1 00:00:15,564 --> 00:00:28,724 Speaker 1: Pushkin. Welcome back to Risky Business, a show about making 2 00:00:28,764 --> 00:00:31,164 Speaker 1: better decisions. I'm Maria Kanakova and. 3 00:00:31,124 --> 00:00:33,684 Speaker 2: I'm Nate Silver. Stay in the show. Look if it 4 00:00:33,724 --> 00:00:36,604 Speaker 2: looks like I'm taping this in candle lights because all 5 00:00:36,644 --> 00:00:39,964 Speaker 2: the power is out in New York City and around 6 00:00:39,964 --> 00:00:42,244 Speaker 2: the world because of the government shutdown. No, there has 7 00:00:42,284 --> 00:00:44,164 Speaker 2: been a government shut down for more than a week 8 00:00:44,204 --> 00:00:46,684 Speaker 2: now as you're hearing this less than new beecause we're 9 00:00:46,724 --> 00:00:48,964 Speaker 2: taping this. We're going to talk about how it's evolved, 10 00:00:49,004 --> 00:00:53,324 Speaker 2: what the polling says so far, and you know, do 11 00:00:53,404 --> 00:00:56,844 Speaker 2: a check in on which side if either is applying 12 00:00:56,844 --> 00:00:57,644 Speaker 2: the right strategy. 13 00:00:58,204 --> 00:01:01,604 Speaker 1: And after that, we are going to talk about one 14 00:01:01,684 --> 00:01:05,844 Speaker 1: of our pet favorite subjects here at Risky Business, which 15 00:01:05,884 --> 00:01:10,684 Speaker 1: is AI and the release of a new AI video 16 00:01:10,764 --> 00:01:13,244 Speaker 1: generation app from open Ai called. 17 00:01:13,284 --> 00:01:15,844 Speaker 2: Sora Sora Too, Sora Too. 18 00:01:15,724 --> 00:01:17,884 Speaker 1: Sorry, I can I can never get it. N Yes, 19 00:01:18,724 --> 00:01:21,924 Speaker 1: the man the names of open Ai products. But the 20 00:01:21,964 --> 00:01:27,324 Speaker 1: segment will be about broader societal concerns that have to 21 00:01:27,364 --> 00:01:29,964 Speaker 1: go beyond whether it's called Sora or Sora Too. 22 00:01:30,524 --> 00:01:32,684 Speaker 2: How dystopian are We're going to answer that question for 23 00:01:32,684 --> 00:01:34,804 Speaker 2: you on a scale of zero to ten, how far 24 00:01:34,844 --> 00:01:40,844 Speaker 2: are down the lines. Are we to a dystopia? 25 00:01:42,604 --> 00:01:45,964 Speaker 1: But before we dive into AI, let's talk a little 26 00:01:45,964 --> 00:01:49,604 Speaker 1: bit about the government shutdown. So we are recording this 27 00:01:49,764 --> 00:01:54,044 Speaker 1: on Monday, October sixth, you'll be hearing it on Wednesday 28 00:01:54,084 --> 00:01:57,964 Speaker 1: the eighth. On the Sex We're still in a government shutdown. 29 00:01:58,004 --> 00:02:01,844 Speaker 1: And Nate, we know you're an advisor to Polymarket. It 30 00:02:01,924 --> 00:02:03,924 Speaker 1: seems to me that if you look at the odds, 31 00:02:03,924 --> 00:02:06,684 Speaker 1: we will probably still be in a shutdown by the 32 00:02:06,684 --> 00:02:09,524 Speaker 1: time listeners hear this episode on Wednesday. 33 00:02:09,684 --> 00:02:13,804 Speaker 2: Polymarket the prediction, Sorry, poly Market, the prediction market for everyone. 34 00:02:15,164 --> 00:02:20,764 Speaker 2: It's poly October anyway. Yeah, according to Polymarket, there's only 35 00:02:20,844 --> 00:02:23,484 Speaker 2: about a three percent chance that will look foolish by 36 00:02:23,564 --> 00:02:26,324 Speaker 2: having released this episode when the sub shutdowns already resolved. 37 00:02:26,724 --> 00:02:28,764 Speaker 2: In about a seventy five percent chance that it will 38 00:02:28,764 --> 00:02:31,204 Speaker 2: be resolved within a week and a half or later. 39 00:02:31,804 --> 00:02:35,724 Speaker 2: Twenty five percent chance for the longest shutdown in American history. 40 00:02:36,164 --> 00:02:39,604 Speaker 1: So, Nate, I am curious, what is the longest the 41 00:02:39,644 --> 00:02:41,084 Speaker 1: government has ever shut down for? 42 00:02:41,964 --> 00:02:45,604 Speaker 2: It was thirty five days in eighteen twenty nineteen. 43 00:02:45,884 --> 00:02:48,564 Speaker 1: And how did that? How did that play out? Was 44 00:02:48,604 --> 00:02:54,324 Speaker 1: the country experiencing any any really negative consequences. Could we 45 00:02:54,444 --> 00:02:57,444 Speaker 1: feel the shutdown as a nation, because I don't actually 46 00:02:57,484 --> 00:03:00,044 Speaker 1: remember what it felt like, to be perfectly honest. 47 00:03:00,404 --> 00:03:02,724 Speaker 2: Have you noticed any effects of the shutdown so far? 48 00:03:03,004 --> 00:03:06,284 Speaker 1: No, I have not gone. But if I were a 49 00:03:06,284 --> 00:03:09,244 Speaker 1: federal employee, I might write because we know that that if. 50 00:03:09,204 --> 00:03:12,444 Speaker 2: You're a federal employee, then you may get furloughed, you 51 00:03:12,484 --> 00:03:14,964 Speaker 2: may stop getting a paycheck, and if you're not furloaed, 52 00:03:15,204 --> 00:03:18,804 Speaker 2: social Security benefits may be delayed. Right. Staffing at some 53 00:03:18,884 --> 00:03:23,004 Speaker 2: agencies like the National Park Service may be limited mildly 54 00:03:23,204 --> 00:03:27,484 Speaker 2: to severely. You know all you know, you have financial 55 00:03:27,484 --> 00:03:29,764 Speaker 2: sector things about how we're making our treasury payments and 56 00:03:29,804 --> 00:03:32,284 Speaker 2: things like that. Anyway, there's a lot of stuff. It's 57 00:03:32,364 --> 00:03:36,844 Speaker 2: kind of the grab bag. The president has both the 58 00:03:36,964 --> 00:03:41,484 Speaker 2: jury and de facto power to play around with things 59 00:03:41,964 --> 00:03:44,004 Speaker 2: a little bit, right. I Mean, there's some question about 60 00:03:44,004 --> 00:03:47,164 Speaker 2: whether Trump wants to make Democrats feel the pain or 61 00:03:47,204 --> 00:03:50,204 Speaker 2: if rather that would turn around on him. But yeah, 62 00:03:50,324 --> 00:03:53,524 Speaker 2: I mean, let's talk about the reasons why nobody expecting 63 00:03:53,524 --> 00:03:54,844 Speaker 2: a quick resolution. 64 00:03:54,724 --> 00:03:57,244 Speaker 1: Right, Yeah, So there, I mean, there are two main 65 00:03:57,284 --> 00:04:00,124 Speaker 1: things the way that I understand it. Thing number one 66 00:04:00,164 --> 00:04:04,204 Speaker 1: healthcare spending, right and or it cuts to healthcare, and 67 00:04:04,404 --> 00:04:07,524 Speaker 1: the Democrats are standing for them on that this is 68 00:04:07,564 --> 00:04:11,164 Speaker 1: a place where we might get some common ground with Republicans, 69 00:04:11,204 --> 00:04:14,884 Speaker 1: because obviously, if your healthcare premiums are about to get 70 00:04:14,924 --> 00:04:17,964 Speaker 1: twice as expensive, if you're going to lose healthcare coverage, 71 00:04:17,964 --> 00:04:20,404 Speaker 1: et cetera, et cetera, et cetera, you care whether you're 72 00:04:20,524 --> 00:04:23,884 Speaker 1: a Republican or a Democrat. So common ground there potentially. 73 00:04:24,604 --> 00:04:28,124 Speaker 1: And number two, one of the things that the Democrats 74 00:04:28,204 --> 00:04:31,124 Speaker 1: seem to be insisting on is some sort of a 75 00:04:31,204 --> 00:04:38,364 Speaker 1: curb on Donald Trump's ability to limit the appropriations stop 76 00:04:38,404 --> 00:04:44,324 Speaker 1: appropriations that Congress has already appropriated. Sorry, I'm saying appropriations 77 00:04:44,364 --> 00:04:47,404 Speaker 1: way too often that I don't know what other My 78 00:04:47,564 --> 00:04:50,644 Speaker 1: vocabulary in this particular case is limited. So they're trying 79 00:04:50,684 --> 00:04:54,804 Speaker 1: to curb executive power somewhat so that they know that, Okay, 80 00:04:54,844 --> 00:04:57,124 Speaker 1: if we allocate these funds, these funds are actually going 81 00:04:57,164 --> 00:05:00,524 Speaker 1: to be used. We want you know that to be 82 00:05:00,604 --> 00:05:03,364 Speaker 1: the case. We want to reclaim congressional power of the purse. 83 00:05:03,764 --> 00:05:06,204 Speaker 1: Did I kind of summarize it the way that you 84 00:05:06,244 --> 00:05:08,004 Speaker 1: would have Nate or Am I getting it a little 85 00:05:08,004 --> 00:05:08,404 Speaker 1: bit wrong? 86 00:05:08,524 --> 00:05:12,604 Speaker 2: I think you summarize Chuck Schumer spill that, Yeah, look, 87 00:05:13,164 --> 00:05:17,484 Speaker 2: what Democrats with their heart is really into is taking 88 00:05:17,524 --> 00:05:22,764 Speaker 2: a stand against the encroachment of all the authority of Congress, 89 00:05:22,884 --> 00:05:27,804 Speaker 2: really against authoritarianism. Put that in quotes if you want, right. 90 00:05:27,884 --> 00:05:30,124 Speaker 2: But like, if you look at kind of what Democrats 91 00:05:30,204 --> 00:05:33,004 Speaker 2: that are arguing for the shutdown the most really care about, right, 92 00:05:33,404 --> 00:05:35,684 Speaker 2: They're like, yeah, healthcare would be would be nice, right, 93 00:05:37,444 --> 00:05:40,444 Speaker 2: And they are on the right side of public opinion 94 00:05:40,524 --> 00:05:43,764 Speaker 2: on that issue. I mean, these cuts are unpopular or 95 00:05:43,804 --> 00:05:46,244 Speaker 2: failing to extend the subseas, I should say, right to 96 00:05:46,244 --> 00:05:48,844 Speaker 2: the extent that you know, you're almost doing Republicans a 97 00:05:48,884 --> 00:05:53,044 Speaker 2: favor if they agree to amend this. Potentially, Chuck Shumer 98 00:05:53,044 --> 00:05:56,844 Speaker 2: will say, Republicans are refusing to take our input on 99 00:05:56,964 --> 00:06:01,564 Speaker 2: a budget and have this negotiation over healthcare that everyone 100 00:06:01,604 --> 00:06:07,364 Speaker 2: wants to have, right, and therefore the shutdown is their fault. 