WEBVTT - Biden Set to Hit China EVs With Tariffs

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<v Speaker 1>Bloomberg Audio Studios, podcasts, radio news. You're listening to the

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<v Speaker 2>Let's stay on the Ebie discussion. We can do that

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<v Speaker 2>with Steve Mann at Global Autos and Industrials research Channels

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<v Speaker 2>for Bloomberg Intelligence joining us from Princeton, Jersey. We got

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<v Speaker 2>a little camera down there, so that is an incentive

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<v Speaker 2>for the folks to stay in Princeton. I guess Steve Man.

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<v Speaker 2>Don't get me started. Steve Man joins us though, Hey, Steve,

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<v Speaker 2>talk to us about I guess President Biden. We're talking

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<v Speaker 2>about tariffs on China evs and other strategic sectors. What's

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<v Speaker 2>going on there.

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<v Speaker 3>It's not surprise that they're going to do that. In

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<v Speaker 3>terms of the impact on the Chinese automaker, obviously nothing

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<v Speaker 3>because they don't sell any vehicles to the US at

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<v Speaker 3>the moment. They have no plans to, you know, byd

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<v Speaker 3>actually explicitly said they have no plans to actually sell

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<v Speaker 3>cars in the US at the moment. The only impact

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<v Speaker 3>is really on the battery side. But I think what

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<v Speaker 3>the government is trying to do is really to protect

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<v Speaker 3>a lot of investments that's already been made to onshore

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<v Speaker 3>EV battery production. You know, through the Inflation Reduction Act,

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<v Speaker 3>there's over one hundred billion dollars already spent on on

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<v Speaker 3>shoring EV battery and other supply chain components for evs

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<v Speaker 3>into the US. So I think, I think for automakers

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<v Speaker 3>there's no impact, but it's it's it seems like it's

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<v Speaker 3>more about posturing for for for the election later this year.

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<v Speaker 4>I have so many thoughts and feelings on this, but

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<v Speaker 4>I do think the timing is so interesting. So we

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<v Speaker 4>break the story on the day that Zeker is going

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<v Speaker 4>public in the US market. I don't know that that

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<v Speaker 4>feels you can buy stock of a China EV's company,

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<v Speaker 4>but you can't buy the China evy car.

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<v Speaker 5>Uh. Is that weird to you?

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<v Speaker 3>Uh, It's probably a good option to have, I think.

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<v Speaker 6>Uh.

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<v Speaker 3>China EV sales is still growing relatively strong. I mean,

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<v Speaker 3>it's slower this year than last year, but it's it's

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<v Speaker 3>you know, it's it's no different than what Neil has done,

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<v Speaker 3>what x Punk and Lee Auto has done. All these

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<v Speaker 3>companies have listed in the US. I think for the

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<v Speaker 3>most part, the purpose of this listing is really to

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<v Speaker 3>build the brand Cachet. You know, they can actually say

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<v Speaker 3>that we're a US listed company that goes really well

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<v Speaker 3>with a lot of the Chinese consumer. So if you

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<v Speaker 3>look at some of the sales Neil sales, Lee Auto

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<v Speaker 3>and Xpunk sales after they listed in the US, they

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<v Speaker 3>actually jumped uh in their in their home home turf.

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<v Speaker 2>I have to backtrack. I mean, you know, Alex is

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<v Speaker 2>way ahead of me on this. Can you tell me

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<v Speaker 2>what Zeker is for the It's the first time in

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<v Speaker 2>my life I've heard about the Zeker. It's a Chinese

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<v Speaker 2>manufactured electric car. Correct, that's right.

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<v Speaker 3>It's a premium evy maker. It actually comes competes with

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<v Speaker 3>the Tesla and the Neil Neils and they sell around

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<v Speaker 3>on average around the thirty seven forty dollars US dollars

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<v Speaker 3>per vehicle. It's part of Jili, the parent company of

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<v Speaker 3>the listed Jili Auto one seven five HK, So this

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<v Speaker 3>is part of the Jili. It's you know, a lot

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<v Speaker 3>of the actually a lot of the technology that Zeker

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<v Speaker 3>has is also part of Vovo they're head of R

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<v Speaker 3>and D is actually in Gothenburg, Sweden.

