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Speaker 1: Hi, I'm Molly John Fast and this is Fast Politics,

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Speaker 1: where we discussed the top political headlines with some of

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Speaker 1: today's best minds. And Donald Trump's approval rating is now

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Speaker 1: lower than it was after January sixth in the Real

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Speaker 1: Clear Politics average, clocking a fifty eight point three percent

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Speaker 1: disapproval rating. We have such a great show for you today.

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Speaker 1: The Nation's ged here stops by to talk about Trump's

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Speaker 1: endless wars in the Middle East. But we'll talk to

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Speaker 1: NBC Newszone Joanna Stern about her new book I Am

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Speaker 1: Not a Robot. My year that I used AI to

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Speaker 1: do everything. But first the news.

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Speaker 2: So, Molly, we saw the Supreme Court actually toss out

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Speaker 2: one of these bogus Republican laws that was cooked up

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Speaker 2: to mess with immigration.

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Speaker 3: Yeah.

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Speaker 1: So this is a politically tinged lawsuit and it's about

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Speaker 1: a high profile crash in Florida last year which involved

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Speaker 1: a commercial t driver from India and three people died.

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Speaker 1: And basically the Supreme Court on Tuesday they threw out

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Speaker 1: a long shot lawsuit where Florida sought to sue California

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Speaker 1: and Washington for allegedly allowing people who entered the country

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Speaker 1: illegally to obtain commercial truck driver's licenses. This is this

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Speaker 1: Stephen Miller stuff where you want to make it so

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Speaker 1: that people who came into this country illegally cannot get

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Speaker 1: jobs and cannot support their families, and basically was thrown

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Speaker 1: out by the court. The people who dissented are Fox

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Speaker 1: News brain Clarence Thomas and Samuel Alito, who have never

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Speaker 1: found a partisan issue. They do not want to be

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Speaker 1: partisan in and the lawsuit alleges that Democratic led states

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Speaker 1: defiance of federal immigration laws led them to flout federal

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Speaker 1: safety regulations. The Washington Attorney General said this was a

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Speaker 1: political student and not a real claim, a political study

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Speaker 1: from the line of Santas in Florida. It cannot be,

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Speaker 1: but in fact it it.

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Speaker 2: Yeah, okay, So now we get to some more interesting matters.

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Speaker 2: We saw that a court has rejected Alabama's house map.

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Speaker 2: It's of course going to get kicked up to the

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Speaker 2: Supreme Court, and we know what they feel about voting

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Speaker 2: these days. But I thought that was the most interesting

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Speaker 2: part of this case is that the Congressional Black Caucus

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Speaker 2: is pressing companies to push back against these redistricting maps,

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Speaker 2: which was interestingly the successful way that things like the

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Speaker 2: film industry got Louisiana and Georgia in trouble, and Georgia,

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Speaker 2: actually I keep thinking about this week that so many

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Speaker 2: of the blockbuster films we're seeing are because Georgia decided

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Speaker 2: to do the right thing in the Louisiana did not.

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Speaker 1: Yeah, it's a really good example. And again this is

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Speaker 1: this idea that look, the Voting Rights Act. Killing the

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Speaker 1: Voting Rights Act was a profoundly racist thing. And we're

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Speaker 1: seeing again South Carolina is trying to steal Jim Clyburn's

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Speaker 1: seat from him. You know, we're seeing this everywhere. Black

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Speaker 1: lawmakers who fought for voting rights, who fought for this

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Speaker 1: multi racial democracy that is so important, they are being

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Speaker 1: drawn out of their seats. And so the CBC, which

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Speaker 1: is like a caucus that is very organized and has

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Speaker 1: really has learned, you know, the lessons of how to

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Speaker 1: preserve democratic power. They are going to corporations and trying

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Speaker 1: to get them to do the right thing. And like,

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Speaker 1: this is really important because when I talk to the

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Speaker 1: Reverend now about this, you know, they did this protest

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Speaker 1: against Target because Target decided to give up on DEI,

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Speaker 1: and giving up on DEI means rejecting anti racism, and

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Speaker 1: when you reject anti racism, you engage in racism. Right.

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Speaker 1: This is just real clear, and I think it's important

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Speaker 1: to remember that I talked to Rever Now about this

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Speaker 1: and like the protesting against Target actually really worked. Target's

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Speaker 1: profits went way down. This Stephen Miller Donald trump fantasy

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Speaker 1: of going back to the nineteen fifties and being racist

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Speaker 1: and discriminating against people. You know, it's popular with a

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Speaker 1: very small percentage of this country, the very magabase, but

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Speaker 1: it's not widely popular. And so people just didn't go

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Speaker 1: to Target and they didn't shop there, and it was

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Speaker 1: really bad for Target. And Target had a bunch of

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Speaker 1: bad quarters. Now they have a new CEO and it

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Speaker 1: worked like protesting works. And so it's great to see

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Speaker 1: the Congressional I mean, it's terrible that the Congressional Black

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Speaker 1: Caucus has to do this. It's terrible that we're seeing

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Speaker 1: this reversal. It's terrible that we're seeing them have to

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Speaker 1: march for things and us and all of us having

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Speaker 1: to march for things like the Voting Rights Act. This

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Speaker 1: is just terrible. But there is knowledge there which is important,

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Speaker 1: and this group is very effective at being able to

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Speaker 1: figure out how wow to do this, and so this

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Speaker 1: is this twenty They're going back to this twenty twenty

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Speaker 1: one coalition business for voting rights. And this was backed

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Speaker 1: by Apple and Amazon and Google and Meta and Tesla

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Speaker 1: before these people went Nazi. So the hope is they

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Speaker 1: will come back. And again it is really important to see,

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Speaker 1: like the only thing worse than fake allyship is no Alan.

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Speaker 2: There's a Buber sticker.

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Speaker 1: I mean, you know, it's we complain about performative allyship,

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Speaker 1: but performative allyship is still better than no Allyship.

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Speaker 2: Yeah, so we're back on this bullshit again. ICE agents

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Speaker 2: have pepper sprayed a sitting senator, Senator Andy Kim.

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Speaker 1: Andy Kim friend of this podcast. He is a quiet guy.

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Speaker 1: There is no world in which Andy Kim did anything first, right,

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Speaker 1: Like there are members of Congress, there are senators who

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Speaker 1: are combative. Andy Kim is not one of them. So

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Speaker 1: the fact that they pepper sprayed him, and they also

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Speaker 1: did not let in the governor of New Jersey also

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Speaker 1: not a particularly obstreperous person. Governor Mikey Cheryl, a person

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Speaker 1: who had served in national security, and Kim told New

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Speaker 1: Jersey dot Com I talked with a pregnant woman who

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Speaker 1: was not getting access to obgyn healthcare in the way

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Speaker 1: she needs. I talked with a woman who had had

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Speaker 1: a miscarriage in this ice facility and was not given

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Speaker 1: any medical support. Talk to an eighteen year old high

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Speaker 1: school senior who just wants to be able to graduate.

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Speaker 1: I mean, we have concentrating hams in this country, and

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Speaker 1: it is really just insane that this is still going on. Insane.

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Speaker 1: And again they're playing it down this administration because they

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Speaker 1: want to win in the midterms. This is still very

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Speaker 1: much going on, and they are still very much not

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Speaker 1: letting and you know, members of Congress, this is their jurisdiction.

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Speaker 1: They are supposed to be able to tour these facilities.

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Speaker 1: That's part of their job, and so the fact that

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Speaker 1: the Trump administration is not letting them in is really

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Speaker 1: just terrible.

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Speaker 2: Yeah, and we saw Governor Cheryl try to get in

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Speaker 2: this week and they were stonewalling her as well. Okay,

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Speaker 2: so we have a really interesting story from Poltico on

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Speaker 2: anti abortion activists. Frustrations are escalating after the abortion pill ruling.

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Speaker 1: Yeah, so this is really interesting. The right is not satisfied,

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Speaker 1: The anti choice activists are not satisfied. Trump is trying

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Speaker 1: to punt to this because he knows that abortion is

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Speaker 1: actually not a winner for him and the rest of

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Speaker 1: the world does not have the kind of abortion restrictions

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Speaker 1: that we have in this country. And again, making it

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Speaker 1: so they can't sell drugs that have had FDA approval

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Speaker 1: for more than twenty years is not going to be

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Speaker 1: popular and I think Trump knows that. And so here

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Speaker 1: we are the Supreme Court. Remember they recently they overturned

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Speaker 1: the conservative Fifth Circuit and so now you can have

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Speaker 1: access to teleh health abortions. And this has made the

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Speaker 1: far right really mad. Our patients has run out. This

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Speaker 1: is the leader of Students for Life, she told Politico.

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Speaker 1: The pro life movement, like many other parts of Trump's coalition,

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Speaker 1: made an investment in Donald Trump, and we want to

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Speaker 1: return in that investment. They want to ban abortion. That's

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Speaker 1: a return on their investment. Both the White House and

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Speaker 1: the Justice Department has held closed door meetings with anti

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Speaker 1: choice activists. My guess is the activists want the president

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Speaker 1: to use executive power more aggressively to cut off the procedure. Remember,

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Speaker 1: abortion activists have been pushing for this replacement, who will

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Speaker 1: be more anti choice. Here's the thing about this. You know,

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Speaker 1: Trump is playing it down for the midterms, and he

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Speaker 1: may after the midterms do more crazy stuff when it

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Speaker 1: comes to abortion. Just because it's a loser for Trump

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Speaker 1: doesn't mean and just you know, Trump doesn't believe in anything. Remember,

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Speaker 1: he is ideologically just completely wacky. So I think anything

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Speaker 1: could happen. And this this is the Supreme Court that

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Speaker 1: overturned raw. Jet Here is a contributor to the nation

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Speaker 1: and the host of the Time of Monsters. Welcome, Welcome

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Speaker 1: jet Here.

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Speaker 3: On the program again.

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Speaker 1: I think the really interesting uh you know, there's this

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Speaker 1: meme where it's like Trump causes a problem and then

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Speaker 1: Trump freaks out and tries to fix the problem, and

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Speaker 1: then Trump gets celebrated for fixing the problem. We're at

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Speaker 1: the like Trump creates a problem moment with Iran, and

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Speaker 1: I would love you know, we saw him the last

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Speaker 1: couple of days like freak out. You could see. I

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Speaker 1: think last week ended with the Republicans freaking out about

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Speaker 1: Trump endorsing Paxton, about gas being a dollar and a

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Speaker 1: half more than it was a year ago in America,

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Speaker 1: and they just started, you know, and it was like

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Speaker 1: all of a sudden, you could see Donald Trump frantically

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Speaker 1: trying to get to the exits, you know, like we

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Speaker 1: can make a deal with Iron, We're gonna make a

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Speaker 1: deal with them. They like us. We are going to

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Speaker 1: bring them into the Abraham Accords discuss.

