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Speaker 1: Hello, welcome to Fast Politics. I'm Molly Johnfest, and this

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Speaker 1: is our anti-election anxiety show where we soothe and distract

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Speaker 1: you while you wait for the results. First, if there's

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Speaker 1: anyone you know in your life who has not voted,

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Speaker 1: please text them now. Later in this show, we'll talk

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Speaker 1: to Better Offline's Ed Zitron about the AI bubble. But first,

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Speaker 1: we have Run for Something's own Amanda Lippman. on the

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Speaker 1: lessons Democrats should consider after this election. Welcome, welcome, Amanda Lipman.

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Speaker 2: Hi, Molly. Glad to be here.

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Speaker 1: Well, I'm really glad to have you. So I'm just

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Speaker 1: going to give like a two second background to this idea.

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Speaker 1: This is going to air on Monday night. And what

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Speaker 1: we're doing here is this is because nobody knows what's

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Speaker 1: going to happen until Tuesday, late, late, maybe Wednesday, we're

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Speaker 1: talking about a sort of scenario that we very much

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Speaker 1: hope will happen, but we don't know yet. And so

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Speaker 1: we are going to talk about a sub stack you wrote.

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Speaker 1: And again, you are relevant because you work on these

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Speaker 1: state races and you have created something called Run for Something,

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Speaker 1: which helps state level candidates run for office. It's wildly successful.

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Speaker 1: What percentage of your candidates are on office now?

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Speaker 2: We've got about 1,700 state and local elected officials and

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Speaker 2: another 394 endorsed candidates on the ballot this week.

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Speaker 1: And that's everyone from dog catcher. Is dog catcher still

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Speaker 1: an elected position?

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Speaker 2: We actually haven't worked with anyone running for dog catcher,

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Speaker 2: but we've worked with people running for coroner, light and

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Speaker 2: plant board, library board, all the way up through state

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Speaker 2: house and state senate races.

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Speaker 1: Coroner to state senator. Who knew you had to run

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Speaker 1: for coroner? Wow. So what we're talking about today is

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Speaker 1: this really brilliant substack you wrote called I'm Worried About Winning.

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Speaker 1: Talk me through your first biggest worry about winning.

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Speaker 2: Well, I should start with the caveat here.

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Speaker 1: We need to win. Yeah.

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Speaker 2: Like, I posted this on Twitter and I got a

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Speaker 2: lot of angry people being like, how dare you say

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Speaker 2: we shouldn't win? No, we need to win. It is

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Speaker 2: good for democracy. It is good for people. It is

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Speaker 2: good for stopping harm. It's good for government. Like, we

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Speaker 2: have to win.

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Speaker 1: Yeah. Because if we don't win, we're going to be

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Speaker 1: in authoritarianism. I mean, we're already practically there. So start by...

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Speaker 1: We know winning is important and that American democracy is

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Speaker 1: a very precarious position. But by the time people have

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Speaker 1: listened to this, they will either have early voted or

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Speaker 1: will be on the walking to the polls. So this

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Speaker 1: is a post-election moment to reflect.

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Speaker 2: So I am worried that if Democrats win big, which...

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Speaker 2: seems possible, if not even probable, it will paper over

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Speaker 2: our party's urgency to actually fix the structural problems. Winning

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Speaker 2: in 2026 does not mean we have done everything right

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Speaker 2: for the last two years. And it does not mean

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Speaker 2: we will win in 2028 if we do not change

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Speaker 2: some really big things about our party. But winning sort

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Speaker 2: of absolves us of the urgency to have that conversation.

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Speaker 2: And I'm really nervous that we won't.

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Speaker 1: Okay, so let's start with people broadly dislike both parties.

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Speaker 1: This is largely true, though I would just add they

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Speaker 1: mostly dislike Republicans more.

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Speaker 3: Yeah.

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Speaker 1: But Democrats also have a problem with the brand, and

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Speaker 1: we see this in Nebraska. We have an independent running.

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Speaker 1: In Iowa, we have Democrats who are really pretending to

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Speaker 1: be independents. Like, clearly the brand in certain parts of

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Speaker 1: the country is really toxic. And in other parts of

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Speaker 1: the country, people aren't so thrilled with either.

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Speaker 2: And it's interesting because the reason that the Democratic Party

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Speaker 2: brand is not popular is because Democrats, who maybe still

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Speaker 2: vote for Democratic candidates, do not like the party. Like,

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Speaker 2: it is people within the family who don't like the family. Yeah.

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Speaker 2: And I think that's really worth noting because we're going

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Speaker 2: to need their energy and their enthusiasm come 2028 and

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Speaker 2: 2030 and 2032.

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Speaker 4: Yeah.

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Speaker 2: We cannot pretend that this win, hopeful win, is because

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Speaker 2: people like us. We can't. It is because Trump has

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Speaker 2: created a political environment where his approval rating is in

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Speaker 2: the trash, the economy is bad, and they have made

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Speaker 2: no argument, no proof point that they're either going to

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Speaker 2: fix it or they even care about fixing it. He

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Speaker 2: has broken a number of promises, including prices go down,

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Speaker 2: no new wars. Well, prices have gone up and he

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Speaker 2: started new wars. So Like, I just I want us

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Speaker 2: to be so clear about the source of this environment

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Speaker 2: so that we don't misread our advantage.

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Speaker 1: So I think that's a really good point. So what

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Speaker 1: do you think? I'm going to ask you a really

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Speaker 1: annoying question here because it's a question I could not

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Speaker 1: fucking tell you the answer to. What do you think

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Speaker 1: Democrats can do to improve their brand?

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Speaker 2: I think part of it is that we need to

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Speaker 2: make sure we keep the promises we can. So in particular,

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Speaker 2: accountability and oversight is. Democratic, especially Democratic primary voters, they

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Speaker 2: wanted fighters. We heard that over and over and over again.

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Speaker 2: They want people with strong backbones who are going to

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Speaker 2: hold Trump accountable. So whether that looks like impeachment hearings,

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Speaker 2: especially I think for cabinet officials, senior staff in the

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Speaker 2: White House, charging people with crimes, like going after corporations

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Speaker 2: that paid bribes or essentially paid bribes, really making sure

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Speaker 2: that we are not trying to like whitewash history and

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Speaker 2: pretend nothing just happened. The second is, I think, being

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Speaker 2: really honest with voters about what is and is not

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Speaker 2: possible around legislation in this moment. Even if we win

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Speaker 2: the House and the Senate, they're not going to be

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Speaker 2: able to really govern in a way people can feel.

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Speaker 2: Like there's just not a lot of room for no

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Speaker 2: guarantee that Trump will sign anything that a Democratic Congress passes.

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Speaker 2: And we don't have a veto-approved majority. And with the filibuster,

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Speaker 2: we probably still won't have enough votes to break through

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Speaker 2: on that front. So I want to be really honest.

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Speaker 2: honest with the american people about what we can deliver

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Speaker 2: to that end i think as much as possible and

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Speaker 2: this will be hard in a presidential cycle and in

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Speaker 2: the next two years but as much as possible directing

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Speaker 2: attention and focus to governors to mayors to state legislators

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Speaker 2: who are going to be able to really govern we

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Speaker 2: are likely to pick up a number of trifectas in

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Speaker 2: the next couple weeks we're likely to pick up flip

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Speaker 2: something like nine state legislatures we're going to break super

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Speaker 2: majorities and a couple more we're going to pick up

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Speaker 2: a bunch of governor's mansions They're going to be able

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Speaker 2: to do stuff that people will feel.

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Speaker 1: At the state level. Maybe not at the federal level,

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Speaker 1: but at the state level, which is important because those

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Speaker 1: are not the same thing.

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Speaker 2: Not the same thing. But there are stories you can

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Speaker 2: tell and attention you can focus on that in a

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Speaker 2: way that can help prove the point that if you

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Speaker 2: elect a Democrat, good things will happen, which is a

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Speaker 2: closing of the trust deficit that we need going into 2028.

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Speaker 2: Because the 2028 presidential candidates are going to promise big things.

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Speaker 2: But if nobody believes they can deliver them, then who cares?

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Speaker 2: So I think for now, the big things are actually

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Speaker 2: keep the promises we've made and direct attention to people

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Speaker 2: who are delivering.

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Speaker 1: So I want you to talk about this idea. We're winning,

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Speaker 1: but not building infrastructure. So Democrats are spending at least

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Speaker 1: $ 2. 4 billion on federal elections this year. A vast majority

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Speaker 1: of that is going to advertising. which is not structural, right?

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Speaker 1: That's not voter registration. Like, for example, and this is

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Speaker 1: something I'm really passionate about, in Mississippi, there is this possibility,

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Speaker 1: it's a long shot, everyone agrees it's a long shot,

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Speaker 1: Scott Column would be the first Black senator from the

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Speaker 1: state of Mississippi since Reconstruction. Mississippi is a majority-minority state, 40%

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Speaker 1: maybe more, people of color, but it's a state where

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Speaker 1: people don't vote for any number of reasons, likely because

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Speaker 1: of voter registration and the infrastructure. And like, there is

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Speaker 1: a sense where if you really want to be a democracy,

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Speaker 1: people in Mississippi should be electing candidates who reflect what

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Speaker 1: the majority of the people in Mississippi are. Totally.

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Speaker 2: And I think when you think $ 2 billion is probably

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Speaker 2: undercounting it at this point. Like, there is... I've seen

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Speaker 2: some folks say that this is going to be the

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Speaker 2: more expensive midterm than the previous presidential, which is insane.

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Speaker 2: Most of that is going to ads. Ads are ephemeral.

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Speaker 2: Ads are direct deposits to local news networks, which is

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Speaker 2: good for them. And when you think about what's going

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Speaker 2: to last post-election day, basically any organization that does year-round

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Speaker 2: organizing or communication or, I'll be honest, candidate recruitment or

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Speaker 2: even a lot of the media stuff, Struggling and likely

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Speaker 2: to do layoffs or cut down staff or scale back

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Speaker 2: going into 2027. That's a really scary place to be in.

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Speaker 2: I will also add one other piece here. So much

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Speaker 2: of the last two years has been so hyper-focused on

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Speaker 2: places that we consider electoral battlegrounds. Wisconsin, Pennsylvania, Michigan, totally.

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Speaker 2: Which is not to say those aren't important. But we're

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Speaker 2: seeing this year, battleground is a dynamic term. Mississippi could

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Speaker 2: be a battleground. South Carolina could be a battleground. Kansas, Iowa, Texas, Florida.