101 00:06:07,604 --> 00:06:07,764 Speaker 1: Right. 102 00:06:08,004 --> 00:06:12,604 Speaker 2: Whereas like for the past month, everyone's been arguing in 103 00:06:12,604 --> 00:06:15,684 Speaker 2: the New York Times that you know about Democrats shut 104 00:06:15,724 --> 00:06:18,044 Speaker 2: down the government to take a stand against Trump, and 105 00:06:18,044 --> 00:06:20,684 Speaker 2: we're not sure what the stand should be about. And 106 00:06:20,764 --> 00:06:24,404 Speaker 2: Chuck Schumer settled on healthcare eventually, right, But like for 107 00:06:24,484 --> 00:06:27,444 Speaker 2: all types of reasons. I suggested it should be about tariffs. 108 00:06:27,444 --> 00:06:30,484 Speaker 2: I guess anyone else like that idea. Right. Some people 109 00:06:30,564 --> 00:06:32,844 Speaker 2: just think that, hey, we cannot in good conscious vote 110 00:06:32,844 --> 00:06:35,564 Speaker 2: to fund this government. Right. And you've seen some like 111 00:06:35,604 --> 00:06:38,444 Speaker 2: seepe to that message, like Santa Chris Murphy, the santer 112 00:06:38,444 --> 00:06:41,444 Speaker 2: from Connecticut who loves to be an MSNBC. Right, but 113 00:06:41,484 --> 00:06:43,204 Speaker 2: he's saying, yeah, it's not just about health here, let's 114 00:06:43,204 --> 00:06:45,644 Speaker 2: be honest. Right. A lot of the Democratic influencers what 115 00:06:45,644 --> 00:06:47,604 Speaker 2: do you call the murdy media personnelity, are kind of 116 00:06:47,644 --> 00:06:50,204 Speaker 2: saying the same thing and so like, So it's it's 117 00:06:50,244 --> 00:06:54,804 Speaker 2: a it's a hard tight rope, yeah, to walk right, 118 00:06:55,164 --> 00:06:55,604 Speaker 2: it's kind of. 119 00:06:55,604 --> 00:06:57,884 Speaker 1: A damned if you do, damned if you don't situation 120 00:06:58,004 --> 00:07:00,724 Speaker 1: if you think about it. You know, since you referenced 121 00:07:01,564 --> 00:07:04,364 Speaker 1: Chuck Schumer, because you remember, last time we talked about 122 00:07:04,404 --> 00:07:07,244 Speaker 1: the shutdown, it was averted because Schumer decided that it 123 00:07:07,284 --> 00:07:09,964 Speaker 1: was time to you know, compromise a vertist shut down, 124 00:07:10,244 --> 00:07:13,324 Speaker 1: and he got pillaraied for it. His approval ratings were horrific. 125 00:07:13,364 --> 00:07:15,684 Speaker 1: People are like, oh, we can't believe you caved. Now, 126 00:07:15,804 --> 00:07:18,244 Speaker 1: you know, he's like, okay, fine, we're going to stand strong, 127 00:07:18,284 --> 00:07:21,564 Speaker 1: we'll shut down the government, and his approval ratings still suck, right. 128 00:07:21,644 --> 00:07:23,844 Speaker 1: He now he's getting blamed for the fact that the 129 00:07:23,884 --> 00:07:27,124 Speaker 1: government shut down. So it's it is, you know, as 130 00:07:27,164 --> 00:07:30,884 Speaker 1: you say, it's a tight it's a hard tightrope, tight 131 00:07:30,924 --> 00:07:33,564 Speaker 1: tight rope. It's a hard type rope to walk, and 132 00:07:33,804 --> 00:07:38,284 Speaker 1: there seems to be little way to walk it short 133 00:07:38,444 --> 00:07:42,164 Speaker 1: of some of sides that are willing to compromise, and 134 00:07:42,204 --> 00:07:45,404 Speaker 1: that doesn't seem likely in the in the immediate term. 135 00:07:45,764 --> 00:07:47,324 Speaker 2: No, I mean, first of all, there's some degree of 136 00:07:47,364 --> 00:07:49,524 Speaker 2: like posturing it. First. I think it was very predictable 137 00:07:49,524 --> 00:07:51,804 Speaker 2: that you'd have at least this amount of shutdown, right, 138 00:07:53,124 --> 00:07:58,964 Speaker 2: you know, poles show that more the public blames Republicans 139 00:07:58,964 --> 00:08:02,324 Speaker 2: and Democrats right now, right, but like, I'm not sure 140 00:08:02,324 --> 00:08:04,724 Speaker 2: that really tells us who's winning the shutdown. Sure, I mean, 141 00:08:04,764 --> 00:08:06,764 Speaker 2: we'll just kind of assume as a default that Republicans 142 00:08:06,804 --> 00:08:10,444 Speaker 2: are more obstructionists, I think think, right, and so like 143 00:08:10,484 --> 00:08:13,324 Speaker 2: Democrats will kind of the they kind of buy about it, 144 00:08:13,364 --> 00:08:15,484 Speaker 2: and they say, oh, we are you know, our hand 145 00:08:15,564 --> 00:08:17,924 Speaker 2: is forced right where it's like, you know, people can 146 00:08:17,964 --> 00:08:19,684 Speaker 2: read the New York Times where you argue about this 147 00:08:19,804 --> 00:08:23,884 Speaker 2: and like your ham was not forced. Now what you 148 00:08:23,924 --> 00:08:26,164 Speaker 2: could say is like, look, you know what, you got 149 00:08:26,204 --> 00:08:28,884 Speaker 2: your majorities in Congress. The majority can change the rules 150 00:08:28,924 --> 00:08:31,324 Speaker 2: that it needs to write. We have so many problems 151 00:08:31,364 --> 00:08:34,644 Speaker 2: with a the budget and b everything that Donald Trump 152 00:08:34,684 --> 00:08:37,244 Speaker 2: is doing that we're not going to fucking support anything 153 00:08:37,244 --> 00:08:40,124 Speaker 2: that Donald Trump does. It requires affirmative Democratic votes, right, 154 00:08:40,284 --> 00:08:42,804 Speaker 2: so go ahead and break the filibuster. You're insincere about 155 00:08:42,804 --> 00:08:45,204 Speaker 2: it anyway, Right, they already keep pairing away at it 156 00:08:45,564 --> 00:08:48,084 Speaker 2: as Democrats have two in some cases. Right, So you 157 00:08:48,124 --> 00:08:50,364 Speaker 2: have the votes, pass your budget, and we'll see you 158 00:08:50,364 --> 00:08:53,004 Speaker 2: at the midterms. But instead there's this like spin that 159 00:08:53,084 --> 00:08:55,804 Speaker 2: like I think is designed to like like designed to 160 00:08:55,844 --> 00:08:58,404 Speaker 2: win this pole question, like the first Washington Post pole 161 00:08:58,444 --> 00:09:01,764 Speaker 2: that comes out says, Okay, who is more blame for 162 00:09:01,804 --> 00:09:03,564 Speaker 2: the shutdown? And just that one question because the other 163 00:09:03,644 --> 00:09:07,044 Speaker 2: questions aren't necessarily so good for Democrats or Republicans, then 164 00:09:07,084 --> 00:09:09,764 Speaker 2: maybe you win that. But like you know, when you're 165 00:09:09,804 --> 00:09:13,284 Speaker 2: in a repeated game to ground this in the show 166 00:09:13,524 --> 00:09:17,644 Speaker 2: a little bit, then credibility matters. I don't mean so 167 00:09:17,724 --> 00:09:21,084 Speaker 2: much with Republicans, right, there's not very much trust there anyway. 168 00:09:21,084 --> 00:09:23,684 Speaker 2: But I mean with I mean with voters, yeah, a 169 00:09:23,724 --> 00:09:27,044 Speaker 2: little bit, right, Whereas if you had owned the shutdown, 170 00:09:27,044 --> 00:09:29,244 Speaker 2: it might have been more unpopular at first. Now look, 171 00:09:29,404 --> 00:09:32,204 Speaker 2: part of it is at Schumer's job is probably a 172 00:09:32,244 --> 00:09:36,764 Speaker 2: fair bit of risk. In fact, a plurality of Democrats 173 00:09:36,804 --> 00:09:39,964 Speaker 2: in a new pupil disapprove of Chuck Schumer have an 174 00:09:40,004 --> 00:09:43,444 Speaker 2: unfavorable view of him. Not a majority, but plurality. You know, 175 00:09:43,484 --> 00:09:47,084 Speaker 2: he is also very much threatened by AOC or other 176 00:09:47,124 --> 00:09:49,284 Speaker 2: primary candidates in twenty twenty eight when he runs in 177 00:09:49,324 --> 00:09:53,284 Speaker 2: I guess three years from now. He's also old as fuck. 178 00:09:53,324 --> 00:09:54,844 Speaker 2: I mean, he's not old as fuck, He's just old, 179 00:09:55,244 --> 00:09:57,084 Speaker 2: not as fuck, just old Nate. 180 00:09:57,124 --> 00:09:59,004 Speaker 1: We have other we have other people for whom we 181 00:09:59,044 --> 00:10:00,164 Speaker 1: should reserve the a s. 182 00:10:00,204 --> 00:10:04,124 Speaker 2: Fuck. Yeah, he's just old. He's just old, not old 183 00:10:04,164 --> 00:10:09,124 Speaker 2: af But like so, he probably wants to like give 184 00:10:09,244 --> 00:10:12,404 Speaker 2: the annoying did I say annoying? He probably wants to give, 185 00:10:12,444 --> 00:10:18,124 Speaker 2: like the partisan Democrats a win. Right that we have 186 00:10:18,204 --> 00:10:20,724 Speaker 2: flexed our power. This keeps the base happy. We've gotten 187 00:10:20,804 --> 00:10:25,244 Speaker 2: some some token or maybe not so token concessions on 188 00:10:25,284 --> 00:10:28,484 Speaker 2: healthcare and things like that. Right, this shows it when 189 00:10:28,484 --> 00:10:31,924 Speaker 2: we stand and fight that we win right, yeah, big 190 00:10:32,924 --> 00:10:35,964 Speaker 2: morale boost. Does it provide much more than morale? I 191 00:10:35,964 --> 00:10:38,324 Speaker 2: don't know. Does it help you win the midterms? I 192 00:10:38,324 --> 00:10:40,764 Speaker 2: don't know. Right, the people that care about this are 193 00:10:40,804 --> 00:10:43,484 Speaker 2: people are gonna vote anyway for the most part. But like, 194 00:10:43,764 --> 00:10:47,284 Speaker 2: but if Schumer blows it, right, if he caves too soon, 195 00:10:48,404 --> 00:10:51,604 Speaker 2: then he's gonna look like he's weak, and then he 196 00:10:51,684 --> 00:10:53,724 Speaker 2: might be out of power. Right. If it goes on 197 00:10:53,804 --> 00:10:57,204 Speaker 2: too long, then A everybody kind of