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Speaker 3: Yeah, I do think it has been sort of Trump's

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Speaker 3: motus APPLEHANDI to kind of like create problems and then

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Speaker 3: sort of like back down and then say the other

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Speaker 3: problem has been solved. And this is the sort of

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Speaker 3: thing is the famous you know Trump tackle meme right

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Speaker 3: like like yeah, like the tariffs. You know, like he

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Speaker 3: created like these trade wars and then you know, now

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Speaker 3: you know, like he created like a lot of strife

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Speaker 3: with China, but now he's sort of decided, well, China's

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Speaker 3: our friend. And then he could say I solve that.

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Speaker 3: No one else could have done that. Well, yeah, no

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Speaker 3: one else could have created a problem, and then like

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Speaker 3: totally surrendered. It is not but for his I mean

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Speaker 3: that works for him because his you know, his base

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Speaker 3: of personality cult MAGA supporters think like, well, this is

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Speaker 3: showing he's doing stuff. It beats Trump's ego. It all

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Speaker 3: plays into the sort of you know, performative wrestling politics

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Speaker 3: that he does. I think the Iran thing is very

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Speaker 3: interesting and to be honest, terrifying as hell, because he

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Speaker 3: has created a problem that he cannot easily walk out of.

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Speaker 3: He cannot go his way out of. I mean, I

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Speaker 3: think maybe his thinking all along was to pull out

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Speaker 3: of the Iran nuke deal and then you know, like

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Speaker 3: do a lot of bluster bombings and then you know,

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Speaker 3: come back and get something very similar and then put

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Speaker 3: his name on it. You know, it's the Trump Nuclear Deal,

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Speaker 3: and then you know, right, yeah, like that exactly, like

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Speaker 3: he's this is pattern. I think that the problem with

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Speaker 3: the like in Iran, which he's finding now is that

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Speaker 3: you know, like if you constantly like kill people like

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Speaker 3: well are you're negotiating with them, they don't trust you.

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Speaker 3: If you like post costs on them, they want retribution.

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Speaker 3: And in this case, you know, Iran really does have

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Speaker 3: a stranglehold all in the straight up our rules, and

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Speaker 3: which is like a huge kind of problem, and he's

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Speaker 3: not going to be able to easily claim I think

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Speaker 3: what trumb would ideally want to do is like, you know,

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Speaker 3: get back to the status quo before and just claim victory,

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Speaker 3: like you know, like just you know, call it a day.

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Speaker 3: It's like and he's been doing that. He's been saying,

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Speaker 3: we want an incredible victory. You know. There we just

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Speaker 3: put the Iranian navy, which a few chefs. We've destroyed

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Speaker 3: the air force, which really consisted of like Soviet era

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Speaker 3: web airplanes. But the funderble fact is that they controlled

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Speaker 3: the Strait of Removes and all their allies are still

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Speaker 3: like in place, and you know, like oil is like

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Speaker 3: you know, the four dollars worth fifty and it's gonna

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Speaker 3: starting to hit the point like we're seeing this like

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Speaker 3: especially in Asia and Africa, but it's gonna spread to

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Speaker 3: the United States where like what was writing, there's like

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Speaker 3: lately So wants to pull this, but he can't because

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Speaker 3: any real settlement of the war includes you know, acknowledging

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Speaker 3: I'm making actual concessions around because they actually do have

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Speaker 3: a struggle and they actually do have a card to flight.

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Speaker 1: It's interesting because we had been warned that this was

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Speaker 1: going to be you know, the reason why all of

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Speaker 1: the Hawks since w have not gone into Iran is

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Speaker 1: because they are a big country, very ideological, and there

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Speaker 1: was this scenario where they took the straits. Now they've

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Speaker 1: taken the trades we've seen a case study that it

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Speaker 1: actually works for them. And now Donald Trump is trying

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Speaker 1: to He's not even remembered that he went to war

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Speaker 1: to get rid of their nukes. And now this is

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Speaker 1: not even about the nukes, this is about the Straits.

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Speaker 3: Yeah, no, no, absolutely absolutely yeah and uh. And there

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Speaker 3: are a few red lines where like it absolutely looks

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Speaker 3: like they will not want to see they don't want

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Speaker 3: to give up is it right to enrichment, which honestly,

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Speaker 3: like you know, I don't think the United any position

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Speaker 3: to like demand that, because the US itself under Trump

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Speaker 3: is going to be building more nuclear power plats. We're

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Speaker 3: encouraging everyone to do that because it is actually, you know,

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Speaker 3: one pathway out of this climate catastrophe.

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Speaker 1: Uh.

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Speaker 3: And then the US itself was the origins of the

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Speaker 3: Iranian nuclear program because on the SHAW we encourage Radians

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Speaker 3: to build nukes to university by their economy. So so

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Speaker 3: so that's an old starter, and they want the US

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Speaker 3: to around to give up supportive hesiblaw, which is also

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Speaker 3: a non starter. So odd so all these like red

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Speaker 3: lines that Iran will not cross, which Trump you know,

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Speaker 3: like if there were serious negotiations. Now what's interesting what

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Speaker 3: we've seen over the weekend is that he's gone into

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Speaker 3: this like new fantasyland, Yeah, which is the Abraham Accords.

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Speaker 3: And I think this is very interesting.

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Speaker 1: He just wrote a column about this, so explain.

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Speaker 3: Yes, yeah, yeah, He's like, now, the Abraham Accords is

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Speaker 3: like one thing where Trump can turn and point to

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Speaker 3: as a successful thing by mainstream standards, because the Democrats

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Speaker 3: embraced this as well. Like in twenty twenty, they signed

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Speaker 3: this agreement that had the United Emirates and Bahrain and

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Speaker 3: a few other countries going to the deal. Now it's

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Speaker 3: always a love of a dubious because actually the countries

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Speaker 3: that signed the deal had never you know, we're not

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Speaker 3: at war with Israel. It was a normalization process. The

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Speaker 3: idea is like, we'll get the countries to diplomatically normalized

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Speaker 3: and have traded and arms agreements, and that this will

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Speaker 3: be the kind of like a path through the piece

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Speaker 3: in the Middle East. And crucially, the biding inveistiration picked

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Speaker 3: up on this and they wanted to also expand the

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Speaker 3: Agram Records to bring in Saudi Arabia. Now, I gotta say,

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Speaker 3: like I have always thought that this is like a

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Speaker 3: terrible idea because it's basically a piece of the elite

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Speaker 3: based on Israel under NAO under you know, like an unvarnished,

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Speaker 3: apart eyed state that wants ethnic cleansing and does never

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Speaker 3: wants a Palestinian state shaking hands with all these Arab

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Speaker 3: autocrats like in the uae Vrain eventually Saudi Arabia with

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Speaker 3: arms deals. Right in Washington loves it because it's a

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Speaker 3: great program if you want to sell arms.

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Speaker 1: And this is.

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Speaker 3: It's a great program, like all these elites. But two

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Speaker 3: things were shut out of this. One was a Palestinian issue,

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Speaker 3: right and and we know that like for Hamas, this

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Speaker 3: was like one of the you know, real issues with

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Speaker 3: October seven. Like they thought like this like thing goes through,

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Speaker 3: we're going to be totally shut it aside. This is

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Speaker 3: like so like Doc seventh was a last grasp effort

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Speaker 3: to like, you know, bring their issue back to the forefront,

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Speaker 3: which honestly, it's like, you know.

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Speaker 1: I mean they're all dead. I mean they all died.

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Speaker 3: Yeah, yeah, I mean I don't know that it worked, no, no, no, no,

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Speaker 3: but it absolutely reminded the world that like you know, yeah, yeah,

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Speaker 3: I mean like there's there's no way and this is

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Speaker 3: actually like you know, it was designed to like derail

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Speaker 3: the Abram Records and to largest and did because like

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Speaker 3: Saudi Arabia and others, like there's no way they can

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Speaker 3: make a deal unless the Palestine issue is settled. But

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Speaker 3: the other issue is that it was designed to be

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Speaker 3: an anti Iran coalition and it was designed to like

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Speaker 3: get all these countries that were the sun air botocracies

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Speaker 3: and Israel. They all didn't like Israel, and so it

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Speaker 3: really prepared the groundworks for this current war and for

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Speaker 3: this you know, like a period of Israeli US attempts

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Speaker 3: to say, you know, impose a military solution. So the

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Speaker 3: Apram Records is actually the I would argue, the root

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Speaker 3: cause for a lot of these problems. It's root of

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Speaker 3: this idea. You know, it reinforces the fantasy that you

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Speaker 3: can have peace and the release without a settlement of

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Speaker 3: the Palestinian mischue. So you know, the totally reinforced Netan

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Speaker 3: yahoo uh been years sort of a program and it

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Speaker 3: totally reinforced kind of you know, this assault on Iran.

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Speaker 3: So the fact that Trump is going back to this

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Speaker 3: and he's going back to this because he's facing a

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Speaker 3: lot of political opposition to the very fact that he's negotiating.

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Speaker 3: So he has to throw a bone to the Hawks.

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Speaker 3: And so this is like a bone to crazy and

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Speaker 3: it's also, to be honest, a bone to democrats.

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Speaker 1: Jones. Yeah. Wait, so let's just pause for a second.

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Speaker 1: So here's the thing Trump desperately wants. I mean, I

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Speaker 1: think it's important to just do the tensions for a minute.

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Speaker 1: This was a war that he never sold to anyone,

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Speaker 1: that didn't make any sense to most Americans, Right, why

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Speaker 1: are we going to war with Iran? The reality was

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Speaker 1: we're going to war with Iran because nat Yahoo said

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Speaker 1: that it was necessary for Trump, and Trump sort of

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Speaker 1: bought that. Nanya, who has been trying to sell that

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Speaker 1: war to Republican and Democratic presidents for decades, and because

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Speaker 1: Iran has, you know, funded terrorism in the Middle East,

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Speaker 1: that said, it's not a real American you know, it

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Speaker 1: became America's problem, but it doesn't really need to be.

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Speaker 1: And what it's done, I think as a Jew, this

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Speaker 1: is what's really upset me is that it's really fueled

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Speaker 1: the anti Semitism writ large, because the feeling is we

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Speaker 1: are now America's in a war for Israel. True or

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Speaker 1: not true. They nobody made a case that America's in

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Speaker 1: this war for America, right, so.

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Speaker 3: Now absolutely, I would I want to understand this the Arabs,

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Speaker 3: because the Arara Cords ties America, not just Israel, but

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Speaker 3: to like all the Herb autocracies who like you know,

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Speaker 3: like don't have the interest of either their own people

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Speaker 3: or the United States ad AD, and like for them.

