | Everyone assumes a static lump of work.

00:58:45.560 | I think what happens particularly in things like developer tools, is the developer can

00:58:50.320 | do so much more.

00:58:51.680 | And then we generate so much more output.

00:58:53.680 | And so the overall productivity goes up, not down.

00:58:56.940 | So it's pretty exciting as these.

00:58:59.120 | And remember, like, like, we were talking on the AMA the other night, Adobe Photoshop

00:59:03.360 | was a tool for photographers.

00:59:05.200 | So you didn't have to take the perfect photograph and then print it.

00:59:08.040 | You could, you know, you could use the software to improve the quality of your photograph.

00:59:12.360 | And I think that that's what we see happening with all software.

00:59:15.360 | In the creative process is it helps people do more than they realize they could do before.

00:59:19.480 | And that's pretty powerful.

00:59:20.480 | And it opens up all these new avenues of interest and things we're not even imagining today.

00:59:24.240 | Alright, so SCOTUS is going to hear two cases for section 230.

00:59:28.940 | The family of Nohema Gonzalez, a 23 year old American college student who was killed in

00:59:33.520 | an ISIS terrorist attack in Paris back in 2015.

00:59:36.780 | Remember those horrible attacks, just claiming that YouTube helped and aided and abetted

00:59:41.740 | ISIS.

00:59:42.940 | The family's argument is YouTube's algorithm was recommending videos that make it that

00:59:47.500 | makes it a publisher of content, as you know, it's section 230, common carrier.

00:59:51.260 | If you make editorial decisions, if you promote certain content, you lose your 230 protections.

00:59:57.700 | In court papers filed in 2016, they said the company quote, no only permitted ISIS to post

01:00:01.660 | on YouTube hundreds of radicalizing videos inciting violence, which helped the group

01:00:06.540 | recruit, including some who were actually involved in the terrorist attack.

01:00:10.220 | So they have made that connection.

01:00:11.340 | Well, look, let's let's be honest, we can we can we can put a pin in this thing.

01:00:14.940 | Because I think it would be shocking to me if this current SCOTUS all of a sudden founded

01:00:21.500 | in the cockles of their heart to protect big tech.

01:00:23.940 | I mean, they've dismantled a lot of other stuff that I think is a lot more controversial

01:00:32.980 | than this.

01:00:34.860 | And so you know, we've we've basically looked at gun laws, we've looked at affirmative action,

01:00:40.420 | we've looked at abortion rights.

01:00:42.900 | So well, I mean, I think as we've said, I think we all know where that die is, unfortunately

01:00:48.020 | going to get cast.

01:00:50.360 | So to me, it just seems like this could be an interesting case where it's actually nine

01:00:54.140 | zero in favor for complete for completely different sets of reasons.

01:00:58.940 | I mean, if you think of the liberal left part of the court, they have their own reasons

01:01:02.940 | for saying that there are 230 protections for big tech.

01:01:05.760 | And if you look at the far right, or the right leaning parts, members of this of SCOTUS,

01:01:10.700 | they have they have another set of do you think you're gonna make a point?

01:01:12.860 | A political decision, not illegal?

01:01:14.340 | No, but even even in their politics, they actually end up in the same place.

01:01:18.060 | They both don't want the protections, but for different reasons.

01:01:21.820 | So there there is a reasonable outcome here where you know, Roberts is going to have a

01:01:25.900 | really interesting time trying to pick who writes the majority opinion.

01:01:29.020 | There was a related case in the Fifth Circuit in Texas, where do you guys see this Fifth

01:01:33.900 | Circuit decision, where Texas passed a law imposing common carrier restrictions on social

01:01:41.340 | media companies.

01:01:42.340 | The idea being that social media companies need to operate like phone companies, and

01:01:46.660 | they can't just arbitrarily deny you service or deny you access to the platform.

01:01:51.820 | And the argument why previously that had been viewed actually as unconstitutional, was this

01:01:58.340 | idea of compelled speech that you can't compel a corporation to support speech that they

01:02:03.860 | don't want to because that was a violation of their own First Amendment rights.

01:02:07.820 | And what the First, the Fifth Circuit said is no, that doesn't make any sense.