gets more egg 198 00:10:57,204 --> 00:10:59,244 Speaker 2: on their faces. B he loses control of the mestry 199 00:10:59,284 --> 00:11:01,284 Speaker 2: doesn't really have to begin with. And so like, he 200 00:11:01,284 --> 00:11:05,764 Speaker 2: would love to have that that you know early, that 201 00:11:05,964 --> 00:11:09,644 Speaker 2: October tenth to fourteenth window that probably market says one 202 00:11:09,684 --> 00:11:11,764 Speaker 2: and four chance, he would love He would love for 203 00:11:11,804 --> 00:11:14,484 Speaker 2: that window to happen. Right, the more it goes on, 204 00:11:14,564 --> 00:11:17,204 Speaker 2: and the more you already have influential Democrats saying, you know, 205 00:11:17,484 --> 00:11:19,964 Speaker 2: it shouldn't really be about healthcare, come on, right, then 206 00:11:21,004 --> 00:11:26,404 Speaker 2: it becomes this big uncertain morass. And look, you know 207 00:11:26,844 --> 00:11:30,284 Speaker 2: you already have some swing state Democrats who are worried 208 00:11:30,524 --> 00:11:34,564 Speaker 2: about this. Right, John Fetterman is not on board with 209 00:11:34,604 --> 00:11:38,804 Speaker 2: a shutdown center from Wyoming. She's not on board with it. 210 00:11:38,844 --> 00:11:41,804 Speaker 2: Angus King from Maine. Jared Golden is a representative from 211 00:11:41,844 --> 00:11:45,324 Speaker 2: main very in the Trumpy District, Independent minded, right, So, like, 212 00:11:46,124 --> 00:11:50,364 Speaker 2: I don't think Democrats feel like this is gonna be 213 00:11:50,404 --> 00:11:54,604 Speaker 2: a big win for them, right, but like, but you 214 00:11:54,644 --> 00:11:58,244 Speaker 2: know whatever, I mean whatever, have fun, you know, have fun. 215 00:11:58,364 --> 00:12:00,084 Speaker 2: It's kind of it's kind of actually not that high 216 00:12:00,124 --> 00:12:02,004 Speaker 2: stakes compared to a lot of things are going on, 217 00:12:02,084 --> 00:12:03,444 Speaker 2: which is kind of to me why it feels like 218 00:12:03,444 --> 00:12:07,524 Speaker 2: a little bit discordant. But they have to make it, 219 00:12:07,524 --> 00:12:09,564 Speaker 2: they have to make a move, right, and they like, 220 00:12:09,604 --> 00:12:11,204 Speaker 2: they couldn't do nothing or Sumer might lose his job 221 00:12:11,204 --> 00:12:13,004 Speaker 2: if he did nothing right, and they don't really have 222 00:12:13,044 --> 00:12:15,684 Speaker 2: a great hand to play. But Republicans aren't popular either, 223 00:12:15,764 --> 00:12:19,764 Speaker 2: and they're insincere in their own ways, believe me. And 224 00:12:19,804 --> 00:12:24,204 Speaker 2: what Democrats are asking for is fundamentally pretty reasonable. I think, 225 00:12:24,684 --> 00:12:26,964 Speaker 2: uh yeah, so yeah, so there we are. 226 00:12:27,444 --> 00:12:29,324 Speaker 1: Yeah, No, I think I think this is that was 227 00:12:29,364 --> 00:12:32,084 Speaker 1: a good summary. And one other thing that I will 228 00:12:32,124 --> 00:12:35,844 Speaker 1: say is, you know, note the language that you've been using, 229 00:12:35,884 --> 00:12:38,684 Speaker 1: which is also the language that they're using, which is, oh, 230 00:12:38,724 --> 00:12:40,644 Speaker 1: we need to win. You know, we're fighting a war, 231 00:12:40,684 --> 00:12:42,924 Speaker 1: et cetera. So if we couch this back in game 232 00:12:42,964 --> 00:12:46,564 Speaker 1: theoretic terms. You know, they are making this into more, 233 00:12:47,164 --> 00:12:49,364 Speaker 1: you know, of a zero sum contest than it has 234 00:12:49,444 --> 00:12:52,324 Speaker 1: to be. Right, it's like a US versus them, like 235 00:12:52,404 --> 00:12:55,364 Speaker 1: we we need to win, as opposed to Okay, let's 236 00:12:55,484 --> 00:12:59,604 Speaker 1: let's figure out this is just positive some like let's compromise, 237 00:12:59,804 --> 00:13:03,364 Speaker 1: let's let's do what's good for the country. That language 238 00:13:03,364 --> 00:13:06,404 Speaker 1: has largely disappeared right now, even though at the end 239 00:13:06,404 --> 00:13:08,484 Speaker 1: what we will need is some sort of a compromise. 240 00:13:08,524 --> 00:13:10,804 Speaker 1: And as you point out, Nate, some of the things 241 00:13:10,844 --> 00:13:13,844 Speaker 1: like healthcare, those are going to help Republicans as well 242 00:13:14,524 --> 00:13:19,684 Speaker 1: in the midterm elections. If Democrats actually can get that changed, 243 00:13:20,364 --> 00:13:22,324 Speaker 1: it'll help the general public, but it will also help 244 00:13:22,404 --> 00:13:24,884 Speaker 1: Republicans because a lot of people who will otherwise be 245 00:13:24,964 --> 00:13:28,324 Speaker 1: incredibly pissed off will no longer be pissed off. And probably, 246 00:13:28,364 --> 00:13:30,604 Speaker 1: as you say, you know, they're the people who already 247 00:13:30,604 --> 00:13:32,844 Speaker 1: pay attention to this, and then those who don't. I'm 248 00:13:32,884 --> 00:13:34,564 Speaker 1: guessing that a lot of those people who would have 249 00:13:34,644 --> 00:13:40,204 Speaker 1: lost health care coverage if we if nothing was done right, 250 00:13:40,444 --> 00:13:43,604 Speaker 1: won't even realize that this happened, right. They won't even 251 00:13:43,684 --> 00:13:45,884 Speaker 1: realize that there was a change that there was a 252 00:13:45,924 --> 00:13:48,484 Speaker 1: compromise that Democrats had anything to do with it. They'll 253 00:13:48,524 --> 00:13:50,324 Speaker 1: be like, yeah, see, we knew that Trump would never 254 00:13:50,364 --> 00:13:52,724 Speaker 1: take away our health care because if you're not paying 255 00:13:52,724 --> 00:13:57,004 Speaker 1: attention to those kinds of behind the scenes machinations, then 256 00:13:57,044 --> 00:13:59,324 Speaker 1: you might not even you might not even realize that's 257 00:13:59,324 --> 00:14:03,284 Speaker 1: the case. So that'll be a win for Republicans as well, 258 00:14:03,284 --> 00:14:05,604 Speaker 1: as you said. And so I think that it's just 259 00:14:05,964 --> 00:14:09,844 Speaker 1: very interesting to see how, you know, politics has always 260 00:14:09,844 --> 00:14:12,524 Speaker 1: been a fight to some extent, but the extent to 261 00:14:12,564 --> 00:14:15,924 Speaker 1: which the language has become kind of zero sum radicalized 262 00:14:15,964 --> 00:14:18,804 Speaker 1: as opposed to a more you know, we all serve 263 00:14:18,884 --> 00:14:21,964 Speaker 1: the people type of rhetoric, which would be I think 264 00:14:22,004 --> 00:14:24,884 Speaker 1: a little bit more appropriate here, but clearly not the 265 00:14:24,924 --> 00:14:26,844 Speaker 1: stage at which we find ourselves. 266 00:14:27,084 --> 00:14:30,004 Speaker 2: Yeah, so it's not zero someone if you're an incumbent, right, 267 00:14:30,044 --> 00:14:32,484 Speaker 2: because one sure pause block cooming is that people get 268 00:14:32,524 --> 00:14:36,244 Speaker 2: really mad at incumbents in general. And so, you know, unfortunately, 269 00:14:36,844 --> 00:14:38,484 Speaker 2: given the type of incumbents we have, you know, we're 270 00:14:38,484 --> 00:14:41,964 Speaker 2: represented in Congress by this current generation of representatives, and 271 00:14:42,884 --> 00:14:45,404 Speaker 2: if there's a pack book on other houses, then they 272 00:14:45,524 --> 00:14:48,484 Speaker 2: might lose. Where the split between the parties goes right. Yeah, 273 00:14:49,084 --> 00:14:52,164 Speaker 2: but yeah, you know, Poland shows pretty clearly that Democrats 274 00:14:53,124 --> 00:14:56,884 Speaker 2: no longer want compromise with Republicans, right, They want they 275 00:14:56,964 --> 00:15:00,524 Speaker 2: want to fight. You know, I as always am contrarian 276 00:15:00,604 --> 00:15:02,404 Speaker 2: because I thought that the spring was a better time 277 00:15:02,484 --> 00:15:05,724 Speaker 2: to shut down than now. You had Elon Musk as 278 00:15:05,764 --> 00:15:10,084 Speaker 2: a good, good scapegoat. I think I'm more salient scapegoat 279 00:15:10,084 --> 00:15:15,004 Speaker 2: slash you know, relevant excuse because he was fucking with 280 00:15:15,044 --> 00:15:16,924 Speaker 2: the budget, right, and I think they were a little 281 00:15:16,924 --> 00:15:19,404 Speaker 2: slow to move then and now it's like, I mean, 282 00:15:19,884 --> 00:15:23,604 Speaker 2: the fact is that when you're out of shut out 283 00:15:23,684 --> 00:15:28,084 Speaker 2: of Congress, shut out of of the Supreme Court, basically right, 284 00:15:28,604 --> 00:15:32,964 Speaker 2: you're shout out of the presidency. You're even losing control 285 00:15:33,244 --> 00:15:38,604 Speaker 2: over blue academic media institutions that you once controlled. I mean, 286 00:15:38,644 --> 00:15:43,124 Speaker 2: you know, Democrats are are number one very angry, right, 287 00:15:43,324 --> 00:15:47,364 Speaker 2: and number two feel like they need something, anything that 288 00:15:47,404 --> 00:15:48,444 Speaker 2: they can call a win. Yep. 289 00:15:48,804 --> 00:15:51,844 Speaker 1: Yeah, I think that the optics are incredibly important. So yeah, 290 00:15:51,884 --> 00:15:55,204 Speaker 1: let's see what happens. Let's see if Chuck Schumer can 291 00:15:55,284 --> 00:15:59,404 Speaker 1: hit his magic window or not. Holymarket odds are against it, 292 00:15:58,924 --> 00:16:02,884 Speaker 1: but we shall see. On that note, let's take a 293 00:16:02,924 --> 00:16:05,444 Speaker 1: quick break and when we come back, we're going to 294 00:16:05,444 --> 00:16:10,284 Speaker 1: talk about the new open AI release sore or sorry, Nate, 295 00:16:10,484 --> 00:16:21,924 Speaker 1: Sora too. So last week, last Friday, October third, open 296 00:16:22,044 --> 00:16:26,044 Speaker 1: a I released a new app. And even if you're 297 00:16:26,284 --> 00:16:30,444 Speaker 1: you know, not terminally online, unless you're living under a 298 00:16:30,564 --> 00:16:32,924 Speaker 1: rock and you have some form of social media, you 299 00:16:32,964 --> 00:16:35,764 Speaker 1: were probably aware that Soora was released, because I don't 300 00:16:35,764 --> 00:16:38,684 Speaker 1: know about your news feed, Nate, but my newsfeed briefly 301 00:16:38,724 --> 00:16:43,484 Speaker 1: became just like completely Sora with the announcement video, all 302 00:16:43,524 --> 00:16:47,564 Speaker 1: these other videos, just people were just going crazy. So 303 00:16:47,764 --> 00:16:50,484 Speaker 1: for a good twenty four hours, Sora was my newsfeed. 