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Speaker 2: Like if we are not thinking expansively about what the

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Speaker 2: map could be, not just in 2028, but in 2030

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Speaker 2: and then 2032 post redistricting and building towards that, we

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Speaker 2: are going to set ourselves up to be unable to

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Speaker 2: win when the headwinds are against us because eventually they

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Speaker 2: will be against us again.

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Speaker 1: I want you to talk about the organizations because so

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Speaker 1: what you're saying is that because Democrats at this moment

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Speaker 1: have been so, you know, they were disheartened and dismayed.

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Speaker 1: And then all of a sudden we see all these

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Speaker 1: good polls and everybody's pouring money. We got a Bloomberg

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Speaker 1: coming in with a two million. I've been talking to

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Speaker 1: an independent expenditure guy who told me they're up to

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Speaker 1: 14 million in the state of Kansas, which is insane. But.

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Speaker 1: I want you to explain to us what these organizations are.

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Speaker 1: You run an organization, run for something, is an organization

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Speaker 1: focused on flipping state ledge, and state house races writ large.

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Speaker 1: But talk us through some of these other organizations that

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Speaker 1: people might not know about.

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Speaker 2: You know, some of my favorites are groups like the

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Speaker 2: Alliance for Youth Action, which is a network of state-based

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Speaker 2: groups that does youth work. They're amazing. It's all hyper-local organizing.

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Speaker 2: And Struggling places like the state and local election alliance,

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Speaker 2: which has done pretty well this year, but who knows

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Speaker 2: what their work will be like next year. They do

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Speaker 2: things like supporting local election administrators, that kind of positions

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Speaker 2: actually oversee elections. We've done great partnerships with them over

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Speaker 2: the years. You know, even organizations where you think like

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Speaker 2: state parties where you don't necessarily are not super excited

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Speaker 2: about them. Those are the places that often recruit the candidates,

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Speaker 2: keep the data clean, engage volunteers year-round. And the state

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Speaker 2: parties that have been well-supported, places like Wisconsin and North Carolina,

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Speaker 2: thanks to the incredible Anderson Clayton down in North Carolina

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Speaker 2: in particular, are well set up this year. And places

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Speaker 2: where they're not are having a harder time.

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Speaker 1: Yeah. And so, I mean, that's also Ben Wickler and

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Speaker 1: now his heir apparent, whose name escapes me, but he's

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Speaker 1: really good in Wisconsin. And I want you to talk

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Speaker 1: about leadership in the Democratic Party and how it sometimes

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Speaker 1: undermines a message, a more local message.

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Speaker 2: Yeah, you know, we've seen this over the last two years, really,

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Speaker 2: from in Mamdani's campaign in New York, where a number

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Speaker 2: of major Democratic leaders declined to endorse him. We've seen

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Speaker 2: it in Michigan this year, where I don't know what

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Speaker 2: the fuck Haley Stevens is doing, but like.

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Speaker 1: She's campaigning, though she is campaigning this weekend. So that's good.

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Speaker 2: A step in the right direction. You know, even in Maine,

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Speaker 2: it took Janet Mills a while to sort of like.

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Speaker 3: Yeah, she just did.

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Speaker 2: Yeah, this week.

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Speaker 1: I just want to pause for a second and just say,

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Speaker 1: because I live in Manhattan, a lot of Democrats were

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Speaker 1: happy to endorse Eric Adams, who immediately looked like deeply

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Speaker 1: corrupt the minute he got on the scene, but were

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Speaker 1: unwilling to endorse Mayor Mondami. And it's like just in

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Speaker 1: a baffling way.

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Speaker 2: Baffling and i think like more broadly you know we

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Speaker 2: see in focus groups voters will say both parties are

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Speaker 2: corrupt they're obviously they're not the same the way the

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Speaker 2: republican party is corrupt is unprecedented yeah and they look

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Speaker 2: at like insider trading or you know people's kids starting

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Speaker 2: businesses in the same line that their parents are regulating

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Speaker 2: and and say like these guys are corrupt too they

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Speaker 2: see them as the same um so i think it

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Speaker 2: is really uh dangerous to for our leadership to be

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Speaker 2: undermining the broader argument. They're like, no, we are on

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Speaker 2: this fight of democracy. We are all on the same team.

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Speaker 2: Once we get through a primary, we work together. If

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Speaker 2: they're not going to, you know, put their money where

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Speaker 2: their mouth is on that front.

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Speaker 1: Yeah. And again, we're talking about, I mean, there are

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Speaker 1: a number of issues, but it One that stands out

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Speaker 1: is Senator Gillibrand, our senator, who is supposed to be

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Speaker 1: regulating the really scary crypto. And she has a son

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Speaker 1: who works in crypto. And it's just like we can't expect.

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Speaker 1: It's already complicated to get Congress to legislate. And then

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Speaker 1: when you look so conflicted, it's really bad for the party.

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Speaker 2: Well, and, you know, I wrote that and her office

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Speaker 2: called me to clarify, like, a whole bunch of things

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Speaker 2: that were left out of that article and a bunch

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Speaker 2: of stuff that she has been doing to regulate, you know,

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Speaker 2: insider trading.

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Speaker 1: Does her son not work in crypto?

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Speaker 2: I will say, regardless of whether this seems on the ground,

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Speaker 2: it looks bad. And, like, politics is about optics. And

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Speaker 2: it looks bad. So I implored them and anyone else, like,

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Speaker 2: even if there is nothing sketchy going on, if it

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Speaker 2: looks sketchy, it feels sketchy. that sense of what it

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Speaker 2: looks like is what it is in a political environment.

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Speaker 1: Agreed. And it's also more than that, right? It may

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Speaker 1: not always be this case. Someday we may have Republicans

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Speaker 1: who are not completely sketchy, but we haven't had it yet.

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Speaker 1: I'd love you to talk about when we look at

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Speaker 1: these state races, one of the things I'm so struck

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Speaker 1: by is, and I actually believe that you're some, I

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Speaker 1: was actually thinking about this today at morning show. I

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Speaker 1: was thinking like, Amanda Lipman is a little bit responsible

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Speaker 1: for this, which is that a lot of these candidates

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Speaker 1: are really good candidates, like really good local candidates. And

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Speaker 1: they remind me of like arena circa 2016, where they,

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Speaker 1: we had a speaker, I was at this event where

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Speaker 1: they were having speakers talk to democratic leader, you know,

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Speaker 1: not leaders, but you know, activists, et cetera. And they

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Speaker 1: were talking, one of them said, you know, go back

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Speaker 1: to where you're from and run for office. Like, You know,

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Speaker 1: you are not from D.C. You know, you're from somewhere real.

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Speaker 1: Not that there's anything wrong with D.C. I actually am

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Speaker 1: the one person who likes it. But, you know, like

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Speaker 1: go go, you know, be from your place. And like

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Speaker 1: when I think about some of these candidates, Rob Sand

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Speaker 1: is a really good one. You know, he is he

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Speaker 1: is of the place he is from. Or I think

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Speaker 1: about Adam Hamilton is a great example of a candidate.

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Speaker 1: He's like a unicorn.

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Speaker 2: I mean, I don't know.

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Speaker 1: You know, in Kansas, he's a liberal megachurch pastor. So

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Speaker 1: I'd love you to talk about the thought process there,

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Speaker 1: because I do see that on the Democratic side. I

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Speaker 1: don't see that on the Republican side.

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Speaker 2: No, I think what we've learned, and again, this year

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Speaker 2: in particular, is something that honestly run for something it's

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Speaker 2: been saying for a decade now, which is who the

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Speaker 2: candidate is really matters. Polarization is obviously fundamentals, but the

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Speaker 2: candidate quality shapes what is possible. And we do not

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Speaker 2: need to be tight on ideology in order to have

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Speaker 2: really good candidates. We need to be tight on values

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Speaker 2: and be very flexible in how those values show up

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Speaker 2: in policy in order to ultimately be growing in the

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Speaker 2: same direction. So even though you've got people like Rob Sand,

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Speaker 2: you've got Adam Hamilton. You've got Angie Nixon, who I

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Speaker 2: think is one of the hardest working candidates in the

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Speaker 2: country down in Florida. You've got Annie Andrews in South Carolina.

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Speaker 2: You've got Abdul El-Sayed in Michigan. Ideologically, a little bit

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Speaker 2: all over the place, but so clearly of the place

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Speaker 2: they're in, in a way that speaks so loudly about

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Speaker 2: a sense of community and about love for the people

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Speaker 2: they're trying to represent. And at the same time, the

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Speaker 2: Republicans are running, you know, just loser after loser after loser.

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Speaker 2: You've got like, who is the worst candidate on the ballot?

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Speaker 1: Is it, um, Cindy Hyde-Smith.

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Speaker 2: Cindy Hyde-Smith. Is it Ken Paxton? Is it Roger Marshall?

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Speaker 2: Is it Darlene Graham? Is it like you can go

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Speaker 2: down the list and just find a bunch of nothing burgers.

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Speaker 2: So I do. I think I'm I'm very hopeful that

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Speaker 2: this election cycle will teach us a party an important lesson,

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Speaker 2: which is candidate recruitment is really important. My favorite is.

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Speaker 1: The rapper who is running for Stefanik's seat, where he

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Speaker 1: has this relationship with Roger Stone and he's running against

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Speaker 1: the dairy farmer. Yeah, that's my favorite. That's Jesse's favorite

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Speaker 1: bad candidate. Hey, Jesse, who's your favorite bad candidate?

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Speaker 5: My favorite bad kid is Anthony Constantino of New York 21.

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Speaker 1: Yeah, New York 21. That is the rapper who employs

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Speaker 1: Roger Stone, but is running for at least Stefanik's old seat.

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Speaker 1: It's a really good example of a candidate who just

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Speaker 1: is terrible. And even like Marsha Blackburn, who they think

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Speaker 1: of as like one of their greats, is not so great.

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Speaker 3: Nope.

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Speaker 2: I think that's like, you know, a good sign that

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Speaker 2: at this point, good people don't want to really run

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Speaker 2: as Republicans. Like the future of the Republican Party is bleak,

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Speaker 2: which I will say maybe this is for a later conversation,

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Speaker 2: but I don't think we're talking enough about how much

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Speaker 2: of a clusterfuck the Republican primary is going to be.

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Speaker 2: How messy the fight for the future of the Republican

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Speaker 2: Party is going to be post-Trump. This era that we're

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Speaker 2: about to enter where he is a lame duck, he's

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Speaker 2: politically toxic. People are going to be running away from

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Speaker 2: him while also trying to keep on the MAGA sort

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Speaker 2: of base. It's going to be a shit show. And I,

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Speaker 2: for one, can't wait.

