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Speaker 1: Hey, guys, Saga and Crystal here.

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Speaker 2: Independent media just played a truly massive role in this election,

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Speaker 1: We need your help to build the future of independent

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Speaker 2: Let's go ahead and talk about John Stewart and Stephen

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Speaker 2: A here, because this is a little more fun than

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Speaker 2: nuclear war. Death in nuclear war and ping fascism is.

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Speaker 3: Fleeing.

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Speaker 2: It's on par I'd say it's likely roughly equivalent. So

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Speaker 2: in any case, Steven A goes on with John Sire.

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Speaker 2: I watched the whole interview. You know, they were growing out,

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Speaker 2: they were like bonding over their next tragic result, which

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Speaker 2: you know, I have some of that going on in

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Speaker 2: my household as well. In any case, the part of

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Speaker 2: the conversation. People were really interested in though, is Stephen

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Speaker 2: a is this like very centristy figure. He also just

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Speaker 2: like he's not a I know, he's like floated this

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Speaker 2: idea of running for president, but his political takes a

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Speaker 2: pret surface level. He's not like deep in the weeds

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Speaker 2: with this kind of stuff, you know. And John Stewart

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Speaker 2: obviously is like deeply in the weeds with this kind

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Speaker 2: of stuff. So they get into a bit of a

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Speaker 2: battle over what is the reason why the Democratic Party

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Speaker 2: with Kamal Harris lost to Trump in this last election cycle.

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Speaker 2: Let's go ahead and listen to how that went down.

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Speaker 4: The left definitely blew it. I thought that I thought

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Speaker 4: that the progressive left, the extreme left, really really ruined

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Speaker 4: the election for the Democrats. And that's that's how I

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Speaker 4: view this is the truth, and I think that I

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Speaker 4: think that because that happened, it positioned him to get

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Speaker 4: away with a lot of things. Then you're putting forth

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Speaker 4: some of these policies, whether it's transgender issues of other

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Speaker 4: stuff that they would bring up. You had a lot

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Speaker 4: of people to say, that's not where our focus should lie.

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Speaker 5: When don't you think that's their focus focus?

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Speaker 3: The right?

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Speaker 5: I feel like the right is far more focused on

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Speaker 5: where you go to the bathroom. They allowed what people

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Speaker 5: would consider to be a centrist old guard Democrat to

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Speaker 5: run again for president, and then you say, the progressive

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Speaker 5: left wing is the one that didn't. They didn't choose

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Speaker 5: Joe Biden. They don't have the power in that party.

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Speaker 5: Chuck Schumer is out there, you know, writing eight strongly

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Speaker 5: worded questions to Donald Trump about getting The Progressives are

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Speaker 5: the one going people in this country want medicare for all,

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Speaker 5: do something. They're the ones that have a principal agenda

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Speaker 5: that they are going to put forth that all the

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Speaker 5: Democrats have run from in under the guise of we

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Speaker 5: have to be moderate and we have to be centrist.

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Speaker 5: But that doesn't appeal to changing the culture and dynamic

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Speaker 5: of how this government should work by the people, for

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Speaker 5: the people of the people.

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Speaker 4: That's a great argument, and I don't disagree.

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Speaker 6: What I'm saying to you is that we're done here.

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Speaker 2: So Johnston then goes on Emily to make a case

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Speaker 2: that I think he'll be maybe more sympathetic too than

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Speaker 2: that one.

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Speaker 7: I mean that one.

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Speaker 2: I totally agree with rights like utterly obsessed with gender

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Speaker 2: identity and genitals at this point, but well, we can

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Speaker 2: pause that debate at.

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Speaker 7: The moment.

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Speaker 3: On that people can look it up and fought with.

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Speaker 7: Piers Morgan on this you can oh much better than me.

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Speaker 2: But in any case, he goes on and make this

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Speaker 2: case of like, don't you think the real problem is

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Speaker 2: that Democrats didn't actually like deliver or promise to deliver

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Speaker 2: anything real. And so when you've got Bernie Sanders and

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Speaker 2: AOC and that they're doing this like stop oligarchy to doing,

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Speaker 2: they're saying like, we're going to help you with your wages,

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Speaker 2: We're going to get you held gere, Like, isn't that

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Speaker 2: really the problem is that Democrats had no credibility on

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Speaker 2: actually addressing people's he didn't put it this way, but

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Speaker 2: addressing people's material concerns. And Steve and A couldn't really

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Speaker 2: couldn't really respond to that. He goes, well, that's a

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Speaker 2: good point, and then went on to say, you know,

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Speaker 2: some other stuff. But you know, I thought John did

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Speaker 2: an effective job of positioning this because to just frame

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Speaker 2: it in terms of left right, I think is deeply confusing.

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Speaker 2: And this gets to this other piece of news that

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Speaker 2: just came out, like Josh Holly is now introducing fifteen

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Speaker 2: dollars minimum wage bill in the Senate. I don't know

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Speaker 2: if it'll make it to the floor. I'm sure there's

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Speaker 2: like literally no other Senate Republicans who would vote for it.

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Speaker 2: But really getting to the left of some of the

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Speaker 2: Democrats on an issue that should be a core democratic issue,

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Speaker 2: and no one would look at that and be like, oh,

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Speaker 2: this is going to cause like Josh Holly to lose,

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Speaker 2: or this is going to be bad for Republicans. No,

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Speaker 2: there is a broad recognition that any of this sort

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Speaker 2: of more populous pieces that whether it's Josh Holly or

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Speaker 2: Trump has pushed with the no tax on tips or whatever,

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Speaker 2: that these are good ideas that are also really popular.

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Speaker 2: And so John Stewart's point is basically like Democrats should

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Speaker 2: do more of that, and that would be you know,

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Speaker 2: an effective way of winning people. Oh or if they

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Speaker 2: actually feel like you have credibility delivering on something real

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Speaker 2: to them.

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Speaker 3: Yeah, And I mean, I think this is one of

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Speaker 3: the biggest problems but also opportunities for the left. Opportunities

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Speaker 3: in the sense that Bernie Sanders and Alexandre Acostio Cortez

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Speaker 3: on the fighting aligarchy tour have leaned into I think

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Speaker 3: the opportunities presented by that challenge. So it's actually a

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Speaker 3: challenge that I think forces Democrats to confront those questions.

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Speaker 3: On the other hand, I think part of the problem

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Speaker 3: here is that politically, the left wing of the Democratic

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Speaker 3: Party has the most attractive, appealing, palatable economic agenda. You know,

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Speaker 3: a lot of my conservative friends, like Blanche at that

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Speaker 3: be like what the hell you're talking about. But it's

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Speaker 3: actually much easier to sell voters on Medicare for all

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Speaker 3: than it is to sell voters on the status quo

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Speaker 3: or some tweak to the status Yeah.

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Speaker 2: Like we're going to negotiate drug prices or medicare on

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Speaker 2: like five drugs. It's not a don't you feel like

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Speaker 2: your life is transformed by that? I mean it's like, okay,

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Speaker 2: well that's better, I guess, right.

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Speaker 3: But the problem is when the left wing's economic solutions

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Speaker 3: are the most palatable and appealing, then you also have

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Speaker 3: the people who have signed the ACLU pledges like Kamala

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Speaker 3: Harris did. That allowed the Republicans to run that ad

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Speaker 3: on supporting taxpayer funded I think it was transition surgeries

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Speaker 3: for incarcerated migrants the most specific thing possible. But I

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Speaker 3: think that's where you see Bernie Sanders and Alexandria Cosio

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Speaker 3: Cortez and Holly is trying to do this from the right,

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Speaker 3: by the way, because it's also a challenge slash opportunity

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Speaker 3: for the right where you have the most MAGA people

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Speaker 3: having economics that are easier to sell, more palatable to

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Speaker 3: sell to general election populations, statewide populations, and all of that,

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Speaker 3: but they're also the ones that are now super super

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Speaker 3: tied down to the MAGA brand. If you look at Holly,

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Speaker 3: for example, a lot of people remember Holly because the

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Speaker 3: image of him with the raised fist outside of the

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Speaker 3: capital in the Mourning of January sixth. So it's not

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Speaker 3: as though any side has like the upper hand politically

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Speaker 3: on the question. It's it's actually something that they're both

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Speaker 3: confronting in their own ways, which is quite interesting.

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Speaker 2: I think there's a lot to that because what always

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Speaker 2: irritates me is, first of all, number one, it is

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Speaker 2: one Republicans who bring up transgender issues more regularly than

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Speaker 2: Democrats because they feel like it's a good issue for them.

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Speaker 7: There's a.

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Speaker 2: Go ahead, try meum, So there's an obsession.

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Speaker 7: There is an obsession there. I mean, there's no doubt

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Speaker 7: about it.

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Speaker 2: But what drives me crazy is I'm like, Okay, so

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Speaker 2: let's accept that, like democratic positioning on the specific issue,

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Speaker 2: not even transgender issues in general, the specific issue of

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Speaker 2: transgender girls in sports, let's accept that democratic positioning on

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Speaker 2: that is not in sync with public opinion. Look at

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Speaker 2: how many wildly unpopular things that Trump says, does and embraces,

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Speaker 2: pardoning the violent January six ers, some of whom beat

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Speaker 2: up cops pulls at about like ten percent, Okay, wildly unpopular,

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Speaker 2: a bunch of the things that were done at DOJE

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Speaker 2: wildly unpopular, insane across the board, haphazard tariffs, wildly unpopular.

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Speaker 2: So then you ask the question, Okay, so if if

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Speaker 2: both sides have and I would say that Trump has

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Speaker 2: specifically more issues that pull way worse than the overall

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Speaker 2: broad like democratic program or what Kamway Harris specifically ran

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Speaker 2: on where you know she intentionally in her campaign didn't

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Speaker 2: talk about a lot of the things that were less popular.

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Speaker 7: For if Trump has, let's just call it even.