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<v Speaker 2>All right, Just for the IPO folks out there, this

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<v Speaker 2>is a company. They priced their IPO last night at

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<v Speaker 2>twenty one dollars a share. We just got a spread

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<v Speaker 2>just popped up on my terminal. Bid twenty three, ask

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<v Speaker 2>is twenty five has not opened yet, that's where the

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<v Speaker 2>bid ass spread is. Won't keep you up to date

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<v Speaker 2>on that throughout the day. So again, a cool new

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<v Speaker 2>IPO out there, which is good Goldman Sachs, Morgan Stanley,

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<v Speaker 2>Bank of America and China International Capital Corporate the underrator,

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<v Speaker 2>So some big time name supporting this company.

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<v Speaker 4>What's interesting is that Glee, the parent company, said it's

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<v Speaker 4>indicated it will subscribe to more than ninety percent of

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<v Speaker 4>the stock.

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<v Speaker 5>So yeah, so clearly that's still.

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<v Speaker 4>Wariness that exists in the EV market when that takes place.

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<v Speaker 5>Here is my deep question. It's not very deep, by

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<v Speaker 5>the way. If your goal is to.

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<v Speaker 4>Decarbonize the world as fast as possible, why would you

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<v Speaker 4>not flood the US market with the EV with cheap

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<v Speaker 4>China EV cars. And if that's not the goal, and

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<v Speaker 4>you want to green the economy, but also do it

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<v Speaker 4>profitably for US companies.

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<v Speaker 5>That's okay. Just let's say that. I mean, is that?

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<v Speaker 5>Can I say that out loud? Can I say the

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<v Speaker 5>quiet part out loud? Steve? Does this make sense to you?

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<v Speaker 7>Yeah?

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<v Speaker 3>I mean I think you have a very legitimate question.

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<v Speaker 3>But I think you know, with with the geop uh

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<v Speaker 3>wrangling between the two countries, uh, you know that that

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<v Speaker 3>that so you know this comes to play, right, you know,

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<v Speaker 3>do we flood the US? Do we protect the US industry?

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<v Speaker 3>I think I think especially during this year when there's

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<v Speaker 3>a lot of you know, to and fro on related

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<v Speaker 3>to the election, I think there's gonna be more and

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<v Speaker 3>more discussion around you know, do we let you know,

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<v Speaker 3>Chinese products come in for example, Evy batteries and solar panels.

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<v Speaker 3>So it's a very legitimate question. I think the government

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<v Speaker 3>will have to answer that for you.

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<v Speaker 2>All right, Steve, thanks so much for joining US. Steve

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<v Speaker 2>Man Global Autos and Industrials Research Channels for Bloomberg Intelligence.

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<v Speaker 2>You know, Alex I was up in Boston yesterday meeting

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<v Speaker 2>with some of the really smart folks at BCG Boston

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<v Speaker 2>Consulting Group, and one of the folks I spoke to

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<v Speaker 2>was Ahead of their EVY consulting practice, and he acknowledged

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<v Speaker 2>that the industry you know, probably put out some inferior products,

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<v Speaker 2>didn't price them. Well, there's a lot of you know,

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<v Speaker 2>the kind of a bumpy start to send the ev business.

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<v Speaker 2>But he says that being said, a lot of good

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<v Speaker 2>product is coming. Oh yeah, they're figuring out and they

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<v Speaker 2>will lower their cost structure. And he thinks ultimately, you know,

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<v Speaker 2>within a reasonable period of time, whether that's five years,

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<v Speaker 2>ten years, he thinks forty percent of the cars in

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<v Speaker 2>the US will be EV's. Oh interesting, So that's and

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<v Speaker 2>I've heard that number a little bit more frequently recently

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<v Speaker 2>because people are trying to make the sense we could

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<v Speaker 2>ever beat a one hundred percent EVS And I think

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<v Speaker 2>ultimately some people would like you to believe that, but

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<v Speaker 2>that may or may not happen. But I kind of

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<v Speaker 2>where they're coming from. They're kind of thinking about forty

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<v Speaker 2>percent within that you know, kind of ten ten year time.