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Speaker 1: It's so funny because I was on a show today

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Speaker 1: and the guy was saying that, like, Democrats, Republicans are

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Speaker 1: going to lose definitively. And Democrats are going to like

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Speaker 1: take the wrong message and they'll be in disarray and

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Speaker 1: Republicans will be fixed. And I was like, your whole

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Speaker 1: party is based around one person who is now wildly unpopular.

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Speaker 1: Like you don't get I mean, it's there are no

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Speaker 1: heir apparent. Like the closest heir apparent is Don Jr.

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Speaker 1: I mean, who is is J.D.

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Speaker 2: Vance?

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Speaker 3: No.

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Speaker 1: It's James Blair? I mean, who?

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Speaker 2: They're screwed. And I think they are, we collectively are

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Speaker 2: underrating how hard it's going to be for them. You know,

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Speaker 2: we've had 10 years in the wilderness. Welcome to the jungle.

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Speaker 1: Yeah, exactly. 10 years in the wilderness. Welcome to the jungle.

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Speaker 1: Thank you. Thank you. Thank you, Amanda Lemon. Thanks, Molly.

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Speaker 5: Hi, it is time for the headlines. Obviously, we've been

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Speaker 5: through quite the fun. tour with hayley stevens what are

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Speaker 5: you making of all this since we last spoke.

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Speaker 1: So hayley stevens basically hayley stevens decided on saturday she

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Speaker 1: was she basically got mad that debbie jing uninvited her

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Speaker 1: from a campaign event with Dr. Abdul. And now she

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Speaker 1: has endorsed Mike Rogers. And she has this I mean,

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Speaker 1: there's more to this story. She has this fiance who

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Speaker 1: may have an outsized influence in her life, who's a

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Speaker 1: Republican candidate. donor who's big into AIPAC. After this all happened,

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Speaker 1: she went on both the Free Press and also talked

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Speaker 1: to Scott Jennings. So I think we can all agree

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Speaker 1: now that she was probably not a great pick for Senate.

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Speaker 1: I mean, obviously, she was not able to win the

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Speaker 1: nomination despite having tens of millions of dollars in APAC money.

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Speaker 1: But the other thing which I think is so relevant

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Speaker 1: and important here is that she never believed any of

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Speaker 1: the stuff she said she believed. And you don't just

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Speaker 1: change your mind because you weren't invited to a barbecue.

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Speaker 1: You never believed any of it. And I think that

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Speaker 1: the fact that Schumer picked her over Mallory McMorrow and Dr.

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Speaker 1: Abdul El-Saeed shows that really he was not like vetting

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Speaker 1: these people. And so and look, it's great that she

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Speaker 1: didn't win because it really does make now it all

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Speaker 1: makes sense. But, like, think about Fetterman. Like, there are

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Speaker 1: people in the Senate right now who tricked us all. So,

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Speaker 1: you know, I'm glad she lost, and I'm glad that

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Speaker 1: this is going to be over soon. But, man, whoa,

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Speaker 1: what a.

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Speaker 5: Yeah, I know you're not as on TikTok as I am, but— No.

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Speaker 5: It was a striking amount of her staffers decided to

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Speaker 5: take to the waves of Twitter and TikTok.

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Speaker 1: Yeah.

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Speaker 5: And, you know, one of the things I will say is, like,

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Speaker 5: you and I have been watching this game for a

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Speaker 5: long time and you don't see people like Mallory McMorrow's

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Speaker 5: husband who came out and said, like, this is a

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Speaker 5: person who's been acting erratic. There's staffers who say we've

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Speaker 5: been protecting her from her erratic behavior and worst instincts

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Speaker 5: for years. And now they're set free and her staff resigned.

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Speaker 5: And it's.

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Speaker 1: Other thing is like, she was not a good candidate

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Speaker 1: because she was not a good speaker. And because she spoke.

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Speaker 5: Bad instincts too.

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Speaker 3: Right.

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Speaker 1: Well, that for sure. But like, So what why did

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Speaker 1: establishment crown her?

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Speaker 5: Well, I was told the establishment vets candidates by near attendant.

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Speaker 1: Well, I'm just saying I don't want to get into

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Speaker 1: an ideological fight here, though. Sure, there's room for that, too.

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Speaker 1: But like. The question is, here's a candidate who is

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Speaker 1: not a good speaker, who is not good retail politician

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Speaker 1: and who's also turns out to be completely insane. Like,

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Speaker 1: don't make this person the nominee when there are two

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Speaker 1: other candidates who are real candidates who are not insane.

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Speaker 1: And I just think, like, if you're Chuck Schumer. You

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Speaker 1: should be horrified by this.

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Speaker 3: Yeah, yeah.

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Speaker 5: I think that we've had a lot of people who

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Speaker 5: are much more inside this game of the DNC and

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Speaker 5: the recruiting that have said that the way they recruit things,

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Speaker 5: and you and I have talked about this, that when

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Speaker 5: we talk to certain people who thought about running, they're

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Speaker 5: often asked, well, can you raise the money? And some

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Speaker 5: of what her staffers are saying is she was only

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Speaker 5: picked because they knew she would have the APAC money.

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Speaker 1: Another moment that I think was really really bad was that, like,

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Speaker 1: she went to a Black church to endorse a white

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Speaker 1: Republican who's, you know, going to hurt Black people and

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Speaker 1: who lives in Florida. I mean, it just was, like,

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Speaker 1: so incredibly gross. And as a woman and a white woman,

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Speaker 1: I mean, obviously I'm Jewish. She is not, though she

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Speaker 1: is very Zionist. I just feel like it is, it's

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Speaker 1: like a very bad look.

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Speaker 2: Yeah.

432
00:23:51,530 --> 00:23:55,290
Speaker 5: OK, we should move on to SCOTUS rejecting Trump's bid

433
00:23:55,470 --> 00:23:57,390
Speaker 5: over disputed California ballots.

434
00:23:57,750 --> 00:24:01,609
Speaker 1: It's a good sign that SCOTUS is not taking Donald

435
00:24:01,630 --> 00:24:06,350
Speaker 1: Trump's crazy election stuff seriously. Let me rephrase that. Thank

436
00:24:06,470 --> 00:24:10,969
Speaker 1: God that these Republican hacks in the Supreme Court are

437
00:24:11,030 --> 00:24:15,820
Speaker 1: at least taking this stuff seriously enough to not even

438
00:24:15,880 --> 00:24:20,340
Speaker 1: touch it. So Trump is trying to make life hard

439
00:24:20,520 --> 00:24:25,520
Speaker 1: for people who like democracy. And so one of the

440
00:24:25,560 --> 00:24:28,920
Speaker 1: things that he and Todd Blanchard are doing are just

441
00:24:29,560 --> 00:24:34,020
Speaker 1: trying every which way they can to fuck this election up.

442
00:24:34,680 --> 00:24:37,920
Speaker 1: But the Supreme Court has denied the Trump administration's request

443
00:24:38,000 --> 00:24:42,600
Speaker 1: on Monday to hear its case regarding Trump. 20,000 California,

444
00:24:42,619 --> 00:24:46,620
Speaker 1: but ballots that President Trump claims were cast by non-citizens.

445
00:24:46,640 --> 00:24:49,120
Speaker 5: There's a really interesting thing here from Mark Elias, too, that,

446
00:24:49,280 --> 00:24:52,110
Speaker 5: you know, since we're trying to calm people's anxiety, Trump's

447
00:24:52,140 --> 00:24:56,070
Speaker 5: DOJ is one for 29 up against Mark Elias right now.

448
00:24:56,130 --> 00:24:58,290
Speaker 5: And I know you and I aren't sports people, but

449
00:24:58,330 --> 00:25:01,110
Speaker 5: we're smart enough to know that's them not doing so well.

450
00:25:01,270 --> 00:25:04,040
Speaker 1: Yeah. I mean, I think that's really important. These guys

451
00:25:04,140 --> 00:25:08,530
Speaker 1: are the dumbest of the dumb. Thank God. Right. Thank

452
00:25:08,590 --> 00:25:12,430
Speaker 1: God they're morons, because otherwise we'd all be in a

453
00:25:12,490 --> 00:25:15,690
Speaker 1: lot of trouble. And they keep losing because they have

454
00:25:16,010 --> 00:25:20,490
Speaker 1: no nothing to stand on. Mark Elias said that Donald

455
00:25:20,540 --> 00:25:24,100
Speaker 1: Trump is like obsessed with him. But but basically Donald

456
00:25:24,119 --> 00:25:26,060
Speaker 1: Trump is really mad because there are some really good

457
00:25:26,100 --> 00:25:31,800
Speaker 1: lawyers who are who are winning against him. We're going

458
00:25:31,840 --> 00:25:34,160
Speaker 1: to see more of it. I think Alaska is going

459
00:25:34,180 --> 00:25:37,310
Speaker 1: to be the state that Trump world is pretty focused on.

460
00:25:37,890 --> 00:25:41,070
Speaker 1: you know, they'll probably try to throw out some ballots

461
00:25:41,290 --> 00:25:46,609
Speaker 1: in the big cities in Georgia and in Michigan because

462
00:25:46,650 --> 00:25:51,399
Speaker 1: those cities are Black and they want to disenfranchise Black voters.

463
00:25:51,800 --> 00:25:56,300
Speaker 1: But there's a real question since Calais of this Black

464
00:25:56,400 --> 00:26:02,840
Speaker 1: voters striking back towards this administration's white supremacy trying to

465
00:26:02,880 --> 00:26:05,919
Speaker 1: steal their votes. And I think, I still think the

466
00:26:05,960 --> 00:26:08,890
Speaker 1: Calais story, like, I mean, again, we don't know what's

467
00:26:08,930 --> 00:26:13,750
Speaker 1: going to happen tomorrow or today. But I think, like,

468
00:26:13,990 --> 00:26:17,110
Speaker 1: if I were to guess, I think the Calais story,

469
00:26:17,170 --> 00:26:21,649
Speaker 1: the story of Black voters, the story of Latino voters

470
00:26:21,970 --> 00:26:25,600
Speaker 1: taking back their power from this administration that has really

471
00:26:26,160 --> 00:26:32,500
Speaker 1: been just ultimately very much about oppressing people of color,

472
00:26:32,600 --> 00:26:34,960
Speaker 1: I think will be a big story. Agreed.

473
00:26:35,160 --> 00:26:38,320
Speaker 5: Trump's Talking about pardoning Elon Musk, I read you my

474
00:26:38,359 --> 00:26:39,020
Speaker 5: favorite quote of this.

