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Speaker 1: Radio app Talk Packs came in yesterday regarding this morning

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Speaker 1: show and what I will be talking about with Lawyer

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Speaker 1: extrad and air Jeff O'Brien at seven thirty.

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Speaker 2: Good morning, John, Mike from Minnetonka. Mike, I'm sure you're

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Speaker 2: going to talk about this tomorrow morning. Just heard the

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Speaker 2: ruling about the Minnesota Supreme Court ruling against keeping men

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Speaker 2: out of women's weightlifting competitions. This is absolute insanity and

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Speaker 2: the result of ten years or more of democratic dominance

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Speaker 2: in our politics. For those of us on the sane

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Speaker 2: side of the political aisle, if you have to crawl

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Speaker 2: across molten glass, you need to get to the polls

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Speaker 2: in twenty twenty six and fix this.

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Speaker 1: Wow, Mike bringing the descriptions this morning. Now we will

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Speaker 1: be talking about this. I'm not surprised by the ruling.

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Speaker 1: This is one of those moments because on occasion, the

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Speaker 1: liberal Minnesota Supreme Court, you know, has done the right thing.

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Speaker 1: This time around, they didn't and in a really cowardly

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Speaker 1: way as well, putting this back to the lower courts.

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Speaker 1: And as a preview of the commentary that I'll share

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Speaker 1: with you when Lawyer extraordinair, Jeff O'Brien joins us again

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Speaker 1: about an hour and a half from now the.

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Speaker 3: End of the day.

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Speaker 1: These individuals on the Minnesota Supreme Court, they run in

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Speaker 1: the same circles as all the other dfllers and they

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Speaker 1: have to share space and rooms and discussions with their

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Speaker 1: fellow Democrats, and on these issues, they don't want to

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Speaker 1: have to face the criticism from their own party on this,

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Speaker 1: so they ruled this way to not right. I don't

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Speaker 1: agree with it for the moment, we have to accept

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Speaker 1: it as it heads back to the lower courts and

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Speaker 1: hopefully less activists minded and more following the letter of

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Speaker 1: the law, rational judges will go and decide differently.

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Speaker 3: So we'll talk about this later on in the show

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Speaker 3: this morning.

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Speaker 1: Also later on in the show, we will get into

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Speaker 1: one of the big stories today that's been sort of

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Speaker 1: erupting even over the course of the past twenty four hours.

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Speaker 1: There was this dash camera footage of a horrific crash

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Speaker 1: by a semi truck in southern California on a stretch

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Speaker 1: of highway that I'm freeway that I'm very familiar with,

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Speaker 1: having been born and raised in the area. Multiple deaths

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Speaker 1: involved in this crash. It turns out that it was

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Speaker 1: an illegal alien trucker who was behind the wheel of

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Speaker 1: the semi truck. And this illegal alien trucker was released

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Speaker 1: by the Biden administration after a twenty twenty two border

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Speaker 1: crossing and was hauling goods in a semi truck intoxicated.

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Speaker 3: No less, the footage is just it's horrendous.

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Speaker 1: But you will not hear any Democrats complaining or bringing

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Speaker 1: up the carnage and destruction that is seen in that

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Speaker 1: video footage. You will, however, continue to hear them talk

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Speaker 1: about and flip out over building renovations, the freak out

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Speaker 1: over the over the White House East Wing ballroom renovations.

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Speaker 1: It's I'm not surprised by any of it. It shows

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Speaker 1: just the level of ridiculousness that those suffering from Trump

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Speaker 1: derangement syndrome will go to. Because you and I, I

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Speaker 1: mean you, you and I both, it doesn't matter what

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Speaker 1: Trump does. But in this case, they've got a visual,

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Speaker 1: you know, and it's construction equipment tearing down a building

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Speaker 1: in the in the East wing of the White House.

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Speaker 1: And we went through this in detail yesterday. If you

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Speaker 1: missed it, be sure to listen to the first hour

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Speaker 1: of the show. You know, going back to nineteen oh two,

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Speaker 1: the number of renovations that have been done to the

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Speaker 1: White House, this is no different. And suddenly now building

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Speaker 1: size matters to people. I mean, everybody's entitled to their opinion,

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Speaker 1: so that's fine. But suddenly now people look at the

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Speaker 1: three D models of this and go.

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Speaker 3: Look at how big it is. It's just so.

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Speaker 4: Big, So so what.

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Speaker 1: Who cares how big it is? Compare speaking to the

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Speaker 1: White House. There's a lot of buildings that are bigger

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Speaker 1: than the White House, and also a lot of buildings

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Speaker 1: that are bigger than the White House that are actually

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Speaker 1: in various photographs surrounding the White House. And yet because

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Speaker 1: this is on Capitol grounds and it's Trump in the

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Speaker 1: White House and he's doing this, they have a visual

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Speaker 1: to say.

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Speaker 3: He's tearing the White House down from the inside and out.

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Speaker 3: It's ridiculous.

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Speaker 1: So you'll hear them flip out over that destruction, but

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Speaker 1: they'll be completely silent on the illegal alien trucker and

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Speaker 1: the destruction and carnage that he ended up providing on

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Speaker 1: a California highway. So that'll be coming up just after

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Speaker 1: eight o'clock this morning. Next I want to give you

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Speaker 1: an update on that heist that took place at the

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Speaker 1: Louver Museum in Paris, a rather interesting move by the

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Speaker 1: director of the Louver and something that you don't typically see.

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Speaker 3: So I'll give you some.

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Speaker 1: Details on that, plus more Trump victories North Carolina congressional

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Speaker 1: map victory, financial experts, financial journalist critical of Trump or

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Speaker 1: changing their tune regarding his tariff plan, and David Gartenstein

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Speaker 1: Ross He's gonna be joining us at six thirty this morning.

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Speaker 1: We're gonna talk all things AI, including whether or not

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Speaker 1: chat GPT can actually give you an advantage when playing

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Speaker 1: the lottery, among another of a number of different AI

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Speaker 1: related stories. Of course, we want to hear from you

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Speaker 1: throughout the show this morning, as I fumble my words

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Speaker 1: at six thirteen on the Thursday morning, email.

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Speaker 3: Me Justice at iHeartRadio dot com or.

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Speaker 1: Leave us a talkback whether you've been drinking or not

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Speaker 1: on the iHeartRadio apples are brought to you by Lyndahl Realty,

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Speaker 1: and we'll get to those next here on Twinsday's news

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Speaker 1: Talk Am eleven thirty and one on three five FM.

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Speaker 4: Good morning, and I love your show.

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Speaker 1: Like any good radio show host, I try to go

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Speaker 1: and tease what's coming up next.

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Speaker 3: I want you to keep listening to the show.