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Speaker 3: You hear that, I mean, it's not just and I

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Speaker 3: actually do think I mean, on the anti Semitism in front,

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Speaker 3: I have constantly argue with people on the left who

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Speaker 3: merely see this as Israel is dragging the US at

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Speaker 3: the war, because it's not just Israel, like you know,

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Speaker 3: like Buran and UAE are like as anti Iran, and

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Speaker 3: I have been you know, like they're less vocal about

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Speaker 3: it because their own population don't want this war.

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Speaker 1: And there's also money to be made. And I think

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Speaker 1: that is there's like money to be made with selling arms,

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Speaker 1: but there's also money to be made. Like Dubai is

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Speaker 1: not the Dubai of Gulf War one or Golf War two.

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Speaker 1: It's a huge shopping mall filled with wealthy people who

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Speaker 1: really do not want drones blowing stuff up. So there

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Speaker 1: are all different tensions here.

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Speaker 3: No, there, they're absolutely all different tensions. And actually, I

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Speaker 3: mean this is the other reason why I think the

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Speaker 3: apra of records caused the real problem, because it actually

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Speaker 3: did sort of tie Dubai, uh you a, to the

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Speaker 3: US and Israel. And so you know, you've been seeing

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Speaker 3: Iranian attacks. You know, it's not just that the Iran

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Speaker 3: can control the Straight, but now they can attack Dubai.

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Speaker 3: And you know, like you have a you know, like

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Speaker 3: a place which you said, a shopping mall for the

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Speaker 3: ridge for all these expats who don't want to pay taxes,

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Speaker 3: want to have cheap servants.

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Speaker 1: Uh you know, yeah, there's moms go off.

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Speaker 3: Is very bad for a place like that. You can't

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Speaker 3: build an economy, uh. And so it's all very vulnerable.

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Speaker 3: And I think that the this fantasy, this Abraham ac

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Speaker 3: Courts fantasy, that you can have this kind of like

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Speaker 3: military alliance that's based on the elites, that's based on

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Speaker 3: you know, basically keeping the air world permanently in their

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Speaker 3: autocracy and also keeping Israel permanently as apart right state

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Speaker 3: without a settlement for the Palestinians. Brand the Abraham Courts

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Speaker 3: is this fantasy that you can have that world and

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Speaker 3: I that's not a stable world. And we've already seen it,

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Speaker 3: like both in what's happened in Gaza and also what's

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Speaker 3: happening in Iran, and it's a recipe for future wars.

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Speaker 3: So beyond the fact like that Trump is stuck and

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Speaker 3: he doesn't know how to get out of this, is

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Speaker 3: the fact that the US bipartisan foreign policy lead, the

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Speaker 3: blog The Blob, they're really committed to bred like vision

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Speaker 3: of the world which offers no past really for peace

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Speaker 3: or for prosperity, and is really like it's great for

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Speaker 3: the armist dealers, it's great for the Brett McGirk works for,

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Speaker 3: but I mean for America world.

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Speaker 1: But and I think more importantly than any of that

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Speaker 1: than the ideological problems, what you and I both have

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Speaker 1: with this is that it's not popular. The American people

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Speaker 1: used to like wars. They don't anymore. They really this

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Speaker 1: is like actually an eighty twenty issue. People don't like it,

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Speaker 1: they don't want to be there. Even Republicans don't like this.

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Speaker 1: So here's the question, because I think this is where

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Speaker 1: we are now. So Trump has Republican donors like Marion Atolson,

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Speaker 1: people for whom Israel is the one issue and that

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Speaker 1: you know, and then you have Democrats for whom Israel

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Speaker 1: is the one issue, okay, and then you have a

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Speaker 1: very unpopular war, and you have Donald Trump trying to

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Speaker 1: get out of a very unpopular war, which he can't.

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Speaker 1: So my question to you is, you know, we we

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Speaker 1: saw Debbie Wasserman Schultz yesterday and Achen Class both Schultz

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Speaker 1: complaining about the getting out of the war in the

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Speaker 1: same way that we saw yeah, and Lindsey Graham, and

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Speaker 1: so talk to me about what you think there.

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Speaker 3: Yeah, No, I I think that this is a real

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Speaker 3: problem that the Democrats in particular have, I mean the

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Speaker 3: relicus as well, Like the both parties are splintering on

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Speaker 3: this because you have kind of you know, foreign policy

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Speaker 3: establishment that's Washington based, that is like really tied in

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Speaker 3: with like you know, the donor class and also i'd

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Speaker 3: say with the military industrial complex. Yeah that like you know,

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Speaker 3: he's totally committed, uh, to this very unpopular war and

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Speaker 3: to the then to the larger vision of the world

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Speaker 3: of that war is an alliance with you know, Arab

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Speaker 3: dictators and with Israel, you know, to exploit the region.

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Speaker 3: But the American people aren't on board with this, and

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Speaker 3: I think like Washerwin Schuilds obviously represents like that older

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Speaker 3: uh Strand and you know like uh Jack, you know

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Speaker 3: aucken Claus, like like I who has now said, you know, uh,

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Speaker 3: he won't support Platner in Maine, which which you know,

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Speaker 3: like basically means he's like supporting Susan Collins, which basically

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Speaker 3: you know, means he's supporting the Republicans controlling the Senate.

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Speaker 3: Like like like, I think that really shows you how

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Speaker 3: deep the divide is. And it's all going to be settled,

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Speaker 3: maybe not this year, but like in the coming years

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Speaker 3: in the primaries, I think, like you know, Democratic primaries

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Speaker 3: and Republican primaries, as we saw with Massy, like you know,

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Speaker 3: like there's a ton of my going in and there's

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Speaker 3: going to be like these like you know, real gorilla

433
00:25:03,440 --> 00:25:07,800
Speaker 3: fights between popular sentiment and what the donor class is

434
00:25:07,840 --> 00:25:10,520
Speaker 3: pushing and to the extent that they can support it.

435
00:25:10,560 --> 00:25:13,120
Speaker 3: I mean, like in terms of public opinion. And then

436
00:25:13,240 --> 00:25:16,639
Speaker 3: with the Massy election, is we're really telling on this

437
00:25:16,680 --> 00:25:20,600
Speaker 3: point the one group that really still supports this foreign

438
00:25:20,680 --> 00:25:23,679
Speaker 3: policy of forever wars in the Middle East are like

439
00:25:23,880 --> 00:25:27,640
Speaker 3: older Republicans, Like you have a Republican who's like six

440
00:25:27,760 --> 00:25:30,919
Speaker 3: years and older, who like watches Fox News and you know,

441
00:25:31,000 --> 00:25:34,639
Speaker 3: believes the sort of Sean Hannity vision of the world

442
00:25:34,680 --> 00:25:37,560
Speaker 3: right like otherwise, like you know, like because Massy won

443
00:25:37,760 --> 00:25:41,920
Speaker 3: like overwhelming among young Republicans, they just weren't enough of them.

444
00:25:42,840 --> 00:25:45,199
Speaker 3: But it's it's going to play out. I think, you know,

445
00:25:45,320 --> 00:25:48,119
Speaker 3: like both parties are going to be deeply divided about this.

446
00:25:48,440 --> 00:25:51,760
Speaker 1: So let's talk about achen class. We have in Maine

447
00:25:52,320 --> 00:25:56,960
Speaker 1: a candidate who's been on this podcast, Grand Platner. He

448
00:25:57,160 --> 00:26:02,240
Speaker 1: has when he was twenty, he was in Croatia. He

449
00:26:02,400 --> 00:26:07,280
Speaker 1: got a cross tattoo. Turned out to be some cross

450
00:26:07,359 --> 00:26:12,639
Speaker 1: that is associated with Nazis. He has apologized for it.

451
00:26:12,760 --> 00:26:15,560
Speaker 1: He said he was drunk. I have talked to someone

452
00:26:15,600 --> 00:26:18,119
Speaker 1: who is with one of the other Marines in the

453
00:26:18,160 --> 00:26:22,040
Speaker 1: group who was also drunk. Anyway, the point is Auchenclous

454
00:26:22,280 --> 00:26:26,440
Speaker 1: feels that this is anti Semitic, goes out and says

455
00:26:26,480 --> 00:26:29,240
Speaker 1: that he is going to support Susan Collins. Very upset.

456
00:26:29,480 --> 00:26:32,520
Speaker 1: Susan Collins posted a photo this weekend of her with

457
00:26:32,640 --> 00:26:36,879
Speaker 1: the main militia, an anti semitic militia group. So I

458
00:26:37,240 --> 00:26:40,280
Speaker 1: just am confused when it comes to auchenclass what is

459
00:26:40,320 --> 00:26:43,840
Speaker 1: anti Semitism and what is not anti Semitism. Plattner has

460
00:26:43,880 --> 00:26:48,040
Speaker 1: a Jewish tetfather, he has a Jewish sister in law

461
00:26:48,160 --> 00:26:53,639
Speaker 1: and a Jewish stepbrother. I was assured that having Jewish

462
00:26:53,680 --> 00:26:58,359
Speaker 1: relatives means you can't possibly be anti Semitic because Donald Trump,

463
00:26:58,760 --> 00:27:02,120
Speaker 1: who has posted anti's semitic means and had a dinner

464
00:27:02,320 --> 00:27:05,840
Speaker 1: with Nick Fuentes, that guy. I was told that because

465
00:27:05,840 --> 00:27:08,119
Speaker 1: he had Jewish relatives, he couldn't be a desemitic. So

466
00:27:08,440 --> 00:27:11,320
Speaker 1: I just wonder what the standard is.