475
00:26:39,359 --> 00:26:40,400
Speaker 3: I would if I needed to.

476
00:26:40,480 --> 00:26:40,780
Speaker 1: Sure.

477
00:26:40,800 --> 00:26:42,189
Speaker 3: I would not even think about it.

478
00:26:42,380 --> 00:26:42,810
Speaker 1: I do it.

479
00:26:43,160 --> 00:26:44,560
Speaker 3: I did it for January 6th.

480
00:26:46,020 --> 00:26:49,119
Speaker 1: He's pardoned real criminals who beat up cops. He can

481
00:26:49,160 --> 00:26:53,869
Speaker 1: pardon a guy who might have done. Look, President. OK,

482
00:26:53,910 --> 00:26:56,090
Speaker 1: first of all, I want to talk about this. A

483
00:26:56,150 --> 00:26:59,609
Speaker 1: pre-taped interview on The New York Post. Pod Force One

484
00:26:59,770 --> 00:27:05,629
Speaker 1: with Miranda Devine. You may remember her as being of

485
00:27:05,730 --> 00:27:09,730
Speaker 1: no importance. So the Democrats were planning to arrest Musk,

486
00:27:10,010 --> 00:27:13,350
Speaker 1: seize his factories, and nationalize SpaceX. By the way, they

487
00:27:13,369 --> 00:27:16,450
Speaker 1: should nationalize SpaceX. That actually is true. But you know

488
00:27:16,490 --> 00:27:21,670
Speaker 1: who nationalized companies before Democrats? Donald J.

489
00:27:21,710 --> 00:27:22,090
Speaker 2: Trump?

490
00:27:23,510 --> 00:27:26,890
Speaker 1: It won't happen. It's talk. But if it's talk, I'll

491
00:27:26,970 --> 00:27:31,139
Speaker 1: have to protect them, said Donald Trump. Donald Trump known

492
00:27:31,200 --> 00:27:33,939
Speaker 1: to protect people. I wouldn't even think about it. I

493
00:27:33,960 --> 00:27:39,159
Speaker 1: did it for J6. Yeah, it's amazing. He's an icon. Musk.

494
00:27:39,440 --> 00:27:43,340
Speaker 1: Musk has poured so much money into this election, half

495
00:27:43,700 --> 00:27:48,000
Speaker 1: a billion dollars into all he wants. I mean, his

496
00:27:48,119 --> 00:27:51,300
Speaker 1: nightmare scenario is that Abbott loses. I mean, I wonder

497
00:27:51,359 --> 00:27:54,220
Speaker 1: if Abbott loses if he leaves the country. It's just

498
00:27:54,260 --> 00:27:59,520
Speaker 1: such a good story because the truth is, Elon Musk,

499
00:28:00,270 --> 00:28:02,990
Speaker 1: knows he's done a lot wrong. Those of us who've

500
00:28:03,070 --> 00:28:08,540
Speaker 1: seen the Alex Gibney documentary know that Elon Musk has

501
00:28:08,560 --> 00:28:12,660
Speaker 1: done a lot wrong. And basically, he's just paid off everyone,

502
00:28:12,720 --> 00:28:17,879
Speaker 1: the people who've died in Tesla autopilot accidents, the women

503
00:28:17,920 --> 00:28:20,980
Speaker 1: who've had children for him. You know, everybody gets a

504
00:28:21,109 --> 00:28:26,650
Speaker 1: check and an NDA. And so I do think that

505
00:28:26,950 --> 00:28:32,230
Speaker 1: there probably is some criminal culpability for Elon Musk. And

506
00:28:33,210 --> 00:28:34,850
Speaker 1: we're probably going to see some of that.

507
00:28:35,410 --> 00:28:35,630
Speaker 3: Yeah.

508
00:28:35,770 --> 00:28:38,490
Speaker 5: So, Molly, lastly, we have to discuss one of you

509
00:28:38,530 --> 00:28:42,170
Speaker 5: and I's least favorite members of Congress, Congressman Mike Lawler,

510
00:28:42,710 --> 00:28:48,010
Speaker 5: who in a late-breaking story right before the election, right

511
00:28:48,030 --> 00:28:51,810
Speaker 5: before we vote, Bloomberg says that canvassers are suing Mike

512
00:28:51,870 --> 00:28:55,570
Speaker 5: Lawler for unpaid wages and discrimination. Could not see that

513
00:28:55,590 --> 00:28:57,310
Speaker 5: coming from a guy known for wearing blackface.

514
00:28:58,210 --> 00:29:00,520
Speaker 1: I just want to say one thing about Mike Lawler,

515
00:29:00,580 --> 00:29:03,060
Speaker 1: which is there are a lot of reasons that we

516
00:29:03,100 --> 00:29:09,360
Speaker 1: don't like him. One is because he said that Chuck

517
00:29:09,380 --> 00:29:14,770
Speaker 1: Schumer was anti-Semitic and that his complaining about Haley Stevens

518
00:29:14,810 --> 00:29:19,770
Speaker 1: was ashanda. Like, my culture is not your costume, Mike Lawler.

519
00:29:21,500 --> 00:29:26,780
Speaker 1: The legal Bloomberg newspaper covered this, the defendants that there's

520
00:29:26,840 --> 00:29:30,810
Speaker 1: this that these defendants are suing the Lawler campaign. They're

521
00:29:30,870 --> 00:29:33,989
Speaker 1: saying that the campaign engaged in a fraudulent bait and

522
00:29:34,050 --> 00:29:37,610
Speaker 1: switch by using recruiting materials for the New York Republicans

523
00:29:37,710 --> 00:29:43,610
Speaker 1: campaign and promising a base outlaw. of $ 1, 200 to $ 3, 000

524
00:29:43,290 --> 00:29:47,350
Speaker 1: free housing and travel costs. And you know what they did?

525
00:29:47,430 --> 00:29:51,530
Speaker 1: They did not do that. But Mike Lawler, ever the

526
00:29:51,570 --> 00:29:58,030
Speaker 1: classy guy, has decided to strip Bloomberg of all of

527
00:29:58,370 --> 00:30:02,980
Speaker 1: its press credentials for the campaign. And the people from

528
00:30:03,000 --> 00:30:07,440
Speaker 1: Bloomberg who try to attend will be treated as trespassers.

529
00:30:07,720 --> 00:30:14,790
Speaker 1: Way to stick it to Mike Bloomberg. Mike Lawler. Ed

530
00:30:14,890 --> 00:30:18,600
Speaker 1: Zitron is the author of Where's Your Ed At? and

531
00:30:18,620 --> 00:30:22,940
Speaker 1: the host of Better Offline. Welcome, welcome, Ed.

532
00:30:23,800 --> 00:30:24,600
Speaker 3: Thanks for having me.

533
00:30:25,100 --> 00:30:29,960
Speaker 1: So today is, we are doing sort of counter-programming because

534
00:30:30,100 --> 00:30:32,380
Speaker 1: we're waiting for returns to come in. And so we

535
00:30:32,420 --> 00:30:36,719
Speaker 1: wanted to talk about one of the biggest pieces of

536
00:30:36,820 --> 00:30:42,890
Speaker 1: this midterm program and also of our current life in crisis,

537
00:30:42,970 --> 00:30:46,090
Speaker 1: which is AI. And so I'd love you to start.

538
00:30:46,110 --> 00:30:52,100
Speaker 1: It feels like we're in the beginning of it. You know,

539
00:30:52,300 --> 00:30:57,720
Speaker 1: when during the dot-com bubble, there was this cover of

540
00:30:57,770 --> 00:31:02,110
Speaker 1: a magazine with the money sort of going up in flames.

541
00:31:02,450 --> 00:31:05,630
Speaker 1: I feel like we're sort of at that moment, the

542
00:31:05,790 --> 00:31:09,090
Speaker 1: precipice of like all of this may have been bullshit

543
00:31:09,350 --> 00:31:10,650
Speaker 1: all along. Yes.

544
00:31:10,950 --> 00:31:13,210
Speaker 3: It's kind of what I've been saying for months, which

545
00:31:13,310 --> 00:31:15,390
Speaker 3: is that all of this ends when the money runs

546
00:31:15,590 --> 00:31:18,470
Speaker 3: out and the companies at the top, the anthropics and

547
00:31:18,490 --> 00:31:21,210
Speaker 3: the open AIs, they will only continue as long as

548
00:31:21,250 --> 00:31:25,550
Speaker 3: they are able to access effectively unlimited resources. Anthropic today,

549
00:31:25,610 --> 00:31:31,060
Speaker 3: as we're speaking, added the Q3 2026 information. to its

550
00:31:31,120 --> 00:31:34,760
Speaker 3: filing to go public and it showed growth is slowing

551
00:31:34,800 --> 00:31:39,700
Speaker 3: in revenue but the costs are increasing dramatically over $ 437

552
00:31:38,960 --> 00:31:42,200
Speaker 3: billion worth of non-cancellable commitments and it came out the

553
00:31:42,240 --> 00:31:45,730
Speaker 3: other day a couple days ago actually that the ratings

554
00:31:45,790 --> 00:31:48,310
Speaker 3: agencies are likely to give it a below investment grade

555
00:31:48,390 --> 00:31:51,830
Speaker 3: rating so junk. This is a big problem when you're

556
00:31:51,870 --> 00:31:55,290
Speaker 3: a company that loses billions of dollars and also plans

557
00:31:55,310 --> 00:31:59,750
Speaker 3: to spend hundreds of billions of dollars. And then Only

558
00:31:59,790 --> 00:32:02,430
Speaker 3: a day or two ago, it came out that OpenAI,

559
00:32:02,470 --> 00:32:06,430
Speaker 3: who has been trying to raise a $ 1. 4, $ 1. 5 trillion valuation,

560
00:32:06,790 --> 00:32:10,110
Speaker 3: trying to raise $ 30 billion, raised $ 11 billion at a

561
00:32:10,110 --> 00:32:15,450
Speaker 3: $ 1. 25 trillion valuation. From the Middle East, from the venture

562
00:32:15,470 --> 00:32:18,080
Speaker 3: capitalists and the investors who are best known for investing

563
00:32:18,120 --> 00:32:22,820
Speaker 3: in basically anything that sounds remotely hype-cyclic. And on top

564
00:32:22,880 --> 00:32:25,160
Speaker 3: of that, the debt markets, according to Wall Street Journal,

565
00:32:25,180 --> 00:32:28,490
Speaker 3: are effectively shut off. to the AI data center companies,

566
00:32:28,510 --> 00:32:31,670
Speaker 3: to the ones below the hyperscalers, below the Microsofts and

567
00:32:31,690 --> 00:32:35,140
Speaker 3: Googles of the world. I mean, it is both that

568
00:32:35,220 --> 00:32:37,620
Speaker 3: everything's kind of flaming out, but also that just the

569
00:32:37,660 --> 00:32:40,440
Speaker 3: money is running out. That there is only so much

570
00:32:40,540 --> 00:32:43,480
Speaker 3: you can do and so much money you can ask

571
00:32:43,520 --> 00:32:45,900
Speaker 3: for before someone at the other end goes, hey, wait

572
00:32:45,960 --> 00:32:48,709
Speaker 3: a minute. Was this a good idea? Are you going

573
00:32:48,730 --> 00:32:51,190
Speaker 3: to make enough money to pay me? And I'm not

574
00:32:51,250 --> 00:32:54,270
Speaker 3: even being particularly sarcastic. That's basically what, like, not very

575
00:32:54,330 --> 00:32:58,330
Speaker 3: much outside of those stories. Not very much has happened

576
00:32:58,650 --> 00:33:02,180
Speaker 3: to sour people on the whole AI trade other than

577
00:33:02,240 --> 00:33:06,000
Speaker 3: just vibes getting worse. And there's even more I can

578
00:33:06,040 --> 00:33:08,040
Speaker 3: go into, but I've already said like 90 things.