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<v Speaker 1>I want to think about the implications for electricity production,

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<v Speaker 1>the grid and balancing the grid.

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<v Speaker 5>You were talking to the right people, Yes, they need

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<v Speaker 5>more of it.

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<v Speaker 4>And you want to know why utilities are some of

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<v Speaker 4>the best performing stocks in the SMP this year.

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<v Speaker 5>I mean they're defensive names. Are they defensive anymore?

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<v Speaker 4>They're growth stocks now they have actual growth in their

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<v Speaker 4>business because of stuff like EV's.

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<v Speaker 5>I have so many more thoughts.

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<v Speaker 1>You're listening to the Bloomberg Intelligence Podcast to catch us

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<v Speaker 1>live weekdays at ten am Eastern on applecar Play and

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<v Speaker 1>Androud Auto with a Bloomberg Business Act. You can also

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<v Speaker 1>listen live on Amazon Alexa from our flagship New York

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<v Speaker 1>station Just Say Alexa playing Bloomberg eleven thirty.

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<v Speaker 4>Joanne Shu joins us. She's University of Michigan Surveys of

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<v Speaker 4>Consumers Director.

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<v Speaker 5>This is her numbers. She does the data.

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<v Speaker 4>Joanne, can you just walk us through that decline of

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<v Speaker 4>US consumer sentiment right at a six month low?

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<v Speaker 5>Now? What let us here?

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<v Speaker 7>So it's not just the worst of both worlds. I

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<v Speaker 7>would say it's the worst on a number. It's worse

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<v Speaker 7>on a number of dimensions. Not only do consumers expect

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<v Speaker 7>inflation to rise a bit in the year ahead, but

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<v Speaker 7>they are also expecting unemployment to worsen, and they're also

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<v Speaker 7>expecting interest rates to rise, and so these are all

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<v Speaker 7>things that had kind of been in a holding pattern

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<v Speaker 7>for most of twenty twenty four, and suddenly in the

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<v Speaker 7>month of May, consumers are seeing a deterioration on all

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<v Speaker 7>of these dimensions.

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<v Speaker 2>So JO just like to call out the magnitude here again,

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<v Speaker 2>the consensus was seventy six point two for the University

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<v Speaker 2>Mission Sentiment Indicator seventy seven point two. Last period, it

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<v Speaker 2>came in its sixty seven point four and on an

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<v Speaker 2>order of magnitude, I don't remember seeing those types of

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<v Speaker 2>variances in the past. How unusual is this may reading

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<v Speaker 2>to you.

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<v Speaker 7>This is a statistically significant to decrease, but I wouldn't

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<v Speaker 7>call it a plummeting or a plunge. It definitely feels

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<v Speaker 7>like that because the last four months, you know, since January,

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<v Speaker 7>we've been only seeing little wiggles of one two points

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<v Speaker 7>per month. Essentially, we've had no change for four months,

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<v Speaker 7>and prior to that we had two months of really

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<v Speaker 7>strong surges. So this is the first real downward movement

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<v Speaker 7>we've seen in quite a bit of time. So I

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<v Speaker 7>think it is jarring to people who watch the data,

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<v Speaker 7>but it's not a plunge. It is a decline. It

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<v Speaker 7>is a signific can decline, but it's not a plunge.

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<v Speaker 4>You mentioned that those surveyed are worried about interest rates,

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<v Speaker 4>and they're worried the interest rates are going to rise.

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<v Speaker 4>Am I hearing that correctly, not that the Fed is

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<v Speaker 4>going to keep them steady or not cut, but they

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<v Speaker 4>expect higher interest rates.

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<v Speaker 7>Well, more people expect higher interest rates than last month.

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<v Speaker 7>Fewer people expect interest rates to fall, so I believe

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<v Speaker 7>about only about a quarter of consumers expect interest rates

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<v Speaker 7>to fall in the year ahead. So overall, consumer expectations

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<v Speaker 7>over interest rates are worse than they were last month.

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<v Speaker 2>Joanne, thank you so much for joining us. Really appreciate

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<v Speaker 2>you hopping on there. Joinshew, Surveys of Consumers Director for

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<v Speaker 2>the University of Michigan.