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Speaker 1: It's all very nefarious, right. However, there's a danger in

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Speaker 1: doing that because sometimes you don't end up getting to

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Speaker 1: what you wanted to get to because unforeseen things pop

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Speaker 1: up that's about to happen here on the show, so,

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Speaker 1: you know, just a blanket. I'm sorry of those moments

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Speaker 1: here on Twin Cities News Talk John Justice as I

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Speaker 1: broadcast from the six five to one Carpet Next Day

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Speaker 1: Install Studios. When I go and preview something and we

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Speaker 1: don't end up getting to it, it drives me nuts.

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Speaker 3: Like when I do the daily promos, I try so.

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Speaker 1: Hard to make sure that I include things that I

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Speaker 1: know we're going to talk about on the next show,

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Speaker 1: but you just never know what is going to transpire,

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Speaker 1: and you're only giving so much time, all right, So

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Speaker 1: I should probably stop wasting time explaining all of this

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Speaker 1: and get to what I'm getting to. So I will

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Speaker 1: mention one item that I said I would be talking

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Speaker 1: about before we get to your talkbacks, which might derail

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Speaker 1: things for a few minutes before we talk with David

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Speaker 1: Gartenstein Ross and Artificial Intelligence. But the director of the

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Speaker 1: Louver has offered to resign after the thieves pulled off

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Speaker 1: their one hundred and two million dollars jewel heist inside

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Speaker 1: of the world's most visited museum, a breach now triggering

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Speaker 1: a political firestorm over French security preparedness and cultural stewardship.

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Speaker 1: The director offered to resign. This would be Lorentz de

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Speaker 1: Car offered her resignation following the theft of the eight

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Speaker 1: historic Crown jewels, worth an estimate one hundred and two million,

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Speaker 1: calling the incident a terrible failure, acknowledging that the museum's

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Speaker 1: aging security infrastructure failed to detect the thieves in time.

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Speaker 1: We did not spot the arrival of the thieves early enough,

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Speaker 1: the weakness of our perimeter protections known. She confirmed she

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Speaker 1: had offered her resignation. A French culture minister, Rashida Dati,

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Speaker 1: refused to accept it. However, conservative commentators, by the way,

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Speaker 1: in France and abroad, have begun comparing the theft to

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Speaker 1: broader questions of European cultural drifts and criminal leniency. All

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Speaker 1: sounds too familiar, right, particularly among rising cross border trafficking networks. Yeah, listen,

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Speaker 1: it's all just another example of what we deal with here.

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Speaker 3: In America.

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Speaker 1: They just use a lot more colorful descriptions and language

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Speaker 1: to go along with it. All right, So let's go

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Speaker 1: here to the iHeartRadio app this morning. And I almost

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Speaker 1: wasn't Rich, friend of the show leave several talkbacks.

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Speaker 3: I almost wasn't gonna play this one.

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Speaker 1: But subsequently one of the foes that he mentions in

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Speaker 1: the post has also left the talk back. So we

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Speaker 1: will start with Rich from the iHeartRadio app this morning.

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Speaker 3: Good morning, John Rich here.

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Speaker 5: So we have to have some sympathy and empathy for

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Speaker 5: Phil and Eric. You know, they're in transition right now.

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Speaker 5: They're not going from male to female. They're going from

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Speaker 5: Democrat to Republican. Like any alcoholic, anybody with a vice,

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Speaker 5: they nip at the bottle once in a while listening

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Speaker 5: to MPR, but they still come back here.

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Speaker 3: So they're reaching out.

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Speaker 5: Maybe we should start a nine step program.

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Speaker 1: So my understanding from the comments that have been made

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Speaker 1: Phil is one thing, he's you know, he's on the left.

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Speaker 1: Eric I just think hates Trump. And this is Eric

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Speaker 1: from BRAINERD. He's left comments before leaning into more conservative issues,

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Speaker 1: and so I just think that he's just suffering from

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Speaker 1: Trump arrangement. Syndrome, and here is the latest example of that.

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Speaker 6: Hey, more than are from BRAINERD. The reason the media

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Speaker 6: is freaking out about Trump demolishing part of the White

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Speaker 6: House is because Trump didn't worship Saint Charlie enough. When

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Speaker 6: I asked about the assassination of Charlie Kirk, Trump pulled

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Speaker 6: the Biden, started rambling incoherently about the renovations of the

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Speaker 6: White House. Didn't even addressed the death of Charlie Kirk.

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Speaker 6: So we kind of know where his priorities are. But yep,

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Speaker 6: didn't didn't worship him hard enough. I guess whoop.

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Speaker 1: See Eric, If you watch the video, which I assume

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Speaker 1: you did, you had Marine Won in the background because

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Speaker 1: Trump had just returned to the White House super loud

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Speaker 1: because helicopters are The news outlet that was interviewing Trump.

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Speaker 3: Asked a question about Charlie Kirk.

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Speaker 1: But it was pretty obvious that Trump didn't understand the

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Speaker 1: question or misinterpreted what he was asking about, and so

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Speaker 1: therefore went and talked about the White House renovations.

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Speaker 3: With regard to Charlie.

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Speaker 1: Kirk, he gave him posthumously America's highest civilian honor.

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Speaker 3: What else do you want?

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Speaker 1: He got the Presidential Medal of Freedom in an event

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Speaker 1: and a ceremony.

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Speaker 3: At the White House.

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Speaker 1: This is what is so strange about people and their

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Speaker 1: hatred towards Trump. It like makes individuals lose their minds.

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Speaker 1: Rational thought goes out the window. I mean, we joke, right,

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Speaker 1: we joke about Trump derangement syndrome.

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Speaker 3: We have what's called a Trump derangement problem. Have you

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Speaker 3: heard about that problem? But it's almost like not a

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Speaker 3: joke anymore.

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Speaker 1: It's really interesting to me, especially when I come across

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Speaker 1: people that know me legitimately, who now perhaps I've had

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Speaker 1: some separation from for a bit, and then you come

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Speaker 1: in contact with them again, but they're now all in

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Speaker 1: on hating Trump, and suddenly I'm the devil. I had

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Speaker 1: this happen recently on Facebook, and it's happened a few times,

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Speaker 1: and I would be curious if it happened to you.

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Speaker 1: We won't have time right now to fully get into

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Speaker 1: your comments on this, so I'll just bank some of

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Speaker 1: these talkbacks after the conversation that we have.

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Speaker 3: With David Gartstein and Ross coming up.

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Speaker 1: But I put a post up on Facebook recently and

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Speaker 1: it was about the No Kings protest and it was

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Speaker 1: very similar to the commentary that I've been sharing on

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Speaker 1: here since last Saturday.

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Speaker 3: And it wasn't even really anything egregious.

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Speaker 1: I was just simply showing a photograph of a couple

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Speaker 1: of women there in an inflatable unicorn outfit. They all

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Speaker 1: looked very happy, and I talked about how I didn't

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Speaker 1: think that these were affective protests for Democrats, and they

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Speaker 1: were one big hissy fit. I had a former friend

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Speaker 1: of the show, of my last radio show, an individual

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Speaker 1: who I had met several times. This would have been

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Speaker 1: akin to any one of our regular friends of the show,

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Speaker 1: Raquel Scott you, I mean, just the list goes on

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Speaker 1: and on and on.