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Speaker 2: And they both have unpopular issues that you know that

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Speaker 2: they support and that they have said things about in

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Speaker 2: the past. Why does it matter for one and not

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Speaker 2: the other? And to me, the reason is number one,

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Speaker 2: Trump is just a talented figure. Number two, he has

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Speaker 2: a story that I think is wrong and bad and

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Speaker 2: evil in all of those things, but makes some sense.

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Speaker 2: The reason your life is bad is because of immigrants,

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Speaker 2: trans people, cultural elites. So if people sort of broadly

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Speaker 2: get on board with that narrative, they're willing to forgive

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Speaker 2: and forget some of the bullshit that is like wildly indefensible.

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Speaker 2: Democrats first of all, don't seem like they stand for

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Speaker 2: literally anything at all, which is why this whole like

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Speaker 2: pull testing. Let me put my finger in the wind,

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Speaker 2: let me try to reposition on trans issues, let me

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Speaker 2: like focus test some response on girls in sports that's

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Speaker 2: gonna not piss off this person, but is gonna appeal

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Speaker 2: to a moderate, etc. People can smell that a mile away.

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Speaker 2: So if you don't have a coherent story to tell

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Speaker 2: and there aren't things that you are demonstrably willing to

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Speaker 2: fight for, then yeah, they're going to have a lot

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Speaker 2: more room to tar you with your least popular positions

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Speaker 2: and frame them in the way that they want to

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Speaker 2: frame them. So to me, That's the big difference between

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Speaker 2: why the rights, why Trump specifically unpopular issues don't seem

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Speaker 2: to matter as much as Democrats is because Democrats frequently

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Speaker 2: don't have a spine, don't stand out for themselves, don't

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Speaker 2: have principles they're willing to stand on, aren't willing to

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Speaker 2: fight for anything, and don't have a coherent story about

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Speaker 2: what has gone wrong or honesty really about the struggles

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Speaker 2: that people are facing. Bernie Sanders is the most popular

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Speaker 2: politician in the country right now, most popular politician in

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Speaker 2: the country. Does he have a different position on trans issues?

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Speaker 3: Now?

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Speaker 7: No, he doesn't.

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Speaker 2: The reason is because he has credibility on fighting for

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Speaker 2: things that people care about, and he has a story

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Speaker 2: that makes sense to people about who is screwing them over,

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Speaker 2: why things have gone sideways in this country, and how

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Speaker 2: critically it could be put back on track.

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Speaker 3: Right people believe him speaking of the opportunities created by

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Speaker 3: all of this. Let's put d three on the screen

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Speaker 3: and talk about zomentum so zoron momdani his Actually he's

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Speaker 3: now trailing according to this new pole Andrew Cuomo in

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Speaker 3: a one to one race. So keep that in mind.

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Speaker 3: He's now trailing Cuomo by only two points. My suspicion

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Speaker 3: is that that's at least pretty close to the margin

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Speaker 3: of error. I'll have to go look at the pole here.

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Speaker 3: But that was also conducted before the Alexandria Occassio Cortes

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Speaker 3: endorsement of Zoron, Mom, Donnie, and we can put D

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Speaker 3: four on the screen as well.

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Speaker 7: Dan before the debate, which went well for Zoron.

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Speaker 3: Yes, yes, and went very poorly for Cuomo. So big

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Speaker 3: thing for Zoron here across the board. The places where

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Speaker 3: he lower net favorabilities are just the places where fewer

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Speaker 3: voters know about him to have an opinion. No demo

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Speaker 3: has particularly high unfavorables for him overall. And then another post,

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Speaker 3: a tale of two candidates. Zoron plus forty three net

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Speaker 3: favorability twenty eight percent haven't heard enough, Cuomo minus one

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Speaker 3: net favorability, three percent haven't heard enough, So.

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Speaker 7: Crystal room to grow for Zoran.

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Speaker 3: Well, also, you heard it here first, truly, you heard

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Speaker 3: it here first. Zomentum is absolutely realm is absolutely real.

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Speaker 7: Yeah, no, there's no doubt about it.

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Speaker 2: I mean, listen, I want to be fair and say

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Speaker 2: there are other poles out Salem's rejoined the chat. There

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Speaker 2: are other poles out that show a larger gap in

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Speaker 2: favor of Cuomo. This particular polling outfit, though, was extremely

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Speaker 2: accurate in the Eric Adams mayor race. It's not a

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Speaker 2: crazy poll right, No, they do have a track record.

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Speaker 2: I don't think anyone denies that Zoron has significant momentum

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Speaker 2: right now. And this poll coming before the debate before

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Speaker 2: AOC endorses, is significant because interestingly, in terms of the

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Speaker 2: gender divide, Zoran is actually doing better with men. So Democrats,

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Speaker 2: if you're looking for a candidate who can appeal to men,

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Speaker 2: and specifically actually white men, there's something to learn from

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Speaker 2: Zoron's candiacy because he's doing really well with young white

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Speaker 2: men are probably his, I'm quite confident are his best demographic.

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Speaker 2: So he's got some answers for you in this whole

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Speaker 2: man conversation. But you know, the point about the groups

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Speaker 2: where he's faring the poorest are also the ones that

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Speaker 2: know the least about him, I think is a really

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Speaker 2: important one and may help to explain some of why

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Speaker 2: women are voting more for Cuomo than Zoran at this point,

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Speaker 2: which disturbing and really a little bit head scratching, but

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Speaker 2: women also knew less about Zoran at this point, so

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Speaker 2: there may be rooms to grow there. You know, he

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Speaker 2: still has some of the demographic challenges that have plagued

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Speaker 2: the left. I think, you know, older black voters are

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Speaker 2: the weakest demographic for him. Makes sense because you have

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Speaker 2: a larger number of conservatives who you know, older black

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Speaker 2: voters tend to as a group be more conservative than

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Speaker 2: some of the other parts of the Democratic He's he's

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Speaker 2: doing pretty well with Latinos though, so he's gained a

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Speaker 2: lot of ground there, and you know, we'll see they're

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Speaker 2: hitting him quite hard. They're really trying to go after

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Speaker 2: him in all sorts of ways, and it's it's going

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Speaker 2: to be continue to be a difficult hill to climb.

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Speaker 2: But I think if you're looking at who has room

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Speaker 2: to grow, obviously he is way more favorable, way higher

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Speaker 2: favorability than Cuomo. I think he's you know, he's really

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Speaker 2: got a shot in this thing, which is pretty wild

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Speaker 2: and pretty extraordinary commentary on the way that the Democratic

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Speaker 2: base has moved in Trump two point zero as well.

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Speaker 3: Yeah, that favorability gap is insane and it'll be really

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Speaker 3: sad if New Yorkers are stuck with Andrew Cuomo because

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Speaker 3: I don't think anybody wants that. Even people who are

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Speaker 3: saying they're maybe favorable to Andrew Cuomo are probably favorable

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Speaker 3: to him in the respect that they don't like the

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Speaker 3: other candidates, and they're like, Okay, he's fine, I know him,

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Speaker 3: but really, nobody wants Andrew Cuomo to be the mayor

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Speaker 3: of New York City. I mean, maybe like five people

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Speaker 3: want Andrew Cuomo to the mayor of New York City.

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Speaker 3: They're all named Cuomo. So if voters are stuck, maybe

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Speaker 3: they're uncomfortable with Zoron's full democratic socialist platform. And honestly,

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Speaker 3: that's fully understandable, especially after the city was mismanaged under

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Speaker 3: build the Blasio Like, I get it, I get it.

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Speaker 3: So if you're forced to choose between Andrew Cuomo and

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Speaker 3: somebody who's, by his own admission, on the left flank,

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Speaker 3: that just sucks for voters. So if it ends up Cuomo,

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Speaker 3: I mean, good luck. Everyone, Like, that's just I'm sorry

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Speaker 3: that those are your those are your choices.

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Speaker 2: Let me let me say something about the way Zorn's

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Speaker 2: run his campaign though, too. Yeah, because to the you

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Speaker 2: know circles back to the like John Stair Stephen A

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Speaker 2: Smith to tion and positioning. So Zoron has you know,

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Speaker 2: all the lefty positions, no doubt about it. His tagline

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Speaker 2: is Zoron for New York you can afford. And every

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Speaker 2: time we've played as clips, you guys talk to him,

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Speaker 2: like every time you talk to him, you know what

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Speaker 2: his policy platform is. Freeze the rent, you know, government

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Speaker 2: owned grocery stores to compete so that you can, you know,

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Speaker 2: in food desert, so that you have some fresh fruit

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Speaker 2: food options. He is laser focused on housing in particular,

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Speaker 2: free bussing, you know, so that people can ride public transit,

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Speaker 2: and additional investments in public transit. People know what his

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Speaker 2: platform is, they know what he stands for. It's really clear.

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Speaker 2: He has a very clear message, very clear story about

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Speaker 2: what has gone wrong in New York, the way it's

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Speaker 2: become an intentionally a luxury good under mayors like Bloomberg

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Speaker 2: and Juliani and Eric Adams. And he wants to make

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Speaker 2: it so that you can live in New York and

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Speaker 2: it doesn't have to be such a struggle. Now, will

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Speaker 2: one person be able to accomplish that in one term? No,

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Speaker 2: But he's put forward some achievable. I think concrete goals

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Speaker 2: that people can really wrap their heads around. And that's

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Speaker 2: why his campaign has taken And it's not just against Cuomo,

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Speaker 2: Like there are a bunch of long time established New

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Speaker 2: York City palls in this race who were getting no traction.

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Speaker 2: He has sucked up all of the oxygen, all of

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Speaker 2: the like not Cuomo oxygen. And it's a real testament

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Speaker 2: to the strength of his very straightforward proposition and his

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Speaker 2: direct pitch to people's materials concerns and his story about

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Speaker 2: you know, how how we got.

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Speaker 7: Here and what went wrong as well.

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Speaker 2: Yeah, and you know the white the white bros, the

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Speaker 2: young men, Emily, they're eating it up again. Lessons to

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Speaker 2: be learned here.