01:02:11.060 | Facebook or Twitter can still advocate for whatever speech they want as a corporation.

01:02:15.700 | But as a platform, they if Texas requires them to not discriminate against people on

01:02:21.820 | the basis of viewpoint, then Texas has the right to to impose that because that doesn't

01:02:27.100 | it, their quote was that does not chill speech, if anything, it chills censorship.

01:02:31.620 | So what's the right legal decision here in your mind, putting aside politics, if you

01:02:35.140 | can, for a moment, putting on your legal hat, what is the right thing for society?

01:02:39.940 | What is the right legal issue around section 230?

01:02:43.260 | Specifically in the YouTube case, and just generally, should we look at YouTube?

01:02:46.780 | Should we look at a blogging platform like medium or blogger, Twitter, should we look

01:02:51.660 | at those as common carrier, and they're not responsible for what you publish on them,

01:02:57.580 | obviously, they have to take stuff down if it breaks our terms of service, etc.

01:03:00.820 | Or if it's illegal,

01:03:01.820 | I've made the case before that I do think that common carrier requirements should apply

01:03:05.940 | on some level of the stack to protect the rights of ordinary Americans to have their

01:03:11.100 | speech in the face of these giant monopolies, which could otherwise deplatform them for

01:03:15.440 | arbitrary reasons.

01:03:17.100 | Just to you know, just explain this a little bit.

01:03:19.860 | So historically, there was always a debate between so called positive rights and negative

01:03:26.300 | rights.

01:03:27.300 | So where the United States start off as a country was with this idea of negative rights

01:03:31.460 | that what a right meant is that you'd be protected from the government taking some action against

01:03:36.540 | you.

01:03:37.540 | And if you look at the Bill of Rights, you know, the original rights are all about protecting

01:03:41.660 | the citizen against intrusion on their liberty by a state or by the federal government.

01:03:46.780 | In other words, Congress shall make no law, it was always a restriction.

01:03:50.540 | So the right was negative, it wasn't sort of positively enforced.

01:03:53.140 | And then with the progressive era, you started seeing, you know, more progressive rights

01:03:58.340 | like, for example, American citizens should have the right to healthcare, right?

01:04:03.180 | That's not protecting you from the government.

01:04:05.140 | That's saying that the government can be used to give you a right that you didn't otherwise

01:04:09.380 | have.

01:04:10.380 | And so that was sort of the big progressive revolution.

01:04:12.880 | My take on it is I actually think that the problem we have in our society right now is

01:04:17.740 | that free speech is only a negative right.

01:04:19.780 | It's not a positive right.

01:04:20.940 | I think it actually needs to be a positive right.

01:04:23.300 | I'm embracing a more progressive version of rights, but on behalf of sort of this original

01:04:29.780 | negative right.

01:04:30.780 | So, and the reason is because the town square got privatized, right?

01:04:34.620 | I mean, you used to be able to go anywhere in this country, there'd be a multiplicity

01:04:37.860 | of town squares, anyone could pull out their soapbox, draw a crowd, they could listen.

01:04:41.900 | That's not how speech occurs anymore.

01:04:43.220 | It's not on public land or public spaces.

01:04:46.660 | The way that speech, political speech especially, occurs today is in these giant social networks

01:04:51.620 | that have giant network effects and are basically monopolies.

01:04:55.340 | So if you don't protect the right to free speech in a positive way, it no longer exists.

01:05:00.700 | So you not only believe that YouTube should keep its section 230, you believe that YouTube

01:05:06.740 | shouldn't be able to de-platform as a private company, you know, Alex Jones as but one example,

01:05:13.900 | they should have their free speech rights, and we should lean on that side of forcing

01:05:17.300 | YouTube to put Alex Jones or Twitter to put Trump back on the platform.

01:05:22.020 | Is that your position?

01:05:23.780 | I'm not saying that the Constitution requires YouTube to do anything.

01:05:28.100 | What I'm saying is that if a state like Texas or if the federal government wants to pass

01:05:33.700 | a law saying that YouTube, if you are say of a certain size, you're a social network

01:05:39.020 | of a certain size, you have monopoly network effects, I wouldn't necessarily apply this

01:05:42.060 | to all the little guys.