579
00:33:09,040 --> 00:33:14,390
Speaker 1: Okay, so I want to start. Jesse said, it looks

580
00:33:14,410 --> 00:33:17,030
Speaker 1: like I'm texting someone, but I'm looking for the Elon

581
00:33:17,110 --> 00:33:20,510
Speaker 1: Musk tweets because I feel like that was a really...

582
00:33:20,910 --> 00:33:24,670
Speaker 1: important cycle, which was he had every AI is going

583
00:33:24,690 --> 00:33:28,350
Speaker 1: to make us all rich beyond belief to grok AI

584
00:33:28,450 --> 00:33:31,990
Speaker 1: is basically cost. So talk us through the, the Elon

585
00:33:32,030 --> 00:33:36,650
Speaker 1: Musk triptych of tweets and explain. Mr.

586
00:33:36,710 --> 00:33:40,620
Speaker 3: Musk has had a very bizarre adventure himself because when

587
00:33:40,640 --> 00:33:43,600
Speaker 3: he took SpaceX public, they were a rocket company with

588
00:33:43,640 --> 00:33:46,760
Speaker 3: an internet service provider in Starlink with this kind of

589
00:33:46,920 --> 00:33:51,040
Speaker 3: nascent AI cloud. He'd been building these data centers. Fast

590
00:33:51,120 --> 00:33:53,820
Speaker 3: and loose, no permits, whatever it happens. We don't have

591
00:33:54,300 --> 00:33:58,719
Speaker 3: regulators out in Texas. Actually, it might be Mississippi. Forgive me.

592
00:33:58,760 --> 00:34:02,350
Speaker 3: But nevertheless, don't have regulators out there either. And so,

593
00:34:02,440 --> 00:34:05,230
Speaker 3: Musk has been building out this capacity for Grog, only

594
00:34:05,270 --> 00:34:08,410
Speaker 3: to realize, crap, not enough users for Grog, so we're

595
00:34:08,430 --> 00:34:11,250
Speaker 3: going to sell the capacity to Anthropic. So, more than,

596
00:34:11,690 --> 00:34:15,300
Speaker 3: I think it was 34.5% of SpaceX's last quarterly revenue

597
00:34:15,340 --> 00:34:16,960
Speaker 3: was Anthropic paying it for compute.

598
00:34:17,700 --> 00:34:17,899
Speaker 1: Now.

599
00:34:18,570 --> 00:34:20,850
Speaker 3: On top of that, Elon Musk has still tried to

600
00:34:20,890 --> 00:34:23,989
Speaker 3: make Grok work, trying to make Grok into something, and

601
00:34:24,010 --> 00:34:29,000
Speaker 3: made something called a GrokBot, which is basically MetaMuse or

602
00:34:29,160 --> 00:34:35,040
Speaker 3: OpenAI.org or AnthropicDuet, but musky. And then the other day,

603
00:34:35,080 --> 00:34:38,000
Speaker 3: about three days ago, he said, yes, well, we will

604
00:34:38,040 --> 00:34:41,319
Speaker 3: not need cash in the future. We'll be totally without cash.

605
00:34:41,360 --> 00:34:46,160
Speaker 3: We'll be fine. Then yesterday he said, Yeah, actually, we

606
00:34:46,180 --> 00:34:48,960
Speaker 3: had to reduce usage limits on Grokbot because it was

607
00:34:49,080 --> 00:34:51,140
Speaker 3: unsustainable when compared to the revenue.

608
00:34:50,860 --> 00:34:51,250
Speaker 5: It brought in.

609
00:34:52,200 --> 00:34:55,520
Speaker 3: So, I don't know. It's a crazy idea. It's almost

610
00:34:55,560 --> 00:34:58,060
Speaker 3: as if Elon Musk just says things when they come

611
00:34:58,100 --> 00:35:00,580
Speaker 3: off the top of his head, and then when reality

612
00:35:00,620 --> 00:35:04,259
Speaker 3: turns up, just completely changes tune. Almost as if he

613
00:35:04,300 --> 00:35:07,200
Speaker 3: never said anything. It's almost like there's never really been

614
00:35:07,239 --> 00:35:09,770
Speaker 3: any accountability for what he says, the amount of times

615
00:35:09,810 --> 00:35:11,610
Speaker 3: he's lied, the amount of times he's made stuff up.

616
00:35:11,870 --> 00:35:15,640
Speaker 3: I'm just guessing here. But nevertheless... If Elon Musk, who

617
00:35:15,680 --> 00:35:19,940
Speaker 3: just raised $ 40 billion to buy more Nvidia chips, is

618
00:35:20,000 --> 00:35:23,880
Speaker 3: saying that the cost is unsustainably high, the cost is

619
00:35:23,920 --> 00:35:26,930
Speaker 3: unsustainably high for the richest man in the world, how

620
00:35:26,969 --> 00:35:28,190
Speaker 3: do you think the rest of them are going to

621
00:35:28,210 --> 00:35:30,390
Speaker 3: work this out? And what's funny is, if you go

622
00:35:30,430 --> 00:35:32,089
Speaker 3: to the AI boosters and tell you this, they just go,

623
00:35:32,870 --> 00:35:34,890
Speaker 3: they'll work it out. They just look at you like

624
00:35:35,070 --> 00:35:38,810
Speaker 3: you're reading algebra to a dog. We'll just work it out,

625
00:35:38,870 --> 00:35:39,390
Speaker 3: get bigger.

626
00:35:40,330 --> 00:35:46,210
Speaker 1: So, again, from... I want you to pause and I

627
00:35:46,230 --> 00:35:49,230
Speaker 1: want to like, one of the big criticisms of you

628
00:35:49,410 --> 00:35:53,110
Speaker 1: is that you have been saying that AI is a

629
00:35:53,150 --> 00:35:54,330
Speaker 1: bubble for a long time.

630
00:35:55,010 --> 00:35:57,330
Speaker 3: So I've been saying it because, and this is as

631
00:35:57,410 --> 00:35:59,530
Speaker 3: true today as it was three years ago when I started,

632
00:35:59,950 --> 00:36:03,150
Speaker 3: the underlying economics don't make sense. The companies are all unprofitable.

633
00:36:03,410 --> 00:36:07,270
Speaker 3: They're all unsustainable. To grow an AI company requires you

634
00:36:07,290 --> 00:36:09,569
Speaker 3: to either buy a bunch more compute or commit large

635
00:36:09,610 --> 00:36:13,190
Speaker 3: amounts to open AI and anthropic. the AI data centers themselves,

636
00:36:13,270 --> 00:36:16,170
Speaker 3: it's unclear if you can even really run one of

637
00:36:16,230 --> 00:36:19,840
Speaker 3: those profitably. It's kind of an open question. And if

638
00:36:19,880 --> 00:36:22,140
Speaker 3: you can, when I say profitably, I mean in a

639
00:36:22,180 --> 00:36:24,500
Speaker 3: way that pays off all of the money you've spent

640
00:36:24,520 --> 00:36:27,700
Speaker 3: to build the bloody thing. The reason that people criticize

641
00:36:27,719 --> 00:36:29,850
Speaker 3: me is, yeah, in 2024, I thought this would be

642
00:36:29,890 --> 00:36:32,690
Speaker 3: wrapped up quicker. That's the biggest mistake I made. I

643
00:36:32,730 --> 00:36:37,109
Speaker 3: had this very naive belief that there was a kind

644
00:36:37,130 --> 00:36:39,730
Speaker 3: of a sensible nature to the system beyond the whole

645
00:36:40,050 --> 00:36:42,299
Speaker 3: market hypothesis. I hate when people are like, oh, the

646
00:36:42,340 --> 00:36:45,260
Speaker 3: market can stay irrational longer than you can stay sovereign.

647
00:36:45,580 --> 00:36:48,360
Speaker 3: What I meant was that the largest companies in the

648
00:36:48,420 --> 00:36:51,260
Speaker 3: world would not blow a trillion plus dollars on something

649
00:36:51,520 --> 00:36:54,839
Speaker 3: without a financial return that they're proud enough to share

650
00:36:54,860 --> 00:36:58,489
Speaker 3: with us. Microsoft, Google, Amazon, Meta haven't mentioned their actual

651
00:36:58,530 --> 00:37:02,570
Speaker 3: AI revenues once. But that is a big sign. People

652
00:37:02,630 --> 00:37:06,080
Speaker 3: love to point at me saying in 2024, 2025, oh, look,

653
00:37:06,100 --> 00:37:08,120
Speaker 3: he said it would be over by now. Look, look

654
00:37:08,140 --> 00:37:08,399
Speaker 3: at that.

655
00:37:08,930 --> 00:37:11,210
Speaker 1: Right, it's always hard to get the timing right.

656
00:37:11,489 --> 00:37:15,089
Speaker 3: Well, they do that because by doing that, they don't

657
00:37:15,110 --> 00:37:17,950
Speaker 3: have to read. They don't actually have to engage with

658
00:37:18,010 --> 00:37:19,870
Speaker 3: any of the numbers. They don't have to engage with

659
00:37:19,890 --> 00:37:22,690
Speaker 3: the arguments. They can just go, well, look, look what

660
00:37:22,730 --> 00:37:26,330
Speaker 3: he said in February 2025. I've got him. That invalidates

661
00:37:26,390 --> 00:37:29,030
Speaker 3: the hundreds of thousands of words since. And you know what?