467
00:27:11,840 --> 00:27:14,720
Speaker 3: Yeah, no, I mean, like I mean, I think it's

468
00:27:14,720 --> 00:27:16,720
Speaker 3: like we are like actually, you know, in the Trump era,

469
00:27:17,200 --> 00:27:20,159
Speaker 3: and you know, like his cabinet is full of people

470
00:27:20,440 --> 00:27:24,080
Speaker 3: I believe like Sebastian Gorka is like some sort of

471
00:27:24,160 --> 00:27:28,240
Speaker 3: like senior counter terrorism right. This guy like he'll received

472
00:27:28,320 --> 00:27:33,040
Speaker 3: like a Hungarian medal on behalf of like that had

473
00:27:33,119 --> 00:27:36,000
Speaker 3: like the name of the sort of you know, fascist

474
00:27:36,080 --> 00:27:39,000
Speaker 3: dictator who like worked with Hitler in Hungary in the

475
00:27:39,040 --> 00:27:41,359
Speaker 3: in the thirties and forties. I think the question like

476
00:27:41,359 --> 00:27:43,919
Speaker 3: for voters is like, you know, like where are people

477
00:27:44,000 --> 00:27:47,119
Speaker 3: now like where actually, uh, you know, like there's nothing

478
00:27:47,160 --> 00:27:51,080
Speaker 3: in like Patner's behavior or any of his falsies that

479
00:27:51,200 --> 00:27:56,119
Speaker 3: shows like any sort of I mean there's one is

480
00:27:56,119 --> 00:27:59,240
Speaker 3: obviously the you know, all the sort of alter right

481
00:27:59,240 --> 00:28:02,560
Speaker 3: people that Trump is. But the other aspect is that

482
00:28:02,720 --> 00:28:06,040
Speaker 3: like the whole scenario that they're putting forward, you know,

483
00:28:06,119 --> 00:28:07,840
Speaker 3: I mean Trump is like repeatedly like you know, like

484
00:28:07,920 --> 00:28:11,119
Speaker 3: he gave like the American highest civilian honor to like

485
00:28:11,520 --> 00:28:15,560
Speaker 3: a mirrym Elman and as I said that she's to

486
00:28:15,600 --> 00:28:18,639
Speaker 3: be giving them uh. And I said, like you know,

487
00:28:18,680 --> 00:28:21,679
Speaker 3: like she wanted me to add the embassy in Jerusalem

488
00:28:21,680 --> 00:28:23,960
Speaker 3: and I gave it to her. Like if you want

489
00:28:23,960 --> 00:28:27,119
Speaker 3: to ask you like fuel anti Semitism, then like I

490
00:28:28,119 --> 00:28:30,600
Speaker 3: say that, like all these policies, I'll be with this

491
00:28:30,640 --> 00:28:33,680
Speaker 3: big Jewish donor uh. And we're also going to war

492
00:28:33,720 --> 00:28:36,920
Speaker 3: with Iran. I mean that's actually going to cause actual,

493
00:28:37,040 --> 00:28:40,600
Speaker 3: real exist uh anti Semitism in the present. Like I'm

494
00:28:40,640 --> 00:28:44,560
Speaker 3: sorry that that's just a reality. And I think that uh,

495
00:28:45,120 --> 00:28:47,520
Speaker 3: unless people, unless people want to actually like you know,

496
00:28:47,560 --> 00:28:50,360
Speaker 3: confront what I had, the actual you know, like causes

497
00:28:50,400 --> 00:28:52,720
Speaker 3: of the search and anti Semitism, then I can't take

498
00:28:52,760 --> 00:28:53,400
Speaker 3: them seriously.

499
00:28:54,200 --> 00:28:58,000
Speaker 1: Yeah, And I also think, you know, when we're when

500
00:28:58,040 --> 00:29:01,560
Speaker 1: we're talking about Platner, I I just think it's more,

501
00:29:02,240 --> 00:29:06,200
Speaker 1: you know, this is obviously he's out there campaigning with

502
00:29:06,280 --> 00:29:10,760
Speaker 1: Bernie Sanders, Bernie Sanders Jewish. It doesn't strike me, you know,

503
00:29:10,800 --> 00:29:14,880
Speaker 1: Bernie Sanders has been pretty clear on his support, you know,

504
00:29:15,360 --> 00:29:17,880
Speaker 1: as much as someone on the left can support Israel,

505
00:29:17,920 --> 00:29:20,840
Speaker 1: he really has, you know what I mean, And so

506
00:29:21,360 --> 00:29:23,719
Speaker 1: it strikes me that this is not a good faith

507
00:29:24,240 --> 00:29:27,080
Speaker 1: worry that this is some of this is just partisan

508
00:29:27,120 --> 00:29:27,680
Speaker 1: hack right.

509
00:29:28,560 --> 00:29:31,280
Speaker 3: Yeah, I no, I do feel like it's sort of

510
00:29:31,280 --> 00:29:33,440
Speaker 3: like person hackery, except that I mean, I think what

511
00:29:34,440 --> 00:29:37,120
Speaker 3: it does show is that, you know, like there are

512
00:29:37,160 --> 00:29:40,360
Speaker 3: forces within the Democratic Party that are really still like

513
00:29:40,480 --> 00:29:43,480
Speaker 3: very as opposed to any sort of like populist economic

514
00:29:43,560 --> 00:29:46,320
Speaker 3: message and are also like very committed to the foreign

515
00:29:46,360 --> 00:29:49,480
Speaker 3: policy consensus, right, Like yeah, I mean, like, I you know,

516
00:29:49,560 --> 00:29:51,520
Speaker 3: like as much as I hate Trump and you know,

517
00:29:51,600 --> 00:29:53,960
Speaker 3: disagree with him a lot of the stuff, he's okay,

518
00:29:54,000 --> 00:29:56,280
Speaker 3: he was stupid enough that he wants his war, but

519
00:29:56,480 --> 00:29:59,320
Speaker 3: all the sort of pre conditions for the war, all

520
00:29:59,360 --> 00:30:02,080
Speaker 3: the sort of with the Abraham records, that was a

521
00:30:02,080 --> 00:30:05,200
Speaker 3: bi partisan consensus, right, and like you have to actually

522
00:30:05,320 --> 00:30:08,400
Speaker 3: unless that has changed, unless there's actually and we're seeing

523
00:30:08,440 --> 00:30:12,120
Speaker 3: it like yeah, I think in Today's New York Times,

524
00:30:13,600 --> 00:30:15,600
Speaker 3: was it Van Holland.

525
00:30:15,640 --> 00:30:18,400
Speaker 1: Has Chris Yeah.

526
00:30:18,040 --> 00:30:19,960
Speaker 3: As a very important op ed where he says, like

527
00:30:20,240 --> 00:30:23,160
Speaker 3: the people like in the Democratic Party who had been

528
00:30:23,280 --> 00:30:26,000
Speaker 3: responsible for this catastrophe, you know who it like.

529
00:30:27,000 --> 00:30:27,920
Speaker 1: We need a new.

530
00:30:29,360 --> 00:30:29,560
Speaker 4: Yeah.

531
00:30:29,600 --> 00:30:31,479
Speaker 3: I think we're gonna see that, we're gonna see all

532
00:30:31,520 --> 00:30:33,880
Speaker 3: this played on in primary people is gonna take the

533
00:30:34,000 --> 00:30:36,000
Speaker 3: you know, Van Holland line, which is that you know,

534
00:30:36,080 --> 00:30:39,320
Speaker 3: like we can't hire Anthony F. Lincoln again, we cannot

535
00:30:39,360 --> 00:30:43,040
Speaker 3: hear people have no future role in the Democratic Party,

536
00:30:43,240 --> 00:30:45,520
Speaker 3: and you know, like I honestly think it's.

537
00:30:45,400 --> 00:30:47,680
Speaker 1: Also gonna honestly think he's right too.

538
00:30:47,920 --> 00:30:50,640
Speaker 3: Yeah yeah, And I think it's the Republican Party as well,

539
00:30:51,200 --> 00:30:54,320
Speaker 3: in a more I think dangerous way, because I think

540
00:30:54,400 --> 00:30:57,280
Speaker 3: they're the sort of like anti Israel voices will be

541
00:30:57,360 --> 00:31:01,720
Speaker 3: Tucker Carlton and Nick Flenttez, you know, like I don't

542
00:31:01,720 --> 00:31:07,920
Speaker 3: have any trouble at all, say.

543
00:31:04,640 --> 00:31:10,040
Speaker 1: Yeah, yeahah, yeah, seems pretty clear.

544
00:31:10,560 --> 00:31:12,440
Speaker 3: H yeah, which is I mean, I think that's actually

545
00:31:12,520 --> 00:31:14,520
Speaker 3: and I actually like where there were I mean, if

546
00:31:14,520 --> 00:31:17,240
Speaker 3: we're really thinking about like antiseptism and dealing with this seriously,

547
00:31:17,680 --> 00:31:21,400
Speaker 3: like you know, unless there's a real honest grappling with

548
00:31:21,440 --> 00:31:24,040
Speaker 3: the American Forum folicy and the problems with that, that's

549
00:31:24,080 --> 00:31:26,080
Speaker 3: only going to help the Tucker Carltons of the world.

550
00:31:26,120 --> 00:31:29,000
Speaker 3: Like we're to create a condition where you know, you

551
00:31:29,040 --> 00:31:32,320
Speaker 3: could have a possible for Carlton run for the residency,

552
00:31:32,360 --> 00:31:34,120
Speaker 3: which is like, I think, terrifying.

553
00:31:34,560 --> 00:31:37,920
Speaker 1: Yeah, get here. I hope you'll come back. I suppose that.

554
00:31:38,560 --> 00:31:43,000
Speaker 3: Was great to talk, even under these dire circumstances.

555
00:31:43,160 --> 00:31:48,560
Speaker 1: Yes. Joanna Stern is the chief Tech Analyst and NBC

556
00:31:48,640 --> 00:31:51,200
Speaker 1: News and the author of I Am Not a Robot

557
00:31:51,320 --> 00:31:56,720
Speaker 1: By Year Using AI to Do Everything. Welcome, Welcome, Joanna.

558
00:31:56,720 --> 00:31:59,280
Speaker 4: Welcome to me. I mean, this is such an honor.

559
00:31:59,280 --> 00:32:00,600
Speaker 4: Thank you for having.

560
00:32:00,600 --> 00:32:02,520
Speaker 1: Oh I'm so excited to have you. First of all,

561
00:32:02,560 --> 00:32:05,040
Speaker 1: tell us about this book because it's such a cool

562
00:32:05,200 --> 00:32:06,280
Speaker 1: and interesting idea.

563
00:32:06,520 --> 00:32:09,320
Speaker 4: It's called I Am Not a Robot My Year using

564
00:32:09,360 --> 00:32:12,200
Speaker 4: AI to do almost everything. And then when I tell

565
00:32:12,200 --> 00:32:13,680
Speaker 4: you the title, you don't even really need me to

566
00:32:13,720 --> 00:32:15,720
Speaker 4: explain it because it told you everything.

567
00:32:15,880 --> 00:32:16,160
Speaker 1: Right.

568
00:32:16,280 --> 00:32:19,360
Speaker 4: If only it was that simple, it did. But I

569
00:32:19,400 --> 00:32:21,160
Speaker 4: think there's actually something to be very clear here. So

570
00:32:21,320 --> 00:32:24,680
Speaker 4: I did try to use AI in almost every part

571
00:32:24,720 --> 00:32:27,560
Speaker 4: of my life in the year twenty twenty five. But

572
00:32:27,800 --> 00:32:30,120
Speaker 4: AI has a lot of definitions, and the beginning of

573
00:32:30,120 --> 00:32:32,360
Speaker 4: the book talks a lot about those different definitions that

574
00:32:32,440 --> 00:32:34,480
Speaker 4: talk about the AI zoo, as I call it. So

575
00:32:34,560 --> 00:32:36,920
Speaker 4: right now, everyone really is very focused on chatbots and

576
00:32:37,000 --> 00:32:40,400
Speaker 4: large language models and the Geminis and the chatchy BET's

577
00:32:40,440 --> 00:32:42,200
Speaker 4: of the world and the Clauds and all of that.

578
00:32:42,400 --> 00:32:46,440
Speaker 4: But I brought into that vision here to talk about robotics,

579
00:32:46,680 --> 00:32:48,320
Speaker 4: which is very clearly AI.