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<v Speaker 1>You're listening to the Bloomberg Intelligence podcast. Catch us live

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<v Speaker 4>You're having S and P just up by two tens

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<v Speaker 4>of one percent, so we're definitely off the highs, but still.

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<v Speaker 4>Kim Forrest, founder and CIO Booth of Capital Partners joined us. Now,

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<v Speaker 4>Kim help me understand what's happening survey data, ism services,

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<v Speaker 4>and if I be youmish pointing to things that are

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<v Speaker 4>not that good. Equity market still near record highs, corporate profits,

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<v Speaker 4>high earnings holding up really really well.

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<v Speaker 5>What gives.

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<v Speaker 6>Well?

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<v Speaker 8>I would say that the investors are looking past this

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<v Speaker 8>information and they are planning for a longer time period

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<v Speaker 8>than the consumer.

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<v Speaker 6>We'll put it that way.

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<v Speaker 5>But does that mean that? So how do you trade

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<v Speaker 5>on that?

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<v Speaker 4>Like, do you trust the underlying data and say, look,

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<v Speaker 4>I mean the survey data and say, okay, maybe we're

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<v Speaker 4>going to hit some speed bumps, I mean, protect myself

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<v Speaker 4>for that.

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<v Speaker 5>Or do you say it.

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<v Speaker 4>Doesn't matter because these themes, even momentum and effect, a rotation.

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<v Speaker 5>Or AI, all of that just going to outweigh it.

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<v Speaker 6>I think it's a little bit of both.

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<v Speaker 8>First, I think that the con zoomer once again is

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<v Speaker 8>being shocked by things they do pretty much at least

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<v Speaker 8>once a week, if not more often, and that's fill

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<v Speaker 8>up their car or truck with gasoline. And the second

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<v Speaker 8>thing they do is go to the grocery store or

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<v Speaker 8>a restaurant and prices are just crazy. So everybody has

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<v Speaker 8>recency bias, and I believe strongly that food prices in

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<v Speaker 8>particular and gas are just you know, demoralizing.

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<v Speaker 6>Have you gone to the grocery store lately. I have?

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<v Speaker 6>It is you know, it's just it's crazy.

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<v Speaker 8>You just you can't believe the basket of groceries costs

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<v Speaker 8>that much, right, That's that's the first thing. And I

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<v Speaker 8>do think that we have great expectation, probably deservedly so,

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<v Speaker 8>although not on what I think Wall Street's timeline is

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<v Speaker 8>for AI to increase productivity. It's been a decade or

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<v Speaker 8>more since we've had something, a technology that we think

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<v Speaker 8>could deliver productivity, productivity in.

0:12:02.280 --> 0:12:03.320
<v Speaker 6>A meaningful way.

0:12:04.559 --> 0:12:09.560
<v Speaker 8>And I think that's what has captured investors imagination around AI.

0:12:10.200 --> 0:12:13.079
<v Speaker 2>And Kim, I was up at the Boston visiting the

0:12:13.120 --> 0:12:17.880
<v Speaker 2>Boston Consulting Group yesterday, and the senior consultants that are

0:12:17.920 --> 0:12:21.559
<v Speaker 2>you ask them what are your CEO clients asking you about?

0:12:21.600 --> 0:12:26.800
<v Speaker 2>And to a person AI, I mean, it's it's just amazing.

0:12:26.840 --> 0:12:29.440
<v Speaker 2>So Kim, as an investor, and as you talk to

0:12:29.440 --> 0:12:31.960
<v Speaker 2>your clients and as you talk to other money managers,

0:12:32.480 --> 0:12:36.599
<v Speaker 2>how do you suggest people really get a fundamental exposure

0:12:36.720 --> 0:12:38.800
<v Speaker 2>to AI? Did they just run out and hold their

0:12:38.840 --> 0:12:41.760
<v Speaker 2>nose of buying in video or how do you recommend

0:12:41.800 --> 0:12:43.760
<v Speaker 2>the investors kind of get some exposure here.