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Speaker 3: Yeah, it's how aoc one and it's not like esoteric

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Speaker 3: DSA naval gazing. It's yeah, a huge lesson I think

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Speaker 3: for for National Democrats. So Crystal, should we move on

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Speaker 3: to updates in the case of Gretit Sunberg.

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Speaker 7: Yeah, let's do that.

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Speaker 2: We've been covering the Gaza Aid Freedom flotilla, which was

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Speaker 2: intercepted in international waters illegally by Israel. The members of

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Speaker 2: that vessel were, including Greta Tunberg, were arrested, in President

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Speaker 2: Trump's words and mine as well, kidnapped by Israel, taken

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Speaker 2: to an Israeli prison, and then they, in order to

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Speaker 2: effectuate their release, were made to sign these forms that

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Speaker 2: would consent to their deportation but also would confess to

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Speaker 2: basically illegally entering Israel, which is not what happened whatsoever.

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Speaker 2: So some of the activists have refused to sign those forms.

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Speaker 2: Greta did sign the form and has been released. She

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Speaker 2: was flown back to Sweden. Reporters spoke to her there

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Speaker 2: about the ordeal and why she did what she did.

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Speaker 2: Let's go ahead and take a listen to a little

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Speaker 2: bit of what she had to say.

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Speaker 3: Berg is speaking to reporters. Let's take a listen to

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Speaker 3: what she has to say.

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Speaker 8: Yeah, on international waters, we were illegally attacked and kidnapped

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Speaker 8: by Israel and the taken against our will to Israel,

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Speaker 8: where we were detained and then some of us deported.

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Speaker 3: Some are still there.

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Speaker 4: Uh, there are very.

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Speaker 8: Big uncertainties because it was quite chaotic and uncertainly, so

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Speaker 8: I don't really know what's going on. I haven't had

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Speaker 8: a phone for many days, and I the last few

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Speaker 8: hours they were absolutely nothing compared to what what people

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Speaker 8: are going through in Palestine, especially Gaza right now, and

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Speaker 8: it is this is yet another international a violation of

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Speaker 8: international rights, adding to the list of countless of such

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Speaker 8: especially towards Palestinian that Israel are committing by blocking and

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Speaker 8: preventing humanitarian aid from entering Gaza.

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Speaker 3: That is a that is illegal.

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Speaker 8: Sandwiches. Yeah, they probably have posted lots of pr stunt videos.

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Speaker 8: As I said, I have not seen anything, but they

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Speaker 8: did an illegal act by kidnapping us on international waters

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Speaker 8: and against our will, bringing us to Israel, keeping us

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Speaker 8: in the bottom of the boat, not letting us getting

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Speaker 8: out and so on. But that is not the real

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Speaker 8: story here.

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Speaker 3: The real story is.

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Speaker 8: That there's a genocide going on in Gaza and a

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Speaker 8: systematic starvation following the seas and blockade now which is

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Speaker 8: leading to food, medicine, water that are desperately needed to

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Speaker 8: get into Gaza is prevented from doing so. But of

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Speaker 8: course there are many attempts like this mission, both by

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Speaker 8: seeing and land to break that siege and open up

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Speaker 8: a humanitarian corridor. And this was a mission of attempting

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Speaker 8: to once again bring aid to Gaza, which is desperately needed,

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Speaker 8: but also to send solidarity and say that we see you,

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Speaker 8: we see what is happening, and we cannot accept just

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Speaker 8: see witnessing all this and doing nothing.

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Speaker 3: That can never be an option.

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Speaker 2: She also spoke more about how these horrors in Gaza

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Speaker 2: have been allowed to persis. Let's go ahead and take

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Speaker 2: a listen to her reaction there.

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Speaker 9: Why do you think so many countries governments around the

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Speaker 9: world are just ignoring what is happening in Gaza because

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Speaker 9: of racism, That's the simple answer.

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Speaker 8: I would say, racism and basically desperately trying to defend

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Speaker 8: a destructive, deadly system that systematically puts a short term

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Speaker 8: economic profit.

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Speaker 9: And to maximize due political power over the well being

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Speaker 9: of humans and the planet.

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Speaker 3: And right now.

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Speaker 9: It's very, very difficult to mortally defend that. It is impossible,

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Speaker 9: but still they are desperately trying, which is absurd is

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Speaker 9: not the word, but in words to describe it.

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Speaker 2: And Emily, I'm curious for your reaction to this. You know,

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Speaker 2: there's a lot I think that she said there that

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Speaker 2: is interesting, but in particular I want to highlight that

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Speaker 2: she mentions this isn't part of just one effort, and

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Speaker 2: in fact, the organization that she's involved with. We spoke

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Speaker 2: with the the spokesperson for the Freedom Flotilla group. They

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Speaker 2: plan to attempt this many more times, and there are

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Speaker 2: two separate additional efforts that they're in coordination with to

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Speaker 2: also try to break the siege. So there's been a

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Speaker 2: lot of discussion about Greta in particular. What do you

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Speaker 2: make of of what's gone down here?

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Speaker 3: Yeah, I mean I don't have a like super I

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Speaker 3: think it kind of went exactly as I expected that

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Speaker 3: it would, and you know, it's better actually than it

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Speaker 3: could have gone. It's a little interesting.

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Speaker 6: Was low.

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Speaker 3: The bar was really low. Bar was really low, and

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Speaker 3: you know, the it's interesting that people still over there.

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Speaker 3: I also think, hey, this is an example of people

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Speaker 3: putting their money where their mouth is and actually doing

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Speaker 3: the thing, walking the walk. So you know, even if

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Speaker 3: I disagree with her, I'm not mad about it.

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Speaker 7: To be honest, it was genuinely courageous. I mean, that's

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Speaker 7: to me.

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Speaker 2: And the other thing is, look, she is a high

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Speaker 2: profile person. People paid much more attention to this effort

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Speaker 2: because she was on board. I think Israel's reaction, which

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Speaker 2: I don't want to underplay, I mean, it was illegal,

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Speaker 2: like they acted as pirates kidnapping even like I said

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Speaker 2: before Trump called it, so they kidnapped her, right, but

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Speaker 2: they didn't kill her, which they've done before with you know,

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Speaker 2: humanitarian activists previously back in twenty ten. And in fact,

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Speaker 2: the spokesperson we talked to was on board that ship

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Speaker 2: that they attacked and killed ten people who were on board,

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Speaker 2: So they didn't do that. And you know, I think

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Speaker 2: that level of quote unquote restraint also is partly attributable

437
00:23:46,400 --> 00:23:48,000
Speaker 2: to the fact that they realized, like, shit, we can't

438
00:23:48,040 --> 00:23:50,720
Speaker 2: kill Greta Tunberg, like this is gonna there's gonna be

439
00:23:50,760 --> 00:23:53,800
Speaker 2: a lot of international attention to that. So I think

440
00:23:53,840 --> 00:23:57,360
Speaker 2: she very effectively used the large platform that she has

441
00:23:57,960 --> 00:24:02,119
Speaker 2: undertook an action that was genuinely courageous, that entailed genuine

442
00:24:02,160 --> 00:24:06,240
Speaker 2: personal discomfort and extreme risk in order to try to

443
00:24:06,359 --> 00:24:10,359
Speaker 2: do something. And so you know, if you have I

444
00:24:10,400 --> 00:24:13,480
Speaker 2: saw Omar Botder front of the show tweeting like if

445
00:24:13,480 --> 00:24:16,400
Speaker 2: you had one thousand people who did the same thing

446
00:24:17,000 --> 00:24:20,480
Speaker 2: and also sent ships trying to break the siege, it

447
00:24:20,520 --> 00:24:23,359
Speaker 2: would become you know, intercepting all of those people and

448
00:24:23,400 --> 00:24:27,440
Speaker 2: trying to hold on to this blockade preventing starving people

449
00:24:27,440 --> 00:24:31,160
Speaker 2: from getting food like that would become unsustainable with even

450
00:24:31,280 --> 00:24:36,399
Speaker 2: you know, a comparatively small level of organization that Greta

451
00:24:36,440 --> 00:24:38,720
Speaker 2: has you know, made much more likely and much more

452
00:24:38,760 --> 00:24:41,879
Speaker 2: possible here. So I think what she did was extraordinary.

453
00:24:41,880 --> 00:24:45,200
Speaker 2: I think I don't know, I don't really understand why

454
00:24:45,280 --> 00:24:48,080
Speaker 2: the right really fixates on her and really hates her

455
00:24:48,200 --> 00:24:50,280
Speaker 2: in the way that they do. But I think she's

456
00:24:50,320 --> 00:24:52,320
Speaker 2: also proved a lot of the haters wrong who felt

457
00:24:52,359 --> 00:24:55,200
Speaker 2: like she had this sort of like corporate friendly level

458
00:24:55,320 --> 00:25:01,040
Speaker 2: of you know, approved activism. This was certainly not in

459
00:25:01,280 --> 00:25:04,880
Speaker 2: that vein both because of the danger of the action

460
00:25:05,080 --> 00:25:09,159
Speaker 2: and also because there is nothing corporate approved about standing

461
00:25:09,240 --> 00:25:11,160
Speaker 2: up for Palestinian rights.

462
00:25:11,359 --> 00:25:13,280
Speaker 3: Yeah, I mean, I think she had like a youthful

463
00:25:13,400 --> 00:25:16,639
Speaker 3: naivete that was easy to make fun of. On the

464
00:25:16,680 --> 00:25:19,720
Speaker 3: climate issue, I genuinely feel badly for anybody who gets

465
00:25:19,720 --> 00:25:23,359
Speaker 3: famous as a child, whether it was their choice, their parents' choice,

466
00:25:23,480 --> 00:25:27,920
Speaker 3: or nobody's choice. So I feel like she's growing up

467
00:25:28,080 --> 00:25:31,719
Speaker 3: in front of cameras and panopticon and that just sucks.

468
00:25:32,280 --> 00:25:36,840
Speaker 3: But much more this is a this is a version

469
00:25:37,160 --> 00:25:40,320
Speaker 3: of Greta Tunberg that has definitely grown up compared to them.