580
00:32:48,760 --> 00:32:50,840
Speaker 1: I talked about self driving cars.

581
00:32:51,320 --> 00:32:54,200
Speaker 4: I talked about AI and medical, which is not the

582
00:32:54,240 --> 00:32:56,640
Speaker 4: same necessarily, it doesn't always have to be the same

583
00:32:56,680 --> 00:32:59,760
Speaker 4: as those chatbots. So I went the whole way. I

584
00:32:59,880 --> 00:33:02,320
Speaker 4: was said, let's do it all. Let's see how this

585
00:33:02,400 --> 00:33:04,360
Speaker 4: is all looking. I want to look at what this

586
00:33:04,440 --> 00:33:06,640
Speaker 4: hype is and I want to come with a clear

587
00:33:07,200 --> 00:33:08,760
Speaker 4: vision of what the future looks.

588
00:33:08,560 --> 00:33:11,520
Speaker 1: Like, and what does the future look like. It's very funny.

589
00:33:12,040 --> 00:33:13,720
Speaker 4: This is one thing is if we're going to have

590
00:33:13,760 --> 00:33:16,840
Speaker 4: these robots doing these things for us, they do hilarious things.

591
00:33:17,200 --> 00:33:21,000
Speaker 4: The chatbots say hilarious things, So there's a lot of

592
00:33:21,040 --> 00:33:23,920
Speaker 4: funny parts of this book. The future can also be terrifying.

593
00:33:24,040 --> 00:33:25,920
Speaker 4: Where I come at the end is that I paint

594
00:33:25,960 --> 00:33:28,480
Speaker 4: the utopian version of what this could look like, and

595
00:33:28,520 --> 00:33:30,000
Speaker 4: I paint the dystopian version.

596
00:33:30,080 --> 00:33:30,200
Speaker 3: Right.

597
00:33:30,240 --> 00:33:33,680
Speaker 4: The utopian version is that everything these tech CEOs have

598
00:33:33,760 --> 00:33:37,680
Speaker 4: been saying comes true. Aisops cancer. Our kids are smarter

599
00:33:37,760 --> 00:33:40,760
Speaker 4: than ever because they've got this personalized tutor. We never

600
00:33:40,800 --> 00:33:43,440
Speaker 4: have a car accident again because the self driving cars

601
00:33:43,440 --> 00:33:45,760
Speaker 4: are so good, We don't have to do our laundry,

602
00:33:45,760 --> 00:33:48,120
Speaker 4: because the humanoid robots in our house are so good.

603
00:33:48,400 --> 00:33:50,960
Speaker 4: It's this perfect vision of the future. And then there's

604
00:33:51,000 --> 00:33:54,960
Speaker 4: the dystopian version, which all of that goes wrong, and

605
00:33:55,160 --> 00:33:58,800
Speaker 4: we also have massive power outages because of data centers,

606
00:33:58,800 --> 00:34:02,160
Speaker 4: and we have drought, and we also have people who

607
00:34:02,160 --> 00:34:04,560
Speaker 4: aren't thinking because we don't remember how to think because

608
00:34:04,560 --> 00:34:07,080
Speaker 4: we're just using AI to do these things, and we

609
00:34:07,160 --> 00:34:10,239
Speaker 4: have rogue robots who are Lord knows what they're doing

610
00:34:10,239 --> 00:34:13,560
Speaker 4: in our homes. So, yeah, you get to decide which

611
00:34:13,640 --> 00:34:14,960
Speaker 4: version of the future you think.

612
00:34:14,880 --> 00:34:17,600
Speaker 1: Is going to happen. Choose your own Adventuremally, I want

613
00:34:17,640 --> 00:34:20,319
Speaker 1: to go to this idea of not thinking because I

614
00:34:20,320 --> 00:34:23,680
Speaker 1: don't use AI because I don't think it's good for us,

615
00:34:23,880 --> 00:34:27,200
Speaker 1: and also because it's ruined Google search. One of the

616
00:34:27,200 --> 00:34:30,200
Speaker 1: things that I'm struck by is that it doesn't so

617
00:34:30,400 --> 00:34:34,120
Speaker 1: I'll like be google searching. Here's a great example. This

618
00:34:34,400 --> 00:34:37,040
Speaker 1: should be an easy Google search. I would have gotten

619
00:34:37,040 --> 00:34:41,680
Speaker 1: it before Google. When full AI? When are the midterm elections?

620
00:34:41,840 --> 00:34:45,040
Speaker 1: I got like a date range and it didn't give

621
00:34:45,080 --> 00:34:48,399
Speaker 1: you the exact day of the election. It just gave

622
00:34:48,440 --> 00:34:51,760
Speaker 1: me a range how many days until the midterm election?

623
00:34:51,880 --> 00:34:55,360
Speaker 1: And it gave me like a range of three days. Like.

624
00:34:55,440 --> 00:34:58,920
Speaker 4: It didn't say this should be the first Tuesday of November.

625
00:34:59,200 --> 00:35:03,200
Speaker 1: No, so it was wrong, which I find a lot,

626
00:35:03,400 --> 00:35:07,560
Speaker 1: but more than that, it processed information wrong. So I

627
00:35:07,719 --> 00:35:09,800
Speaker 1: wanted to get that in there because I hate AI.

628
00:35:09,960 --> 00:35:13,640
Speaker 1: But I want you to now talk about what does

629
00:35:13,680 --> 00:35:16,520
Speaker 1: it mean to have something that thinks for you? And

630
00:35:16,920 --> 00:35:20,319
Speaker 1: what would that you said, the sort of nightmare scenario

631
00:35:20,719 --> 00:35:23,120
Speaker 1: is that we no longer think for ourselves because we

632
00:35:23,239 --> 00:35:25,480
Speaker 1: have this information process for us.

633
00:35:25,680 --> 00:35:29,360
Speaker 4: Yeah, well, it can happen in so many different parts

634
00:35:29,480 --> 00:35:32,040
Speaker 4: of where we're using AI. But I think that the

635
00:35:32,440 --> 00:35:33,920
Speaker 4: one where I talk about this in the book, and

636
00:35:33,960 --> 00:35:36,840
Speaker 4: I can probably be explained. The clearest is for students.

637
00:35:37,080 --> 00:35:39,600
Speaker 4: And I know you have kids that are either in

638
00:35:39,880 --> 00:35:42,480
Speaker 4: high school or going to college, and I are probably

639
00:35:42,600 --> 00:35:45,120
Speaker 4: seeing these tools, and I'd be very interested in how

640
00:35:45,120 --> 00:35:48,960
Speaker 4: they're approaching it. But if the task at hand can

641
00:35:49,040 --> 00:35:52,200
Speaker 4: be done so simply by AI. So if the task

642
00:35:52,320 --> 00:35:58,160
Speaker 4: is write a paper about comparing media and politics in

643
00:35:58,239 --> 00:36:02,640
Speaker 4: twenty twenty six versus twenty sixteen, and you don't even

644
00:36:02,760 --> 00:36:05,040
Speaker 4: have to really think through what is going to be

645
00:36:05,239 --> 00:36:07,279
Speaker 4: need to even be in that paper. Right, let's pretend

646
00:36:07,320 --> 00:36:09,080
Speaker 4: we're going to write that paper together. Right, me and

647
00:36:09,120 --> 00:36:10,880
Speaker 4: you went to college at a time when we wrote

648
00:36:10,880 --> 00:36:13,360
Speaker 4: that paper. I wrote that not you know, not that

649
00:36:13,400 --> 00:36:15,560
Speaker 4: exact paper, but we worked on assignments like that. So

650
00:36:15,600 --> 00:36:17,560
Speaker 4: the first step there is, Okay, I'm going to go

651
00:36:17,600 --> 00:36:20,799
Speaker 4: do the research on that. Okay, Step one, I'm going

652
00:36:20,840 --> 00:36:22,040
Speaker 4: to do the research. Maybe I'm going to go to

653
00:36:22,040 --> 00:36:24,160
Speaker 4: the library and I'm going to google around for that. Well,

654
00:36:24,200 --> 00:36:27,160
Speaker 4: now that's already all done by AI, right, so I

655
00:36:27,200 --> 00:36:28,160
Speaker 4: don't even have to pull the.

656
00:36:28,200 --> 00:36:30,760
Speaker 1: Research and do the research. That's step one of that task.

657
00:36:31,120 --> 00:36:33,400
Speaker 4: Step two of that task is I now need to

658
00:36:33,440 --> 00:36:35,560
Speaker 4: make an outline of that. I probably need to make

659
00:36:35,600 --> 00:36:38,160
Speaker 4: a thesis. I probably need to do some analysis of

660
00:36:38,200 --> 00:36:40,000
Speaker 4: this thing that I need to do. I don't have

661
00:36:40,040 --> 00:36:42,920
Speaker 4: to do that either, because AI can do that summary.

662
00:36:42,960 --> 00:36:44,959
Speaker 4: I could take all of that research that AI found

663
00:36:45,000 --> 00:36:46,799
Speaker 4: and said, now I'm going to prompt it. I'm going

664
00:36:46,880 --> 00:36:49,319
Speaker 4: to have you write that full research outline for me.

665
00:36:49,600 --> 00:36:52,000
Speaker 4: I want you to structure a really strong thesis statement.

666
00:36:52,280 --> 00:36:54,640
Speaker 4: We need to give me supporting paragraphs. I don't have

667
00:36:54,680 --> 00:36:56,759
Speaker 4: to do that task either. And now I can say that,

668
00:36:56,920 --> 00:36:58,520
Speaker 4: now that you've got that outline, I want you to

669
00:36:58,520 --> 00:37:00,680
Speaker 4: write me a first draft, and I want you to

670
00:37:00,680 --> 00:37:02,480
Speaker 4: not sound like AI. I want you to I'm going

671
00:37:02,520 --> 00:37:04,239
Speaker 4: to upload a few samples with my writing. I want

672
00:37:04,280 --> 00:37:06,640
Speaker 4: you to sound like that. And so that whole process

673
00:37:06,960 --> 00:37:08,560
Speaker 4: and not even the writing. I actually don't think the

674
00:37:08,560 --> 00:37:10,680
Speaker 4: writing's actually the hard part there, because me and you

675
00:37:10,719 --> 00:37:12,920
Speaker 4: know that that the actual like the writing, like if

676
00:37:12,960 --> 00:37:14,680
Speaker 4: you know once you're going to write, it's probably easier

677
00:37:14,719 --> 00:37:15,560
Speaker 4: to sit and write.

678
00:37:15,600 --> 00:37:16,919
Speaker 1: At least I feel that as a writer.

679
00:37:17,239 --> 00:37:19,520
Speaker 4: Not one time during that whole process did I have

680
00:37:19,560 --> 00:37:22,360
Speaker 4: to think right, Because the AI was getting better and

681
00:37:22,400 --> 00:37:24,000
Speaker 4: better and was able to do all of those things.