0:12:44.920 --> 0:12:46.960
<v Speaker 8>Well, there are going to be people that want to

0:12:47.000 --> 0:12:50.360
<v Speaker 8>own in Vidio because again I love the concept of

0:12:50.440 --> 0:12:53.199
<v Speaker 8>recency bias, like it's gone up a lot, so it

0:12:53.240 --> 0:12:56.559
<v Speaker 8>should keep going up, you know, forever and ever. And

0:12:56.920 --> 0:12:59.640
<v Speaker 8>I think that's true to some extent. But we are

0:12:59.720 --> 0:13:02.760
<v Speaker 8>probably probably going to hit a pothole where they miss

0:13:02.800 --> 0:13:06.679
<v Speaker 8>expectations and that's just because of the and I'm getting

0:13:06.679 --> 0:13:09.000
<v Speaker 8>in the weeds here, but go with me. The data

0:13:09.040 --> 0:13:11.400
<v Speaker 8>center build out, I don't care if it's for regular

0:13:11.480 --> 0:13:14.640
<v Speaker 8>data centers or AI. You reach a point at which

0:13:14.640 --> 0:13:17.480
<v Speaker 8>you stop spending and you use the technology you have

0:13:17.600 --> 0:13:20.199
<v Speaker 8>in a building for a while, and then you add more.

0:13:20.559 --> 0:13:23.320
<v Speaker 8>So we're at the beginning of the build out of AI,

0:13:23.520 --> 0:13:27.720
<v Speaker 8>so everybody wants to own in Nvidia. I think AMD

0:13:27.840 --> 0:13:30.280
<v Speaker 8>and Intel are fine companies that are going to be

0:13:30.320 --> 0:13:32.800
<v Speaker 8>able to participate in this, and they are going to

0:13:33.040 --> 0:13:37.520
<v Speaker 8>push in Vidia to probably sell their product at a

0:13:37.559 --> 0:13:42.319
<v Speaker 8>somewhat lower margin. And I think that both Intel a

0:13:42.400 --> 0:13:46.079
<v Speaker 8>AMD can play in this space. So those are some ideas.

0:13:46.480 --> 0:13:50.120
<v Speaker 8>I also think people should look further and keep their

0:13:50.120 --> 0:13:53.120
<v Speaker 8>eye out for good software that is going to answer

0:13:53.200 --> 0:13:56.440
<v Speaker 8>some problems. And what I mean by that is that

0:13:56.480 --> 0:14:00.840
<v Speaker 8>it's not entertainment, only that you know, those CEOs would

0:14:00.920 --> 0:14:06.120
<v Speaker 8>want to buy it to enhance their productivity, and that

0:14:06.240 --> 0:14:08.960
<v Speaker 8>I think is the surest way to having a happy

0:14:09.040 --> 0:14:11.880
<v Speaker 8>portfolio in five to seven years.

0:14:12.480 --> 0:14:14.520
<v Speaker 4>So huh, a new thing in five to seven years.

0:14:14.520 --> 0:14:15.719
<v Speaker 4>And I'm going to ask you about the next two

0:14:15.720 --> 0:14:17.880
<v Speaker 4>and a half weeks. But we do get in video earnings.

0:14:17.880 --> 0:14:20.880
<v Speaker 4>On May twenty second, I was talking to immobile servemen

0:14:20.920 --> 0:14:23.440
<v Speaker 4>of RBC. She runs all their derivative strategy, and she

0:14:23.520 --> 0:14:25.480
<v Speaker 4>was saying, this is the first time that they haven't

0:14:25.520 --> 0:14:29.560
<v Speaker 4>seen a lot of sentiment upside going into nvideo. There's

0:14:29.560 --> 0:14:33.080
<v Speaker 4>not the exuberants that we're used to seeing. What do

0:14:33.120 --> 0:14:34.800
<v Speaker 4>you think the mark how do you think the market's

0:14:34.800 --> 0:14:37.520
<v Speaker 4>going to react and take to in video's earnings?

0:14:40.280 --> 0:14:40.960
<v Speaker 6>Well, they're going.