304 00:16:50,764 --> 00:16:54,204 Speaker 1: And this is kind of the next gen video generation 305 00:16:54,404 --> 00:16:58,804 Speaker 1: app that they've been working on. And yeah, it's as 306 00:16:58,844 --> 00:17:02,484 Speaker 1: of now invite only, but man, it's as of now 307 00:17:02,524 --> 00:17:05,844 Speaker 1: still free, even though there are you know, already murmurs 308 00:17:05,844 --> 00:17:08,124 Speaker 1: that it's not going to be for for much longer, 309 00:17:08,164 --> 00:17:12,324 Speaker 1: because Sam Altman said that he wasn't he wasn't prepared 310 00:17:12,364 --> 00:17:15,004 Speaker 1: for how many people would be making those those videos, 311 00:17:15,244 --> 00:17:19,164 Speaker 1: which I've seemed very strange, Like, of course, of course 312 00:17:19,204 --> 00:17:22,164 Speaker 1: you're you should be prepared for the volume that it's 313 00:17:22,204 --> 00:17:24,764 Speaker 1: going to generate at this stage, right, you seem to 314 00:17:24,764 --> 00:17:27,044 Speaker 1: be unprepared every single time you release something new. 315 00:17:27,124 --> 00:17:31,044 Speaker 2: Well, that can be a classic tactics in politics too. Absolutely, 316 00:17:31,084 --> 00:17:33,284 Speaker 2: Oh my god, we have an overflow capacity here at 317 00:17:33,324 --> 00:17:35,724 Speaker 2: this tiny venue that you should pick a bigger venue. Right. Yeah, 318 00:17:35,724 --> 00:17:39,084 Speaker 2: it looks good on TV. Yeah, it generates buzz right yep. 319 00:17:39,884 --> 00:17:43,604 Speaker 1: So yeah, let's let's talk about soa. Let's talk about 320 00:17:43,964 --> 00:17:47,964 Speaker 1: you know, the good, the bad, the ugly. But first, 321 00:17:48,564 --> 00:17:51,364 Speaker 1: just a brief explanation. So, Nate, have you used Sora 322 00:17:51,484 --> 00:17:52,164 Speaker 1: yourself yet? 323 00:17:52,884 --> 00:17:54,484 Speaker 2: I am not. I don't know why I was going 324 00:17:54,564 --> 00:17:56,524 Speaker 2: to use it in preparation for this program. I don't 325 00:17:56,564 --> 00:18:00,244 Speaker 2: have any What would I do with it? Yeah? 326 00:18:00,284 --> 00:18:01,564 Speaker 1: I feel the exactly. 327 00:18:02,164 --> 00:18:03,844 Speaker 2: I mean, it seems it's like, oh, you can generate 328 00:18:03,844 --> 00:18:05,324 Speaker 2: in the image, and it's like, okay, I have a 329 00:18:05,364 --> 00:18:07,244 Speaker 2: good imagination, you know what I mean? Yeah, Like what 330 00:18:08,084 --> 00:18:09,924 Speaker 2: I do with it? Do I feel like the old person? 331 00:18:10,084 --> 00:18:12,324 Speaker 1: I mean no, I mean I actually feel I feel 332 00:18:12,364 --> 00:18:14,964 Speaker 1: the same way, which is that it's using a whole 333 00:18:15,004 --> 00:18:17,044 Speaker 1: lot of capacity for something that I don't know how 334 00:18:17,124 --> 00:18:19,244 Speaker 1: much of this we need. But the idea is that 335 00:18:19,284 --> 00:18:22,404 Speaker 1: you give it a text prompt and then it creates 336 00:18:22,884 --> 00:18:25,844 Speaker 1: a video from it. And the video is when I 337 00:18:25,844 --> 00:18:28,204 Speaker 1: said next, jen A, I really is. It does look 338 00:18:28,204 --> 00:18:32,164 Speaker 1: a lot better than AI generated videos did before. You 339 00:18:32,364 --> 00:18:37,204 Speaker 1: can even upload your own photograph. I would actually urge 340 00:18:37,244 --> 00:18:39,724 Speaker 1: you not to do that because this is more training 341 00:18:39,804 --> 00:18:43,364 Speaker 1: material for the apps and you kind of lose a 342 00:18:43,364 --> 00:18:45,444 Speaker 1: lot of control over it. Anyway, we can, we can 343 00:18:45,484 --> 00:18:48,884 Speaker 1: talk about that. But so you can upload your own image, 344 00:18:48,924 --> 00:18:52,404 Speaker 1: you can use historical figures. People have been making videos 345 00:18:52,764 --> 00:18:57,524 Speaker 1: of MLK. The most famous one that has been circulated 346 00:18:57,684 --> 00:19:03,724 Speaker 1: is sam Altman shoplifting from a target. So yeah, you can. 347 00:19:04,044 --> 00:19:06,684 Speaker 1: You can give it lots of prompts and it spits 348 00:19:06,724 --> 00:19:09,364 Speaker 1: out the output and as of now it is water 349 00:19:09,524 --> 00:19:13,604 Speaker 1: are marked, but the watermarks are already being removed. They're 350 00:19:13,644 --> 00:19:16,644 Speaker 1: not very sticky, it turns out. But yeah, so it's 351 00:19:16,684 --> 00:19:21,244 Speaker 1: this new frontier of video generation. And even though it 352 00:19:21,284 --> 00:19:24,964 Speaker 1: says that they have guardrails about usage, you know, consent, 353 00:19:25,804 --> 00:19:31,604 Speaker 1: celebrity usage, political violence, people have already demonstrated that Zura 354 00:19:31,764 --> 00:19:39,764 Speaker 1: is capable of creating incredibly realistic images of things like protests, 355 00:19:40,604 --> 00:19:46,804 Speaker 1: violence in the streets, you know, things that border closely 356 00:19:46,844 --> 00:19:50,124 Speaker 1: on political violence, which it says it doesn't allow, and 357 00:19:50,284 --> 00:19:56,124 Speaker 1: some experts in video AI generation and detection. So there 358 00:19:56,164 --> 00:20:00,604 Speaker 1: are people who have companies that actually are supposed to, 359 00:20:00,764 --> 00:20:03,764 Speaker 1: you know, be very very very accurate at figuring out 360 00:20:03,804 --> 00:20:06,804 Speaker 1: is a SAI generated or not? Are saying, hey, this 361 00:20:06,924 --> 00:20:09,244 Speaker 1: is really tough, right, Like, some of these are actually 362 00:20:09,604 --> 00:20:14,564 Speaker 1: quite good, and it's becoming very difficult to figure out 363 00:20:15,204 --> 00:20:17,684 Speaker 1: whether or not this is real or not, which is 364 00:20:17,724 --> 00:20:22,284 Speaker 1: scary because once we stop having that capacity, what happens 365 00:20:22,324 --> 00:20:25,524 Speaker 1: with video evidence, right, what happens with deep fakes, what 366 00:20:25,724 --> 00:20:29,204 Speaker 1: happens with kind of the ability to tell what is 367 00:20:29,324 --> 00:20:33,164 Speaker 1: real from what isn't real. We're already We're at the doorstep. 368 00:20:33,204 --> 00:20:36,004 Speaker 1: We're almost there, and I think Sarah will probably take 369 00:20:36,084 --> 00:20:37,244 Speaker 1: us over the doorstep. 370 00:20:38,284 --> 00:20:41,004 Speaker 2: Yeah. Look, you can have a watermark, or you can 371 00:20:41,044 --> 00:20:44,724 Speaker 2: have some type of digital signature embedded, right, or you 372 00:20:44,724 --> 00:20:47,524 Speaker 2: can just say, okay, there are artifacts that suggest that 373 00:20:47,524 --> 00:20:51,444 Speaker 2: this is artificial, right. You know, I suspect we're probably 374 00:20:52,444 --> 00:20:55,004 Speaker 2: a few years away from because there's more going on 375 00:20:55,124 --> 00:20:57,404 Speaker 2: with vision than text, you know what I mean? You know, 376 00:20:57,764 --> 00:21:00,204 Speaker 2: these AI detectors for like college essays I think are 377 00:21:00,244 --> 00:21:04,564 Speaker 2: not very reliable, but like visual artifacts are like harder. 