01:05:43.800 | But for those big monopolies, we know who they are, if the federal government or a state

01:05:49.600 | wanted to say that they are required to be a common carrier, and they cannot discriminate

01:05:54.440 | against certain viewpoints, I think the government should be allowed to do that because it furthers

01:05:58.920 | a positive right.

01:06:00.480 | Historically, they've not been able to do that because of this idea of compelled speech,

01:06:05.960 | meaning that it would infringe on YouTube's speech rights.

01:06:08.600 | I don't think it would.

01:06:09.840 | I mean, Google and YouTube can advocate for whatever positions they want.

01:06:13.280 | They can produce whatever content they want.

01:06:15.840 | But the point that, and I think Section 230 kind of makes this point as well, is that

01:06:19.840 | they are platforms, they're distribution platforms, they're not publishers.

01:06:23.080 | So if they want, so especially if they want Section 230 protection, they should not be

01:06:27.320 | engaging in viewpoint discrimination.

01:06:28.800 | So now there is a rub here.

01:06:29.800 | Can I just say, can I just say, go ahead.

01:06:31.680 | Your explanation, David, your explanation that you just gave before was so excellent.

01:06:36.400 | Thank you.

01:06:37.400 | That it allows me to understand it even more clearly.

01:06:39.760 | That was really good.

01:06:40.760 | So Shumoff, do you think the algorithm is an act of editorialization?

01:06:44.600 | Yes, yes, yes, yes.

01:06:47.120 | And so then should YouTube, TikTok?

01:06:49.520 | Look guys, at the end of the day, let me, let me break down an algorithm for you.

01:06:53.080 | Okay.

01:06:54.080 | Effectively, it is a mathematical equation of variables and weights.

01:06:59.360 | An editor 50 years ago was somebody who had that equation of variables and weights in

01:07:06.440 | his or her mind.

01:07:08.280 | Okay.

01:07:09.280 | And so all we did was we translated again, this multimodal model that was in somebody's

01:07:14.560 | brain into a model that's mathematical, that sits in code.

01:07:20.680 | You're talking about the front page editor of the New York Times.

01:07:23.000 | Yeah.

01:07:24.000 | And I think it's a fake leaf to say that because there is not an individual person who writes

01:07:27.840 | point two in front of this one variable and point eight in front of the other, that all

01:07:33.200 | of a sudden that this isn't editorial decision making is wrong.

01:07:36.400 | We need to understand the current moment in which we live, which is that these computers

01:07:42.280 | are thinking actively for us.

01:07:45.780 | They're providing this, you know, computationally intensive decision making and reasoning.

01:07:53.700 | And I think it's, it's pretty ridiculous to assume that that isn't true.

01:07:58.200 | That's why when you go to Google and you search for, you know, Michael Jordan, we know what

01:08:03.960 | the right Michael Jordan is because it's reasoned.

01:08:07.140 | There is an algorithm that is doing that.

01:08:09.000 | It's making an editorial decision around what the right answer is.

01:08:12.280 | They have deemed it to be right.

01:08:14.520 | And that is just true.

01:08:15.920 | And so I think we need to acknowledge that because I think it allows us at least to be

01:08:19.880 | in a position to rewrite these laws through the lens of the 21st century.

01:08:25.320 | And we need to update our understanding for how the world works today.

01:08:30.520 | And you know, Chamath, there's such an easy way to do this.

01:08:32.640 | If you're tick tock, if you're YouTube, if you want section 230, if you want to have

01:08:37.280 | common carrier and not be responsible with their when a user signs up, it should give

01:08:41.720 | them the option, would you like to turn on an algorithm?

01:08:44.920 | Here are a series of algorithms which you could turn on, you could bring your own algorithm,

01:08:49.140 | you could write your own algorithm with a bunch of sliders, or here are ones that other

01:08:52.980 | users and services provide like an app store.

01:08:56.160 | So you Chamath could pick one for your family, your kids, that would be I want one that's

01:09:00.680 | leaning towards education and takes out conspiracy theories takes out cannabis use takes out

01:09:04.960 | this one.

01:09:05.960 | It's a wonderful what you're saying is so wonderful.

01:09:08.000 | Because for example, like, you know, this organization, common sense media.

01:09:10.720 | Yes, I love that website.