682
00:37:24,080 --> 00:37:27,840
Speaker 4: There was no cognitive action there was just cognitive melt,

683
00:37:28,000 --> 00:37:31,080
Speaker 4: cognitive dissonance't I didn't need to do anything. There was

684
00:37:31,160 --> 00:37:34,600
Speaker 4: brain rot basically, so you're not thinking. But that's the

685
00:37:34,640 --> 00:37:36,959
Speaker 4: worst case scenario of it, which I in the book

686
00:37:37,000 --> 00:37:40,360
Speaker 4: talk to some students who certainly do that. But then

687
00:37:40,400 --> 00:37:42,640
Speaker 4: there's the flip side of people who said, I actually

688
00:37:42,640 --> 00:37:44,319
Speaker 4: have now seen that it can do all of this.

689
00:37:44,520 --> 00:37:46,640
Speaker 4: I see that my brain is rotting, and I don't

690
00:37:46,680 --> 00:37:49,040
Speaker 4: want that, so I'm now using the AI in ways

691
00:37:49,120 --> 00:37:50,680
Speaker 4: to help me but also.

692
00:37:50,480 --> 00:37:53,280
Speaker 1: To push me. Okay, how would to push you?

693
00:37:53,280 --> 00:37:55,040
Speaker 4: You could say, I want you to pull all this

694
00:37:55,120 --> 00:37:57,440
Speaker 4: research and then you could work with it to form

695
00:37:57,480 --> 00:38:00,680
Speaker 4: that thesis statement. You could go back and forth it said, no,

696
00:38:00,880 --> 00:38:04,319
Speaker 4: I actually really believe this, and I was thinking about this.

697
00:38:04,400 --> 00:38:05,920
Speaker 1: Can you help me sharpen that thought?

698
00:38:06,680 --> 00:38:08,799
Speaker 4: Still you might not be thinking as much, but you're

699
00:38:08,800 --> 00:38:12,280
Speaker 4: having this active debate with a computer around this topic.

700
00:38:12,600 --> 00:38:15,360
Speaker 4: There's also the idea that in education that instead of

701
00:38:15,360 --> 00:38:18,200
Speaker 4: these being answer machines, that they'll end up being sort

702
00:38:18,200 --> 00:38:21,000
Speaker 4: of explaining machines and help you be that tutor along

703
00:38:21,040 --> 00:38:21,319
Speaker 4: the way.

704
00:38:21,680 --> 00:38:25,520
Speaker 1: One of the things that I read with this sort

705
00:38:25,520 --> 00:38:29,680
Speaker 1: of in a pro AI argument. Again, remember I'm not

706
00:38:29,760 --> 00:38:32,879
Speaker 1: a fan. Is that we said these kind of things

707
00:38:32,880 --> 00:38:36,440
Speaker 1: about computers, and in fact they didn't put people out

708
00:38:36,480 --> 00:38:39,440
Speaker 1: of work, they just made different kind of work. And

709
00:38:39,480 --> 00:38:40,000
Speaker 1: I think.

710
00:38:39,800 --> 00:38:42,960
Speaker 4: That's largely true about what's happening with AI right now. People,

711
00:38:43,120 --> 00:38:45,440
Speaker 4: especially let's say in the world of coding, they're not

712
00:38:45,600 --> 00:38:48,600
Speaker 4: they are taking the jobs of certain coders, but a

713
00:38:48,600 --> 00:38:50,440
Speaker 4: lot of coders are now saying I can work a

714
00:38:50,440 --> 00:38:53,160
Speaker 4: lot faster, I can work on better types of projects,

715
00:38:53,160 --> 00:38:55,240
Speaker 4: and I can work side by side with this AI.

716
00:38:55,280 --> 00:38:57,839
Speaker 4: It's now this assistant to me. And that's a big

717
00:38:57,880 --> 00:39:00,640
Speaker 4: theme of the book is where I see men's working

718
00:39:00,680 --> 00:39:03,319
Speaker 4: hand in hand with AI. Of this chapter about my

719
00:39:03,400 --> 00:39:07,120
Speaker 4: breast ultrasound and mammogram, which I just posted a fun

720
00:39:07,239 --> 00:39:10,799
Speaker 4: video about today, and when I went to go get

721
00:39:10,800 --> 00:39:13,680
Speaker 4: my mammogram and ultrasound read I went to Mount Sinai

722
00:39:13,920 --> 00:39:16,840
Speaker 4: and I had it done using AI. But also the

723
00:39:16,920 --> 00:39:20,360
Speaker 4: radiologist uses it side by side, and she's seeing things

724
00:39:20,400 --> 00:39:23,840
Speaker 4: on those ultrasounds now that she didn't necessarily see before.

725
00:39:24,200 --> 00:39:26,400
Speaker 4: And so she's working hand in hand with this AI

726
00:39:26,560 --> 00:39:29,680
Speaker 4: to do her job better. So ask me your question

727
00:39:30,000 --> 00:39:32,239
Speaker 4: when you say I hate it, break it down for me.

728
00:39:32,520 --> 00:39:33,719
Speaker 4: What parts specifically.

729
00:39:34,239 --> 00:39:38,200
Speaker 1: First of all, fundamentally, I hate what it's done to earnconomy.

730
00:39:38,400 --> 00:39:41,160
Speaker 1: They's sort of a binary right now, which is either

731
00:39:41,200 --> 00:39:43,640
Speaker 1: it works and it puts us on our business and

732
00:39:43,800 --> 00:39:46,840
Speaker 1: the people never read anything written by human ever again,

733
00:39:47,560 --> 00:39:51,799
Speaker 1: or it doesn't work and the economy crashes because billions

734
00:39:51,800 --> 00:39:55,359
Speaker 1: of dollars have been spent investing in data centers and

735
00:39:55,360 --> 00:39:57,680
Speaker 1: lives and that. Then the other thing is I hate

736
00:39:57,760 --> 00:40:01,239
Speaker 1: data centers. I mean their enormous, they're expensive, they're bad

737
00:40:01,280 --> 00:40:04,839
Speaker 1: for the environment, they use water and electricity to do

738
00:40:05,000 --> 00:40:08,800
Speaker 1: things like make photos of things that never happened, which

739
00:40:08,920 --> 00:40:12,439
Speaker 1: in my mind has no value. And then also it's

740
00:40:12,480 --> 00:40:14,560
Speaker 1: made it so that much of what I see on

741
00:40:14,560 --> 00:40:15,720
Speaker 1: the internet I don't believe.

742
00:40:15,880 --> 00:40:17,920
Speaker 4: I think you bring up all of the best arguments

743
00:40:17,920 --> 00:40:21,240
Speaker 4: for why it's terrible. Have you done anything with AI

744
00:40:21,520 --> 00:40:23,920
Speaker 4: to see where it could be kind of amazing?

745
00:40:24,120 --> 00:40:27,319
Speaker 1: I mean I try to search and it doesn't work anymore.

746
00:40:27,400 --> 00:40:29,080
Speaker 1: So that was not good.

747
00:40:29,520 --> 00:40:30,960
Speaker 4: I feel like I need to come over and spend

748
00:40:31,040 --> 00:40:32,719
Speaker 4: some time seeing what your workflow is.

749
00:40:32,840 --> 00:40:35,319
Speaker 1: I mean, I just have not had any experiences with

750
00:40:35,400 --> 00:40:38,120
Speaker 1: it that have been I mean, I know Jesse uses

751
00:40:38,160 --> 00:40:40,800
Speaker 1: it to get noise out of the podcast that works

752
00:40:40,840 --> 00:40:43,920
Speaker 1: really well for him. So I respect that I have

753
00:40:43,960 --> 00:40:46,239
Speaker 1: a lot of people I respect to like it, but

754
00:40:46,440 --> 00:40:49,319
Speaker 1: I have not had anything that it has done for

755
00:40:49,400 --> 00:40:50,680
Speaker 1: me that has changed mind.

756
00:40:51,000 --> 00:40:53,600
Speaker 4: Are there things in your personal life where you ever

757
00:40:53,640 --> 00:40:56,520
Speaker 4: find I would have googled this, or you're googling something

758
00:40:56,560 --> 00:40:59,200
Speaker 4: and you would have wanted a better answer, something more

759
00:40:59,600 --> 00:41:01,319
Speaker 4: that's playing something better to you.

760
00:41:01,840 --> 00:41:04,040
Speaker 1: What I want is to be able to go to

761
00:41:04,080 --> 00:41:07,360
Speaker 1: a primary source. The whole point of AI seems to

762
00:41:07,400 --> 00:41:09,560
Speaker 1: be to try to get me away from a primary

763
00:41:09,600 --> 00:41:12,799
Speaker 1: source and to trust the thinking a computer as. I

764
00:41:12,840 --> 00:41:15,040
Speaker 1: don't know why I need to trust the thinking of

765
00:41:15,160 --> 00:41:18,360
Speaker 1: computer as when I can just have my own thinking

766
00:41:18,520 --> 00:41:21,200
Speaker 1: that comes from my brain. Do you see what I'm saying? Like,

767
00:41:21,360 --> 00:41:24,560
Speaker 1: because both on the left and the right, our elected

768
00:41:24,880 --> 00:41:28,600
Speaker 1: officials have decided that legislation and regulation is not in

769
00:41:28,640 --> 00:41:31,440
Speaker 1: their wheelhouse. We don't know what's real and what's not real,

770
00:41:31,640 --> 00:41:34,160
Speaker 1: So it just makes my life ten times harder.

771
00:41:34,440 --> 00:41:37,759
Speaker 4: And I think what you're really describing is there's so

772
00:41:37,920 --> 00:41:42,000
Speaker 4: much unnecessary slop from these tools that.

773
00:41:41,880 --> 00:41:44,960
Speaker 1: We just have not seen any benefit from, not for

774
00:41:45,120 --> 00:41:45,840
Speaker 1: me anyway.

775
00:41:46,040 --> 00:41:47,800
Speaker 4: No, And I think a lot of people are feeling

776
00:41:47,800 --> 00:41:49,600
Speaker 4: this way, and I actually think that that is a

777
00:41:49,600 --> 00:41:51,399
Speaker 4: big difference when I wrote the book in twenty twenty

778
00:41:51,400 --> 00:41:55,080
Speaker 4: five verses. Now we're seeing that pushback in terms of

779
00:41:55,160 --> 00:41:57,880
Speaker 4: people not and it goes further than the data center arguments,

780
00:41:58,000 --> 00:42:00,960
Speaker 4: which I think are really important. Talking about the jobs,

781
00:42:00,960 --> 00:42:03,080
Speaker 4: I mean, this class coming out of college this year

782
00:42:03,280 --> 00:42:06,880
Speaker 4: seems to be only blaming AI, and maybe rightfully so.