470
00:25:40,400 --> 00:25:43,399
Speaker 3: There's there's no question about it. Big w Forgreta on

471
00:25:43,440 --> 00:25:45,000
Speaker 3: this one, not for Bebe.

472
00:25:45,640 --> 00:25:47,800
Speaker 2: Yeah, I would say, so, let's go and put this

473
00:25:47,840 --> 00:25:50,280
Speaker 2: image up on the screen. This is the latest video

474
00:25:50,320 --> 00:25:52,639
Speaker 2: of a quote unquote AID and the way that this

475
00:25:52,760 --> 00:25:55,439
Speaker 2: operation looks, I know drop site is reporting that you

476
00:25:55,520 --> 00:26:00,159
Speaker 2: had another massacre in the context of Hungary Palace to

477
00:26:00,200 --> 00:26:06,080
Speaker 2: means attempting to obtain AID from this Gaza Humanitarian Foundation.

478
00:26:06,640 --> 00:26:08,960
Speaker 2: You know, hundreds of people now who have been killed

479
00:26:09,920 --> 00:26:14,280
Speaker 2: because of this setup of this aid effectively trap at

480
00:26:14,320 --> 00:26:17,400
Speaker 2: this point, and at the same time, we also have

481
00:26:18,080 --> 00:26:21,880
Speaker 2: the ambassador to Israel, Mike Kakabee, who is kat Oh

482
00:26:21,920 --> 00:26:22,520
Speaker 2: my god.

483
00:26:23,080 --> 00:26:25,080
Speaker 3: The ambassador at a breaking points.

484
00:26:24,800 --> 00:26:30,440
Speaker 2: Salem, Salem, Yes, Mike Kakabee significantly changing US policy and

485
00:26:30,560 --> 00:26:32,879
Speaker 2: just I mean making it plain now we don't we

486
00:26:32,920 --> 00:26:35,600
Speaker 2: don't support the previous contours of any idea of a

487
00:26:35,600 --> 00:26:36,320
Speaker 2: two state solution.

488
00:26:36,400 --> 00:26:37,520
Speaker 7: Let's go ahead and take a listen to that.

489
00:26:37,960 --> 00:26:40,800
Speaker 10: I think the question is what does that Palestinian state

490
00:26:40,840 --> 00:26:43,239
Speaker 10: look like? Okay, where is it? Where is it going

491
00:26:43,320 --> 00:26:45,440
Speaker 10: to be? Well, what does it have to be in

492
00:26:46,119 --> 00:26:49,040
Speaker 10: Judae and Samaria? Does it need to be somewhere different.

493
00:26:49,119 --> 00:26:51,439
Speaker 10: Does it need to be an opportunity for people to

494
00:26:51,520 --> 00:26:55,560
Speaker 10: have a true place that is completely their own, or

495
00:26:55,600 --> 00:26:58,959
Speaker 10: is it going to be in the existing areas that

496
00:26:59,000 --> 00:27:02,160
Speaker 10: are currently under the dominion of the PA. So there's

497
00:27:02,200 --> 00:27:03,920
Speaker 10: a lot of questions. That's why I'm saying I don't

498
00:27:03,920 --> 00:27:06,959
Speaker 10: believe anybody can say it's impossible, it will never happen.

499
00:27:07,800 --> 00:27:10,439
Speaker 10: But if someone wants to declare that this is the

500
00:27:10,520 --> 00:27:13,560
Speaker 10: exact strip of geography that is going to be the

501
00:27:13,600 --> 00:27:16,800
Speaker 10: future Palestinian state, that's where the complication comes from.

502
00:27:16,880 --> 00:27:19,200
Speaker 3: Well, maybe the word is exact, that's the problem.

503
00:27:19,320 --> 00:27:23,320
Speaker 5: Are you suggesting that somewhere other than mandatory Palestine area,

504
00:27:23,359 --> 00:27:25,720
Speaker 5: that it could be in Saudi Arabia or something.

505
00:27:25,800 --> 00:27:28,560
Speaker 10: I'm just saying that I think every option should be

506
00:27:28,600 --> 00:27:29,800
Speaker 10: and could be on the table.

507
00:27:30,320 --> 00:27:32,240
Speaker 2: Wow, what did you Yeah, what did you make of that?

508
00:27:32,320 --> 00:27:35,439
Speaker 2: Emily So he's designed but not in Palestine.

509
00:27:35,480 --> 00:27:38,719
Speaker 3: Well yeah, but he's also like being clever and winking

510
00:27:38,760 --> 00:27:43,320
Speaker 3: and nodding and saying, basically, Palestine is impossible because none

511
00:27:43,359 --> 00:27:46,720
Speaker 3: of that land belongs to Palestine from his perspective, and.

512
00:27:46,840 --> 00:27:48,000
Speaker 7: It stands the area.

513
00:27:48,320 --> 00:27:52,680
Speaker 3: Yeah. It reminds me of when the Biden administration. Joe

514
00:27:52,680 --> 00:27:56,120
Speaker 3: Biden is mister two state solution is funding a war

515
00:27:57,200 --> 00:28:00,520
Speaker 3: for Netsan, Yaho, who is explicitly against the idea of

516
00:28:00,520 --> 00:28:03,280
Speaker 3: a two state solution. Here you have Mike Kuckabee actually

517
00:28:03,320 --> 00:28:06,800
Speaker 3: saying something very different than the president himself. And Trump

518
00:28:06,840 --> 00:28:08,919
Speaker 3: is hard to pin down on this issue obviously, like

519
00:28:09,040 --> 00:28:13,119
Speaker 3: is his Mara Gaza idea technically under the umbrella of

520
00:28:13,160 --> 00:28:18,000
Speaker 3: a Palestinian state as Propalestine activists would define it. No,

521
00:28:18,240 --> 00:28:21,840
Speaker 3: absolutely not. But he also would not be comfortable with

522
00:28:22,000 --> 00:28:27,680
Speaker 3: just saying, hey, screw it, give it all to Israel

523
00:28:28,400 --> 00:28:30,480
Speaker 3: because he has all of these relationships with other Arab

524
00:28:30,560 --> 00:28:34,320
Speaker 3: states that he thinks are important and contingent upon finding

525
00:28:34,320 --> 00:28:36,879
Speaker 3: a fair solution or a solution that they see is

526
00:28:36,920 --> 00:28:41,000
Speaker 3: fair for the people of Palestine. So Mike Cockabe in

527
00:28:41,040 --> 00:28:44,640
Speaker 3: that clip, it's not at all surprising. He's saying exactly

528
00:28:44,840 --> 00:28:52,280
Speaker 3: what you know, people from the dispensationalist evangelical movement believe

529
00:28:52,520 --> 00:28:55,800
Speaker 3: about that land, and it happens to be what a

530
00:28:55,840 --> 00:28:59,560
Speaker 3: lot of Israelis believe about that land. But it's not

531
00:28:59,760 --> 00:29:04,040
Speaker 3: what the quote unquote America First movement believes about that

532
00:29:03,440 --> 00:29:05,080
Speaker 3: that believes about the solution.

533
00:29:05,640 --> 00:29:09,160
Speaker 2: Yeah, well, I mean it is quite consistent with Trump's

534
00:29:09,200 --> 00:29:12,800
Speaker 2: like We're going to ethnically cleanse Gaza and push them

535
00:29:12,840 --> 00:29:16,960
Speaker 2: somewhere else. Like it certainly fits with that. And we

536
00:29:17,040 --> 00:29:19,080
Speaker 2: know there have been discussions that have been ongoing with

537
00:29:19,160 --> 00:29:22,120
Speaker 2: a variety of different countries to try to push them

538
00:29:22,160 --> 00:29:27,960
Speaker 2: to accept these Palestinian refugees that Trump wants to create.

539
00:29:28,520 --> 00:29:30,440
Speaker 2: And so you know, I think it's I think it's

540
00:29:30,480 --> 00:29:31,520
Speaker 2: part and parcel with that.

541
00:29:32,040 --> 00:29:32,240
Speaker 3: You know.

542
00:29:32,360 --> 00:29:35,000
Speaker 2: The easy thing to say here is like, okay, well,

543
00:29:35,000 --> 00:29:37,480
Speaker 2: if you've made it impossible to have a two state

544
00:29:37,600 --> 00:29:42,080
Speaker 2: solution with the ongoing settlement, illegal settlement project, which all

545
00:29:42,160 --> 00:29:44,480
Speaker 2: Israeli prime ministers have engaged in, by the way, but

546
00:29:44,520 --> 00:29:47,880
Speaker 2: have really ramped up in recent years under net Yahoo

547
00:29:47,880 --> 00:29:52,440
Speaker 2: and specifically post October seven, if you've made that impossible, okay,

548
00:29:52,760 --> 00:29:55,440
Speaker 2: then how about everybody gets equal rights?

549
00:29:55,960 --> 00:29:56,520
Speaker 7: How about that?

550
00:29:57,120 --> 00:30:00,360
Speaker 2: How about you just live up to your rhetoric about

551
00:30:00,400 --> 00:30:03,200
Speaker 2: how you know, you don't discriminate in your democracy and

552
00:30:03,200 --> 00:30:08,000
Speaker 2: making an actual democracy where everybody's actually treated equally. Because

553
00:30:08,440 --> 00:30:11,440
Speaker 2: if a two state solution is not going to be possible,

554
00:30:11,560 --> 00:30:13,560
Speaker 2: and you're putting that off the table and you're doing

555
00:30:13,600 --> 00:30:16,000
Speaker 2: everything you can which is a stated goal of Nan

556
00:30:16,120 --> 00:30:19,400
Speaker 2: Yahoo to make it. So that's impossible. Well, that's the

557
00:30:19,440 --> 00:30:21,200
Speaker 2: other solution that's available to you.