378 00:21:04,644 --> 00:21:10,044 Speaker 2: I believe to entirely eradicate potentially, right, you know, just 379 00:21:10,244 --> 00:21:12,084 Speaker 2: to be able to manipulate photos for a long time. 380 00:21:12,124 --> 00:21:14,724 Speaker 2: I'm not sure it's changed things all all that much. 381 00:21:14,964 --> 00:21:17,564 Speaker 2: I don't know. I mean everyone, like people are always 382 00:21:17,604 --> 00:21:23,804 Speaker 2: like misinformation, this misinformation that, right, Like I think it's important, 383 00:21:24,244 --> 00:21:27,564 Speaker 2: but I think that's something people will adapt to. And 384 00:21:27,604 --> 00:21:29,604 Speaker 2: I don't know. I mean, I'm more interested in, like 385 00:21:30,284 --> 00:21:34,364 Speaker 2: what this tells us about, like what open AI wants 386 00:21:34,404 --> 00:21:36,804 Speaker 2: to be, right, and how will AI become a part 387 00:21:37,084 --> 00:21:39,844 Speaker 2: of our life. I suppose you know, this is not 388 00:21:41,284 --> 00:21:44,924 Speaker 2: what you would call a particularly high brow product, you 389 00:21:44,964 --> 00:21:45,484 Speaker 2: know what I mean? 390 00:21:45,684 --> 00:21:46,964 Speaker 1: No, it is not. 391 00:21:48,964 --> 00:21:51,644 Speaker 2: It's refined and interesting and I'm sure you'll see interesting 392 00:21:51,804 --> 00:21:54,244 Speaker 2: art produced with it, and you know, I say that 393 00:21:54,324 --> 00:21:57,164 Speaker 2: on ironically and things like that, but like this is 394 00:21:57,444 --> 00:22:03,924 Speaker 2: kind of a facebooky product, not to not to demean 395 00:22:04,164 --> 00:22:09,044 Speaker 2: our friends at meta, right, but like this is consumer focused, 396 00:22:09,884 --> 00:22:15,004 Speaker 2: middle brow, right. They seem not to be terribly concerned 397 00:22:15,004 --> 00:22:20,324 Speaker 2: about like you know, the use of copyrighted characters and 398 00:22:20,604 --> 00:22:22,964 Speaker 2: whether that falls under fair use is a legal debate, right, 399 00:22:24,284 --> 00:22:26,684 Speaker 2: But like you know, I think if they were trying 400 00:22:26,724 --> 00:22:28,484 Speaker 2: to go for a more intellectual crowd, they might have 401 00:22:28,884 --> 00:22:31,724 Speaker 2: draw more guardbrails over that. But like to me, it 402 00:22:31,804 --> 00:22:37,164 Speaker 2: seems like open ai is is turning into a more 403 00:22:37,244 --> 00:22:43,084 Speaker 2: legibly normal consumer product technology company, you know what I mean. 404 00:22:43,364 --> 00:22:46,644 Speaker 2: Now you can say they're doing that as an interim phase, right, 405 00:22:47,004 --> 00:22:51,684 Speaker 2: in order to then raise enough money to take over 406 00:22:51,724 --> 00:22:54,724 Speaker 2: the world with AI, right, or build AGI or a 407 00:22:55,004 --> 00:23:00,684 Speaker 2: SI artificial superintelligence. But like, this is not quite the 408 00:23:00,804 --> 00:23:07,164 Speaker 2: AI timeline that maybe the p Doomers feared exactly, right. 409 00:23:07,244 --> 00:23:14,444 Speaker 2: You know, I compare this with the somewhat underwhelming release 410 00:23:14,484 --> 00:23:16,804 Speaker 2: of GPT five, right, And we did an episode on 411 00:23:16,844 --> 00:23:19,244 Speaker 2: GPT five. What was it like a month or so ago, right, 412 00:23:19,564 --> 00:23:26,284 Speaker 2: Like I've remained a little bit underwhelmed by it, I 413 00:23:26,324 --> 00:23:28,724 Speaker 2: would say, right, you know, look, I pay two dollars 414 00:23:28,724 --> 00:23:31,724 Speaker 2: a month in the promode, use the pro mode, and 415 00:23:30,764 --> 00:23:35,204 Speaker 2: and it's better, right, but like you know, it still 416 00:23:35,404 --> 00:23:38,604 Speaker 2: feels very limited in capabilities, and it still feels like 417 00:23:38,644 --> 00:23:40,364 Speaker 2: I gave to tell you this to the last show. 418 00:23:40,684 --> 00:23:42,484 Speaker 2: So my friend has like I think she's like eight 419 00:23:42,564 --> 00:23:45,844 Speaker 2: years old or in second grade, right, and you know 420 00:23:45,924 --> 00:23:48,684 Speaker 2: I had chet ChiPT so there's this, you know, the 421 00:23:48,724 --> 00:23:51,964 Speaker 2: machine that can like solve math Olympia problems. Right, So 422 00:23:52,044 --> 00:23:56,884 Speaker 2: she had a Snoopy coloring book right where it's like 423 00:23:56,964 --> 00:23:59,764 Speaker 2: you have to like fill in you know, little word 424 00:23:59,804 --> 00:24:03,124 Speaker 2: puzzles that are very simple, unscramble of words and like 425 00:24:03,604 --> 00:24:06,684 Speaker 2: you know Snoopy and the people are in Charlie Brown, 426 00:24:06,764 --> 00:24:08,964 Speaker 2: Lucy whatever lineus, they're in a spaceship. You have to 427 00:24:08,964 --> 00:24:11,124 Speaker 2: like connect the cord with the person in the in 428 00:24:11,124 --> 00:24:14,804 Speaker 2: the space soup. It's designed for eight year olds. Every 429 00:24:14,844 --> 00:24:20,644 Speaker 2: single problem chat ChiPT totally fucked up. It like cheated, right, 430 00:24:20,684 --> 00:24:25,684 Speaker 2: that's amazing. Yeah, yeah, you wh when you had Snoopy, 431 00:24:25,764 --> 00:24:27,404 Speaker 2: you had you know, you're looking forward and you have 432 00:24:27,764 --> 00:24:29,564 Speaker 2: the left half is filled out with an image of 433 00:24:29,644 --> 00:24:34,244 Speaker 2: Snoopy in the profile facing the child who's filling out 434 00:24:34,284 --> 00:24:37,044 Speaker 2: this cartoon, right, and the right half is blank. So 435 00:24:37,044 --> 00:24:39,444 Speaker 2: it is we're just teaching you to mirror Snoopy on 436 00:24:39,484 --> 00:24:41,884 Speaker 2: both sides, right. So I'm like, oh, I'm not going 437 00:24:41,964 --> 00:24:44,124 Speaker 2: to even give this to chat cheepy Tea. It's too easy, right, 438 00:24:44,644 --> 00:24:49,284 Speaker 2: So I photograph Snoopy and give it the puzzle, and 439 00:24:49,764 --> 00:24:54,444 Speaker 2: instead it turns Snoopy around ninety degrees. So Snoopy is 440 00:24:54,444 --> 00:24:56,484 Speaker 2: now not excuse me, he was facing you. Now he's 441 00:24:56,484 --> 00:24:59,364 Speaker 2: in profile, right, you see his snout is facing right, 442 00:24:59,404 --> 00:25:02,324 Speaker 2: so like it totally it totally cheated and like it 443 00:25:02,444 --> 00:25:04,484 Speaker 2: gas lights you. Right, It's like you know you have 444 00:25:04,564 --> 00:25:06,084 Speaker 2: like that, you know you have to think of you like, 445 00:25:06,124 --> 00:25:09,044 Speaker 2: find the things that are different in the image, right, 446 00:25:09,484 --> 00:25:12,884 Speaker 2: will like make up things that are different in its 447 00:25:12,964 --> 00:25:15,284 Speaker 2: version of the image, but weren't different in the original image. 448 00:25:15,284 --> 00:25:17,684 Speaker 2: It just gaslights you. You know. By the way, I 449 00:25:17,764 --> 00:25:21,284 Speaker 2: don't think GPT five is like a total miss or 450 00:25:21,604 --> 00:25:23,844 Speaker 2: or anything. Right, We're getting toward the end of the year. 451 00:25:25,124 --> 00:25:27,764 Speaker 2: Some people I think predicted bigger breakthroughs this year in 452 00:25:27,804 --> 00:25:30,044 Speaker 2: terms of agentic capabilities, for example, what they'd be able 453 00:25:30,084 --> 00:25:32,124 Speaker 2: to do in terms of you know, computer usage and 454 00:25:32,164 --> 00:25:34,604 Speaker 2: stuff like that, and like, you know, this has been 455 00:25:35,884 --> 00:25:38,124 Speaker 2: as far as like core AI, you know, we have 456 00:25:38,204 --> 00:25:41,524 Speaker 2: not been We've been it's been getting better, it's been improving. Right. 457 00:25:42,324 --> 00:25:43,804 Speaker 2: This is the year where it's felt like we're not 458 00:25:44,004 --> 00:25:48,164 Speaker 2: on an exponential curve, right, It's felt more linear now 459 00:25:48,164 --> 00:25:50,764 Speaker 2: that could be some type of punctuated equilibrium where there 460 00:25:50,804 --> 00:25:54,324 Speaker 2: needs to be some some new technology, you know. I 461 00:25:54,364 --> 00:25:58,364 Speaker 2: think people mostly feel that like maybe merely adding more 462 00:25:58,604 --> 00:26:02,364 Speaker 2: compute will produced diminishing returns at some point it might 463 00:26:02,404 --> 00:26:05,004 Speaker 2: need some new techniques. Right. And by the way, if 464 00:26:05,044 --> 00:26:06,724 Speaker 2: you were at the best Urn of AI, you know 465 00:26:06,764 --> 00:26:09,364 Speaker 2: they're private company for now, right, But like I were 466 00:26:09,404 --> 00:26:12,084 Speaker 2: investor in Opening, I be fine with this. I mean, 467 00:26:12,124 --> 00:26:16,324 Speaker 2: all the big you know, Google, Facebook, et cetera. Companies 468 00:26:16,724 --> 00:26:21,204 Speaker 2: are are very profitable and have very you know, rich valuations. Right. 469 00:26:21,764 --> 00:26:26,804 Speaker 2: But yeah, it's becoming it's becoming a little Uh. What's 470 00:26:26,844 --> 00:26:28,924 Speaker 2: the words slop is the word that some people use 471 00:26:28,964 --> 00:26:30,404 Speaker 2: for this type of image generation. Oh? 472 00:26:30,404 --> 00:26:32,884 Speaker 1: Absolutely, I think slop is the right word. And I 473 00:26:32,924 --> 00:26:35,844 Speaker 1: think that I would actually go a step further and 474 00:26:35,924 --> 00:26:40,244 Speaker 1: say that as opposed to a step towards kind of 475 00:26:40,284 --> 00:26:43,764 Speaker 1: AGI or kind of bigger advancements, you know, P doom 476 00:26:43,764 --> 00:26:47,324 Speaker 1: et cetera. I phrased that poorly. It makes it seem 477 00:26:47,364 --> 00:26:50,524 Speaker 1: like P doom would be an advancement. P doom is bad, right, 478 00:26:50,564 --> 00:26:54,324 Speaker 1: that's why it's called P doom. But the advancements that 479 00:26:54,364 --> 00:26:59,164 Speaker 1: would potentially lead us to U P doom scenario. This 480 00:26:59,244 --> 00:27:04,484 Speaker 1: seems to be basically the only uses I can see 481 00:27:04,484 --> 00:27:06,684 Speaker 1: of this with some limited things. I don't want a 482 00:27:06,724 --> 00:27:09,404 Speaker 1: straw man and say like there's nothing good that comes up, 483 00:27:09,884 --> 00:27:12,284 Speaker 1: But I think that a lot of the kind of 484 00:27:12,404 --> 00:27:15,284 Speaker 1: a lot of the downstream effects of this are actually negative. Right, 485 00:27:15,284 --> 00:27:18,364 Speaker 1: They're not doing anything to be a net positive value 486 00:27:18,404 --> 00:27:22,044 Speaker 1: to society. Instead, it's creating kind of slop. It is, 487 00:27:22,364 --> 00:27:26,364 Speaker 1: you know, taking taking up your time with activities that 488 00:27:26,484 --> 00:27:28,924 Speaker 1: might dead in some of your brain cells and would 489 00:27:28,964 --> 00:27:32,964 Speaker 1: probably be better used doing something else. And it can 490 00:27:33,004 --> 00:27:36,324 Speaker 1: create really bad downstream effects if we look at the 491 00:27:36,324 --> 00:27:40,444 Speaker 1: types of videos that it is capable of generating. You 492 00:27:40,484 --> 00:27:44,564 Speaker 1: know what happens if someone, as they get more realistic, thinks, oh, 493 00:27:44,644 --> 00:27:47,964 Speaker 1: there really is violence here, There's really an uprising here, right, 494 00:27:48,004 --> 00:27:51,924 Speaker 1: there was really a bomb here. The Democrats did this, 495 00:27:52,164 --> 00:27:54,324 Speaker 1: you know, the Republicans did that. I think this can 496 00:27:54,364 --> 00:27:56,804 Speaker 1: go on both sides of the party line. So let's 497 00:27:56,804 --> 00:27:59,804 Speaker 1: not let's not say one party or the other. What 498 00:27:59,924 --> 00:28:02,524 Speaker 1: happens Nate. If someone can get your face and makes 499 00:28:02,564 --> 00:28:06,764 Speaker 1: it seem like Nate Silver did something very bad or 500 00:28:06,804 --> 00:28:10,764 Speaker 1: Maria konnikovid did something very bad or said something, you 501 00:28:10,764 --> 00:28:14,164 Speaker 1: could probably implicate people in crimes they did and commit. 