0:14:40.960 --> 0:14:43.480
<v Speaker 8>To do it Wall Street does, which is overreact in

0:14:43.520 --> 0:14:46.080
<v Speaker 8>the short term whichever way that is, and then get

0:14:46.080 --> 0:14:48.760
<v Speaker 8>it right in the long term. I mean, I hate

0:14:48.760 --> 0:14:51.880
<v Speaker 8>to be so perfunctory about it, but that is the

0:14:51.960 --> 0:14:54.680
<v Speaker 8>last twenty five years of my life. Is you know,

0:14:54.720 --> 0:14:57.000
<v Speaker 8>you get that if it is higher than expected. You're

0:14:57.000 --> 0:15:00.280
<v Speaker 8>going to get a huge sugar rush upwards. It's a

0:15:00.320 --> 0:15:03.480
<v Speaker 8>little bit you're going to get a sell off because

0:15:03.520 --> 0:15:06.440
<v Speaker 8>there are people that are expecting this thing to just

0:15:06.560 --> 0:15:09.640
<v Speaker 8>keep marching up straight, you know that forty five degree

0:15:09.680 --> 0:15:10.480
<v Speaker 8>angle to the right.

0:15:10.680 --> 0:15:10.880
<v Speaker 6>Right.

0:15:12.800 --> 0:15:15.440
<v Speaker 8>But I don't think you should give up hope. This

0:15:15.600 --> 0:15:17.720
<v Speaker 8>is just what markets do in the short term.

0:15:18.320 --> 0:15:22.960
<v Speaker 4>So you mentioned AMD and Intel, what else? What other

0:15:23.000 --> 0:15:26.200
<v Speaker 4>stocks you like to play this trend? You mentioned Synopsis

0:15:26.520 --> 0:15:28.480
<v Speaker 4>SNPs is the ticker.

0:15:28.560 --> 0:15:29.400
<v Speaker 5>What's that company?

0:15:30.520 --> 0:15:36.000
<v Speaker 8>Well, it's for people that design chips and we're going

0:15:36.080 --> 0:15:39.440
<v Speaker 8>to need not just the AI chips, but to get

0:15:39.680 --> 0:15:42.400
<v Speaker 8>real productivity, we're going to have to have armies of

0:15:43.120 --> 0:15:46.600
<v Speaker 8>Internet of things and those will be robots, those will

0:15:46.680 --> 0:15:49.360
<v Speaker 8>be sensors, and they're all going to be talking to

0:15:49.640 --> 0:15:52.760
<v Speaker 8>back to the main computer and have the main computer

0:15:52.880 --> 0:15:53.640
<v Speaker 8>tell them what to.

0:15:53.520 --> 0:15:56.800
<v Speaker 6>Do, or at least gather that information. So there are

0:15:56.800 --> 0:15:57.400
<v Speaker 6>going to have to.

0:15:57.320 --> 0:16:01.960
<v Speaker 8>Be more chips made and improvements to chips made. So

0:16:02.200 --> 0:16:07.120
<v Speaker 8>that is how Synopsids benefits is from companies having to

0:16:07.120 --> 0:16:12.360
<v Speaker 8>buy additional licenses or additional capabilities to its products. So

0:16:13.360 --> 0:16:16.760
<v Speaker 8>I'm a strong believer. There's another company called Cadence. They

0:16:16.840 --> 0:16:20.320
<v Speaker 8>are also in the same area, but we like Synopsis

0:16:20.560 --> 0:16:23.479
<v Speaker 8>because of its breadth of product offerings.

0:16:24.280 --> 0:16:27.720
<v Speaker 4>What about What do you make of the consumer space?

0:16:27.760 --> 0:16:30.720
<v Speaker 4>Do you like anything in the consumer space? The read

0:16:30.760 --> 0:16:34.440
<v Speaker 4>through from earnings has been really confusing, like Starbucks and

0:16:34.560 --> 0:16:37.360
<v Speaker 4>McDonald's warning on the consumer, but then Dutch Bros. Just

0:16:37.800 --> 0:16:39.680
<v Speaker 4>crushed it yesterday.

0:16:39.720 --> 0:16:40.440
<v Speaker 5>What are we learning?

0:16:41.960 --> 0:16:42.720
<v Speaker 6>We're learning we.

0:16:42.680 --> 0:16:46.400
<v Speaker 8>Have a very picky consumer, as we should, right, I mean,

0:16:46.440 --> 0:16:49.120
<v Speaker 8>I'm a picky consumer. I'm thinking you're a picky consumer.