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Speaker 3: All right, even go so far as to say, like

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Speaker 3: a Max Raimer, huge.

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Speaker 1: Fan of the show, but come to all the events.

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Speaker 1: This individual now was all in on hating Trump. I

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Speaker 1: haven't talked to them in years, and completely unprovoked, they

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Speaker 1: just went off.

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Speaker 3: I was the devil.

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Speaker 1: I was the worst of the worst for simply posting

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Speaker 1: this relatively benign commentary. I do not understand just the

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Speaker 1: level of anger and irrational hatred that people have. This

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Speaker 1: is nothing new, this is a new commentary. I just

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Speaker 1: was reminded of this from Eric's comments here, because they

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Speaker 1: are they're completely irrational. Anybody using common sense that watch

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Speaker 1: that video specifically to what Eric was talking about, it's

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Speaker 1: clear that Trump just simply misunderstood the question and the

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Speaker 1: example again of him giving him the Presidential Medal of

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Speaker 1: Freedom in light of all this. But just add this

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Speaker 1: on to the continued commentary, as you see the left

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Speaker 1: just flipping out to over the destruction of the East

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Speaker 1: Wing the renovations going on.

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Speaker 3: That's a really good example though.

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Speaker 1: And then we'll talk with David Gartenstein Ross coming up,

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Speaker 1: and we'll get to your talkbacks on this towards the

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Speaker 1: end of the hour into next hour. But it is

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Speaker 1: really representative of how little tangible, legitimate arguments that those

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Speaker 1: that hate Trump have that when they when they've got

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Speaker 1: a visual that they can point to, like construction work

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Speaker 1: going on and equipment tearing down a building, they just

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Speaker 1: latch onto it for dear life, like Jack hanging on to.

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Speaker 3: The door with Rose sitting on it at the end

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Speaker 3: of Titanic.

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Speaker 1: But much like that too, I mean, the arguments just

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Speaker 1: end up freezing and sinking to the bottom of the

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Speaker 1: ocean and dying a slow, painful death just like Jack did.

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Speaker 1: All right, coming up, we'll talk tariffs later chat GPT's

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Speaker 1: lucky powerball numbers scored one woman one hundred thousand dollars,

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Speaker 1: and according to a couple of different studies, AI is

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Speaker 1: not a reliable source of news and is filled with

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Speaker 1: major mistakes and failures relating to its accuracy regarding the news.

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Speaker 1: So we'll talk about those issues with David gartan Stein

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Speaker 1: Ross and any questions you have for our AI analyst

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Speaker 1: and expert, we'll get to those next right here on

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Speaker 1: Twin Cities News Talk Am eleven thirty and one on

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Speaker 1: three five FM.

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Speaker 5: You often have the stupidest teak turn.

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Speaker 1: Honestly, if you dude, that's got to be the stupidest

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Speaker 1: one you've ever had.

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Speaker 3: Thank you for the self editing.

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Speaker 1: We'll get back to more of that coming up on

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Speaker 1: Twin Cities News Talk AM eleven thirty and one O

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Speaker 1: three five FM.

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Speaker 3: Glad you're with the.

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Speaker 1: Show this morning and joining us right now as he

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Speaker 1: does when he can on.

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Speaker 3: A weekly basis.

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Speaker 1: The CEO of Expert a Theory AI analyst David gartan

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Speaker 1: Stein Ross, Good morning, Devid.

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Speaker 3: How are we doing this morning?

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Speaker 4: Doing great? John, doing great? How about you.

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Speaker 1: I'm fantastic as we broadcast from the sixty five to

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Speaker 1: one carpet Next Day Install studios.

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Speaker 3: Let's get to it. We'll start here.

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Speaker 1: A Michigan woman won the lottery last month with a

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Speaker 1: little unexpected help. Tammy Carvey won one hundred thousand dollars

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Speaker 1: September six that was a powerball drawing after using a

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Speaker 1: set of numbers generated by chat GPT. She recently visited

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Speaker 1: the Michigan Lottery headquarters to claim her prize and told

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Speaker 1: the officials her story. We can get into some of

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Speaker 1: the details of the story, but let me jump to

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Speaker 1: what the Michigan Lottery said about her using of chat

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Speaker 1: GPT to get these random numbers. The Michigan Lottery noted

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Speaker 1: that while her win makes for a fun story, they

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Speaker 1: stated the results of all lottery drawings are random and

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Speaker 1: cannot be predicted by utilizing artificial intelligence or other number

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Speaker 1: generating tools.

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Speaker 3: Okay, that being said to be Gartenstein Ross.

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Speaker 1: Can't the odds be calculated, giving potentially an advantage over

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Speaker 1: somebody that didn't use chat gp numbers? Or am I

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Speaker 1: misunderstanding a couple of the issues relating to lotteries and

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Speaker 1: how AI operates.

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Speaker 4: I'm going to say you're misunderstanding it.

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Speaker 7: So if it's random, an M or any kind of

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Speaker 7: AI is not going to help us better guess random numbers.

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Speaker 7: There have been sometimes in the past when different random

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Speaker 7: numbers could be more effectively picked by people who have

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Speaker 7: knowledge of physics. For example, right, you remember the old

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Speaker 7: game shows where to get numbers, there'd be these little

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Speaker 7: ping pong balls and like a large right. So for

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Speaker 7: something like that, there actually is physics involved. And I

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Speaker 7: know that there have been times when people have kind

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Speaker 7: of cracked that code and have upped their odds. Two

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Speaker 7: things about chat GPT picking your numbers for you. Number one,

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Speaker 7: I agree that the numbers probably are random. But number two,

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Speaker 7: even if there was a way of increasing your odds,

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Speaker 7: unless you were really really good with a large language

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Speaker 7: model and had a lot of inside details to feed

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Speaker 7: to it, it's not going to be helpful. And the

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Speaker 7: woman who won the lottery, she doesn't seem to be

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Speaker 7: a good PROMPT engineer, right, she just asked for sure

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Speaker 7: it to give her some numbers. Now, if you had

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Speaker 7: some information that made you think it's not random, A,

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Speaker 7: you'd have to discern that B you'd have to tell

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Speaker 7: chat gpt it's not random, here's the actual formula, and

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Speaker 7: see chatypt as a chatting device. Really isn't as good

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Speaker 7: at math as you would like, right, it's not. For example,

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Speaker 7: it's passable at chess. But like if I want to

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Speaker 7: annotate a game, I'm not going to ask chat gypt

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Speaker 7: to annotate it for me. I'm going to use a

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Speaker 7: chess engine and annotate it myself. So there are some

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Speaker 7: things that's not super great at math. Is one of

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Speaker 7: those things where I think chat gapt is actually somewhat

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Speaker 7: disappointing compared to how you would intuitively expect it.