558
00:30:21,720 --> 00:30:24,400
Speaker 3: But in a sense, I don't think he's being honest

559
00:30:24,400 --> 00:30:28,120
Speaker 3: about it. In a sense, it's the like sadly realistic

560
00:30:28,560 --> 00:30:33,480
Speaker 3: situation on the ground is that that area in I mean,

561
00:30:33,520 --> 00:30:37,960
Speaker 3: he's in Jerusalem right there, but you know, the Temple mount,

562
00:30:38,080 --> 00:30:42,600
Speaker 3: I mean the Al Aksa, Like nobody wants that to

563
00:30:42,680 --> 00:30:46,760
Speaker 3: be given to the other side, is least of all

564
00:30:47,720 --> 00:30:53,320
Speaker 3: people who are of the like dispensationalist ideology as people

565
00:30:53,360 --> 00:30:56,920
Speaker 3: like Mike Huckabee. Are you know that's so?

566
00:30:57,360 --> 00:30:57,560
Speaker 4: Yeah?

567
00:30:57,640 --> 00:30:59,440
Speaker 3: I mean is it impossible?

568
00:30:59,680 --> 00:30:59,880
Speaker 5: Sure?

569
00:31:00,040 --> 00:31:03,440
Speaker 3: Or should it be impossible? No? And I think that's

570
00:31:03,440 --> 00:31:06,800
Speaker 3: where the just depressing reality sets in.

571
00:31:07,480 --> 00:31:07,800
Speaker 7: Yeah.

572
00:31:08,000 --> 00:31:11,640
Speaker 2: Indeed, we've got a great guest standing by to talk

573
00:31:11,680 --> 00:31:14,920
Speaker 2: about some extraordinary developments with regard to AI, So let's

574
00:31:14,920 --> 00:31:19,280
Speaker 2: go ahead and get to that. There have been a

575
00:31:19,600 --> 00:31:23,360
Speaker 2: bunch of very significant developments with regard to AI development

576
00:31:23,400 --> 00:31:25,120
Speaker 2: that we did not want to lose sight of this week.

577
00:31:25,160 --> 00:31:27,560
Speaker 2: So really excited to be joined this morning by tarn

578
00:31:27,640 --> 00:31:31,640
Speaker 2: Steinbreckner Kaufman. She's the CEO of the Golden Gate Institute,

579
00:31:31,720 --> 00:31:33,960
Speaker 2: for AI On. She's going to take us through some

580
00:31:34,000 --> 00:31:35,760
Speaker 2: of these developments. Taran is great to have you, great

581
00:31:35,760 --> 00:31:36,120
Speaker 2: to meet you.

582
00:31:36,880 --> 00:31:38,600
Speaker 6: Thanks so much. It's lovely to be here.

583
00:31:39,240 --> 00:31:42,400
Speaker 2: So, first of all, in the wake of the protests,

584
00:31:42,480 --> 00:31:45,600
Speaker 2: which at times turned violent in La, there has been

585
00:31:45,720 --> 00:31:50,120
Speaker 2: an effort to spread all kinds of propaganda, as is

586
00:31:50,240 --> 00:31:53,800
Speaker 2: quite typical in these news cycles these days, but added

587
00:31:53,840 --> 00:31:57,080
Speaker 2: to the mix now we have these just outright AI

588
00:31:57,520 --> 00:32:01,320
Speaker 2: generated scenes, which this one in particular, I'm about to show.

589
00:32:01,400 --> 00:32:03,480
Speaker 2: It's kind of crazy to me that some people even

590
00:32:03,520 --> 00:32:06,800
Speaker 2: thought this was real, because it takes a very surreal

591
00:32:06,840 --> 00:32:10,240
Speaker 2: turn quite quickly, but some people did just watch a

592
00:32:10,280 --> 00:32:13,280
Speaker 2: piece of it and actually think that it was real. So,

593
00:32:14,040 --> 00:32:18,160
Speaker 2: you know, that's putting aside more sort of sophisticated efforts

594
00:32:18,200 --> 00:32:22,160
Speaker 2: to create these images and shape people's understanding of events

595
00:32:22,160 --> 00:32:24,480
Speaker 2: that are happening in this country. So let's go ahead

596
00:32:24,560 --> 00:32:27,280
Speaker 2: and take a look at this one video in particular

597
00:32:27,400 --> 00:32:28,800
Speaker 2: AI generated that went viral.

598
00:32:29,240 --> 00:32:33,120
Speaker 6: Why are you rioting? I don't know.

599
00:32:33,160 --> 00:32:35,040
Speaker 3: I was paid to be here and I just want

600
00:32:35,080 --> 00:32:38,760
Speaker 3: to destroy stuff? Is what you are doing illegal?

601
00:32:40,160 --> 00:32:42,680
Speaker 7: Well, these are peaceful protests. Even that guy over there

602
00:32:42,720 --> 00:32:48,560
Speaker 7: says so and so Taren.

603
00:32:48,600 --> 00:32:51,400
Speaker 2: This really speaks to one of the concerns people have

604
00:32:51,480 --> 00:32:55,480
Speaker 2: with regard to AI development, which is just how difficult

605
00:32:55,840 --> 00:32:59,280
Speaker 2: it makes it to sort through what even is reality.

606
00:33:01,040 --> 00:33:04,680
Speaker 11: Yeah, this is really important issue. Uh, these are going

607
00:33:04,720 --> 00:33:05,880
Speaker 11: to get One thing to know is that these are

608
00:33:05,880 --> 00:33:07,280
Speaker 11: going to get much more realistic.

609
00:33:07,600 --> 00:33:08,240
Speaker 6: Like these are.

610
00:33:08,960 --> 00:33:11,080
Speaker 11: You know, if you're a discerning eye and you sort

611
00:33:11,080 --> 00:33:12,880
Speaker 11: of know what you're looking for, it's pretty obvious that

612
00:33:12,880 --> 00:33:14,760
Speaker 11: this is AI generated. Still it kind of has like

613
00:33:14,800 --> 00:33:18,600
Speaker 11: a sheene, it doesn't quite kind of look exactly photo realistic.

614
00:33:19,240 --> 00:33:21,560
Speaker 11: And they've gotten much more realistic in the last year,

615
00:33:21,600 --> 00:33:23,480
Speaker 11: and they're going to keep getting much more realistic. I

616
00:33:23,480 --> 00:33:25,840
Speaker 11: would assume that a year from now I won't be

617
00:33:25,880 --> 00:33:30,600
Speaker 11: able to tell easily whether something like that is real

618
00:33:30,680 --> 00:33:35,240
Speaker 11: or not based on the actual images and audio. The

619
00:33:35,320 --> 00:33:37,000
Speaker 11: second thing, you know is this is a really hard

620
00:33:37,040 --> 00:33:40,240
Speaker 11: problem to solve. I've heard, you know, lots of solutions

621
00:33:40,240 --> 00:33:44,840
Speaker 11: floating around, like we should you know, require AI generated

622
00:33:44,920 --> 00:33:48,040
Speaker 11: video to have a water mark, or we should you know,

623
00:33:48,400 --> 00:33:51,400
Speaker 11: It's just really hard. It's not really clear how any

624
00:33:51,400 --> 00:33:56,160
Speaker 11: policy solution at scale could work to to you know,

625
00:33:56,240 --> 00:34:00,360
Speaker 11: to stop bad actors from making videos and circular them

626
00:34:00,360 --> 00:34:03,800
Speaker 11: that are not real. And the third thing which you

627
00:34:03,880 --> 00:34:06,200
Speaker 11: pointed to, I think is this is sort of what

628
00:34:06,240 --> 00:34:09,560
Speaker 11: I call the truth fog, which is it goes beyond

629
00:34:09,600 --> 00:34:14,400
Speaker 11: the effect of the actual negative videos, the false videos,

630
00:34:14,680 --> 00:34:16,799
Speaker 11: which is to say that like now, I can't you know,

631
00:34:17,080 --> 00:34:18,440
Speaker 11: in a year, I don't think I'm going to be

632
00:34:18,440 --> 00:34:20,600
Speaker 11: able to tell whether a video is real, which is

633
00:34:20,640 --> 00:34:25,439
Speaker 11: going to make me discredit true videos evidence as well. Right,

634
00:34:25,680 --> 00:34:28,080
Speaker 11: And that's a problem too, because anything, you know, I'll

635
00:34:28,080 --> 00:34:30,080
Speaker 11: be able to share. If I share a video of

636
00:34:30,680 --> 00:34:33,120
Speaker 11: you know, a candidate for president saying something and I'm like,

637
00:34:33,120 --> 00:34:34,799
Speaker 11: you should vote for them because they said this, people

638
00:34:34,840 --> 00:34:36,440
Speaker 11: are going to be like, how do I know they

639
00:34:36,480 --> 00:34:37,279
Speaker 11: actually said that?

640
00:34:37,880 --> 00:34:38,160
Speaker 6: Right?

641
00:34:38,800 --> 00:34:41,959
Speaker 2: And we are I think Emily already seeing some of that.

642
00:34:42,080 --> 00:34:45,200
Speaker 2: I know there was an incident with regard to a

643
00:34:45,239 --> 00:34:49,920
Speaker 2: doctor in Gaza whose kids were killed by the Israelis,

644
00:34:50,520 --> 00:34:54,479
Speaker 2: and there was you know, video of the aftermath, the

645
00:34:54,600 --> 00:34:57,160
Speaker 2: horrific aftermath of that, and there are all these claims

646
00:34:57,160 --> 00:34:59,840
Speaker 2: that the videos weren't real, that these were just AI generated.

647
00:35:00,160 --> 00:35:02,080
Speaker 2: I think we're already seeing some of that fall out

648
00:35:02,080 --> 00:35:02,960
Speaker 2: that Tarren's talking about.

649
00:35:03,120 --> 00:35:04,600
Speaker 3: Yeah, it's a good point. I mean, it just becomes

650
00:35:04,680 --> 00:35:07,520
Speaker 3: harder to trust anything at all. I wanted to ask

651
00:35:07,600 --> 00:35:10,120
Speaker 3: about this news both from The New York Times in

652
00:35:10,160 --> 00:35:15,640
Speaker 3: Bloomberg that Mark Zuckerberg is announcing a nine figure by

653
00:35:16,160 --> 00:35:19,840
Speaker 3: to bring top talent on for quote unquote super intelligence.