0:16:49.600 --> 0:16:51.800
<v Speaker 8>I might get an eye roll on this one, but

0:16:51.960 --> 0:16:56.200
<v Speaker 8>I really love companies like Urban Outfitters that know that

0:16:56.280 --> 0:17:02.240
<v Speaker 8>they can satisfy this really picky base of a customer set.

0:17:02.280 --> 0:17:05.199
<v Speaker 8>They're not trying to sell product to everybody. They know

0:17:05.280 --> 0:17:08.359
<v Speaker 8>who their consumer is and they have been through the

0:17:08.440 --> 0:17:12.280
<v Speaker 8>decades able to delight and surprise this consumer, to get

0:17:12.320 --> 0:17:14.080
<v Speaker 8>her to open her wallet. And it is a her

0:17:14.440 --> 0:17:17.680
<v Speaker 8>in their case, to get to open the wallet and

0:17:17.720 --> 0:17:21.320
<v Speaker 8>to spend for the new looks. And I think you

0:17:21.480 --> 0:17:24.920
<v Speaker 8>need to know you don't want to you don't want

0:17:24.920 --> 0:17:29.400
<v Speaker 8>to buy mass marketing. You want to buy narrow, niche

0:17:29.480 --> 0:17:32.720
<v Speaker 8>kind of products that companies that do it well.

0:17:33.000 --> 0:17:35.879
<v Speaker 4>So interesting it's like a very different story than just

0:17:35.920 --> 0:17:36.840
<v Speaker 4>even a few years ago.

0:17:36.920 --> 0:17:40.119
<v Speaker 5>Right, So what other? Yeah, go ahead.

0:17:40.600 --> 0:17:41.199
<v Speaker 6>Yeah.

0:17:41.040 --> 0:17:44.080
<v Speaker 8>I think that they do well in any kind of

0:17:44.200 --> 0:17:49.680
<v Speaker 8>market because they have this small consume, small consumer base

0:17:49.880 --> 0:17:53.680
<v Speaker 8>that has money and they want to look fresh and new,

0:17:53.840 --> 0:17:57.200
<v Speaker 8>and that is what their secret is. They keep inventing

0:17:57.320 --> 0:18:00.400
<v Speaker 8>and finding those designers and finding those shapes we don't

0:18:00.400 --> 0:18:01.200
<v Speaker 8>have in our closet.

0:18:01.960 --> 0:18:04.080
<v Speaker 5>Do you do you shop at Urban Outfitters?

0:18:04.640 --> 0:18:05.199
<v Speaker 6>Not really.

0:18:05.320 --> 0:18:07.040
<v Speaker 8>I'm kind of a hipnie, but not that much of

0:18:07.080 --> 0:18:08.800
<v Speaker 8>a hippie, but I get what they.

0:18:08.760 --> 0:18:09.520
<v Speaker 6>Do, you know.

0:18:10.680 --> 0:18:11.119
<v Speaker 5>Yeah, I know.

0:18:11.160 --> 0:18:13.840
<v Speaker 9>I was like, you're extending this into a fashion discussion,

0:18:13.960 --> 0:18:14.840
<v Speaker 9>you know, Yeah, and.

0:18:15.040 --> 0:18:15.760
<v Speaker 6>Yeah, we do that.

0:18:16.040 --> 0:18:16.480
<v Speaker 5>We do that.

0:18:17.040 --> 0:18:19.320
<v Speaker 4>I've known Kim for a long time and inevitably the

0:18:19.480 --> 0:18:22.160
<v Speaker 4>end of our interview always goes to fashion. We've had

0:18:22.160 --> 0:18:24.480
<v Speaker 4>this like hope and dream that one day we'll actually

0:18:24.520 --> 0:18:26.120
<v Speaker 4>go shopping in New York together, but that has yet

0:18:26.160 --> 0:18:26.800
<v Speaker 4>to materialize.

0:18:26.840 --> 0:18:29.320
<v Speaker 6>Yes, Oh my gosh, I can't wait.