502 00:28:14,644 --> 00:28:17,484 Speaker 1: And if you and I had exonerating evidence and we're like, hey, 503 00:28:17,484 --> 00:28:20,444 Speaker 1: we were taping the Risky Business podcast at that exact 504 00:28:20,444 --> 00:28:23,724 Speaker 1: time look at the video of it. If you can't 505 00:28:23,724 --> 00:28:27,684 Speaker 1: distinguish that right from the video that they generated, or 506 00:28:27,804 --> 00:28:31,484 Speaker 1: it can be incredibly difficult, time consuming to distinguish it. 507 00:28:31,484 --> 00:28:34,084 Speaker 1: It's not as easy as doing it immediately. That could 508 00:28:34,084 --> 00:28:37,804 Speaker 1: still upend our lives and kind of lead us into 509 00:28:37,964 --> 00:28:40,924 Speaker 1: a very uncomfortable spiral where even if at the end 510 00:28:40,964 --> 00:28:43,244 Speaker 1: of the day we are vindicated, it will still have 511 00:28:43,364 --> 00:28:48,524 Speaker 1: been you know, very long time, financial resources, emotional cost, 512 00:28:48,644 --> 00:28:51,484 Speaker 1: all of these things, and those seem to be kind 513 00:28:51,524 --> 00:28:56,084 Speaker 1: of the main uses of video generation of the sort 514 00:28:56,404 --> 00:28:59,724 Speaker 1: that or you know, disrupting Hollywood and saying okay, fine, well, 515 00:29:00,004 --> 00:29:03,044 Speaker 1: you know, create entire AI movies, which I don't think 516 00:29:03,084 --> 00:29:04,924 Speaker 1: you can do at this point with SOA in any 517 00:29:04,964 --> 00:29:08,724 Speaker 1: believable way. But that might be kind of one of 518 00:29:08,764 --> 00:29:11,604 Speaker 1: the one of the other downstream effects. But I'm more 519 00:29:12,004 --> 00:29:15,284 Speaker 1: kind of what worries me, especially as someone you know, 520 00:29:15,604 --> 00:29:20,084 Speaker 1: who studies a barent behavior con artists, et cetera, and 521 00:29:20,124 --> 00:29:22,564 Speaker 1: who's working on a book about cheating. What worries me 522 00:29:22,604 --> 00:29:24,764 Speaker 1: are kind of all of the nefarious uses of this 523 00:29:24,924 --> 00:29:30,084 Speaker 1: for scamming, for for conning, for cheating, and you do 524 00:29:30,324 --> 00:29:34,404 Speaker 1: have this thing called the liar's dividend that I think 525 00:29:34,884 --> 00:29:38,164 Speaker 1: is very prominent in something like Sore, where I mean 526 00:29:38,204 --> 00:29:43,004 Speaker 1: Trump even said it himself that basically, you know, if 527 00:29:43,404 --> 00:29:46,404 Speaker 1: you don't trust this evidence anymore, then you can just 528 00:29:46,524 --> 00:29:49,564 Speaker 1: blame AI for everything. And Trump said and this isn't 529 00:29:49,604 --> 00:29:52,244 Speaker 1: a this is a quote. If something happens that's really bad, 530 00:29:52,364 --> 00:29:55,644 Speaker 1: maybe I'll have to just blame AI, right, And that's 531 00:29:55,724 --> 00:29:59,244 Speaker 1: that's the liar's dividend that like now, liars are actually 532 00:29:59,244 --> 00:30:02,284 Speaker 1: the ones who are benefiting cheaters, are the ones that 533 00:30:02,324 --> 00:30:05,244 Speaker 1: are benefiting con artists, scammers, They're the ones that are 534 00:30:05,524 --> 00:30:08,764 Speaker 1: deriving the most benefit from this. So if your stated 535 00:30:08,764 --> 00:30:10,364 Speaker 1: goal has a come he is to make the world 536 00:30:10,404 --> 00:30:13,924 Speaker 1: a better place. And all you're doing is a good 537 00:30:13,964 --> 00:30:15,964 Speaker 1: on a good day, just generating slop and on a 538 00:30:16,004 --> 00:30:21,284 Speaker 1: bad day, creating content that might actually diletoriously affect people's 539 00:30:21,364 --> 00:30:24,284 Speaker 1: lives on an individual level, on a broader level, depending 540 00:30:24,324 --> 00:30:27,524 Speaker 1: on what we're talking about, then I start to question, 541 00:30:27,604 --> 00:30:30,004 Speaker 1: you know, what, what are we even doing? Right, Like, 542 00:30:30,204 --> 00:30:33,044 Speaker 1: what's the point of this because chat GPT. You know, 543 00:30:33,164 --> 00:30:35,964 Speaker 1: I'm not a fan of GPT five, We've talked about this, 544 00:30:36,524 --> 00:30:38,444 Speaker 1: it's still you know, there are things that we don't like, 545 00:30:38,684 --> 00:30:40,884 Speaker 1: but they are good uses of it, right and large 546 00:30:40,964 --> 00:30:44,204 Speaker 1: language models. I can see like there are really great 547 00:30:44,284 --> 00:30:47,444 Speaker 1: ways that this can you know, speed scientific discoveries do 548 00:30:47,964 --> 00:30:50,644 Speaker 1: make work easier. There there are lots of really great things. 549 00:30:50,684 --> 00:30:53,484 Speaker 1: And what's sore? I'm like, well, you know at this point, 550 00:30:53,524 --> 00:30:56,164 Speaker 1: like what are give me the strong man case for 551 00:30:56,244 --> 00:30:59,764 Speaker 1: what the amazing uses of this are as opposed to 552 00:31:00,564 --> 00:31:05,444 Speaker 1: just the meh or the just dead weight on society 553 00:31:05,604 --> 00:31:11,604 Speaker 1: or the actually actively bad society societal effect. And Nate, 554 00:31:11,604 --> 00:31:13,484 Speaker 1: I can already see by your face that you know 555 00:31:13,924 --> 00:31:16,644 Speaker 1: this is you'll rise to the challenge, even though you 556 00:31:16,724 --> 00:31:19,884 Speaker 1: were you started off by by being negative on sour 557 00:31:19,964 --> 00:31:21,444 Speaker 1: and saying that you don't even see why you would 558 00:31:21,684 --> 00:31:24,164 Speaker 1: why you would want to use it. But let's I'd 559 00:31:24,204 --> 00:31:26,524 Speaker 1: actually really like to try to figure out, Okay, what 560 00:31:26,644 --> 00:31:30,084 Speaker 1: are some really great like what is the societal good 561 00:31:30,724 --> 00:31:33,324 Speaker 1: from this? What is the thing from this that might 562 00:31:33,404 --> 00:31:38,204 Speaker 1: actually be you know, medical breakthrough, don't discoveries? I don't know, Like, 563 00:31:38,484 --> 00:31:41,644 Speaker 1: let's let's try to figure out, you know, is there 564 00:31:41,724 --> 00:31:45,004 Speaker 1: a way to spend this in a more positive light 565 00:31:45,084 --> 00:31:50,444 Speaker 1: than the one that I have just recounted, which is yeah, 566 00:31:50,484 --> 00:31:53,644 Speaker 1: well exactly. Yeah, No, I mean I haven't mentioned all 567 00:31:53,724 --> 00:31:58,364 Speaker 1: the negative stuff. Yeah, please, I mean that's a huge thing. Porn, revenge, porn, 568 00:31:59,204 --> 00:32:01,444 Speaker 1: just all all sorts of things that you can do 569 00:32:02,004 --> 00:32:07,484 Speaker 1: with with this technology, and with the access to photographs 570 00:32:07,724 --> 00:32:14,244 Speaker 1: and the frankly not great protections on image likeness, and 571 00:32:14,364 --> 00:32:19,244 Speaker 1: the ability to kind of use things that are copyright protected. 572 00:32:19,404 --> 00:32:22,444 Speaker 2: And and and fanfic y, which which may also have 573 00:32:22,484 --> 00:32:27,244 Speaker 2: some commercialization that you have you're on some weird wormhole 574 00:32:27,244 --> 00:32:30,444 Speaker 2: on the internet, right, and you have something involving these 575 00:32:30,524 --> 00:32:34,044 Speaker 2: characters that's not canon, and and and now you can 576 00:32:34,244 --> 00:32:40,924 Speaker 2: create your own movie universe, cinematic universe, right, especially for 577 00:32:41,044 --> 00:32:43,444 Speaker 2: fantasy type stuff where maybe the realism is not super 578 00:32:43,484 --> 00:32:46,244 Speaker 2: super important, and some of them will be popular, some 579 00:32:46,284 --> 00:32:48,484 Speaker 2: of them will be good even, right, They'll be interesting 580 00:32:48,604 --> 00:32:53,764 Speaker 2: art produced with this tool. But but yeah, I think 581 00:32:53,844 --> 00:32:56,444 Speaker 2: large language models are fascinating. It's partly because I'm like 582 00:32:56,484 --> 00:32:58,964 Speaker 2: kind of like a language guy. 583 00:32:59,204 --> 00:33:01,444 Speaker 1: I mean, you know, I totally totally agree. 