662
00:37:29,900 --> 00:37:34,100
Speaker 3: That's fine. I'm too busy to worry about that, but

663
00:37:34,670 --> 00:37:37,330
Speaker 3: The thing is, is the Michael Burry quote from The

664
00:37:37,350 --> 00:37:39,730
Speaker 3: Big Show. It's like, I may have been early, but

665
00:37:39,750 --> 00:37:43,089
Speaker 3: I'm not wrong. Because right now, there's a story going

666
00:37:43,110 --> 00:37:46,739
Speaker 3: around related to this Elon Musk Grop bot thing, which

667
00:37:46,840 --> 00:37:50,920
Speaker 3: is that most AI, in fact, every AI service is subsidized,

668
00:37:51,000 --> 00:37:53,820
Speaker 3: meaning that they allow each user to burn several dollars

669
00:37:54,200 --> 00:37:56,960
Speaker 3: worth of AI tokens for every dollar they spend on

670
00:37:56,980 --> 00:38:01,569
Speaker 3: the platform to subscribe. And indeed, an AI founder, CEO

671
00:38:01,610 --> 00:38:04,569
Speaker 3: of Replit said, Get ready for the rug pull for

672
00:38:04,610 --> 00:38:07,469
Speaker 3: your subsidized subscriptions. It's exactly what I've been warning about

673
00:38:07,489 --> 00:38:11,370
Speaker 3: for years. And that's because AI tried to do the

674
00:38:11,390 --> 00:38:13,390
Speaker 3: thing that Silicon Valley does, which is they set the

675
00:38:13,430 --> 00:38:15,469
Speaker 3: prices really low. They get people in the door and

676
00:38:15,489 --> 00:38:18,450
Speaker 3: they crank up the prices. The problem is when they

677
00:38:18,469 --> 00:38:20,850
Speaker 3: crank up the prices, their users scream at them like

678
00:38:20,870 --> 00:38:23,350
Speaker 3: they're being stung to death by bees. Go on Twitter.

679
00:38:23,370 --> 00:38:24,850
Speaker 3: If you really want to laugh right now, and it

680
00:38:24,950 --> 00:38:28,140
Speaker 3: is funny, go on Twitter and look up GrokBot. limits

681
00:38:28,460 --> 00:38:31,100
Speaker 3: and watch people doing like Aaron Sorkin speeches that you'll

682
00:38:31,120 --> 00:38:34,000
Speaker 3: almost be like, so you, I pay $ 200 a month

683
00:38:34,020 --> 00:38:37,200
Speaker 3: for Gronk, ultra heavy premium, and I can't use it

684
00:38:37,260 --> 00:38:40,719
Speaker 3: as much. Mr. Musk, sir. It's genuinely really funny, but

685
00:38:40,760 --> 00:38:43,170
Speaker 3: I've been warning about this for years. This is what

686
00:38:43,210 --> 00:38:46,330
Speaker 3: I call the subprime AI crisis, which is that everyone

687
00:38:46,390 --> 00:38:49,529
Speaker 3: builds their services on top of open AI and Anthropic,

688
00:38:49,989 --> 00:38:52,670
Speaker 3: and they do so getting people through the door. But

689
00:38:52,710 --> 00:38:55,770
Speaker 3: then when they will eventually have to crank up the prices,

690
00:38:55,790 --> 00:38:58,779
Speaker 3: the service operators So the users who are used to

691
00:38:58,840 --> 00:39:01,359
Speaker 3: running their workflows on these platforms at these discounted rates

692
00:39:01,400 --> 00:39:04,380
Speaker 3: will suddenly find that they can't even use them in

693
00:39:04,400 --> 00:39:06,200
Speaker 3: the way they used to. Again, go and look up

694
00:39:06,219 --> 00:39:08,259
Speaker 3: the Grockbot people. People are saying, I can't use it

695
00:39:08,300 --> 00:39:10,500
Speaker 3: how I used to. And that's coming for the entire

696
00:39:10,560 --> 00:39:11,260
Speaker 3: AI industry.

697
00:39:11,580 --> 00:39:13,940
Speaker 1: So I want to get back to something for a

698
00:39:13,960 --> 00:39:20,100
Speaker 1: minute here, which is like, I believe tech companies killed magazines, newspapers, newspapers.

699
00:39:21,590 --> 00:39:24,300
Speaker 1: They did. They killed it. They said, you know, instead

700
00:39:24,360 --> 00:39:29,200
Speaker 1: of reading the Ohio plane dealer right or whatever you

701
00:39:29,239 --> 00:39:33,580
Speaker 1: were reading the facebook page of your grandmother right yeah

702
00:39:33,640 --> 00:39:37,680
Speaker 1: like you're reading boomers say facebook lies to each other

703
00:39:37,860 --> 00:39:42,549
Speaker 1: instead of like your local newspaper And that is— and we

704
00:39:42,590 --> 00:39:46,169
Speaker 1: know why that happened, because they were incentivized to make money.

705
00:39:46,210 --> 00:39:50,730
Speaker 1: But ultimately, people don't pay for Facebook, right? They don't

706
00:39:50,770 --> 00:39:54,330
Speaker 1: pay for Facebook. They don't pay for Twitter, really. I mean,

707
00:39:54,350 --> 00:39:57,859
Speaker 1: some people do, but most people don't. So why would

708
00:39:57,890 --> 00:40:03,670
Speaker 1: they pay for— one of these AI bots when it's

709
00:40:03,730 --> 00:40:07,029
Speaker 1: still the same, ultimately the same thing, which is information.

710
00:40:07,050 --> 00:40:09,690
Speaker 1: Like if you don't pay for the New York Times

711
00:40:09,870 --> 00:40:11,910
Speaker 1: and you don't pay for the Washington Post, I mean,

712
00:40:12,010 --> 00:40:15,080
Speaker 1: I understand there's some functionality if you're a law firm

713
00:40:15,680 --> 00:40:19,640
Speaker 1: or if you're, you know, whatever, you maybe want your

714
00:40:19,719 --> 00:40:24,040
Speaker 1: search through AI, but like ultimately most people don't on

715
00:40:24,080 --> 00:40:27,549
Speaker 1: a consumer level pay for anything like that. So....

716
00:40:29,100 --> 00:40:31,020
Speaker 3: You can do more than just search with it. You

717
00:40:31,060 --> 00:40:33,779
Speaker 3: can generate code. It can do some software engineering things.

718
00:40:33,860 --> 00:40:36,279
Speaker 3: And indeed, that seems to be the place where they're

719
00:40:36,300 --> 00:40:38,940
Speaker 3: making their biggest moves. I've heard terrible things about the

720
00:40:38,980 --> 00:40:42,960
Speaker 3: legal services. But the point is, most people use these

721
00:40:42,980 --> 00:40:46,009
Speaker 3: things like search. And most people, as a result of

722
00:40:46,120 --> 00:40:48,890
Speaker 3: using them like search, are, to your point, unlikely to

723
00:40:48,930 --> 00:40:51,790
Speaker 3: pay for them. Because think of it as you were saying.

724
00:40:52,340 --> 00:40:54,380
Speaker 3: We are not thinking about the cost of search. The

725
00:40:54,420 --> 00:40:56,339
Speaker 3: whole point of search, and it's got much worse because

726
00:40:56,360 --> 00:41:00,370
Speaker 3: they're making more about ads. It's free, and the monetization

727
00:41:00,430 --> 00:41:04,130
Speaker 3: is invisible to us. It's very exploitative, but it's invisible. Also,

728
00:41:04,250 --> 00:41:06,649
Speaker 3: the cost of running a search query is pretty low.

729
00:41:07,130 --> 00:41:11,509
Speaker 3: The problem with an AI service is that your most excitable,

730
00:41:12,050 --> 00:41:15,219
Speaker 3: engaged users are also your most expensive because they use

731
00:41:15,239 --> 00:41:19,700
Speaker 3: the service more. They're generating more. They're pushing the highest-end

732
00:41:19,760 --> 00:41:21,940
Speaker 3: models more. the most because they want to see what

733
00:41:21,960 --> 00:41:25,760
Speaker 3: they do, and the more they pay, the more incentive

734
00:41:25,820 --> 00:41:28,100
Speaker 3: they have to use them more so they get value

735
00:41:28,120 --> 00:41:31,880
Speaker 3: for money. So it's a lose-lose situation all the way

736
00:41:31,900 --> 00:41:35,690
Speaker 3: around because the AI industry is sold on this scam of, yeah,

737
00:41:35,770 --> 00:41:37,810
Speaker 3: the thing that you get three months ago, that's how

738
00:41:37,830 --> 00:41:41,110
Speaker 3: it's always going to be, even though on OpenAI, on Anthropic,

739
00:41:41,450 --> 00:41:43,989
Speaker 3: on Replit, on Cursor, and all of these others, The

740
00:41:44,030 --> 00:41:47,400
Speaker 3: services have got measurably worse over time because, surprise, surprise,

741
00:41:47,600 --> 00:41:49,479
Speaker 3: they were losing a bunch of money and they're trying

742
00:41:49,540 --> 00:41:52,280
Speaker 3: to not lose as much money. It's just a very

743
00:41:52,480 --> 00:41:58,169
Speaker 3: strange industry that I'm not sure. I hear from people

744
00:41:58,190 --> 00:41:59,910
Speaker 3: all the time being like, oh, well, actually, the plan

745
00:41:59,930 --> 00:42:03,070
Speaker 3: is surveillance. Oh, actually, these are the smartest people in

746
00:42:03,090 --> 00:42:05,270
Speaker 3: the world. They wouldn't make this mistake. I think people

747
00:42:05,330 --> 00:42:07,420
Speaker 3: need to subscribe to a much simpler worldview, which is

748
00:42:07,480 --> 00:42:10,060
Speaker 3: no one really had a plan. They saw chat GPT

749
00:42:10,100 --> 00:42:12,460
Speaker 3: take off and everyone went crap. We haven't got any

750
00:42:12,500 --> 00:42:14,319
Speaker 3: other height. We don't have a new iPhone. We don't

751
00:42:14,360 --> 00:42:16,800
Speaker 3: have a new Google search. We don't have a new Facebook.

752
00:42:16,940 --> 00:42:19,660
Speaker 3: We have AI. So let's all buy chips. And they

753
00:42:19,680 --> 00:42:21,620
Speaker 3: got rewarded by the markets, even though none of them

754
00:42:21,640 --> 00:42:24,060
Speaker 3: will talk about how much money they have, how much

755
00:42:24,100 --> 00:42:24,660
Speaker 3: money they're making.