783
00:42:07,000 --> 00:42:09,880
Speaker 4: We still we don't have full data, but they're like,

784
00:42:10,040 --> 00:42:12,960
Speaker 4: no entry level job can be now, every entry level

785
00:42:13,040 --> 00:42:14,439
Speaker 4: level job can be done by AI.

786
00:42:14,520 --> 00:42:17,080
Speaker 1: Why what are we supposed to do? AI is just

787
00:42:17,160 --> 00:42:20,600
Speaker 1: being blamed because it's easier. So we have companies saying

788
00:42:20,600 --> 00:42:23,120
Speaker 1: like we're going to like coin Base just laid off

789
00:42:23,120 --> 00:42:25,360
Speaker 1: a bunch of people. They were like, it's AI this

790
00:42:25,880 --> 00:42:28,360
Speaker 1: you know? Is it AI? No, it's not AI. But

791
00:42:28,600 --> 00:42:31,319
Speaker 1: I want to ask you about this thing I read

792
00:42:31,360 --> 00:42:33,960
Speaker 1: in the ft from Jillian Ted. I want to read

793
00:42:33,960 --> 00:42:36,000
Speaker 1: it to you because I'm curious what you think about.

794
00:42:36,040 --> 00:42:39,319
Speaker 1: This is from her column yesterday. A few months ago

795
00:42:39,360 --> 00:42:42,160
Speaker 1: in New York, Financier told me he had just experienced

796
00:42:42,160 --> 00:42:46,160
Speaker 1: it first. His twenty twenty five summer interns were the

797
00:42:46,200 --> 00:42:49,960
Speaker 1: first true AI natives I have seen. He meant, they

798
00:42:50,000 --> 00:42:54,239
Speaker 1: had grown up not only among digital tech, but AI too,

799
00:42:54,680 --> 00:42:57,400
Speaker 1: So how to go? He winced, Well, those want to

800
00:42:57,440 --> 00:43:01,279
Speaker 1: be masters of the universe. Initially see and wildly impressive.

801
00:43:01,920 --> 00:43:05,759
Speaker 1: When senior financiers later probed their ideas, they found them

802
00:43:06,160 --> 00:43:11,200
Speaker 1: alarmingly shallow. Consequently, this person's company made fewer returnal offers

803
00:43:11,200 --> 00:43:13,920
Speaker 1: and is now focused less on graduates and science, technology

804
00:43:13,920 --> 00:43:18,879
Speaker 1: and engineering, and more in humanity as students. Now, obviously

805
00:43:19,040 --> 00:43:21,960
Speaker 1: this fits my narrative because I have kids who are

806
00:43:22,040 --> 00:43:24,360
Speaker 1: humanities kids. But I'm just curious, and I am a

807
00:43:24,440 --> 00:43:27,439
Speaker 1: humanities kid, but I'm just curious of what you think

808
00:43:27,480 --> 00:43:30,040
Speaker 1: about that idea. And that's sort of the thing that

809
00:43:30,080 --> 00:43:32,799
Speaker 1: I've noticed with the AI thinking is that it does

810
00:43:32,800 --> 00:43:35,680
Speaker 1: seem very shallow. I think that it's sort of the

811
00:43:35,760 --> 00:43:36,480
Speaker 1: chicken in the egg.

812
00:43:36,680 --> 00:43:40,560
Speaker 4: Right. You have this thought that you have this graduating class,

813
00:43:40,600 --> 00:43:43,279
Speaker 4: this class that comes out of college in May will

814
00:43:43,280 --> 00:43:45,279
Speaker 4: be the first class to have had CHATCHYPT.

815
00:43:45,600 --> 00:43:47,600
Speaker 1: When they were freshmen CHATCHYPT launched.

816
00:43:47,719 --> 00:43:50,080
Speaker 4: So they've seen the models get better, they have seen

817
00:43:50,120 --> 00:43:53,840
Speaker 4: it get progressively more infiltrated in their lives and their apps.

818
00:43:54,040 --> 00:43:57,520
Speaker 4: So the idea that they might not be that everything

819
00:43:57,560 --> 00:43:59,719
Speaker 4: they are talking about or every idea they bring into

820
00:43:59,760 --> 00:44:02,319
Speaker 4: the world might have been not generated by them. Is

821
00:44:02,480 --> 00:44:04,760
Speaker 4: that does not seem outlandish to me? That seems pretty

822
00:44:04,800 --> 00:44:07,200
Speaker 4: pretty likely. I mean, I'm sure we have you know,

823
00:44:07,239 --> 00:44:10,160
Speaker 4: we all can think of some stellar student or stellar kid.

824
00:44:10,200 --> 00:44:13,040
Speaker 4: But like, this tool is there, they're they're using it.

825
00:44:13,280 --> 00:44:15,239
Speaker 4: We know that as humans, right, we're going to use

826
00:44:15,280 --> 00:44:18,759
Speaker 4: this if it's easier. I keep asking the question, then,

827
00:44:18,800 --> 00:44:21,400
Speaker 4: how is this generation going to now move through the

828
00:44:21,440 --> 00:44:23,879
Speaker 4: workforce because if they can't get the entry level job

829
00:44:24,120 --> 00:44:26,040
Speaker 4: and they haven't been taught to do the entry level

830
00:44:26,080 --> 00:44:28,279
Speaker 4: stuff because it's being replaced by AI. And I get

831
00:44:28,320 --> 00:44:30,040
Speaker 4: asked this question a lot. What do you recommend to

832
00:44:30,080 --> 00:44:32,319
Speaker 4: this class? You know, how do they make a dent

833
00:44:32,600 --> 00:44:34,080
Speaker 4: in the their industry?

834
00:44:34,400 --> 00:44:36,800
Speaker 1: How do they get jobs? How do they get jobs?

835
00:44:36,840 --> 00:44:38,200
Speaker 1: And they're out learning on the job.

836
00:44:38,280 --> 00:44:40,600
Speaker 4: So you know, my advice has been Okay, you have

837
00:44:40,719 --> 00:44:42,919
Speaker 4: to really look at and this is in the book

838
00:44:42,920 --> 00:44:45,160
Speaker 4: because a lot of the academic research looks at this,

839
00:44:45,640 --> 00:44:47,520
Speaker 4: what are the tasks that are being done by AI?

840
00:44:47,920 --> 00:44:50,480
Speaker 4: Look at them say okay, I know AI is doing that.

841
00:44:50,520 --> 00:44:52,040
Speaker 4: I need to level up and do the tasks that

842
00:44:52,080 --> 00:44:54,120
Speaker 4: are not done by AI. But how do they learn

843
00:44:54,200 --> 00:44:56,120
Speaker 4: how to do those tasks that are not done by AI?

844
00:44:56,160 --> 00:44:59,080
Speaker 4: And that's the big question. You need companies, you need mentors,

845
00:44:59,120 --> 00:45:02,960
Speaker 4: you need people with the deep experience willing to educate.

846
00:45:03,040 --> 00:45:06,360
Speaker 1: Still, yeah, and that's a really good point. So tell

847
00:45:06,400 --> 00:45:09,080
Speaker 1: me what you learned from this experience.

848
00:45:09,360 --> 00:45:11,319
Speaker 4: Well, one thing I learned, and I think I haven't

849
00:45:11,360 --> 00:45:12,920
Speaker 4: really talked about it, but I think because of your

850
00:45:12,960 --> 00:45:15,520
Speaker 4: hate for A, I think it's important to talk about

851
00:45:15,600 --> 00:45:18,520
Speaker 4: it is that there are a lot of places AI

852
00:45:18,719 --> 00:45:21,919
Speaker 4: is begin to be used which you're not actively going

853
00:45:21,960 --> 00:45:25,600
Speaker 4: to use, right, You're not going to invite it into

854
00:45:25,600 --> 00:45:27,319
Speaker 4: your life, but it is going to impact your life,

855
00:45:27,360 --> 00:45:29,359
Speaker 4: and it honestly might be for the better. There are

856
00:45:29,360 --> 00:45:31,120
Speaker 4: some places where it might not be for the better,

857
00:45:31,200 --> 00:45:32,840
Speaker 4: or we know it clearly won't be for the better.

858
00:45:32,960 --> 00:45:35,080
Speaker 1: And I think one of those examples is in healthcare.

859
00:45:35,440 --> 00:45:37,680
Speaker 4: It's the way I start the book, because the book

860
00:45:37,719 --> 00:45:39,840
Speaker 4: is seasonal, and so it starts in the winter, and

861
00:45:39,880 --> 00:45:41,839
Speaker 4: in the winter I went and got this my mammogram

862
00:45:41,840 --> 00:45:43,400
Speaker 4: and rest culture sound read by AI.

863
00:45:43,600 --> 00:45:46,040
Speaker 1: But now I would not go to a hospital that

864
00:45:46,120 --> 00:45:46,920
Speaker 1: didn't use AI.

865
00:45:47,120 --> 00:45:49,319
Speaker 4: To look at these scans of a very high risk

866
00:45:49,360 --> 00:45:53,080
Speaker 4: of breast cancer, and these are able to very clearly

867
00:45:53,200 --> 00:45:56,960
Speaker 4: spot things that humans cannot see. So in certain areas,

868
00:45:57,040 --> 00:46:00,200
Speaker 4: I think we have to really understand that this is

869
00:46:00,239 --> 00:46:04,120
Speaker 4: not just chatbots that we type into or Google or

870
00:46:04,200 --> 00:46:06,719
Speaker 4: Google searches that we chat that we type into and

871
00:46:06,760 --> 00:46:10,000
Speaker 4: get garbage and slop back. I think the other thing

872
00:46:10,040 --> 00:46:12,680
Speaker 4: that I really learned and I want to impart to

873
00:46:12,760 --> 00:46:17,360
Speaker 4: people is that it is very scary the emotional connections

874
00:46:17,400 --> 00:46:20,799
Speaker 4: people can form with these chatbots. And I don't only

875
00:46:20,880 --> 00:46:23,120
Speaker 4: have fears of it with kids and with teens, I

876
00:46:23,160 --> 00:46:24,919
Speaker 4: have fears with adults too.

877
00:46:25,080 --> 00:46:26,319
Speaker 1: I think we are going to see that.

878
00:46:26,360 --> 00:46:28,440
Speaker 4: And I think this is more in the world that

879
00:46:28,520 --> 00:46:29,960
Speaker 4: you cover, and not as much in the world that

880
00:46:30,000 --> 00:46:33,840
Speaker 4: I cover, but in the political discourse and in the

881
00:46:34,040 --> 00:46:37,960
Speaker 4: political division and the divisiveness, it only seems like it's

882
00:46:37,960 --> 00:46:39,799
Speaker 4: only going to get worse because of the way these

883
00:46:39,920 --> 00:46:42,600
Speaker 4: chatbots are programmed. My feeling come at this the end

884
00:46:42,600 --> 00:46:45,960
Speaker 4: of the book is we need something here because these

885
00:46:46,080 --> 00:46:47,879
Speaker 4: bots are just programmed to tell us.