01:09:12.400 | Every time I put in a movie, I put common sense media decide if we should watch it.

01:09:15.840 | Or like an I use it a lot for apps, because they're pretty good at just telling you which

01:09:19.760 | which apps are reasonable and unreasonable.

01:09:22.520 | But you know, if common sense media could raise a little bit more money and create an

01:09:26.240 | algorithm that would help filter stories in Tick Tock for my kids, I'd be more likely

01:09:32.520 | to give my kids Tick Tock when they turn 14.

01:09:35.360 | Right now, I know that they're going to sneak it by going to YouTube and looking at YouTube

01:09:39.160 | shorts and all these other things because I cannot control that algorithm.

01:09:43.320 | And it does worry me what kind of content that they're getting access to.

01:09:47.640 | And you could do this, by the way, Chamath on the operating system level or on the router

01:09:51.560 | level in your house, you could say I want the common sense algorithm, I will pay $25

01:09:56.160 | a month, $100 a year for it, we are put it on your society, then any IP that goes through

01:10:01.080 | it would be programmed properly.

01:10:02.840 | I want less violence, I want less sex, you know, whatever I think we are as a society

01:10:07.280 | sophisticated enough now.

01:10:09.480 | Yes.

01:10:10.480 | To have these controls.

01:10:11.800 | And so I think we need them.

01:10:13.180 | And so I think we do need to have the right observation of the current state of play.

01:10:19.480 | Friedberg.

01:10:20.480 | Where do you sit on this?

01:10:22.000 | Do you think the algorithm should be?

01:10:23.960 | I don't I don't really have 230.

01:10:26.320 | Yeah, I don't fully agree with sex on the monopolistic assumption.

01:10:32.400 | I think that there are a packet, I think that there are other places to access content.

01:10:37.000 | And I think that there is still a free market to compete.

01:10:40.840 | And it is possible to compete.

01:10:42.560 | I think that we saw this happen with Tick Tock, we saw it happen with Instagram, we

01:10:46.440 | saw it happen with YouTube, competing against Google Video and Microsoft Video.

01:10:51.240 | Compared to that, there has been a very significant battle for the attention of kind of being

01:10:57.880 | the next gen of media businesses.

01:10:59.580 | And we have seen Spotify compete, and we're seeing Spotify continue to be challenged by

01:11:04.120 | emerging competitors.

01:11:06.080 | So I don't buy the assumption that these are built in monopolies, and therefore it allows

01:11:12.780 | some regulatory process to come in and say, hey, free speech needs to be actively enforced

01:11:17.600 | because they're monopolies.

01:11:18.600 | This isn't like when utilities laid power lines, and sewer lines and and trains across

01:11:24.720 | the country, and they had a physical monopoly on being able to access and move goods and

01:11:29.520 | services.

01:11:30.520 | The internet is still thank God knock on wood open.

01:11:33.480 | And the ability for anyone to build a competing service is still possible.

01:11:37.300 | And there is a lot of money that would love to disrupt these businesses that is actively

01:11:41.140 | doing it.

01:11:42.140 | And I think every day, look at how big Tick Tock has gotten, it is bigger than YouTube

01:11:46.000 | almost or will be soon.

01:11:48.600 | And there is a competition that happens.

01:11:50.560 | And because of that competition, I think that the the market will ultimately choose where

01:11:55.440 | they want to get their content from and how they want to consume it.

01:11:58.200 | And I don't think that the government should play a role.

01:12:00.600 | Sachs rebuttal to that you buy that?

01:12:02.200 | Well, so not all these companies are monopolies, but I think they act in a monopolistic way

01:12:07.400 | with respect to restricting free speech, which is they act as a cartel, they all share like

01:12:12.880 | best practices with each other on how to restrict speech.

01:12:16.120 | And we saw the the watershed here was better when Trump was thrown off.

01:12:20.680 | First Twitter made the decision.

01:12:22.280 | You know, Jack, I don't know if it was Jack, but basically the company

01:12:25.400 | Jack said it wasn't him.

01:12:26.680 | Actually, he said it was the woman who was running it specifically.

01:12:28.720 | Jack later said it was a mistake.

01:12:29.720 | She got death threats after that.