756
00:42:25,020 --> 00:42:28,299
Speaker 1: But it's fundamentally a business model problem, right? I mean,

757
00:42:28,380 --> 00:42:31,440
Speaker 1: among other things, like there's no way to make the

758
00:42:31,500 --> 00:42:34,020
Speaker 1: money you would need to pay for the building that

759
00:42:34,040 --> 00:42:34,589
Speaker 1: you're doing.

760
00:42:35,300 --> 00:42:39,630
Speaker 3: So this is the problem. Theoretically, you could, after five years,

761
00:42:39,750 --> 00:42:42,330
Speaker 3: pay off what a data center costs if you had

762
00:42:42,670 --> 00:42:46,130
Speaker 3: a consistent client that pays top dollar for the whole time,

763
00:42:46,969 --> 00:42:49,489
Speaker 3: and they agree, and they actually pay. The problem is

764
00:42:49,570 --> 00:42:52,770
Speaker 3: that most AI compute is paid for by OpenAI and Anthropic,

765
00:42:53,130 --> 00:42:55,529
Speaker 3: and the rest is paid for by AI startups that

766
00:42:55,570 --> 00:42:58,069
Speaker 3: are venture backed and lose a bunch of money. So

767
00:42:58,110 --> 00:43:00,649
Speaker 3: you are basically building these data centers for either two

768
00:43:00,730 --> 00:43:04,250
Speaker 3: companies that are unsustainable and unprofitable, or hundreds of others

769
00:43:04,290 --> 00:43:08,839
Speaker 3: that are also unsustainable and unprofitable. So the way to

770
00:43:08,880 --> 00:43:12,399
Speaker 3: break this would be some magical form of tech that

771
00:43:12,440 --> 00:43:15,940
Speaker 3: would bring the cost down by like a hundred times.

772
00:43:16,100 --> 00:43:19,080
Speaker 3: I don't know. They have yet to prove that that's possible.

773
00:43:19,140 --> 00:43:21,950
Speaker 3: We hear a lot of noise every new generation of

774
00:43:22,050 --> 00:43:24,669
Speaker 3: Nvidia chip. Oh, this will be the one that makes

775
00:43:24,710 --> 00:43:29,629
Speaker 3: things cheaper, whatever. Not really happening. Hasn't seemed to... They like,

776
00:43:29,750 --> 00:43:31,509
Speaker 3: oh yeah, it's cheaper in this thing when you look

777
00:43:31,530 --> 00:43:34,490
Speaker 3: at this graph here. But there's never been... a connection

778
00:43:34,510 --> 00:43:36,310
Speaker 3: between it where it just goes, yeah, it's actually going

779
00:43:36,330 --> 00:43:39,280
Speaker 3: to save this much money. Even OpenAI announced their own chip,

780
00:43:39,700 --> 00:43:42,640
Speaker 3: and they didn't even say it would be profitable or

781
00:43:42,760 --> 00:43:45,339
Speaker 3: they didn't really give any financial information. And they're still

782
00:43:45,380 --> 00:43:47,620
Speaker 3: trying to work it out, and there's not enough time.

783
00:43:48,400 --> 00:43:53,480
Speaker 1: So Sam Altman believes people owning RAM is wasteful and

784
00:43:53,520 --> 00:43:58,200
Speaker 1: that it belongs in data centers instead. Mm-hmm. Is that

785
00:43:58,280 --> 00:44:03,720
Speaker 1: because he needs... be able to charge people for being

786
00:44:03,739 --> 00:44:04,640
Speaker 1: in data centers?

787
00:44:05,340 --> 00:44:09,060
Speaker 3: I think it's because he's a fucking asshole. I think

788
00:44:09,080 --> 00:44:12,080
Speaker 3: that it's because he is a disconnected, rich piece of shit.

789
00:44:12,460 --> 00:44:17,040
Speaker 3: I don't think he understands anyone's problems. He said that

790
00:44:17,160 --> 00:44:20,740
Speaker 3: quote on the Cheeky Pint podcast, which is, I kid

791
00:44:20,780 --> 00:44:25,010
Speaker 3: you not, a bar built inside the offices of Stripe.

792
00:44:26,110 --> 00:44:29,230
Speaker 3: And so you are interviewed in this fake pub by

793
00:44:29,370 --> 00:44:32,469
Speaker 3: another billionaire. and thus you'll just say all sorts of

794
00:44:32,510 --> 00:44:36,490
Speaker 3: wackadoo shit, such as Sam Altman saying, yeah, actually RAM

795
00:44:36,770 --> 00:44:39,230
Speaker 3: should be in data centers because it's more important to

796
00:44:39,270 --> 00:44:44,550
Speaker 3: provide intelligence to more people. Complete nonsense wank. Just nonsense.

797
00:44:45,350 --> 00:44:49,450
Speaker 3: Absolute crap. He is just a moron. I think he's

798
00:44:49,489 --> 00:44:53,040
Speaker 3: an absolute bozo. I heard that quote, and it's one

799
00:44:53,120 --> 00:44:55,060
Speaker 3: of many quotes from this guy where you're just like,

800
00:44:55,520 --> 00:44:59,100
Speaker 3: what are you talking about? The other time... He said

801
00:44:59,360 --> 00:45:04,180
Speaker 3: that he couldn't imagine raising a child without chat GPT.

802
00:45:04,330 --> 00:45:07,290
Speaker 3: What do you mean by that? The other thing he said,

803
00:45:07,870 --> 00:45:11,750
Speaker 3: another really bizarre one, where he was saying, oh, yeah, well,

804
00:45:11,770 --> 00:45:13,940
Speaker 3: you complain about the energy cost of data centers. Well,

805
00:45:13,960 --> 00:45:17,300
Speaker 3: it takes a lot of energy to train a child.

806
00:45:18,540 --> 00:45:21,400
Speaker 3: And it's just, you hear these things, you're like, are

807
00:45:21,440 --> 00:45:25,450
Speaker 3: you an alien? What's wrong with you, man? What do

808
00:45:25,489 --> 00:45:27,770
Speaker 3: you do? Do you just, when you're done with business,

809
00:45:27,790 --> 00:45:31,359
Speaker 3: just stare at the wall all day? Goddamn sociopath.

810
00:45:31,640 --> 00:45:33,860
Speaker 1: Okay, so one of the things that's happened in this

811
00:45:33,980 --> 00:45:42,009
Speaker 1: election is that there has been massive backlash to data centers. Massive, massive, massive.

812
00:45:43,810 --> 00:45:46,370
Speaker 1: And a real problem, tech has had a real problem.

813
00:45:46,430 --> 00:45:48,470
Speaker 1: Tech boards have had a real problem explaining to people

814
00:45:48,590 --> 00:45:54,700
Speaker 1: why They should want data centers. What do you think

815
00:45:54,739 --> 00:46:00,069
Speaker 1: this does? Because remember, data centers are beneficiaries of tax write-offs.

816
00:46:00,090 --> 00:46:03,330
Speaker 1: They're beneficiaries of all sorts of things that have made

817
00:46:03,370 --> 00:46:06,609
Speaker 1: it more attractive, easier, and cheaper to build them. But

818
00:46:06,690 --> 00:46:07,989
Speaker 1: now voters don't like them.

819
00:46:08,910 --> 00:46:12,589
Speaker 3: Well, that's the thing. The big mistake they made, even

820
00:46:12,630 --> 00:46:14,379
Speaker 3: though it seemed smart in the beginning, was to call

821
00:46:14,410 --> 00:46:17,800
Speaker 3: them data centers. That's why Sam Altman, Clammy Sammy himself,

822
00:46:17,860 --> 00:46:21,870
Speaker 3: is trying to rename them AI campuses. So they wanted

823
00:46:21,910 --> 00:46:25,250
Speaker 3: to do that because regular data centers, which are very,

824
00:46:25,350 --> 00:46:29,530
Speaker 3: very different, much smaller with different tech inside, are useful.

825
00:46:29,690 --> 00:46:33,420
Speaker 3: So the stuff that gets us Facebook and email and

826
00:46:33,620 --> 00:46:36,719
Speaker 3: our internet content delivery net. Like, we have all sorts

827
00:46:36,760 --> 00:46:40,120
Speaker 3: of useful kinds of data centers. The AI industry intentionally

828
00:46:40,360 --> 00:46:44,180
Speaker 3: used the terminology of data center so that they could

829
00:46:44,219 --> 00:46:46,950
Speaker 3: make the half-arsed centrist moron argument.

830
00:46:46,670 --> 00:46:49,710
Speaker 4: Of, oh, Oh, so you don't like data centers. I

831
00:46:49,750 --> 00:46:51,570
Speaker 4: guess you don't need streaming media.

832
00:46:51,969 --> 00:46:56,860
Speaker 3: Even though an AI data center is magnitudes larger, more expensive,

833
00:46:56,880 --> 00:47:00,680
Speaker 3: and more power intensive. So yeah, understandably, people are a

834
00:47:00,719 --> 00:47:04,379
Speaker 3: little bit pissed off at data centers because for, what,

835
00:47:04,560 --> 00:47:08,680
Speaker 3: three years? Four years? We have all paid more for everything.