584 00:33:02,124 --> 00:33:06,284 Speaker 2: But they're also this kind of remarkable miracle technology, you know, 585 00:33:06,364 --> 00:33:08,444 Speaker 2: I get. Look, I guess in some sense, like there 586 00:33:08,484 --> 00:33:10,924 Speaker 2: is still some like shock and all with these video models, right, 587 00:33:10,964 --> 00:33:14,684 Speaker 2: I you know, given current developments in any eye, I'm 588 00:33:14,724 --> 00:33:18,564 Speaker 2: not surprised, right, but I would have been surprised five 589 00:33:18,684 --> 00:33:19,884 Speaker 2: or ten years ago, I think. 590 00:33:20,084 --> 00:33:20,284 Speaker 1: Right. 591 00:33:22,524 --> 00:33:24,204 Speaker 2: Yeah, look, and you know, and by the way, as 592 00:33:24,204 --> 00:33:28,364 Speaker 2: you get more like customized versions of whatever fanfic you're into, 593 00:33:28,404 --> 00:33:31,884 Speaker 2: that makes society more like atomized also, right, we have 594 00:33:31,964 --> 00:33:34,724 Speaker 2: like fewer and fewer focal points. You know, you'll probably 595 00:33:34,764 --> 00:33:37,444 Speaker 2: get some really cool video games out of it. Right, 596 00:33:37,444 --> 00:33:40,764 Speaker 2: if you can custom generate unique animations and that, you know, 597 00:33:40,804 --> 00:33:43,084 Speaker 2: video games will be cool. It's hard to find the 598 00:33:43,644 --> 00:33:48,044 Speaker 2: legitimate quote unquote positive use cases for it, right, Whereas 599 00:33:48,044 --> 00:33:50,924 Speaker 2: emage image generation you can see plenty of positives, right, 600 00:33:50,964 --> 00:33:55,684 Speaker 2: and certainly language generation. Right. I guess advertising advertising, okay, 601 00:33:55,764 --> 00:33:58,284 Speaker 2: that's fine, right, video games? Right, but these are they 602 00:33:58,284 --> 00:34:00,124 Speaker 2: are these things that are kind of neutral and benign 603 00:34:00,324 --> 00:34:03,244 Speaker 2: ish and then things that are bad, right, And there's 604 00:34:03,284 --> 00:34:06,724 Speaker 2: not a lot that's good. Yeah, I don't think. 605 00:34:07,084 --> 00:34:10,244 Speaker 1: No, I don't think so either, did you see? By 606 00:34:10,244 --> 00:34:13,164 Speaker 1: the way, Nate, just to show like how important video 607 00:34:13,244 --> 00:34:17,684 Speaker 1: gaming data is to open a last year, they offered 608 00:34:17,884 --> 00:34:23,244 Speaker 1: five hundred million dollars for a company called Metal, which 609 00:34:23,324 --> 00:34:29,684 Speaker 1: lets gamers basically upload videos of them playing these video games, 610 00:34:29,884 --> 00:34:33,084 Speaker 1: and the reason that they were willing to offer so 611 00:34:33,204 --> 00:34:37,644 Speaker 1: much is because you know you might be able to 612 00:34:37,724 --> 00:34:42,444 Speaker 1: then train its models on those videos. There was a 613 00:34:42,484 --> 00:34:44,724 Speaker 1: story in a tech site that I read a lot 614 00:34:44,844 --> 00:34:49,004 Speaker 1: the information about this if you're interested in more detail, 615 00:34:49,044 --> 00:34:52,124 Speaker 1: but I thought that this was a really interesting window 616 00:34:52,284 --> 00:34:55,924 Speaker 1: into what open a I might be thinking in terms 617 00:34:55,964 --> 00:34:58,724 Speaker 1: of the future of its video apps. So yeah, I 618 00:34:58,764 --> 00:35:02,084 Speaker 1: think that it's it's funny you say video games will 619 00:35:02,124 --> 00:35:05,844 Speaker 1: be will get better, but it's a they will use 620 00:35:05,964 --> 00:35:09,644 Speaker 1: video game video games themselves to be able to prove 621 00:35:09,724 --> 00:35:12,564 Speaker 1: It's kind of one of these basically the way that 622 00:35:12,644 --> 00:35:15,644 Speaker 1: large language models work, right where you need that feedback, 623 00:35:15,684 --> 00:35:17,684 Speaker 1: You need the good stuff to be able to then 624 00:35:17,804 --> 00:35:21,324 Speaker 1: generate things on your own, and so you need access 625 00:35:21,364 --> 00:35:23,444 Speaker 1: to that data and to that raw data. 626 00:35:26,324 --> 00:35:36,404 Speaker 2: And we'll be right back after this break. Google I 627 00:35:36,444 --> 00:35:40,764 Speaker 2: think is the best cop for open AI, right, probably 628 00:35:40,804 --> 00:35:44,164 Speaker 2: better than Facebook, but that's the second most obvious, right, Yep, 629 00:35:45,364 --> 00:35:47,684 Speaker 2: they both do a lot of different shit, right, Like 630 00:35:47,804 --> 00:35:50,684 Speaker 2: Google would not even claim that like everything they do 631 00:35:50,804 --> 00:35:56,364 Speaker 2: is search or one degree removed from search. Right, they 632 00:35:56,444 --> 00:35:58,364 Speaker 2: have lots of start people, they have lots of capital, 633 00:35:58,764 --> 00:36:00,644 Speaker 2: they buy lots of stuff. Maybe they think it will 634 00:36:00,684 --> 00:36:04,284 Speaker 2: improve search originally and then and then once you're doing 635 00:36:04,324 --> 00:36:07,604 Speaker 2: something else, right, they have you know, electric vehicles and 636 00:36:07,644 --> 00:36:09,324 Speaker 2: things like that, and like I kind of predict like 637 00:36:09,884 --> 00:36:12,044 Speaker 2: this is what we're seeing, Like I don't know. I mean, 638 00:36:12,164 --> 00:36:15,484 Speaker 2: I think Sam Moulton would love to have artificial general 639 00:36:15,484 --> 00:36:19,884 Speaker 2: intelligence AGI. You know, the implications for society good or bad. Right, 640 00:36:20,244 --> 00:36:23,164 Speaker 2: But like this does not feel to me like a 641 00:36:23,204 --> 00:36:25,484 Speaker 2: company that has a lot of competence about its ability 642 00:36:25,484 --> 00:36:27,764 Speaker 2: to like develop AGI in a one to three year timeline. 643 00:36:27,964 --> 00:36:30,444 Speaker 2: They might have something they call AGI, but like, if 644 00:36:30,484 --> 00:36:35,484 Speaker 2: you were on the brink of an intelligence explosion, as 645 00:36:36,244 --> 00:36:38,644 Speaker 2: both the doomers and the optimists sometimes call it, right, 646 00:36:39,964 --> 00:36:47,164 Speaker 2: you I think would not put out this video slop product. Right, Like, 647 00:36:47,204 --> 00:36:49,444 Speaker 2: there are ways where they could have rolled it out 648 00:36:49,444 --> 00:36:52,404 Speaker 2: with like with a little bit more pretense, even though 649 00:36:52,404 --> 00:36:54,284 Speaker 2: probably would be ninety eight percent the same what you 650 00:36:54,364 --> 00:36:56,564 Speaker 2: can do with it, right, but with like it could 651 00:36:56,564 --> 00:36:58,924 Speaker 2: have got a little bit more pretense about like here 652 00:36:58,964 --> 00:37:02,964 Speaker 2: the precautions we're taking about copyright and pornography and here 653 00:37:03,084 --> 00:37:07,364 Speaker 2: are you know, and we hired whatever these artists, We 654 00:37:07,404 --> 00:37:09,364 Speaker 2: did a collab with these artists right to show you 655 00:37:09,524 --> 00:37:13,484 Speaker 2: different ways in which can be used for true creativity, right, 656 00:37:13,604 --> 00:37:16,124 Speaker 2: and here's a way it can use for medical diagnosis. 657 00:37:16,164 --> 00:37:19,444 Speaker 2: And like the marketing was kind of you know, maybe 658 00:37:19,484 --> 00:37:21,004 Speaker 2: they didn't do their market but they're usually a company 659 00:37:21,244 --> 00:37:23,964 Speaker 2: pretty savy about its marketing, right, Yeah, everything Sam Maltman 660 00:37:24,004 --> 00:37:27,084 Speaker 2: says is is savvy. But like, you know, maybe take 661 00:37:27,124 --> 00:37:29,524 Speaker 2: that back. All the different versions of names they have 662 00:37:29,684 --> 00:37:33,004 Speaker 2: for their products can be a little bit confusing, but like, yeah, 663 00:37:33,004 --> 00:37:37,244 Speaker 2: this to me says that like, you know, this company 664 00:37:37,404 --> 00:37:40,884 Speaker 2: is is Google, Facebook, Microsoft. 665 00:37:41,404 --> 00:37:45,164 Speaker 1: Yes, they know that they're they're creating something that will 666 00:37:45,924 --> 00:37:49,284 Speaker 1: potentially cause a lot of negative effects, and they don't 667 00:37:49,284 --> 00:37:53,684 Speaker 1: particularly care right now because that is something that, as 668 00:37:53,724 --> 00:37:55,764 Speaker 1: you say, in the short term, like they don't have 669 00:37:55,844 --> 00:38:00,524 Speaker 1: agi and this is something that can get people's eyeballs 670 00:38:00,564 --> 00:38:04,444 Speaker 1: for a limited amount of time, which means money, et cetera, 671 00:38:04,484 --> 00:38:06,804 Speaker 1: et cetera. So it seems that, you know, we've come 672 00:38:06,844 --> 00:38:11,284 Speaker 1: a long way from Sam Altman's initial goals for the 673 00:38:11,324 --> 00:38:14,404 Speaker 1: company to now like Okay, let's just figure out a 674 00:38:14,444 --> 00:38:18,524 Speaker 1: way that we can increase immediate revenue right now. Consequences 675 00:38:18,604 --> 00:38:22,524 Speaker 1: kind of be damned, and we're going to sure, like 676 00:38:22,604 --> 00:38:25,244 Speaker 1: once we get soon and if something really bad happens, 677 00:38:25,324 --> 00:38:29,244 Speaker 1: we might start adding some guardrails on some of this stuff. 678 00:38:29,284 --> 00:38:32,044 Speaker 1: But in the meantime, you know, let's just put it 679 00:38:32,084 --> 00:38:36,204 Speaker 1: out there and yeah, well we'll see what happens. Nate, 680 00:38:36,284 --> 00:38:39,284 Speaker 1: have you seen the movie that came out, I think 681 00:38:39,324 --> 00:38:42,364 Speaker 1: on Netflix earlier this year, Mountainhead. 