01:12:30.720 | Yeah, Jack actually said it was a mistake.

01:12:32.400 | But in any event, Twitter did it first.

01:12:34.440 | And then all the other companies followed suit.

01:12:36.120 | I mean, even like Pinterest and Okta and Snapchat, like officially passed a policy.

01:12:42.280 | YouTube, everybody.

01:12:43.280 | Yeah.

01:12:44.280 | But Trump was actually on Facebook.

01:12:45.280 | He wasn't on all these other companies.

01:12:46.280 | They still threw him off.

01:12:47.280 | So they all copy each other.

01:12:49.400 | And Jack actually said that in his comments where he said it was a mistake.

01:12:52.680 | He said he didn't realize the way in which Twitter's action would actually cascade.

01:12:59.040 | He said that he thought originally that the action was okay, because it was just Twitter

01:13:03.760 | decided to take away Trump's right to free speech.

01:13:06.720 | But he could still go to all these other companies.

01:13:08.280 | And then all these other companies, basically, they are all subject to the same political

01:13:14.000 | forces.

01:13:15.000 | The leadership of these companies are all sort of, they all drink from the same monocultural

01:13:19.040 | fountain.

01:13:20.040 | They all have the same political biases.

01:13:21.280 | The polls show this.

01:13:22.280 | So the problem with Freeburg is, yeah, I agree, a bunch of these companies aren't quite monopolies,

01:13:26.040 | but they all act the same way.

01:13:27.280 | I hate to say it, but I agree with you, Zach.

01:13:28.280 | I'm agreeing with you.

01:13:29.280 | And so the collective effect is of a speech cartel.

01:13:33.320 | So the question is, how do you protect the rights of Americans to free speech in the

01:13:37.200 | face of a speech cartel that wants to basically block them?

01:13:40.600 | Go ahead, Freeburg, respond.

01:13:41.600 | Here's my argument.

01:13:42.600 | My argument is that these are not public service providers, they're private service providers,

01:13:46.240 | and the market is telling them what to do.

01:13:48.400 | The market is saying, and I think I think that the pressure that was felt by these folks

01:13:53.480 | was that so many consumers were pissed off that they were letting Trump rail on or they

01:13:58.240 | were pissed off about Jan six, they were pissed off about whatever, whatever the current fad

01:14:03.160 | is, the trend is, they respond to the market.

01:14:06.080 | And they say, you know what, this is cross the line.

01:14:08.520 | And this was the case on public television, when nudity came out, and they're like, okay,

01:14:12.400 | you know what, we need to take that off the TV, we need to because the market is telling

01:14:16.040 | us they're going to boycott us.

01:14:17.560 | And I think that there's a market pressure here that we're ignoring, that is actually

01:14:20.960 | pretty, pretty relevant, that as a private service provider, if they're going to lose

01:14:24.840 | half their audience, because people are pissed about one or two pieces of content showing

01:14:29.080 | up that they're acting in the best interest of their shareholders, and in the best interest

01:14:33.360 | of their platform, they're not acting as a public service.

01:14:36.040 | Look, I love market forces as much as the next libertarian.

01:14:40.160 | But I just think that fundamentally, that's just not what's going on here.

01:14:43.040 | This has nothing to do with market forces as everything to do with political forces.

01:14:46.640 | That's what's driving this.

01:14:47.640 | Look, do you think the average consumer, the average user of PayPal is demanding that they

01:14:53.040 | engage in all these restrictive policies throwing off all these accounts who have the wrong

01:14:57.480 | viewpoints?

01:14:58.480 | No, that has nothing to do with it has to do with the vocal minority.

01:15:00.920 | Yeah, it's a small number of people who are political activists who work at these companies

01:15:06.600 | and create pressure from below.

01:15:08.400 | It's also the people from outside, the activists who create these boycott campaigns and pressure

01:15:13.340 | from outside.

01:15:14.540 | And then it's basically people on Capitol Hill who have the same ideology who basically

01:15:18.560 | create threats from above.

01:15:19.720 | So these companies are under enormous pressure from above, below, and sideways, and it's

01:15:24.840 | 100% political.

01:15:25.840 | Hold on, it's not about maximizing profits.

01:15:29.560 | I think it's about maximizing political outcomes.