654
00:35:20,520 --> 00:35:23,120
Speaker 3: You know that that year timescale. I'm glad you mentioned

655
00:35:23,120 --> 00:35:24,239
Speaker 3: it because it was one of the things I asked

656
00:35:24,239 --> 00:35:26,600
Speaker 3: about you. At what point are we going to have

657
00:35:27,239 --> 00:35:30,160
Speaker 3: a situation where even experts have to exert a huge

658
00:35:30,200 --> 00:35:32,399
Speaker 3: amount of effort to tell what's real and what's fake

659
00:35:32,440 --> 00:35:35,160
Speaker 3: on social media and even then maybe have a hard

660
00:35:35,239 --> 00:35:39,480
Speaker 3: time doing it. What does super intelligence mean as defined

661
00:35:39,520 --> 00:35:43,759
Speaker 3: by Mark Zuckerberg and the industry, and is this, you know,

662
00:35:44,600 --> 00:35:49,360
Speaker 3: hurdling us even more quickly along in that timeline.

663
00:35:52,280 --> 00:35:56,240
Speaker 11: Everybody has their own term. There's super intelligence, there's advanced

664
00:35:56,239 --> 00:36:00,719
Speaker 11: general or artificial general intelligence, agi there's a There's all

665
00:36:00,719 --> 00:36:02,919
Speaker 11: these terms floating around, and a lot of people don't

666
00:36:02,920 --> 00:36:07,279
Speaker 11: define what they mean by them. So I don't know

667
00:36:07,320 --> 00:36:10,040
Speaker 11: exactly what Mark Zuckerberg means. Like when I think about this,

668
00:36:10,920 --> 00:36:14,560
Speaker 11: what I'm thinking about is an artificial intelligence that can

669
00:36:14,640 --> 00:36:17,640
Speaker 11: do most or almost all of the things that humans

670
00:36:17,680 --> 00:36:18,680
Speaker 11: can do on a computer.

671
00:36:20,160 --> 00:36:23,600
Speaker 6: And that includes there for many many jobs.

672
00:36:24,120 --> 00:36:26,399
Speaker 11: There are many many jobs that people can do from

673
00:36:26,440 --> 00:36:29,560
Speaker 11: their computer, and when we have an artificial intelligence that

674
00:36:29,600 --> 00:36:33,000
Speaker 11: can do all of those tasks remotely, that's obviously going

675
00:36:33,000 --> 00:36:34,880
Speaker 11: to have very huge economic impacts. It's going to have

676
00:36:34,880 --> 00:36:36,960
Speaker 11: a lot of other impacts on society as well. So

677
00:36:37,000 --> 00:36:39,600
Speaker 11: that's my own definition, and I think it's probably sort

678
00:36:39,640 --> 00:36:43,520
Speaker 11: of roughly aligned with many people in the industry's definition.

679
00:36:43,920 --> 00:36:46,440
Speaker 11: But as I said, different people mean different things. I mean,

680
00:36:46,480 --> 00:36:48,400
Speaker 11: I think the number one thing to take away for

681
00:36:48,880 --> 00:36:51,879
Speaker 11: there's still a large segment of people, I think who

682
00:36:51,920 --> 00:36:55,640
Speaker 11: dismiss AI as hype and you know, sort of like, oh,

683
00:36:55,640 --> 00:36:57,879
Speaker 11: this is kind of like crypto, like this thing these

684
00:36:57,880 --> 00:37:01,319
Speaker 11: tech bros and Silicon Valley are doing. Mark Zuckerberg has

685
00:37:01,400 --> 00:37:06,040
Speaker 11: never paid anyone nine figures for crypto. R AI is

686
00:37:06,080 --> 00:37:09,120
Speaker 11: in a different category, and everybody needs to be taking

687
00:37:09,120 --> 00:37:12,719
Speaker 11: it very seriously. It's going to have extremely large impacts

688
00:37:12,800 --> 00:37:16,560
Speaker 11: on the economy, on the way our democracy works, and

689
00:37:16,600 --> 00:37:19,680
Speaker 11: on the rest of our society in many many ways.

690
00:37:20,000 --> 00:37:22,560
Speaker 11: And then I think that that's my number one takeaway

691
00:37:22,560 --> 00:37:27,759
Speaker 11: from this is like, look, the world's largest corporations are

692
00:37:27,800 --> 00:37:29,360
Speaker 11: taking this extremely seriously.

693
00:37:29,400 --> 00:37:30,479
Speaker 6: And you need to as well.

694
00:37:31,360 --> 00:37:34,040
Speaker 2: What does it look like to you to take this

695
00:37:34,160 --> 00:37:36,920
Speaker 2: extremely seriously, because I feel like that's where I get

696
00:37:36,920 --> 00:37:40,840
Speaker 2: a little lost. I am deeply concerned. I think the

697
00:37:41,320 --> 00:37:44,960
Speaker 2: job loss is going to be extraordinary. I don't put

698
00:37:45,080 --> 00:37:50,120
Speaker 2: off the table some of the most maximust dystopian possibilities

699
00:37:50,160 --> 00:37:52,960
Speaker 2: here as well. But I'm not really sure what to

700
00:37:53,000 --> 00:37:56,520
Speaker 2: do about it at this point, because you have this

701
00:37:56,560 --> 00:37:59,200
Speaker 2: administration that is totally like no breaks on.

702
00:37:59,160 --> 00:38:01,080
Speaker 7: The car Wild Wild West.

703
00:38:01,200 --> 00:38:03,680
Speaker 2: We've got to we're in this race versus China, and

704
00:38:03,760 --> 00:38:05,440
Speaker 2: we've got to be the first to get to it,

705
00:38:05,480 --> 00:38:09,840
Speaker 2: and they, you know, have this very aggressive no regulations

706
00:38:09,960 --> 00:38:14,120
Speaker 2: approach to it. You have people like Sam Altman and

707
00:38:14,440 --> 00:38:17,160
Speaker 2: others who are overtly out there like, yeah, we want

708
00:38:17,200 --> 00:38:19,839
Speaker 2: to replace as much of human labor and possibly all

709
00:38:19,880 --> 00:38:22,600
Speaker 2: of humor human labor as possible, and we're probably going

710
00:38:22,680 --> 00:38:25,080
Speaker 2: to have to completely upend the social contract in order

711
00:38:25,080 --> 00:38:26,160
Speaker 2: for this to all work out.

712
00:38:26,640 --> 00:38:28,560
Speaker 7: And yet, you know, I don't see any.

713
00:38:28,440 --> 00:38:31,600
Speaker 2: Real large scale conversation, which is where you come in,

714
00:38:31,920 --> 00:38:34,439
Speaker 2: about what that new social contract is going to look

715
00:38:34,480 --> 00:38:35,880
Speaker 2: like and how we're going to make sure that people

716
00:38:35,920 --> 00:38:40,279
Speaker 2: outside of trillionaires are okay in that new world. So

717
00:38:40,600 --> 00:38:42,960
Speaker 2: when you say something like we need to be taken seriously,

718
00:38:43,040 --> 00:38:46,400
Speaker 2: like what specific things do you have in mind in particular?

719
00:38:47,239 --> 00:38:50,120
Speaker 11: Yeah, it's hard because the field is moving so fast

720
00:38:50,200 --> 00:38:52,520
Speaker 11: that we haven't had time for a real like civil

721
00:38:52,560 --> 00:38:55,360
Speaker 11: society ecosystem to evolve around AI.

722
00:38:55,920 --> 00:38:56,640
Speaker 6: Yeah.

723
00:38:56,680 --> 00:38:59,920
Speaker 11: So if you're somebody who might have the like interesting

724
00:39:00,040 --> 00:39:03,200
Speaker 11: wherewithal in like starting a new organization around AI, there

725
00:39:03,239 --> 00:39:06,279
Speaker 11: are gaps everywhere. Like here's an example is I don't

726
00:39:06,440 --> 00:39:10,120
Speaker 11: know of any concerted programs to educate state legislators about AI.

727
00:39:10,239 --> 00:39:11,760
Speaker 6: That's just like one example.

728
00:39:12,480 --> 00:39:15,359
Speaker 11: Another example is, like I've heard many experts in the field,

729
00:39:15,440 --> 00:39:18,920
Speaker 11: like Ezra Klein and Kevin Russ and think tank people

730
00:39:18,920 --> 00:39:23,319
Speaker 11: in DC say we don't have nearly enough economic frameworks

731
00:39:23,360 --> 00:39:26,400
Speaker 11: for thinking about how about the labor dislocations that we

732
00:39:26,440 --> 00:39:27,840
Speaker 11: expect are going to happen from AI.

733
00:39:28,600 --> 00:39:30,759
Speaker 6: It's not like there's just gaps everywhere.

734
00:39:30,480 --> 00:39:32,600
Speaker 11: If you're familiar with this field, if you think that

735
00:39:32,680 --> 00:39:35,359
Speaker 11: somebody is doing something about AI, and you've got an

736
00:39:35,400 --> 00:39:37,920
Speaker 11: idea that somebody should be doing something, probably nobody is

737
00:39:38,040 --> 00:39:41,359
Speaker 11: yet or certainly not enough people. So you should go

738
00:39:41,800 --> 00:39:45,280
Speaker 11: like investigate and like maybe think about starting an effort

739
00:39:45,320 --> 00:39:47,799
Speaker 11: in that field. And I don't think that there are

740
00:39:48,160 --> 00:39:52,440
Speaker 11: a lot of pathways yet for people who to like engage,

741
00:39:52,760 --> 00:39:55,319
Speaker 11: but I think, but I think you should call your legislators.