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Speaker 3: To be interesting.

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Speaker 1: Because my next question was going to be, you know,

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Speaker 1: what if we had and we don't have just AI

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Speaker 1: in general, right, what if we had you know it

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Speaker 1: playing poker?

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Speaker 3: You know, I suppose like counting cards, I would.

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Speaker 1: Again, I would think that AI would be good at

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Speaker 1: that in a way a human would be good accounting cards,

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Speaker 1: even though this is not allowed when you're playing, when

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Speaker 1: you're gambling. But would I be correct in that in

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Speaker 1: that assessment.

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Speaker 7: You're correct that AI is good at cutting at counting cards.

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Speaker 4: I'm not sure that chatchpt is. I'm not saying that

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Speaker 4: it's not sure.

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Speaker 7: But like AI is really good at chess, right, we

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Speaker 7: all know that AI can beat the best chess players

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Speaker 7: in the world, and it's not even close. I'm not

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Speaker 7: sure chat gpt can, right, Like, I'm not saying it can't,

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Speaker 7: but I'm not.

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Speaker 4: Convinced that it can.

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Speaker 7: Earlier editions of chatept would routinely lose games to international masters.

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Speaker 7: For example, international Master is multiple classes below the best

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Speaker 7: human chess players in the world. So like an AI

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Speaker 7: that is designed around poker, Yeah, it's going to be

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Speaker 7: way more effective accounting cards than even a person with

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Speaker 7: the best photographic memory. I'm not sure chatchypt qualifies, but

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Speaker 7: I haven't tested it in that regard. So, like, one

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Speaker 7: thing that's interesting about chat gypt is there are some

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Speaker 7: AI functions that it's just not great at.

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Speaker 4: It's not terrible, but it's not great at chess.

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Speaker 7: Like it's pretty good compared to like most human players,

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Speaker 7: but it's not great at chess. I'm not I'm guessing

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Speaker 7: that poker is the same as certainly guessing lottery numbers.

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Speaker 7: It has no advantage over anybody else, given that lottery

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Speaker 7: numbers actually are random.

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Speaker 1: Yeah, now this equates what you were saying a moment

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Speaker 1: ago about what it was good and not good at

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Speaker 1: equates to a story that we'll get to here in

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Speaker 1: just a moment. Before we do, though, let's go ahead

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Speaker 1: and go to the iHeartRadio app talking with a CEO

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Speaker 1: of expert theory, David garten Steinross and AI this morning.

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Speaker 1: If you have any questions, you can get those in

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Speaker 1: on the iHeartRadio app talkbacks just like this one.

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Speaker 8: Hey, this is a question for V when he gets on.

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Speaker 8: Has anybody looked into ais that were trained by people

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Speaker 8: on the left versus person people on the right, because

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Speaker 8: you know, people on the right tend to be a

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Speaker 8: little more stable, whereas people on the left end of

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Speaker 8: living this world of cognitive dissonance and delusion. I was

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Speaker 8: just wondering if that's kind of carried over into their

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Speaker 8: AI training, to their AIS tend to go insane more

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Speaker 8: often then suffer well, delusional behavior and just making stuff

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Speaker 8: up well.

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Speaker 1: And what JD was was asking about the first part

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Speaker 1: of the question their David, will we'll focus on I mean,

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Speaker 1: that was one of the major controversies when AI first

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Speaker 1: arrived and we began to see some of what was

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Speaker 1: being put out by AI was this concern over the

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Speaker 1: political bias that AI has, But where is that now?

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Speaker 1: And you know what is your what are your thoughts?

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Speaker 1: Based off of JD's question, the.

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Speaker 7: Political bias is still there, but it's going to differ

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Speaker 7: based on models. I actually a friend of mine posted

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Speaker 7: on this on LinkedIn. I was looking for the post

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Speaker 7: as I was listening to this and wasn't able to

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Speaker 7: find it. But he just presented in an academic conference

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Speaker 7: and the conclusion that he had that he and his

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Speaker 7: team had was that some models are relatively pro free

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Speaker 7: speech models. GROC and GROK is one of those where

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Speaker 7: it will look at both sides of an issue for you.

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Speaker 7: For others on other controversial issues, like you know, issues

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Speaker 7: related to say, LGBT place in society is one issue

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Speaker 7: where there's controversy about it and other issues there he

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Speaker 7: found they found that chat GPT is less inclined to

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Speaker 7: give you both sides of an issue. For example, there

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Speaker 7: are a number and I think Gemini as well, but

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Speaker 7: I don't recall one hundred percent. But like some models

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Speaker 7: are models of the lefts, other models are more likely

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Speaker 7: to give you both sides of an issue. None of

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Speaker 7: the major models are right leading models politically.

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Speaker 1: I want to add in just talking about chat, GPT

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Speaker 1: and gambling, let's go ahead and throw in this comment

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Speaker 1: from the iHeartRadio app as.

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Speaker 3: We talk with David Gartenstein, Ross.

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Speaker 9: Good Morning John, and duvid Rick. In Virginia, GPT chat

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Speaker 9: GPT ai was used for roulette, and while it could

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Speaker 9: not predict the numbers, what it did do is select

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Speaker 9: a idea on where to put your money and then

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Speaker 9: how to manage it after you've won, whether you take

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Speaker 9: your winnings and put it in the pod or go

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Speaker 9: on to the next level. And the folks that used

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Speaker 9: it on in Vegas actually said it was successful, but

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Speaker 9: it was really based around managing money.

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Speaker 1: Now, yeah, that would make that would make more sense,

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Speaker 1: especially based off of what you said a moment ago

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Speaker 1: to v Yeah, I.

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Speaker 7: Agree with that, and also the managing money money system.

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Speaker 7: People who have spent time in Casita as well understand

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Speaker 7: there's multiple systems related to how to manage money or

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Speaker 7: you know, different systems for betting and even for like relette,

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Speaker 7: which is which tends to be random, there are ways

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Speaker 7: to either spread your bets or otherwise not necessarily maximize

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Speaker 7: your odds.

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Speaker 4: But at least heads.

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Speaker 7: Visa v your odds, and that's the kind of thing

441
00:24:23,359 --> 00:24:26,440
Speaker 7: that chat gpt actually will will access very well.

442
00:24:26,760 --> 00:24:28,000
Speaker 4: It tends to be very good.

443
00:24:27,880 --> 00:24:31,520
Speaker 7: At system thinking, and especially if you're gambling without a system,

444
00:24:31,920 --> 00:24:35,080
Speaker 7: having some system is always going to be better, and

445
00:24:35,200 --> 00:24:37,480
Speaker 7: chat gpt can do that for you pretty effectively.