01:15:33.160 | Yeah, I don't-

01:15:34.160 | And that is what the American people need to be protected from.

01:15:36.840 | Now I will add one nuance to my theory though, which is I'm not sure what level of the stack

01:15:44.320 | we should declare to be common carrier.

01:15:46.580 | So in other words, you may be right actually that at the level of YouTube or Twitter or

01:15:52.560 | Facebook, maybe we shouldn't make them common carrier and I'll tell you why because just

01:15:56.720 | to take the other side of the argument for a second, which is if you don't...

01:16:00.920 | Because those companies do have legitimate reasons to take down some content.

01:16:05.120 | I don't like the way they do it, but I do not want to see bots on there.

01:16:08.600 | I do not want to see fake accounts and I actually don't want to see truly hateful speech or

01:16:14.600 | harassment.

01:16:15.720 | And the problem is I do worry that if you subject them to common carrier, they won't

01:16:20.020 | actually be able to engage in, let's say, legitimate curation of their social networks.

01:16:25.720 | However, so there's a real debate to be had there and it's going to be messy, but I think

01:16:31.160 | there's one level of the stack below that, which is at the level of pipes, like an AWS,

01:16:35.600 | like a CloudFlare, like a PayPal, like the ISPs, like the banks.

01:16:40.320 | They are not doing any content moderation or they have no legitimate reason to be doing

01:16:44.200 | content moderation.

01:16:45.200 | None of those companies should be allowed to engage in viewpoint discrimination.

01:16:48.280 | We have a problem right now where American citizens are being denied access to payment

01:16:52.800 | rails and to the banking system because of their viewpoints.

01:16:55.920 | So wait, hold on, you're saying AWS shouldn't be able to deny service to the Ku Klux Klan

01:16:59.360 | or some hate speech group?

01:17:00.560 | I think that they should be under the same requirements the phone company is under.

01:17:04.480 | Okay.

01:17:05.480 | Yeah, I mean, this is a very complicated shock.

01:17:06.480 | So when you frame it that way, it's, you know, the question is like, look, I could frame

01:17:10.880 | the same question to you.

01:17:12.800 | Should such and such horrible group be able to get a phone account, right?

01:17:17.680 | Yeah, no, no.

01:17:18.680 | And you'd say, no, they shouldn't get anything, but they have that right.

01:17:21.960 | That has been litigated and that's been pretty much protected by the Supreme Court.

01:17:25.720 | You know, even if it's a government conferred monopoly, the Supreme Court has said, okay,

01:17:30.520 | listen, like it's not violating one's constitutional right.

01:17:33.360 | For example, if your water bill gets terminated without you getting due process and the inverse

01:17:39.660 | is also true.

01:17:40.680 | So for whether we like it or not, that Jason, that issue has been litigated.

01:17:45.600 | I think I think I think for me, again, just like practically speaking for the functioning

01:17:52.160 | of civil society, I think it's very important for us to now introduce this idea of algorithmic

01:17:58.200 | choice.

01:17:59.940 | And I don't think that that will happen in the absence of us rewriting section 230 in

01:18:04.720 | a more intelligent way.

01:18:05.920 | I don't know.

01:18:07.040 | I don't know whether this specific case creates enough standing for us to do all of that.

01:18:14.760 | But I think it's an important thing that we have to revisit as a society, because Jason,

01:18:20.040 | what you described as having a breadth of algorithmic choices over time where there

01:18:24.960 | are purveyors and sellers.

01:18:26.880 | Could you imagine that's not a job or a company that the four of us would ever have imagined

01:18:31.000 | could be possible five years ago.

01:18:33.420 | But maybe there should be an economy of algorithms and there are these really great algorithms

01:18:39.040 | that one would want to pay a subscription for because one believes in the quality of

01:18:42.400 | what it gives you.

01:18:44.440 | We should have that choice.

01:18:45.760 | And I think it's an important set of choices that will allow actually, YouTube as an example

01:18:50.640 | to operate more safely as a platform, because it can say, Listen, I've created this set

01:18:55.280 | of abstractions, you can plug in all sorts of algorithms, there's a default algorithm

01:18:59.520 | that works, but then there's a marketplace of algorithms, just like there's a marketplace

01:19:02.980 | of ideas.