742
00:39:55,360 --> 00:39:57,239
Speaker 11: I think you should talk to your legislators about how

743
00:39:57,239 --> 00:39:59,600
Speaker 11: big of an issue this is, how much you're worried

744
00:39:59,600 --> 00:40:02,080
Speaker 11: about it. Right now, AI is still not showing up

745
00:40:02,120 --> 00:40:05,680
Speaker 11: as a big issue in public opinion polling, and legislators

746
00:40:05,680 --> 00:40:07,759
Speaker 11: and politicians aren't hearing that much about it. I mean

747
00:40:07,760 --> 00:40:09,239
Speaker 11: when I say it's not showing if this big issue,

748
00:40:09,280 --> 00:40:11,839
Speaker 11: I mean it's not showing up. Like if you ask

749
00:40:11,920 --> 00:40:14,120
Speaker 11: people what their top issues are, what they're most worried about,

750
00:40:14,160 --> 00:40:16,040
Speaker 11: AI is not showing up on those lists.

751
00:40:16,920 --> 00:40:19,880
Speaker 6: And we need to shift politicians perceptions of that.

752
00:40:20,000 --> 00:40:22,040
Speaker 11: So if you're not going to go start a new organization,

753
00:40:22,160 --> 00:40:23,839
Speaker 11: you can at least like talk to your to your

754
00:40:24,200 --> 00:40:28,200
Speaker 11: legislators and your representatives about why what they You know

755
00:40:28,200 --> 00:40:29,640
Speaker 11: that they need to be paying attention to this.

756
00:40:29,840 --> 00:40:32,480
Speaker 3: And I want to ask, oh, go ahead, go ahead,

757
00:40:32,840 --> 00:40:34,240
Speaker 3: was it? I want to ask about this next element

758
00:40:34,239 --> 00:40:36,160
Speaker 3: we put on the screen the Apple paper that went

759
00:40:36,239 --> 00:40:40,480
Speaker 3: viral and got a lot of reactions to it. This

760
00:40:40,640 --> 00:40:44,439
Speaker 3: post from Reuben Hassid says Apple just proved AI quote

761
00:40:44,480 --> 00:40:47,359
Speaker 3: unquote reasoning models like Claude Deepseagar one, and oh three

762
00:40:47,400 --> 00:40:50,400
Speaker 3: many don't actually reason at all. They just memorize patterns

763
00:40:50,440 --> 00:40:53,719
Speaker 3: really well, here's what Apple discovered parentheses. Hint, we're not

764
00:40:53,760 --> 00:40:56,959
Speaker 3: as close to AGI as the hype suggests. I read

765
00:40:57,000 --> 00:40:59,920
Speaker 3: that post and I think, okay, yeah, I mean, no,

766
00:41:00,320 --> 00:41:03,520
Speaker 3: they don't reason, they just memorize patterns really well, they're

767
00:41:03,560 --> 00:41:08,840
Speaker 3: freaking computers. Yes, that's exactly right. But then, on the

768
00:41:08,880 --> 00:41:14,279
Speaker 3: other hand, to use that as a way to dismiss

769
00:41:14,640 --> 00:41:19,279
Speaker 3: the our current proximity to AGI and to say that

770
00:41:19,880 --> 00:41:22,680
Speaker 3: this is all over hyped and it's it's really moving

771
00:41:22,800 --> 00:41:26,239
Speaker 3: very slowly seems ill advised. And I just kind of

772
00:41:26,239 --> 00:41:29,440
Speaker 3: wanted to get your reaction to I guess your reaction

773
00:41:29,520 --> 00:41:32,640
Speaker 3: to the reaction from that paper. Yeah.

774
00:41:32,920 --> 00:41:35,200
Speaker 11: Look, there's a set of people out there who do

775
00:41:35,440 --> 00:41:37,680
Speaker 11: make their money off of and their and their fame

776
00:41:37,760 --> 00:41:41,080
Speaker 11: off of dismissing AI, and you know they'll sort of

777
00:41:41,080 --> 00:41:43,960
Speaker 11: seize onto papers like this to do that. I don't

778
00:41:43,960 --> 00:41:46,120
Speaker 11: think that this paper is actually that big.

779
00:41:45,960 --> 00:41:46,399
Speaker 6: Of a deal.

780
00:41:46,440 --> 00:41:49,200
Speaker 11: In some sense, it's not saying anything that's particularly surprising.

781
00:41:49,239 --> 00:41:55,759
Speaker 11: As you pointed out, it's it's asking the models to

782
00:41:55,840 --> 00:41:58,920
Speaker 11: do a set of very hard problems, and they at

783
00:41:58,920 --> 00:42:01,200
Speaker 11: some point are like, basically, the models are like, this

784
00:42:01,280 --> 00:42:05,400
Speaker 11: problem is too hard. I'm going to stop now, and

785
00:42:05,440 --> 00:42:08,920
Speaker 11: then the authors, well, I don't even think the authors

786
00:42:08,920 --> 00:42:12,640
Speaker 11: aren't necessarily interpreting that as these models aren't good at

787
00:42:12,640 --> 00:42:14,640
Speaker 11: what they do. It's then there's a sort of a

788
00:42:14,640 --> 00:42:17,839
Speaker 11: whole you know, Twitter ecosystem that's like, well, now we've

789
00:42:17,840 --> 00:42:19,400
Speaker 11: proved that these models aren't good at what they do.

790
00:42:19,560 --> 00:42:21,399
Speaker 11: Guess what if you give me one of these hard

791
00:42:21,440 --> 00:42:24,400
Speaker 11: problems that they gave these models. I'm also going to

792
00:42:24,440 --> 00:42:26,560
Speaker 11: be like, yeah, sorry, I don't have time for this.

793
00:42:26,560 --> 00:42:28,480
Speaker 11: This is like you've because had a said I have

794
00:42:28,520 --> 00:42:30,319
Speaker 11: to solve this. They give the models constraints like you

795
00:42:30,320 --> 00:42:32,680
Speaker 11: have to solve this in a certain context window, which

796
00:42:32,719 --> 00:42:34,600
Speaker 11: is maybe roughly equivalent to telling me I have an

797
00:42:34,640 --> 00:42:36,759
Speaker 11: hour to solve it, and I'm like, I can't solve

798
00:42:36,760 --> 00:42:39,000
Speaker 11: that problem in an hour, So I'm going to stop

799
00:42:39,040 --> 00:42:43,040
Speaker 11: now and then interpreting. But I hope you all think

800
00:42:43,040 --> 00:42:46,239
Speaker 11: that I'm artificial intelligence or sorry, I'm general intelligence, right,

801
00:42:46,840 --> 00:42:48,960
Speaker 11: I'm not artificial intelligence.

802
00:42:48,400 --> 00:42:50,480
Speaker 6: And so they're sort of actually in.

803
00:42:50,440 --> 00:42:53,160
Speaker 11: Some ways behaving a lot like humans in these situations,

804
00:42:53,160 --> 00:42:55,919
Speaker 11: and we're interpreting. Some people are interpreting that as saying

805
00:42:55,960 --> 00:42:57,960
Speaker 11: that they aren't smart, which I think is just not true.

806
00:42:58,960 --> 00:43:03,319
Speaker 2: Another thing that I've wondered is there's this sort of

807
00:43:03,360 --> 00:43:06,440
Speaker 2: like line that's drawn. We were talking about the metaproject

808
00:43:06,520 --> 00:43:10,000
Speaker 2: to develop AGI ared Official General Intelligence, and you were saying,

809
00:43:10,000 --> 00:43:12,960
Speaker 2: there's all kinds of different terminologies for you know, meaning

810
00:43:13,000 --> 00:43:17,560
Speaker 2: whatever this milestone is, Like how much of a just

811
00:43:17,600 --> 00:43:21,200
Speaker 2: sort of like line that you cross is AGI and

812
00:43:21,239 --> 00:43:24,160
Speaker 2: how much of it is more of a sense. And

813
00:43:24,239 --> 00:43:26,280
Speaker 2: it's a little bit hard to tell whether you're there

814
00:43:26,520 --> 00:43:29,439
Speaker 2: or you're not. Like, help me understand when people talk

815
00:43:29,440 --> 00:43:34,040
Speaker 2: about AGI, how much it will be clear when we've

816
00:43:34,080 --> 00:43:37,919
Speaker 2: achieved this whatever this benchmark is. Because I also see

817
00:43:37,920 --> 00:43:40,279
Speaker 2: things like you I see that study from you know,

818
00:43:40,400 --> 00:43:42,320
Speaker 2: I see that particular study that was going around. I

819
00:43:42,360 --> 00:43:45,799
Speaker 2: also see these other studies where AI is scheming to

820
00:43:45,880 --> 00:43:48,040
Speaker 2: make sure it's not getting shut off and trying to

821
00:43:48,040 --> 00:43:50,359
Speaker 2: like blackmail an engineer with an affair that they think

822
00:43:50,360 --> 00:43:53,880
Speaker 2: the engineer is engaged in to avoid having their programming change.

823
00:43:54,200 --> 00:43:57,880
Speaker 2: You know, I see things that are deeply disturbing in

824
00:43:57,960 --> 00:44:01,120
Speaker 2: terms of AI engaging in. I mean those are very

825
00:44:01,200 --> 00:44:06,120
Speaker 2: like human type behaviors and trying to defy the wishes

826
00:44:06,160 --> 00:44:07,520
Speaker 2: of their programmers.

827
00:44:07,800 --> 00:44:11,080
Speaker 7: So where are we in that development and will we.

828
00:44:11,160 --> 00:44:14,440
Speaker 2: Really Is it like this hard fast line of we

829
00:44:14,560 --> 00:44:16,960
Speaker 2: are not at AGI and now we are at AGI

830
00:44:17,040 --> 00:44:18,000
Speaker 2: and it's very clear cut.

831
00:44:19,480 --> 00:44:23,399
Speaker 11: Yeah, this is a great question. I think different. Well,

832
00:44:23,440 --> 00:44:25,719
Speaker 11: I think most people in the industry would say it's

833
00:44:25,719 --> 00:44:28,600
Speaker 11: not a hard line. And there's there's a term that's

834
00:44:28,600 --> 00:44:31,279
Speaker 11: called the jagged frontier. It's like the models can be

835
00:44:31,440 --> 00:44:33,720
Speaker 11: very good at some things and very bad other things

836
00:44:33,880 --> 00:44:36,200
Speaker 11: that humans can do. That by the same token, you

837
00:44:36,200 --> 00:44:38,680
Speaker 11: could say that humans have a jagged frontier, like humans

838
00:44:38,680 --> 00:44:40,400
Speaker 11: are very good at some things compared to AI, and

839
00:44:40,480 --> 00:44:41,760
Speaker 11: very bad at other things.