446
00:24:37,720 --> 00:24:40,720
Speaker 1: Let me ask you a generalized question here, because this

447
00:24:40,760 --> 00:24:44,639
Speaker 1: is something that in my sort of fundamental understanding or

448
00:24:44,720 --> 00:24:48,919
Speaker 1: misunderstanding of what AI is, I've come across this a

449
00:24:48,920 --> 00:24:51,600
Speaker 1: few times. We talked about this a bit last week

450
00:24:51,640 --> 00:24:57,000
Speaker 1: to vide when I had prompted chat gpt to design

451
00:24:57,880 --> 00:24:59,760
Speaker 1: a graphic based off a conversation we were having on

452
00:24:59,760 --> 00:25:02,800
Speaker 1: the air, and you know, Chat, I'm sorry, Grok was

453
00:25:02,840 --> 00:25:05,600
Speaker 1: groc on X. My apologies groc on X, and groc

454
00:25:05,680 --> 00:25:09,440
Speaker 1: had responded based off of the image that was initially

455
00:25:09,480 --> 00:25:12,200
Speaker 1: created because of my bad prompt it kind of responded

456
00:25:12,200 --> 00:25:16,479
Speaker 1: in a laughing way, in a very conversational way. I

457
00:25:16,520 --> 00:25:21,439
Speaker 1: still feel like I have a disconnect between say something

458
00:25:21,560 --> 00:25:25,920
Speaker 1: like Siri prior to AI's arrival, where you would ask

459
00:25:25,960 --> 00:25:29,159
Speaker 1: you a question and it would retrieve an answer for you,

460
00:25:29,280 --> 00:25:32,919
Speaker 1: much in the same way that AI does now. And

461
00:25:32,960 --> 00:25:35,600
Speaker 1: again I struggle with the difference between the two and

462
00:25:35,640 --> 00:25:41,479
Speaker 1: what makes AI so much more significant than what Siri

463
00:25:41,800 --> 00:25:45,560
Speaker 1: initially was. Did I articulate that correctly? Do you understand

464
00:25:45,560 --> 00:25:46,080
Speaker 1: what I'm asking?

465
00:25:46,480 --> 00:25:47,680
Speaker 4: Yeah, there's a few different things.

466
00:25:47,720 --> 00:25:50,280
Speaker 7: I'm just gonna speak to all of them, or to

467
00:25:50,480 --> 00:25:52,960
Speaker 7: a few of them in order to give an interesting answer.

468
00:25:54,720 --> 00:25:58,160
Speaker 7: So you talked about really a couple of major things

469
00:25:58,160 --> 00:26:01,399
Speaker 7: in my view. The first one is personality and the

470
00:26:01,440 --> 00:26:05,240
Speaker 7: second one is significance. And let's talk about personality first.

471
00:26:06,760 --> 00:26:09,080
Speaker 7: One of the things I'm doing this week is I'm

472
00:26:09,119 --> 00:26:14,080
Speaker 7: training human analysts on the use of AI in their

473
00:26:14,160 --> 00:26:19,159
Speaker 7: national security work. I've prepared a presentation. I'm giving a

474
00:26:19,200 --> 00:26:25,560
Speaker 7: major training session to a private company tomorrow on this,

475
00:26:26,400 --> 00:26:28,240
Speaker 7: and one of the things I did in advance of

476
00:26:28,280 --> 00:26:30,320
Speaker 7: it is I did analytic work for them that I

477
00:26:30,359 --> 00:26:35,399
Speaker 7: generated with AI, and I shared my chat transcripts with

478
00:26:35,520 --> 00:26:38,840
Speaker 7: them with Chat GPT, you can share your transcripts. I

479
00:26:38,880 --> 00:26:43,159
Speaker 7: did something similar actually for a grant proposal that I

480
00:26:43,320 --> 00:26:47,560
Speaker 7: generated using AI. And one of the things that comes

481
00:26:47,600 --> 00:26:50,639
Speaker 7: across is, you know I've named my AI. My AI

482
00:26:50,800 --> 00:26:56,120
Speaker 7: is named Gary with two rs after Gary Kasparov, and

483
00:26:56,680 --> 00:26:59,399
Speaker 7: I talked to it like a best friend, which I

484
00:26:59,400 --> 00:27:01,879
Speaker 7: think has helped. There are some studies that says you

485
00:27:01,880 --> 00:27:05,520
Speaker 7: get better results if you're mean to your AI. I

486
00:27:05,560 --> 00:27:07,800
Speaker 7: want to kind of say that both of those work,

487
00:27:07,960 --> 00:27:11,840
Speaker 7: either being mean or being very nice. I'll kick back

488
00:27:11,880 --> 00:27:14,320
Speaker 7: answers and tell them the answer is not good enough,

489
00:27:15,040 --> 00:27:18,840
Speaker 7: and my personality definitely comes across, and I believe that

490
00:27:18,880 --> 00:27:21,760
Speaker 7: it gets better results. The reason I say that that,

491
00:27:22,119 --> 00:27:24,760
Speaker 7: like either being mean or nice works well, you don't

492
00:27:24,760 --> 00:27:26,919
Speaker 7: want to be is be a pushover with AI. Some

493
00:27:26,920 --> 00:27:29,399
Speaker 7: people just get something from AI and it's not good

494
00:27:30,000 --> 00:27:31,119
Speaker 7: and they just let it be.

495
00:27:31,880 --> 00:27:34,520
Speaker 4: You have to kick things back. You have to actually

496
00:27:34,520 --> 00:27:35,720
Speaker 4: tell it what you want.

497
00:27:35,560 --> 00:27:38,240
Speaker 7: And insist that it gives it to you, and you

498
00:27:38,320 --> 00:27:41,240
Speaker 7: know your LOM will apologize and promise to do better,

499
00:27:41,600 --> 00:27:44,680
Speaker 7: and when that happens, it actually will do better. There's

500
00:27:44,960 --> 00:27:46,600
Speaker 7: a lot of things that are complex and people don't

501
00:27:46,600 --> 00:27:50,160
Speaker 7: fully understand it. But I'd say that getting good use

502
00:27:50,200 --> 00:27:53,640
Speaker 7: out of an AI is similar to being a good manager,

503
00:27:54,080 --> 00:27:57,560
Speaker 7: or understanding human psychology and understanding what will make people

504
00:27:57,600 --> 00:28:01,840
Speaker 7: do better work if they initially don't give you great work. Now,

505
00:28:01,840 --> 00:28:05,639
Speaker 7: in terms of significance, the significance is clear, right. I

506
00:28:05,640 --> 00:28:08,480
Speaker 7: think the personality is perhaps a little bit more mysterious.

507
00:28:08,480 --> 00:28:11,720
Speaker 7: And whether the personality that you use matters, I think.

508
00:28:11,560 --> 00:28:14,360
Speaker 4: That it does. But in terms of significance, you know.