01:19:03.980 | I don't discriminate and let people choose.

01:19:07.160 | This is the key thing here.

01:19:08.160 | It's model like if it was on a blockchain, if all the videos, all the video content was

01:19:12.760 | uploaded to a public blockchain, and then distributed on distributed computing system,

01:19:17.680 | then your ability to search and use that media would be a function of a service provider

01:19:23.160 | you're willing to pay for that provides the best service experience.

01:19:25.640 | And by the way, this is also why I think over time to sex to kind of sex and I are both

01:19:30.400 | arguing both sides a little bit, but I think that what will happen, I don't think that

01:19:35.120 | the government should come in and regulate these guys and tell them that they can't take

01:19:38.440 | stuff down and whatnot.

01:19:39.440 | I really don't like the precedent it sets, period.

01:19:42.280 | I also think that it's a terrible idea for YouTube and Twitter to take stuff down.

01:19:48.480 | And I think that there's an incredibly difficult balance that they're going to have to find

01:19:52.840 | because if they do this, as we're seeing right now, the quality of the experience for a set

01:19:57.000 | of users declines, and they will find somewhere else any market will develop for something

01:20:00.760 | else to compete effectively against them.

01:20:03.120 | And so I that's why I don't like the government intervening, because I want to see a better

01:20:06.840 | product emerge when the big company makes some stupid mistake and does a bad job.

01:20:11.240 | And then the market will find a better outcome.

01:20:13.720 | And it just it's messy in the middle.

01:20:15.720 | And as soon as you do government intervention on these things and tell them what they can

01:20:19.680 | and can't take down, I really do think that over time, you will limit the user experience

01:20:24.240 | to what is possible if you allow the free market.

01:20:27.320 | And this is where the the industry needs to police itself.

01:20:30.280 | If you look at the movie industry with the MPAA, and Indiana Jones and the Temple of

01:20:34.960 | Doom, they came out with the PG 13 rating specifically for things that were a little

01:20:38.160 | too edgy for PG.

01:20:40.320 | This is where our industry could get ahead of this, they could give algorithmic choice

01:20:45.240 | and algorithmic app store.

01:20:47.080 | And if you look at the original sin, it was these lifetime bans, like, Trump should not

01:20:50.880 | have been given a lifetime ban, they should have given them a one year ban, they should

01:20:54.160 | have had a process and overreached, we wouldn't be in this position.

01:20:58.400 | But Jason, when you talk about when you talk about like having a industry consortium, like

01:21:03.120 | the MPAA, what you're doing is formalizing the communication that's already taking place

01:21:07.160 | already happening between these companies.

01:21:09.120 | And what is the result of that communication?

01:21:10.840 | They all standardized on overly restrictive policies, because they all share the same

01:21:14.480 | political bias.

01:21:15.480 | No, but if they did it correctly, it's all in the execution sacks, it has to be executed

01:21:19.480 | properly, like the movie industry, it doesn't matter, you'll end up with the same problem

01:21:23.240 | as having the government intervene.

01:21:24.560 | If you have the government intervene, or a private body intervene, any sort of set standard

01:21:28.600 | intervention that prevents the market from complying, I disagree with you, I think you

01:21:32.760 | can create more competition if the government says, Okay, folks, you can have the standard

01:21:37.600 | algorithm, but you need to make a simple, abstracted way for somebody else to write

01:21:43.400 | some other filtering mechanism and to basically you so that users can pick the power users.

01:21:47.760 | Yes, I don't like it.

01:21:48.760 | I don't like it.

01:21:49.760 | What the MPAA did was, I don't understand why you don't like why isn't that more choice?

01:21:54.080 | Because as a product person, as a product company, I don't want to be told how to make

01:21:57.000 | my product, right?

01:21:58.000 | If I'm not YouTube, you have you have an algo, you're now saying that there is this distinction

01:22:02.040 | of the algo from the UX from the data.

01:22:04.140 | And my choice might be to create different content libraries.

01:22:06.800 | For example, YouTube has YouTube kids, and it's a different content library.

01:22:10.560 | And it's a different user interface.

01:22:12.280 | And it's a different algorithm.