840
00:44:41,640 --> 00:44:42,680
Speaker 6: Compared to current AI.

841
00:44:43,560 --> 00:44:46,840
Speaker 11: So I don't think we're going to immediately cross the

842
00:44:46,920 --> 00:44:49,959
Speaker 11: rubicon of like suddenly the models are very are better

843
00:44:50,000 --> 00:44:54,080
Speaker 11: than humans at everything, including for instance, operating a physical

844
00:44:54,080 --> 00:44:56,640
Speaker 11: body in physical space. Right, that's not going to happen

845
00:44:56,840 --> 00:45:05,240
Speaker 11: literally overnight, probably, But you always have to qualify everything

846
00:45:05,239 --> 00:45:07,760
Speaker 11: you say about AI as like I think so probably

847
00:45:07,880 --> 00:45:10,319
Speaker 11: anybody who's not qualifying their statements. I think you have

848
00:45:10,360 --> 00:45:12,600
Speaker 11: to give you have to be worried about how much

849
00:45:12,640 --> 00:45:17,480
Speaker 11: you can trust what they're saying. So, but I think

850
00:45:17,600 --> 00:45:21,719
Speaker 11: that you know we are we at AGI now. I mean,

851
00:45:21,719 --> 00:45:24,759
Speaker 11: it's an interesting question because for a long time, the

852
00:45:24,800 --> 00:45:27,080
Speaker 11: test that everybody thought we were going to use for

853
00:45:27,120 --> 00:45:29,200
Speaker 11: this was called the Turing test, and people who haven't

854
00:45:29,239 --> 00:45:32,960
Speaker 11: heard of it, the Turing test is you put a

855
00:45:33,040 --> 00:45:35,800
Speaker 11: human behind a computer and an AI behind a computer

856
00:45:35,920 --> 00:45:38,120
Speaker 11: or in the computer, and then you have another human

857
00:45:38,200 --> 00:45:41,880
Speaker 11: interview both of them, right, And if the human who's

858
00:45:41,920 --> 00:45:46,279
Speaker 11: interviewing can't tell whether they're talking to the other human

859
00:45:46,360 --> 00:45:48,520
Speaker 11: or to the AI, then the AI has passed the

860
00:45:48,560 --> 00:45:52,600
Speaker 11: Turing test of intelligence. And these models pass that test now,

861
00:45:52,640 --> 00:45:56,360
Speaker 11: the cutting edge models, right, and so by that define

862
00:45:56,440 --> 00:45:59,239
Speaker 11: the definition that we had all used for literally decades,

863
00:45:59,560 --> 00:46:00,760
Speaker 11: invented by Alan Turing.

864
00:46:00,800 --> 00:46:01,919
Speaker 6: We've already gotten there.

865
00:46:02,040 --> 00:46:04,160
Speaker 11: And yet these models are very bad at a lot

866
00:46:04,160 --> 00:46:07,279
Speaker 11: of things that humans are good at. And I don't

867
00:46:07,320 --> 00:46:10,120
Speaker 11: I think anybody is anybody of serious repute as saying

868
00:46:10,160 --> 00:46:15,080
Speaker 11: we've actually hit AGI. So the goalposts are moving over time,

869
00:46:15,400 --> 00:46:16,960
Speaker 11: I think the goalposts will keep moving.

870
00:46:18,680 --> 00:46:23,799
Speaker 3: Yeah, the train of acceleration is just unbelievable. I was

871
00:46:23,800 --> 00:46:26,640
Speaker 3: listening to an old through Line episode on Ralph Nader

872
00:46:27,160 --> 00:46:30,480
Speaker 3: and thinking about this, actually, because that's from car accidents

873
00:46:30,560 --> 00:46:32,640
Speaker 3: used to be like five times as high as they

874
00:46:32,640 --> 00:46:34,600
Speaker 3: are now. It took us actually a really long time

875
00:46:34,680 --> 00:46:39,040
Speaker 3: and a lot of tragedy to adapt our policies to

876
00:46:39,120 --> 00:46:42,080
Speaker 3: make our roads relatively safe. I mean, relatively is an

877
00:46:42,120 --> 00:46:45,839
Speaker 3: important word there. And I guess I'm just wondering how

878
00:46:45,840 --> 00:46:51,440
Speaker 3: optimistic you are about the policy evolution meeting the moment,

879
00:46:52,040 --> 00:46:55,520
Speaker 3: because right now it looks very bleak for some understandable reasons.

880
00:46:55,560 --> 00:46:57,640
Speaker 3: I mean, it's hard to define, it's hard to see

881
00:46:57,640 --> 00:46:59,960
Speaker 3: what the future looks like exactly. Things are moving very quick,

882
00:47:01,200 --> 00:47:05,520
Speaker 3: but it just seems like the reaction is completely non

883
00:47:05,520 --> 00:47:06,959
Speaker 3: existent here in DC.

884
00:47:08,280 --> 00:47:10,560
Speaker 11: Yeah, I mean there's a lot of reasons to be pessimistic.

885
00:47:10,600 --> 00:47:13,400
Speaker 11: I think the reason to be optimistic is sort of like,

886
00:47:13,440 --> 00:47:15,680
Speaker 11: if you take a step back, humanity has faced a

887
00:47:15,719 --> 00:47:18,560
Speaker 11: lot of threats in the past, and so far we've

888
00:47:19,200 --> 00:47:23,920
Speaker 11: survived them all right, And so you know, humans are resilient,

889
00:47:24,160 --> 00:47:28,000
Speaker 11: and we do live in an era when lots of

890
00:47:28,040 --> 00:47:32,200
Speaker 11: people have agency.

891
00:47:31,000 --> 00:47:31,480
Speaker 3: And.

892
00:47:33,080 --> 00:47:36,000
Speaker 11: I hope, I mean, these are choices we are making right.

893
00:47:36,000 --> 00:47:38,440
Speaker 11: These are not inevitable conclusions. These are choices that we

894
00:47:38,480 --> 00:47:40,880
Speaker 11: as individuals and as society are making about how to

895
00:47:40,920 --> 00:47:44,360
Speaker 11: respond to AI. And I hope we can rise to

896
00:47:44,400 --> 00:47:47,120
Speaker 11: this challenge. I don't think it's impossible, but it is

897
00:47:47,160 --> 00:47:49,960
Speaker 11: a huge challenge, there's no doubt about that.

898
00:47:51,640 --> 00:47:51,920
Speaker 7: Karen.

899
00:47:51,960 --> 00:47:54,200
Speaker 2: Thank you so much for joining us this morning. It's

900
00:47:54,200 --> 00:47:56,359
Speaker 2: been great to chat with you. Let people know where

901
00:47:56,400 --> 00:47:58,440
Speaker 2: they can find you and follow the work that you

902
00:47:58,440 --> 00:47:59,040
Speaker 2: guys are doing.

903
00:48:00,000 --> 00:48:02,799
Speaker 11: Thanks so much. Golden Gate Institute dot org. And we've

904
00:48:02,800 --> 00:48:04,920
Speaker 11: got a newsletter that you can subscribe to where we

905
00:48:05,000 --> 00:48:07,480
Speaker 11: try to make sense of what's happening in AI for

906
00:48:08,440 --> 00:48:10,840
Speaker 11: folks who are outside of the Silicon Valley bubble.

907
00:48:11,239 --> 00:48:13,359
Speaker 2: All right, well, I'm definitely going to subscribe to that.

908
00:48:13,760 --> 00:48:15,160
Speaker 2: Thank you so much again, and we'll talk to you

909
00:48:15,200 --> 00:48:15,600
Speaker 2: again soon.

910
00:48:16,160 --> 00:48:16,520
Speaker 6: Thank you.

911
00:48:17,200 --> 00:48:17,520
Speaker 3: All right.

912
00:48:17,560 --> 00:48:19,239
Speaker 7: Really interesting talking to Tarin there.

913
00:48:19,280 --> 00:48:21,800
Speaker 3: Huh, Emily, Christal, I feel like we are at a

914
00:48:21,840 --> 00:48:29,880
Speaker 3: breaking point with regard to the end of the show. Okay,

915
00:48:30,040 --> 00:48:31,880
Speaker 3: see you got me that I walked into it and

916
00:48:31,880 --> 00:48:34,080
Speaker 3: I didn't expect it. But both of us should go

917
00:48:34,120 --> 00:48:35,319
Speaker 3: to prison for what we just did.

918
00:48:36,400 --> 00:48:39,160
Speaker 2: Indeed, all right, I'm sure Trump stormtroopers will be here

919
00:48:39,200 --> 00:48:40,120
Speaker 2: to Russa shortly.

920
00:48:41,239 --> 00:48:41,520
Speaker 7: Emily.

921
00:48:41,640 --> 00:48:43,560
Speaker 2: Always great to see you guys. Thank you so much

922
00:48:43,600 --> 00:48:46,080
Speaker 2: for watching the show. Let's see you today is Wednesday.

923
00:48:46,200 --> 00:48:49,359
Speaker 2: Tomorrow I will be in with Sager, so we'll have

924
00:48:49,440 --> 00:48:51,880
Speaker 2: all of the leadus for you guys. Then, thank you

925
00:48:51,920 --> 00:48:55,000
Speaker 2: for subscribing over at Breaking Points. Make sure you use

926
00:48:55,040 --> 00:48:58,640
Speaker 2: that free promo code BP free and there it is

927
00:48:58,719 --> 00:49:01,640
Speaker 2: up on the screen BP free Breakingpoints dot Com. And

928
00:49:01,680 --> 00:49:03,000
Speaker 2: we will see you guys tomorrow.

929
00:49:03,200 --> 00:49:03,640
Speaker 3: See you then,