682 00:38:43,644 --> 00:38:47,764 Speaker 2: No, so this was I I have fountain Head. 683 00:38:48,324 --> 00:38:51,604 Speaker 1: Yes, yes, and in the movie, so they actually have 684 00:38:51,724 --> 00:38:57,444 Speaker 1: the exact same I think thought process about our intelligence 685 00:38:57,524 --> 00:39:00,524 Speaker 1: as Sam Altman does, which is that, oh, people are 686 00:39:00,524 --> 00:39:02,564 Speaker 1: going to miss this. So the movie even just like 687 00:39:02,604 --> 00:39:05,524 Speaker 1: makes a show. They're like, you called this mountain Head. 688 00:39:05,804 --> 00:39:08,724 Speaker 1: Is that supposed to be like Fountainhead? I was like, guys, guys, 689 00:39:08,764 --> 00:39:12,004 Speaker 1: we got it. You don't have to explicitly put it 690 00:39:12,524 --> 00:39:16,204 Speaker 1: in the dialogue. It's one of those cringe moments. But yes, 691 00:39:16,644 --> 00:39:18,964 Speaker 1: that is in fact a line of dialogue, which is 692 00:39:19,204 --> 00:39:21,844 Speaker 1: which is heartbreaking to me because I wanted to love 693 00:39:21,884 --> 00:39:25,004 Speaker 1: this movie. Disclaimer, I did not because it was written 694 00:39:25,044 --> 00:39:27,364 Speaker 1: by Jesse Armstrong, who was a creator and writer of 695 00:39:27,484 --> 00:39:30,524 Speaker 1: A Succession, which I think is one of the best 696 00:39:30,564 --> 00:39:34,244 Speaker 1: TV shows in recent memories. So that was it was 697 00:39:34,484 --> 00:39:37,964 Speaker 1: sad to me that I not not only did not 698 00:39:38,004 --> 00:39:41,404 Speaker 1: love this movie, but another disclaimer, turned it off probably 699 00:39:41,444 --> 00:39:43,764 Speaker 1: less than halfway through because I felt that it was 700 00:39:44,084 --> 00:39:47,004 Speaker 1: that bad. But the premise of it is that you 701 00:39:47,044 --> 00:39:51,004 Speaker 1: have all of these tech billionaires come to kind of 702 00:39:51,044 --> 00:39:57,524 Speaker 1: a house called mountain Head, like fountain Head, and they're all, 703 00:39:58,124 --> 00:40:01,004 Speaker 1: you know, they're all basically they're they're big dick. Contest 704 00:40:01,164 --> 00:40:03,604 Speaker 1: is you know whose net worth is hired at any 705 00:40:03,644 --> 00:40:06,444 Speaker 1: given moment, And one of the companies has just released 706 00:40:06,444 --> 00:40:11,724 Speaker 1: this AI video generation tool that is starting real world 707 00:40:11,964 --> 00:40:17,124 Speaker 1: riots as they are ensconced in their mountain retreats because 708 00:40:17,644 --> 00:40:21,684 Speaker 1: the videos content is so incredibly realistic. And one of 709 00:40:21,684 --> 00:40:24,444 Speaker 1: the other guys has a company that can actually help 710 00:40:24,484 --> 00:40:28,044 Speaker 1: people very accurately say no, this is AI and distinguish 711 00:40:28,524 --> 00:40:31,284 Speaker 1: from it, and his net worth is rising even faster, 712 00:40:31,444 --> 00:40:33,884 Speaker 1: and so he doesn't. You know, it's kind of yeah, 713 00:40:34,004 --> 00:40:39,964 Speaker 1: it's this kind of battle between morals and like letting 714 00:40:40,004 --> 00:40:42,204 Speaker 1: the world kind of go to hell because we'll be 715 00:40:42,244 --> 00:40:45,604 Speaker 1: fine because we're mega billionaires and look at our net 716 00:40:45,644 --> 00:40:48,844 Speaker 1: worth going up and up and up. But yeah, it 717 00:40:48,924 --> 00:40:51,444 Speaker 1: was very funny because when I saw Sora, I was like, wait, 718 00:40:51,964 --> 00:40:55,364 Speaker 1: this is kind of like that, you know, famous tweet 719 00:40:55,804 --> 00:41:00,604 Speaker 1: from forever ago. It was science fiction author in my book, 720 00:41:00,644 --> 00:41:04,084 Speaker 1: I invented the Torment Nexus as a cautionary tale tech 721 00:41:04,124 --> 00:41:07,284 Speaker 1: company at Long Last, we have created the Torment Nexus 722 00:41:07,324 --> 00:41:12,124 Speaker 1: from classics sci fi novel don'tate the Torment Nexus. So 723 00:41:12,364 --> 00:41:16,244 Speaker 1: this was, Yeah, this seems to be like exactly one 724 00:41:16,284 --> 00:41:18,404 Speaker 1: of those moments where I was like, wait, didn't we 725 00:41:18,604 --> 00:41:20,844 Speaker 1: just have a movie about the fact that maybe we 726 00:41:20,924 --> 00:41:23,564 Speaker 1: don't want to create a tool that does this exact thing. 727 00:41:24,284 --> 00:41:27,844 Speaker 1: So I thought that that was a pretty pretty funny 728 00:41:27,884 --> 00:41:28,484 Speaker 1: funny moment. 729 00:41:30,404 --> 00:41:34,084 Speaker 2: Yeah. Well, look, I mean, for example, companies, if you know, 730 00:41:34,204 --> 00:41:37,244 Speaker 2: you can use their IP like their stock characters unless 731 00:41:37,284 --> 00:41:41,204 Speaker 2: they specifically opt out of it. Like that's kind of presumptuous, right, 732 00:41:41,964 --> 00:41:42,164 Speaker 2: Just it. 733 00:41:42,164 --> 00:41:43,804 Speaker 1: Should be the other way around, by the way, you 734 00:41:43,804 --> 00:41:45,444 Speaker 1: should have to opt in, not opt out. 735 00:41:46,004 --> 00:41:49,444 Speaker 2: I'm sure they've done financial modeling and like, very few 736 00:41:49,444 --> 00:41:54,924 Speaker 2: companies have been truly crippled by by a lawsuit, right, 737 00:41:55,004 --> 00:41:57,604 Speaker 2: Like Napster was kind of like the famous exception, right, 738 00:41:57,644 --> 00:41:59,444 Speaker 2: But for the most part, if you're growing super deeper 739 00:41:59,484 --> 00:42:01,244 Speaker 2: fast and you're going to lose a few of these 740 00:42:01,804 --> 00:42:06,564 Speaker 2: big lawsuits, right, you know, we're already in an environment, 741 00:42:06,844 --> 00:42:12,524 Speaker 2: particularly on Twitter slash x where uh where you have 742 00:42:12,604 --> 00:42:14,644 Speaker 2: you collect videos from the eight billion people in the world, right, 743 00:42:14,684 --> 00:42:17,444 Speaker 2: you collect something that happens somewhere to one of those 744 00:42:17,484 --> 00:42:20,484 Speaker 2: eight billion people, and like Ela Musk tweets, Elamus tweets it, 745 00:42:20,884 --> 00:42:25,324 Speaker 2: and it creates outrage. Right, sometimes those videos are are 746 00:42:25,404 --> 00:42:28,244 Speaker 2: manipulated or even fake, but like often they're not. Right, 747 00:42:28,284 --> 00:42:29,844 Speaker 2: it's just kind of like the cherry picking that you 748 00:42:29,844 --> 00:42:33,444 Speaker 2: get in there. And I think I'm a dystopian meter. 749 00:42:34,324 --> 00:42:38,404 Speaker 2: We're at like eight point two I think, right. 750 00:42:38,364 --> 00:42:40,684 Speaker 1: A point two out of tenya yeah, yeah. 751 00:42:40,604 --> 00:42:43,044 Speaker 2: Yeah, it's not linear. It's a long way to go 752 00:42:43,124 --> 00:42:44,164 Speaker 2: to get to a ten. 753 00:42:44,364 --> 00:42:46,084 Speaker 1: Yeah, but I think I think eight point two we're 754 00:42:46,324 --> 00:42:49,284 Speaker 1: you know, we're certainly I think I think we are 755 00:42:49,324 --> 00:42:52,324 Speaker 1: as well. I think we are as well. And that's 756 00:42:52,324 --> 00:42:56,004 Speaker 1: such a positive note on which to leave our audience 757 00:42:56,924 --> 00:43:00,084 Speaker 1: this this fine week. But yeah, let's uh. I know 758 00:43:00,124 --> 00:43:02,804 Speaker 1: we say this so frequently at the end of our episodes, 759 00:43:02,844 --> 00:43:06,044 Speaker 1: but I really do. I'm very curious to see what's 760 00:43:06,164 --> 00:43:09,004 Speaker 1: going to end up happening with SOA and whether we 761 00:43:09,044 --> 00:43:13,164 Speaker 1: look back on this and are like, holy shit, you know, Nate, 762 00:43:13,244 --> 00:43:16,124 Speaker 1: you and I were so naive or whether we think 763 00:43:16,804 --> 00:43:19,524 Speaker 1: you know, we we were very prescient about this. You know, 764 00:43:19,604 --> 00:43:22,404 Speaker 1: I'm very interested to see what's going to go down, 765 00:43:22,844 --> 00:43:27,364 Speaker 1: and I hope that it's less negative than we have 766 00:43:27,564 --> 00:43:30,364 Speaker 1: envisioned it during the course of the last forty five minutes. 767 00:43:30,564 --> 00:43:34,524 Speaker 1: I hope that we don't hit at ten anytime soon, 768 00:43:34,644 --> 00:43:35,724 Speaker 1: but we shall. 769 00:43:35,484 --> 00:43:41,604 Speaker 2: See log off, log off, flog up, go to. 770 00:43:41,524 --> 00:43:46,044 Speaker 1: The bogging Nate and Maria logging off for today. We'll 771 00:43:46,084 --> 00:43:54,044 Speaker 1: see you all on Saturday. Let us know what you 772 00:43:54,044 --> 00:43:56,364 Speaker 1: think of the show. Reach out to us at Risky 773 00:43:56,404 --> 00:44:01,004 Speaker 1: Business at pushkin dot FM. Risky Business is hosted by 774 00:44:01,044 --> 00:44:02,764 Speaker 1: me Maria Kanakova. 775 00:44:02,484 --> 00:44:05,204 Speaker 2: And by me Nate Silver. The show was a Cool 776 00:44:05,244 --> 00:44:09,204 Speaker 2: production of Pushing Industries and iHeartMedia. This episode was produced 777 00:44:09,204 --> 00:44:12,364 Speaker 2: by as At Carter. Our associate producer is Sonya gerwit 778 00:44:13,244 --> 00:44:16,364 Speaker 2: Lydia Jean Kott and Daphne Chen are our editors, and 779 00:44:16,444 --> 00:44:20,564 Speaker 2: our executive producer is Jacob Goldstein. Mixing by Sarah Brugert. 780 00:44:20,724 --> 00:44:22,884 Speaker 1: If you like the show, please rate and review us 781 00:44:22,924 --> 00:44:25,604 Speaker 1: so other people can find us too, But once again, 782 00:44:25,764 --> 00:44:27,564 Speaker 1: only if you like us. We don't want those bad 783 00:44:27,604 --> 00:44:29,764 Speaker 1: reviews out there, Thanks for tuning in