01:22:13.280 | And you're trying to create an abstraction that may not necessarily be natural for the

01:22:16.720 | evolution of the product set of that company.

01:22:18.840 | I would much rather see them figure it out.

01:22:20.560 | That's not a good argument that again, if you were not a monopoly, I would be more sympathetic.

01:22:25.600 | But because like somebody, somebody feelings would get hurt, a product managers feelings

01:22:29.720 | will get hurt inside of Google feelings, but it's not the reason to not protect free speech.

01:22:33.800 | I think you're unnaturally disrupting the product evolution.

01:22:36.520 | And I don't flock.

01:22:37.520 | That's what that's what happens when you're worth $2 trillion.

01:22:40.200 | And when you impact a billion people on the planet, when you start having massive impact

01:22:44.280 | in society, you have to take some responsibility.

01:22:46.840 | And those companies are not taking responsibility.

01:22:49.240 | If you're not super, super, super successful, it's this is not going to affect you.

01:22:53.180 | So you're not going to worry about you'll see you'll see apps offshore and you'll see

01:22:56.800 | tick tock and other things compete because they'll have a better product experience.

01:23:00.080 | Because they know, no, no, no, no, I was going to create a new Google because they're down

01:23:05.120 | ranking one to 10% of the search results for reasons.

01:23:10.520 | Some accountability, hold on an ideal world companies like Google and so forth would not

01:23:15.000 | take sides in political debates to be politically neutral, but they're not.

01:23:18.280 | You look at all the data around the political leanings, the people running these companies,

01:23:21.920 | and then you look at the actual actions of these companies, and they have become fully

01:23:25.600 | political and they've waded into all these political debates with the result that the

01:23:29.280 | American people's rights to speech and to earn have been reduced.

01:23:33.640 | You have companies like PayPal, which are just engaging in retaliation, basically financial

01:23:38.000 | retaliation purely on based on what political viewpoints they have.

01:23:42.800 | Why it's not like face.

01:23:44.280 | It's not like PayPal needs to be in the business.

01:23:47.560 | Let's continue this conversation.

01:23:49.000 | We're not going to stop calling it calling AMA.

01:23:51.200 | Well, if they can get some servers over that, I don't know, maybe so you got to raise some

01:23:55.560 | money sacks for this app and get some more service.

01:23:57.920 | All right, listen for the dictator who needs to hit the loo to do a number two.

01:24:03.160 | Yes, I am the world's greatest moderator.

01:24:06.560 | Friedberg is the Sultan of science.

01:24:08.640 | And David Sachs is the prince of peace.

01:24:14.200 | See you all next week on the episode.

01:24:16.560 | Wait, wait, is this 98 or 99?

01:24:19.040 | No, it's 99.

01:24:20.040 | It's 99.

01:24:21.040 | We have one episode left.

01:24:22.040 | Wayne Gretzky.

01:24:23.040 | Get it while less enjoy while less that we're wrapping it up here.

01:24:25.760 | All right, we'll see you all next time.

01:24:27.480 | Have a great movement.

01:24:28.480 | Bye bye.

01:24:29.480 | Let your winners ride.

01:24:30.480 | Rain Man David Sachs.

01:24:31.480 | We open source it to the fans and they've just gone crazy with it.

01:24:32.480 | Love you.

01:24:33.480 | Queen of Kinhwa.

01:24:34.480 | Besties are gone.

01:24:52.280 | *laughter*

01:24:54.280 | That is my dog taking a notice your driveway

01:24:56.160 | Sex!

01:24:57.120 | *laughter*

01:24:58.240 | Oh man!

01:24:59.600 | My avatars will meet me at places

01:25:01.600 | We should all just get a room and just have like one big huge orgy

01:25:04.360 | Cause they're all just useless

01:25:05.640 | It's like this like sexual tension that they just need to release somehow

01:25:08.920 | Wet ur bee!

01:25:11.280 | Wet ur bee!

01:25:12.860 | *laughs*

01:25:14.720 | We need to get merch!

01:25:16.380 | I'm going all in!

01:25:18.180 | *music*

01:25:24.420 | I'm going all in!

01:25:26.420 | [Music]

E99: Cheating scandals, Twitter updates, rapid AI advancements, Biden's pardon, Section 230 & more

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