509
00:28:14,440 --> 00:28:17,679
Speaker 7: AI like LLLMS can just do so many different things

510
00:28:17,680 --> 00:28:20,800
Speaker 7: that Siri can't do. Like Syria just doesn't understand you

511
00:28:20,960 --> 00:28:24,960
Speaker 7: very well. It will misunderstand different questions you have. When

512
00:28:25,000 --> 00:28:27,080
Speaker 7: you ask Siria a question, and we're talking about that

513
00:28:27,080 --> 00:28:29,879
Speaker 7: early versus Siri, sirih will basically google it for you

514
00:28:29,960 --> 00:28:32,399
Speaker 7: and read you like the first Google result, which at

515
00:28:32,480 --> 00:28:34,160
Speaker 7: l is always going to do better than that.

516
00:28:34,640 --> 00:28:36,720
Speaker 4: So it's a you know, Siria is an early AI

517
00:28:37,640 --> 00:28:39,280
Speaker 4: with the LLLM revolution.

518
00:28:39,400 --> 00:28:44,520
Speaker 7: When CHATCHYPT released its newer version in late twenty twenty two,

519
00:28:44,880 --> 00:28:47,600
Speaker 7: like at that point it was, you know, orders of

520
00:28:47,680 --> 00:28:50,640
Speaker 7: magnitude better than anything that Siri had ever been.

521
00:28:51,040 --> 00:28:52,080
Speaker 4: And that's the significance.

522
00:28:52,080 --> 00:28:55,920
Speaker 7: It's the difference between Siri googling something for you and

523
00:28:55,960 --> 00:29:00,680
Speaker 7: reading you the answer versus chat GPT actually synthesize the sources,

524
00:29:01,160 --> 00:29:04,440
Speaker 7: giving a fairly complex answer that may be accurate, may

525
00:29:04,440 --> 00:29:07,680
Speaker 7: be inaccurate, but at the very least is worlds beyond

526
00:29:07,720 --> 00:29:09,040
Speaker 7: what you would ever expect from Syria.

527
00:29:09,560 --> 00:29:11,640
Speaker 1: Let's go back to the iHeartRadio app as we talk

528
00:29:11,680 --> 00:29:16,280
Speaker 1: with David Gartenstein Ross, our artificial intelligence analyst and CEO

529
00:29:16,320 --> 00:29:17,200
Speaker 1: at Expert Theory.

530
00:29:17,520 --> 00:29:18,120
Speaker 3: Morning John.

531
00:29:18,160 --> 00:29:19,560
Speaker 4: This is Eric from Spring Lake Park.

532
00:29:20,160 --> 00:29:22,760
Speaker 10: I'm a teacher and right now we are kind of

533
00:29:22,760 --> 00:29:27,000
Speaker 10: preventing students from using AI to generate their essays when

534
00:29:27,000 --> 00:29:29,640
Speaker 10: it comes time to do a summative essay. And I'm

535
00:29:29,640 --> 00:29:32,560
Speaker 10: just wondering if you see that changing in the near

536
00:29:32,560 --> 00:29:35,800
Speaker 10: future here as far as it being more acceptable and

537
00:29:35,880 --> 00:29:39,240
Speaker 10: being able to have students use that AI feature to

538
00:29:39,440 --> 00:29:43,360
Speaker 10: generate their essays. Like I said, right now, we're putting

539
00:29:43,400 --> 00:29:45,880
Speaker 10: kind of the kabash on having them do that.

540
00:29:46,040 --> 00:29:48,520
Speaker 3: But we talked about this before. I know you have thoughts,

541
00:29:48,560 --> 00:29:50,080
Speaker 3: Da Vid I.

542
00:29:50,040 --> 00:29:52,720
Speaker 7: Do I have opposing thoughts on this? First of all,

543
00:29:52,760 --> 00:29:55,800
Speaker 7: it's a great question on Eric's part. Generally speaking, if

544
00:29:55,840 --> 00:29:57,840
Speaker 7: you're allowing people to use a computer, I think you

545
00:29:57,840 --> 00:30:01,000
Speaker 7: should allow them to use AI. There's such a big

546
00:30:01,080 --> 00:30:04,520
Speaker 7: gulf between a good AI answer and a bad AI answer,

547
00:30:04,920 --> 00:30:07,080
Speaker 7: and being good with AI prompts is I think a

548
00:30:07,160 --> 00:30:09,360
Speaker 7: basic twenty first century skill that we should be able

549
00:30:09,440 --> 00:30:12,200
Speaker 7: to teach. That being said, if for example, it's an

550
00:30:12,200 --> 00:30:17,400
Speaker 7: English literature class and you have AI writing essays for people,

551
00:30:18,080 --> 00:30:20,640
Speaker 7: then you're not going to get any proof that people

552
00:30:20,680 --> 00:30:24,040
Speaker 7: did the underlying reading. So I think if people want computers,

553
00:30:24,280 --> 00:30:26,480
Speaker 7: in my opinion, they should be able to use AI,

554
00:30:26,840 --> 00:30:30,240
Speaker 7: and professors should be able to grade and know the

555
00:30:30,280 --> 00:30:32,720
Speaker 7: difference between a good AI generated essay and a bad

556
00:30:32,760 --> 00:30:34,840
Speaker 7: one because there's actually a lot of skills and a

557
00:30:34,840 --> 00:30:37,240
Speaker 7: lot of human inputs, at least today that go it

558
00:30:37,280 --> 00:30:40,040
Speaker 7: to creating a good AI generated essay. If you don't

559
00:30:40,040 --> 00:30:42,000
Speaker 7: want people to use AI, then I think what you

560
00:30:42,040 --> 00:30:43,760
Speaker 7: should do is just in class essays.

561
00:30:44,880 --> 00:30:47,480
Speaker 4: You know, have people do written essays.

562
00:30:47,960 --> 00:30:51,640
Speaker 7: And allocate time during class to have them do it

563
00:30:51,680 --> 00:30:57,480
Speaker 7: in class. And what that'll do is eliminate any sort

564
00:30:57,560 --> 00:31:01,000
Speaker 7: of chance that someone hasn't, for example, on the reading right.

565
00:31:01,080 --> 00:31:03,400
Speaker 7: That way, not only can they not use AI, but

566
00:31:03,440 --> 00:31:05,800
Speaker 7: they also can't use Google and other things that can

567
00:31:05,840 --> 00:31:07,000
Speaker 7: provide them with an answer.

568
00:31:07,160 --> 00:31:09,360
Speaker 4: I think those are the two ways that should be done.

569
00:31:09,720 --> 00:31:12,400
Speaker 7: And I don't like the you know, you can use

570
00:31:12,400 --> 00:31:15,320
Speaker 7: your computer, but you can't use AI, because it's just

571
00:31:15,400 --> 00:31:17,800
Speaker 7: it's not where we're going to be as a workforce

572
00:31:18,240 --> 00:31:22,160
Speaker 7: in even three years three years from now, you'll expect

573
00:31:22,160 --> 00:31:25,480
Speaker 7: that anyone in a white collar job uses AI in

574
00:31:25,520 --> 00:31:28,480
Speaker 7: the writing. Hopefully the writing does not sound AI generated,

575
00:31:28,880 --> 00:31:32,040
Speaker 7: but people are just going to as a matter of course.