836
00:47:08,739 --> 00:47:11,230
Speaker 3: Cost of gas, cost of goods, cost of living, all

837
00:47:11,270 --> 00:47:16,739
Speaker 3: expensive for us. We are overtaxed. We are ultimately... pressured

838
00:47:16,800 --> 00:47:19,690
Speaker 3: and given very little from our government. Unless, of course,

839
00:47:19,710 --> 00:47:22,090
Speaker 3: we're building a data center, then you can get whatever

840
00:47:22,170 --> 00:47:25,890
Speaker 3: funding you need, Jensen Huang for video pay. And also

841
00:47:25,930 --> 00:47:28,310
Speaker 3: these things are huge and they get massive tax abatements

842
00:47:28,330 --> 00:47:30,890
Speaker 3: and they use a bunch of water and electricity. And

843
00:47:30,910 --> 00:47:32,690
Speaker 3: there's all of this risk that is carried by the

844
00:47:32,750 --> 00:47:37,770
Speaker 3: taxpayer while giving the corporation behind the data center, which

845
00:47:37,810 --> 00:47:41,129
Speaker 3: also is allowed to sign NDAs and hide everything. We're

846
00:47:41,150 --> 00:47:43,169
Speaker 3: giving them everything they need. So yeah, people are pissed

847
00:47:43,210 --> 00:47:45,529
Speaker 3: off about this and there is no real fixing this

848
00:47:45,610 --> 00:47:48,350
Speaker 3: because fundamentally in the AI people hate when I say this,

849
00:47:49,010 --> 00:47:50,989
Speaker 3: why do we need more data centers? Why do we

850
00:47:51,010 --> 00:47:53,140
Speaker 3: need them? Who do we need them for? What are

851
00:47:53,180 --> 00:47:56,640
Speaker 3: we not getting as a result of this? If we

852
00:47:56,989 --> 00:48:00,739
Speaker 3: stopped building these things tomorrow, what would stop? Would Anthropic

853
00:48:00,820 --> 00:48:04,379
Speaker 3: or OpenAI service? Nothing. Nothing would actually change other than

854
00:48:04,460 --> 00:48:08,680
Speaker 3: Blackstone and the various other Patagonia gargoyles being able to

855
00:48:09,060 --> 00:48:13,450
Speaker 3: get a shred of fees. And so these moratoriums, for better,

856
00:48:13,489 --> 00:48:16,969
Speaker 3: for worse, for whatever, are just a natural result of

857
00:48:17,030 --> 00:48:20,759
Speaker 3: a tech industry that is unregulated and unrestrained, doing what

858
00:48:20,780 --> 00:48:23,479
Speaker 3: it wants. And then suddenly it turns out that people

859
00:48:23,520 --> 00:48:27,799
Speaker 3: don't like being exploited and having these horrifying monoliths built

860
00:48:27,840 --> 00:48:31,120
Speaker 3: in their community and then going to their town hall meetings.

861
00:48:31,160 --> 00:48:34,170
Speaker 3: And this is what actually radicalized them. Again and again,

862
00:48:34,530 --> 00:48:36,770
Speaker 3: people go to town hall meetings and they say, I

863
00:48:36,790 --> 00:48:39,170
Speaker 3: don't want this. And they spend hours giving the The

864
00:48:39,210 --> 00:48:41,390
Speaker 3: town council saying, I don't want this, I don't want this.

865
00:48:41,790 --> 00:48:44,669
Speaker 3: And then the town council every time greenlights everything. Happened

866
00:48:44,690 --> 00:48:46,930
Speaker 3: in Vineland, New Jersey. It's happened all over the country.

867
00:48:47,489 --> 00:48:51,219
Speaker 3: And so data centers have become this very visible and

868
00:48:51,260 --> 00:48:56,100
Speaker 3: egregious symbol of capitalism, but also a symbol of how

869
00:48:56,219 --> 00:49:02,240
Speaker 3: elected officials are regularly not thinking in the best interest

870
00:49:02,280 --> 00:49:04,879
Speaker 3: of the people, but in the interest of who will

871
00:49:04,920 --> 00:49:07,460
Speaker 3: take them out for dinner, who will make them feel good.

872
00:49:07,760 --> 00:49:10,620
Speaker 3: because the important point with this is data centers do

873
00:49:10,660 --> 00:49:13,980
Speaker 3: not create many jobs. And when they do create them

874
00:49:14,020 --> 00:49:17,670
Speaker 3: for the construction, those jobs are imported in. In a

875
00:49:17,750 --> 00:49:21,719
Speaker 3: very real sense, data center construction is a way of

876
00:49:21,760 --> 00:49:28,560
Speaker 3: the West pillaging the South, Louisiana, Mississippi, Texas especially. And

877
00:49:28,680 --> 00:49:31,120
Speaker 3: so I find the whole thing terribly ugly and I

878
00:49:31,160 --> 00:49:33,000
Speaker 3: find all of the data center centrists who.

879
00:49:32,960 --> 00:49:35,480
Speaker 4: Are like, oh, well, let's look into these water claims

880
00:49:35,500 --> 00:49:37,740
Speaker 4: a little bit more. Oh, data centers are actually creating

881
00:49:37,760 --> 00:49:41,720
Speaker 4: jobs if you do this mathematics here. These people are noxious.

882
00:49:42,080 --> 00:49:45,770
Speaker 4: And I find this build down truly ugly. And I

883
00:49:45,810 --> 00:49:46,810
Speaker 4: can't wait until it's over.

884
00:49:47,210 --> 00:49:49,549
Speaker 1: And it is an 80-20 issue. I mean, where you

885
00:49:49,690 --> 00:49:51,270
Speaker 1: are is where most Americans are.

886
00:49:52,810 --> 00:49:55,250
Speaker 3: I mean, what's in it for them? And people will say, oh,

887
00:49:55,489 --> 00:49:58,279
Speaker 3: we can share the profits of AI. What profits? Everyone's

888
00:49:58,320 --> 00:50:01,700
Speaker 3: losing money. Oh, we'll share the benefits of AI. What benefits?

889
00:50:01,739 --> 00:50:04,759
Speaker 3: Can you be remotely specific? Well, they go, well, in

890
00:50:04,780 --> 00:50:07,739
Speaker 3: the future. No, no, no. Shut up. I don't want that.

891
00:50:07,920 --> 00:50:08,560
Speaker 3: I want now.

892
00:50:09,090 --> 00:50:11,830
Speaker 1: So AI is not going to replace us. There's not

893
00:50:11,850 --> 00:50:14,370
Speaker 1: going to be a Molly bod. No.

894
00:50:15,469 --> 00:50:18,069
Speaker 3: It's just like, we are so.

895
00:50:18,090 --> 00:50:20,009
Speaker 1: But there's also not going to be a Hyperloop to

896
00:50:20,050 --> 00:50:21,980
Speaker 1: take me to DC in four minutes.

897
00:50:22,440 --> 00:50:24,100
Speaker 3: There's not going to be a... Imagine if we put

898
00:50:24,140 --> 00:50:27,800
Speaker 3: this money into a high-speed train. I know the Californian

899
00:50:27,860 --> 00:50:30,360
Speaker 3: one's kind of dodgy, but can you imagine if we

900
00:50:30,380 --> 00:50:33,219
Speaker 3: put this into even a quarter of this money into

901
00:50:33,320 --> 00:50:35,940
Speaker 3: anything for people? Can you imagine if anything good was

902
00:50:35,980 --> 00:50:40,190
Speaker 3: done with it? Instead, it's just Microsoft, Google, Amazon, Neta

903
00:50:40,230 --> 00:50:43,989
Speaker 3: handing money to NVIDIA and stock number go up and

904
00:50:44,050 --> 00:50:49,890
Speaker 3: bubble created. And so it's very frustrating because having had

905
00:50:49,930 --> 00:50:51,509
Speaker 3: a lot of these arguments with a lot of these

906
00:50:51,570 --> 00:50:53,569
Speaker 3: people and they're like, well, look, LLMs have got better

907
00:50:53,610 --> 00:50:56,110
Speaker 3: and all this. It's like, yeah, but compared to the

908
00:50:56,210 --> 00:50:59,410
Speaker 3: trillion plus costs, the hundreds of billions of dollars that

909
00:50:59,450 --> 00:51:04,089
Speaker 3: Anthropic and OpenAIH have promised people, what is the thing

910
00:51:04,160 --> 00:51:06,839
Speaker 3: I meant to look at and be excited about? Like,

911
00:51:06,890 --> 00:51:08,650
Speaker 3: what is the thing that most people, what is a

912
00:51:08,750 --> 00:51:10,390
Speaker 3: regular person meant to do?

913
00:51:10,770 --> 00:51:12,770
Speaker 1: What indeed. Ed Zitron.

914
00:51:13,190 --> 00:51:13,850
Speaker 3: Thanks for having me.

915
00:51:14,330 --> 00:51:19,950
Speaker 1: Thanks for coming on. And now your moment of suffering.

916
00:51:20,310 --> 00:51:21,529
Speaker 1: Jesse Cannon.

917
00:51:22,239 --> 00:51:26,120
Speaker 5: Molly, you know, when we look at the Trump administration,

918
00:51:26,140 --> 00:51:30,520
Speaker 5: you know, they earn every bit of all the pushback

919
00:51:30,540 --> 00:51:33,489
Speaker 5: they get. And in a shocking new report, Half of

920
00:51:33,530 --> 00:51:35,930
Speaker 5: the military says they want to quit over Trump and

921
00:51:35,950 --> 00:51:37,259
Speaker 5: Pete Higgs' treatment of them.

922
00:51:38,360 --> 00:51:41,799
Speaker 1: I am not at all surprised by this because I

923
00:51:41,960 --> 00:51:45,000
Speaker 1: hear a lot of this kind of stuff. But, you know,

924
00:51:45,020 --> 00:51:50,290
Speaker 1: the truth is Pete Higgs is a complete lunatic. It's

925
00:51:50,670 --> 00:51:56,590
Speaker 1: crazy that he is the secretary of defense. It is.

926
00:51:57,739 --> 00:52:01,520
Speaker 1: I mean, he's just so emotional and so unhinged. We're

927
00:52:01,920 --> 00:52:06,460
Speaker 1: minutes from declaring war on Greenland and Canada. I mean,

928
00:52:07,000 --> 00:52:10,480
Speaker 1: as destabilizing as it is for us, imagine if you

929
00:52:10,600 --> 00:52:15,400
Speaker 1: serve in the United States military. 53% of active duty

930
00:52:15,460 --> 00:52:19,299
Speaker 1: service members want to separate from service sooner because they

931
00:52:19,360 --> 00:52:26,330
Speaker 1: say the military is becoming more politicized. This is the legacy.

932
00:52:27,410 --> 00:52:31,690
Speaker 1: of this is the insane legacy of Whiskey Pete.

933
00:52:32,210 --> 00:52:34,650
Speaker 5: Yeah, I can't imagine how it feels great to not

934
00:52:34,710 --> 00:52:36,290
Speaker 5: know from week to week whether you're going to be

935
00:52:36,310 --> 00:52:37,970
Speaker 5: doing a ground invasion in Iran.

936
00:52:38,830 --> 00:52:40,290
Speaker 2: Whiskey Pete, baby!

937
00:52:41,420 --> 00:52:45,830
Speaker 1: That's it for this episode of Fast Politics. Tune in

938
00:52:46,190 --> 00:52:51,530
Speaker 1: every Monday, Wednesday, Thursday, and Saturday to hear the best

939
00:52:51,610 --> 00:52:55,950
Speaker 1: minds in politics make sense of all this chaos. If

940
00:52:55,969 --> 00:52:59,060
Speaker 1: you enjoy this podcast, please send it to a friend

941
00:52:59,560 --> 00:53:02,700
Speaker 1: and keep the conversation going. Thanks for listening.