886
00:46:47,840 --> 00:46:51,000
Speaker 1: What we want, no friction to as you say, tell

887
00:46:51,080 --> 00:46:51,920
Speaker 1: us things that are wrong.

888
00:46:51,960 --> 00:46:54,160
Speaker 4: I don't they have gotten better at that, but it's

889
00:46:54,280 --> 00:46:56,400
Speaker 4: going to continue to be an uphill battle, and we

890
00:46:56,480 --> 00:47:00,800
Speaker 4: need something to keep these ai being that these companies

891
00:47:00,800 --> 00:47:04,440
Speaker 4: are creating from being so compelling to talk to all

892
00:47:04,520 --> 00:47:05,080
Speaker 4: day long.

893
00:47:05,400 --> 00:47:08,799
Speaker 1: Yeah, so we need government regulation something the government has

894
00:47:08,880 --> 00:47:13,719
Speaker 1: been very reticent by doing. Yeah, and this is my

895
00:47:13,800 --> 00:47:14,359
Speaker 1: biggest spirit.

896
00:47:14,400 --> 00:47:15,640
Speaker 4: And I come at this at the end of the

897
00:47:15,680 --> 00:47:17,400
Speaker 4: book and I go to Sam Altman and talk to

898
00:47:17,480 --> 00:47:20,680
Speaker 4: him about this, which is, how can we ensure that

899
00:47:20,760 --> 00:47:24,440
Speaker 4: humans will not just be in relationships with computers and

900
00:47:24,480 --> 00:47:26,600
Speaker 4: talking to computers all the time. And he says, oh,

901
00:47:26,680 --> 00:47:32,040
Speaker 4: humanity will win out. Humans love being with humans, baby,

902
00:47:33,280 --> 00:47:34,640
Speaker 4: I mean, I like being here with you.

903
00:47:35,040 --> 00:47:37,960
Speaker 1: I like you, but we like each other because we

904
00:47:38,120 --> 00:47:41,160
Speaker 1: met once before. How are the self driving cars.

905
00:47:41,320 --> 00:47:43,439
Speaker 4: I think that's another area, like where we just talked

906
00:47:43,440 --> 00:47:46,960
Speaker 4: about with the in medical or healthcare. I think that

907
00:47:47,200 --> 00:47:50,400
Speaker 4: self driving cars are going to join to fundamentally change

908
00:47:50,480 --> 00:47:54,319
Speaker 4: transportation and likely safety on the roads. I mean, there's

909
00:47:54,360 --> 00:47:56,879
Speaker 4: still not hard data that is saying that these are

910
00:47:56,880 --> 00:47:59,239
Speaker 4: absolutely much safer than humans, but there's a lot of

911
00:47:59,320 --> 00:48:00,399
Speaker 4: data pointing to that.

912
00:48:00,840 --> 00:48:01,839
Speaker 1: Have you been in one yet?

913
00:48:02,160 --> 00:48:04,520
Speaker 4: No, You've got to. I know you hate this stuff,

914
00:48:04,560 --> 00:48:05,879
Speaker 4: but you got to. I want to be there when

915
00:48:05,920 --> 00:48:06,440
Speaker 4: you go.

916
00:48:06,480 --> 00:48:09,640
Speaker 1: Well, let's go together. We'll go together, We'll do we'll

917
00:48:09,640 --> 00:48:11,600
Speaker 1: do a video together where I can talk about how

918
00:48:11,680 --> 00:48:12,279
Speaker 1: much I hate it.

919
00:48:12,480 --> 00:48:14,360
Speaker 4: You're going to maybe be nervous. I mean, you're a

920
00:48:14,360 --> 00:48:16,760
Speaker 4: New Yorker, You're you're first of all, you're probably nervous

921
00:48:16,760 --> 00:48:18,520
Speaker 4: about everything. But second level, like you've been in like

922
00:48:18,680 --> 00:48:20,560
Speaker 4: crazy taxis right.

923
00:48:20,800 --> 00:48:23,320
Speaker 1: For sure, it could always be worse. It could always

924
00:48:23,320 --> 00:48:23,560
Speaker 1: be worse.

925
00:48:23,640 --> 00:48:25,440
Speaker 4: But you're you're going to see how quickly you get

926
00:48:25,520 --> 00:48:28,640
Speaker 4: used to it, and you're gonna also realize this is

927
00:48:28,640 --> 00:48:30,439
Speaker 4: what the world is probably going to look like soon.

928
00:48:30,840 --> 00:48:33,000
Speaker 4: If it's not just cars that drive us, it's going

929
00:48:33,040 --> 00:48:34,840
Speaker 4: to be us sitting in cars that are driving us,

930
00:48:34,880 --> 00:48:37,000
Speaker 4: and we only jump in and drive sometimes.

931
00:48:37,280 --> 00:48:37,520
Speaker 1: Wow.

932
00:48:37,800 --> 00:48:40,040
Speaker 4: I took my kids in these way mows we went

933
00:48:40,080 --> 00:48:42,080
Speaker 4: on a spring break, and they just were obsessed with them.

934
00:48:42,080 --> 00:48:44,560
Speaker 4: But they also just then didn't care. They were just like, Okay, yeah,

935
00:48:44,560 --> 00:48:46,480
Speaker 4: this is normal. The car just drives for us. I

936
00:48:46,480 --> 00:48:49,120
Speaker 4: think that's going to be the way the next generation

937
00:48:49,360 --> 00:48:50,600
Speaker 4: used drivers, Joanna.

938
00:48:50,640 --> 00:48:51,560
Speaker 1: I hope you'll come back.

939
00:48:51,760 --> 00:48:54,520
Speaker 4: Well, I need to polish my arguments on why you

940
00:48:54,520 --> 00:48:55,360
Speaker 4: shouldn't hate.

941
00:48:55,160 --> 00:48:59,319
Speaker 1: AI before the AI overlords take us over. Please come back,

942
00:48:59,480 --> 00:49:00,680
Speaker 1: and you need to come to your apartment.

943
00:49:00,719 --> 00:49:03,760
Speaker 4: I'd show you one thing that you see and you say, okay,

944
00:49:03,800 --> 00:49:06,360
Speaker 4: I can see there's some promise here.

945
00:49:06,560 --> 00:49:08,120
Speaker 1: I don't want to convince you that it's all good.

946
00:49:08,160 --> 00:49:09,360
Speaker 4: I don't want to do that, but I want to

947
00:49:09,360 --> 00:49:11,640
Speaker 4: be at least to show you a glimmer of why

948
00:49:11,680 --> 00:49:12,160
Speaker 4: it could.

949
00:49:12,000 --> 00:49:14,600
Speaker 1: Be useful to you. I'll take it. I would love that.

950
00:49:14,920 --> 00:49:17,120
Speaker 1: Thank you so much for coming on. You're the best.

951
00:49:17,280 --> 00:49:24,719
Speaker 1: Of course they're no more. Pectod Jesse Cannon Smiley.

952
00:49:25,400 --> 00:49:29,680
Speaker 2: We are now seeing something quite disturbing. The US government

953
00:49:29,920 --> 00:49:34,120
Speaker 2: is now monitoring anti tech extremism for people who oppose

954
00:49:34,200 --> 00:49:38,600
Speaker 2: data centers, meaning what like seventy to eighty percent of Americans.

955
00:49:38,920 --> 00:49:44,319
Speaker 1: Yeah, this is a wild thing, because Americans really hate

956
00:49:44,400 --> 00:49:47,080
Speaker 1: data centers. There's a reason they hate them. They're expensive,

957
00:49:47,120 --> 00:49:49,719
Speaker 1: they're bad to the environment, they use our water, they

958
00:49:49,800 --> 00:49:54,640
Speaker 1: use our electricity, and they produce Aislam. So I don't

959
00:49:54,719 --> 00:49:57,960
Speaker 1: understand what the point here is. But there are a

960
00:49:58,000 --> 00:50:00,879
Speaker 1: lot of tech companies who want to build that, and

961
00:50:01,160 --> 00:50:05,680
Speaker 1: this administration is now going after people like the ladies

962
00:50:05,680 --> 00:50:08,759
Speaker 1: of Elevate, Utah, who we had on this podcast. You'll

963
00:50:08,800 --> 00:50:12,560
Speaker 1: remember that mister quote unquote wonderful Canadian citizen is trying

964
00:50:12,600 --> 00:50:16,880
Speaker 1: to build a forty thousand acre data center in Utah

965
00:50:17,440 --> 00:50:20,280
Speaker 1: right near the Great Salt Lake that will use water

966
00:50:20,400 --> 00:50:24,680
Speaker 1: and power and land. And this administration does not believe

967
00:50:24,760 --> 00:50:29,640
Speaker 1: in any kind of regulations for building, and so they

968
00:50:29,760 --> 00:50:36,040
Speaker 1: are pretty committed to letting Trump do whatever he wants

969
00:50:36,120 --> 00:50:39,600
Speaker 1: when it comes to data centers. And so here we are,

970
00:50:39,960 --> 00:50:43,840
Speaker 1: and they're going to hunt people down for being activists,

971
00:50:43,840 --> 00:50:48,640
Speaker 1: for being anti American, anti Christian, and anti capitalist. Now

972
00:50:48,880 --> 00:50:53,640
Speaker 1: the good news is, yes, Trump's counter terrorisms are you

973
00:50:53,760 --> 00:50:57,760
Speaker 1: really remember him? From season one? Doctor Sebastian Gorka released

974
00:50:57,760 --> 00:51:02,200
Speaker 1: a public counter terrorism energy claiming that the left wing

975
00:51:02,320 --> 00:51:06,680
Speaker 1: extremists are one of the top three counter terrorism priorities

976
00:51:06,719 --> 00:51:09,319
Speaker 1: facing the United States. I don't know what to tell you.

977
00:51:09,560 --> 00:51:11,480
Speaker 1: I really don't know what to tell you. It was

978
00:51:11,680 --> 00:51:16,040
Speaker 1: like being in the dumbest possible. It's like being in hell,

979
00:51:16,360 --> 00:51:22,040
Speaker 1: the dumbest people doing the dumbest stuff. It is shockingly

980
00:51:22,120 --> 00:51:26,480
Speaker 1: stupid and also bad. That's it for this episode of

981
00:51:26,560 --> 00:51:33,360
Speaker 1: Fast Politics. Tune in every Monday, Wednesday, Thursday, and Saturday

982
00:51:33,520 --> 00:51:36,840
Speaker 1: to hear the best minds and politics make sense of

983
00:51:37,120 --> 00:51:41,359
Speaker 1: all this chaos. If you enjoy this podcast, please send

984
00:51:41,400 --> 00:51:45,080
Speaker 1: it to a friend and keep the conversation going. Thanks

985
00:51:45,120 --> 00:51:45,800
Speaker 1: for listening.