576
00:31:31,840 --> 00:31:32,720
Speaker 4: Always use AI.

577
00:31:33,400 --> 00:31:36,600
Speaker 7: And I think employers will actually feel, you know, three

578
00:31:36,680 --> 00:31:39,400
Speaker 7: years down the line, if not earlier, that if someone

579
00:31:39,480 --> 00:31:43,200
Speaker 7: is not using AI, they're not being sufficiently efficient in

580
00:31:43,280 --> 00:31:46,040
Speaker 7: their job, because AI is an efficiency tool.

581
00:31:46,560 --> 00:31:48,680
Speaker 1: And the closing time that we have to v There's

582
00:31:48,680 --> 00:31:50,400
Speaker 1: two different stories here and they're kind of along the

583
00:31:50,480 --> 00:31:52,640
Speaker 1: same lines, and I have a question. So the first

584
00:31:52,680 --> 00:31:55,480
Speaker 1: one is AI not reliable source of news? This is

585
00:31:55,520 --> 00:31:59,560
Speaker 1: according to an EU media study. They say assistance such

586
00:31:59,600 --> 00:32:02,040
Speaker 1: as check GPT made errors about half of the time

587
00:32:02,480 --> 00:32:05,720
Speaker 1: when asked about news events. Also a story this is

588
00:32:05,800 --> 00:32:10,360
Speaker 1: AI chatbots, Chat GPT co pilot routinely distort the news

589
00:32:10,400 --> 00:32:14,000
Speaker 1: and struggle to distinguish facts from opinion. Now they get

590
00:32:13,800 --> 00:32:19,320
Speaker 1: into the percentages here in terms of the accuracy, sourcing,

591
00:32:19,400 --> 00:32:24,280
Speaker 1: providing context, the ability to editorialize appropriately. What's not in

592
00:32:24,360 --> 00:32:26,520
Speaker 1: the stories that I would personally like to see, and

593
00:32:26,560 --> 00:32:30,400
Speaker 1: I'm curious your thoughts is I would like to see

594
00:32:30,400 --> 00:32:36,320
Speaker 1: the comparison to humans who deliver the news and their

595
00:32:36,440 --> 00:32:40,120
Speaker 1: accuracy before we decide whether or not we're going to

596
00:32:40,240 --> 00:32:44,040
Speaker 1: judge AI based off of its mistake obviously, because are

597
00:32:44,120 --> 00:32:48,240
Speaker 1: humans making the same number of mistakes the same percentage

598
00:32:48,280 --> 00:32:49,520
Speaker 1: amount of time as AI?

599
00:32:49,920 --> 00:32:51,440
Speaker 3: Is your thoughts on.

600
00:32:51,400 --> 00:32:55,120
Speaker 7: That, No, that's precisely where my mind went. Humans are

601
00:32:55,120 --> 00:32:58,640
Speaker 7: also bad at it. News agencies are also often bad

602
00:32:58,640 --> 00:33:01,800
Speaker 7: at delivering the news. As we've talked about there, you know,

603
00:33:01,840 --> 00:33:05,080
Speaker 7: with the rush to publish.

604
00:33:04,320 --> 00:33:07,240
Speaker 4: News, accuracy is not what it used to be.

605
00:33:08,520 --> 00:33:11,400
Speaker 7: And you know it probably is in the aggregate, but

606
00:33:11,520 --> 00:33:13,960
Speaker 7: in any one story, it's much more likely to be

607
00:33:14,120 --> 00:33:17,400
Speaker 7: somewhat inaccurate than was the case, you know, ten to

608
00:33:17,440 --> 00:33:20,680
Speaker 7: fifteen years ago. And that's just a product of speed

609
00:33:20,720 --> 00:33:26,240
Speaker 7: to publish AI. Like, here's kind of my theme in

610
00:33:26,560 --> 00:33:30,080
Speaker 7: these conversations. And again, I've really enjoyed the fact that

611
00:33:30,080 --> 00:33:32,520
Speaker 7: some listeners have reached out and connected to me directly,

612
00:33:32,560 --> 00:33:35,480
Speaker 7: and I've tried to provide helpful advice in terms of

613
00:33:35,520 --> 00:33:38,600
Speaker 7: careers and AI or other things of the sort, or

614
00:33:38,680 --> 00:33:43,080
Speaker 7: even tactical or strategic advice using AI, and basically for

615
00:33:43,200 --> 00:33:45,680
Speaker 7: most things you can do with AI, if you know

616
00:33:45,720 --> 00:33:48,680
Speaker 7: how to use AI, well, it's going to help you

617
00:33:49,720 --> 00:33:51,560
Speaker 7: if you know how to use AI well, it will

618
00:33:51,560 --> 00:33:54,360
Speaker 7: actually help you understand the news better if you just

619
00:33:54,400 --> 00:33:57,520
Speaker 7: ask AI about the news. If you're not sophisticated with prompts,

620
00:33:57,800 --> 00:34:01,520
Speaker 7: it's it's going to be worse than just sitting down

621
00:34:01,560 --> 00:34:05,120
Speaker 7: and reading the news. So for me like this, I'm

622
00:34:05,200 --> 00:34:08,000
Speaker 7: certain the study is accurate. I'm certain they don't do

623
00:34:08,080 --> 00:34:10,239
Speaker 7: a whole lot with prompt engineering to try to up

624
00:34:10,280 --> 00:34:12,680
Speaker 7: their odds that they're going to get good news analysis,

625
00:34:12,719 --> 00:34:14,680
Speaker 7: because you just don't do that in a study of

626
00:34:14,680 --> 00:34:18,800
Speaker 7: that sort. So my main thing is that for everyone

627
00:34:18,880 --> 00:34:24,279
Speaker 7: listening is if you don't use AI particularly well, it's

628
00:34:24,320 --> 00:34:28,120
Speaker 7: not going to give you particularly good results in almost

629
00:34:28,160 --> 00:34:31,160
Speaker 7: anything that you ask it to do. If you develop

630
00:34:31,239 --> 00:34:35,799
Speaker 7: the skill, it's going to really add value when you

631
00:34:35,920 --> 00:34:38,719
Speaker 7: know what you're asking for and when you're specific in

632
00:34:38,760 --> 00:34:40,080
Speaker 7: your demands of your AI.

633
00:34:40,680 --> 00:34:44,359
Speaker 1: We had some follow up questions for those that left

634
00:34:44,360 --> 00:34:47,040
Speaker 1: those talkbacks with follow ups. For David, if you want

635
00:34:47,040 --> 00:34:51,960
Speaker 1: to email me Justice at iHeartRadio dot com, I'm happy

636
00:34:51,960 --> 00:34:54,799
Speaker 1: to go on forward those on to DVD, and I

637
00:34:54,840 --> 00:34:56,520
Speaker 1: have some of the emails here. I may just do

638
00:34:56,560 --> 00:34:58,640
Speaker 1: this on my own because we had a follow up

639
00:34:58,719 --> 00:35:01,440
Speaker 1: question from the teacher. We don't have time to address

640
00:35:01,480 --> 00:35:05,160
Speaker 1: it right now, but David Gartenstein Ross again, CEO at

641
00:35:05,480 --> 00:35:09,040
Speaker 1: Expert Theory, our AI analyst, thank you so much as

642
00:35:09,040 --> 00:35:11,680
Speaker 1: always for the time. I love these I love these conversations, man,

643
00:35:11,719 --> 00:35:14,520
Speaker 1: I really do. And I appreciate you your willingness to

644
00:35:14,520 --> 00:35:16,239
Speaker 1: come on a weekly basis and join us and talk

645
00:35:16,280 --> 00:35:17,719
Speaker 1: about these things like wese.

646
00:35:17,800 --> 00:35:19,759
Speaker 4: John truly enjoyed that. I'm truly enjoy your audience and

647
00:35:19,800 --> 00:35:21,160
Speaker 4: I look forward to talking next week.

648
00:35:21,280 --> 00:35:24,000
Speaker 3: Thanks buddy. So let me go here briefly.

649
00:35:24,040 --> 00:35:26,719
Speaker 1: We got just a moment before we uh head into

650
00:35:26,760 --> 00:35:29,680
Speaker 1: hour two for a Thursday Morning on twin Cdity's News Talk.

651
00:35:29,640 --> 00:35:30,640
Speaker 3: And a Candida Parents.

652
00:35:30,719 --> 00:35:36,280
Speaker 1: On Tuesday, Rob Schmidt, longtime financial journalist New York Times columnist,

653
00:35:36,400 --> 00:35:39,840
Speaker 1: was on Newsmax and admit it that his initial reaction

654
00:35:40,000 --> 00:35:44,600
Speaker 1: to Trump's trade strategy on tariffs was wrong, crediting the

655
00:35:44,600 --> 00:35:50,040
Speaker 1: President for his willingness to adjust course as economic effects unfolded.

656
00:35:50,680 --> 00:35:53,480
Speaker 1: He said, so I'll tell you where I got it wrong.

657
00:35:53,520 --> 00:35:55,080
Speaker 1: I got it wrong, and I think Wall Street got

658
00:35:55,080 --> 00:35:56,560
Speaker 1: it wrong. I think a lot of people got it wrong.

659
00:35:56,560 --> 00:36:00,600
Speaker 1: When he first made his pronouncement on about Liberal Day,

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00:36:01,760 --> 00:36:04,960
Speaker 1: At the time, Sorculd recalled Trump's proposed to tariff's appeared

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00:36:05,000 --> 00:36:09,680
Speaker 1: poised to trigger immediate market chaos. The tariff rates that

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00:36:09,719 --> 00:36:13,080
Speaker 1: he was talking about were high, super high, and immediately

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00:36:13,120 --> 00:36:15,680
Speaker 1: the truth was the bond market freaked out to the

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Speaker 1: president's credit, though, he said, Trump responded strategically.

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00:36:19,760 --> 00:36:21,279
Speaker 3: He actually looked at what was.

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00:36:21,239 --> 00:36:23,200
Speaker 1: Happening with the bond market and he said, you know what,

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00:36:23,440 --> 00:36:24,920
Speaker 1: I'm gonna take some time. I'm going to try to

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00:36:24,920 --> 00:36:27,080
Speaker 1: figure this out. I'm going to negotiate country by country,

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00:36:27,080 --> 00:36:32,160
Speaker 1: and he's been doing that. Sorkin says the more mythological

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00:36:32,320 --> 00:36:36,040
Speaker 1: approach has eased to Wall Street's concerns and their worst fears,

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00:36:36,560 --> 00:36:39,920
Speaker 1: while still leaving unresolved challenges in the trading global system

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00:36:40,640 --> 00:36:42,560
Speaker 1: and getting to what we were talking about with Dab,

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00:36:43,040 --> 00:36:45,000
Speaker 1: he said, there's still a couple of countries he's trying

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00:36:45,040 --> 00:36:47,279
Speaker 1: to figure out. Those are still real issues, and I

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00:36:47,280 --> 00:36:49,080
Speaker 1: think they're going to have an impact on our economy

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00:36:49,080 --> 00:36:49,800
Speaker 1: to some degree.

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00:36:49,880 --> 00:36:50,200
Speaker 5: He says.

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00:36:50,200 --> 00:36:52,440
Speaker 1: One of the things that's fascinating about our economy right

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00:36:52,520 --> 00:36:55,760
Speaker 1: now is the whole AI boom and all the investment

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00:36:55,760 --> 00:36:58,000
Speaker 1: that's being made in America, so tons of money is

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00:36:58,040 --> 00:37:00,879
Speaker 1: coming into the President's credit, by the way, and I'm

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00:37:00,880 --> 00:37:03,680
Speaker 1: here to tell you what's happening. It's amazing what happens

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00:37:03,719 --> 00:37:06,640
Speaker 1: when people put their bias aside and they just look

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00:37:06,680 --> 00:37:10,320
Speaker 1: at things in a comprehensive, rational way with the proper context.

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00:37:11,600 --> 00:37:13,680
Speaker 1: One more on tariffs, by the way, President Donald Trump

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00:37:13,760 --> 00:37:16,759
Speaker 1: yesterday posted that he got a thank you call from

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00:37:16,880 --> 00:37:23,840
Speaker 1: GM and Ford over his tariff strategy. He said, Mary

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00:37:23,920 --> 00:37:26,640
Speaker 1: Bearra of General Motors and Bill Ford to the Ford

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00:37:26,680 --> 00:37:29,440
Speaker 1: Motor Company just called, he said, to thank me for

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00:37:29,440 --> 00:37:33,960
Speaker 1: putting tariffs on mid size and large sized trucks. Trump said,

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00:37:33,960 --> 00:37:36,520
Speaker 1: they appreciate the effect of the trade policies. Their stock

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00:37:36,560 --> 00:37:39,200
Speaker 1: has gone through the roof. They told me that without terrafs,

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00:37:39,239 --> 00:37:42,400
Speaker 1: it would be very a very hard, long slog for

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00:37:42,640 --> 00:37:47,560
Speaker 1: truck and manufacturers in the United States. Coming up, we'll

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Speaker 1: give you an update on the shutdown. So amazing audio

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Speaker 1: to share. And then I want to talk about a post,

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00:37:55,360 --> 00:37:59,560
Speaker 1: a seemingly rather benign post online from Lieutenant Governor and

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00:37:59,680 --> 00:38:02,920
Speaker 1: Senate candidate Peggy Flan Again. I'll share this with you

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Speaker 1: coming up because I want to get your thoughts as well.

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Speaker 1: From the iHeartRadio app here on Twin Cities news talk,