0:18:29.880 --> 0:18:32.000
<v Speaker 4>Come on, I'm across two from Bloomingdale's. We' hit the

0:18:32.000 --> 0:18:34.840
<v Speaker 4>sales sample stores. Okay, Kim, I'll let you go get

0:18:34.880 --> 0:18:37.240
<v Speaker 4>back to your real job. Kim Forrest, founder and CIO

0:18:37.359 --> 0:18:41.000
<v Speaker 4>a Boca Capital Partners standing by.

0:18:41.240 --> 0:18:45.160
<v Speaker 1>You're listening to the Bloomberg Intelligence Podcast. Catch us live

0:18:45.240 --> 0:18:48.280
<v Speaker 1>weekdays at ten am Eastern on Apple car Play and

0:18:48.280 --> 0:18:51.200
<v Speaker 1>Android Auto with a Bloomberg Business Act. You can also

0:18:51.280 --> 0:18:54.760
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0:18:55.119 --> 0:18:58.879
<v Speaker 1>Just say Alexa play Bloomberg eleven thirty.

0:18:59.600 --> 0:19:04.120
<v Speaker 4>Here's super interesting report that came out earlier in the week.

0:19:04.440 --> 0:19:07.880
<v Speaker 4>You know the story, right, the great resignation. People left

0:19:07.960 --> 0:19:12.760
<v Speaker 4>jobs during COVID, they got new jobs. Worker satisfaction isn't great,

0:19:12.840 --> 0:19:17.320
<v Speaker 4>particularly among women, and yet is the grass greener. It's

0:19:17.320 --> 0:19:19.679
<v Speaker 4>something that if you ever contemplated leaving your job for

0:19:19.720 --> 0:19:22.639
<v Speaker 4>something else, you constantly think about, are there problems that

0:19:22.680 --> 0:19:24.680
<v Speaker 4>are at every workplace? Or am I the problem and

0:19:24.720 --> 0:19:27.000
<v Speaker 4>I'm bringing it with me? Well, now we may have

0:19:27.200 --> 0:19:30.879
<v Speaker 4>some answers to this. Alan Swire is principal researcher of

0:19:30.960 --> 0:19:34.199
<v Speaker 4>Human capital over at the Conference Board, and they had

0:19:34.240 --> 0:19:36.920
<v Speaker 4>a survey out that said the workers who jumped CHIP

0:19:37.040 --> 0:19:41.200
<v Speaker 4>during COVID are now regretting it. Alan, thanks for coming.

0:19:41.240 --> 0:19:43.480
<v Speaker 4>This is great. I love this story. Can you walk

0:19:43.520 --> 0:19:44.560
<v Speaker 4>us through your findings?

0:19:45.920 --> 0:19:46.120
<v Speaker 2>Yeah?

0:19:46.200 --> 0:19:49.879
<v Speaker 10>Sure, So that one specifically was a big surprise to

0:19:50.000 --> 0:19:52.200
<v Speaker 10>us because just last year when we ran the survey

0:19:52.280 --> 0:19:56.520
<v Speaker 10>or twenty two, I should say, it was completely opposite

0:19:56.600 --> 0:20:01.600
<v Speaker 10>to what we found this time. Measure of job satisfaction

0:20:01.800 --> 0:20:05.760
<v Speaker 10>was up amongst those who switched jobs, but this year,

0:20:06.600 --> 0:20:10.600
<v Speaker 10>overall job satisfaction was down by over five percentage points,

0:20:11.000 --> 0:20:13.280
<v Speaker 10>and it was down across most of the twenty six

0:20:13.320 --> 0:20:16.480
<v Speaker 10>components of satisfaction that we measure, so a big swing.

0:20:16.680 --> 0:20:20.560
<v Speaker 10>We think we agree with your summary that you know,

0:20:20.640 --> 0:20:24.080
<v Speaker 10>sometimes the grass isn't necessarily greener on the other side,

0:20:24.560 --> 0:20:27.720
<v Speaker 10>and could be for a range of factors that this

0:20:27.880 --> 0:20:32.120
<v Speaker 10>is happening, But we definitely see that this year those

0:20:32.160 --> 0:20:36.200
<v Speaker 10>who have switched jobs during the pandemic are less satisfied

0:20:36.240 --> 0:20:37.159
<v Speaker 10>than those who stayed.

0:20:37.840 --> 0:20:40.440
<v Speaker 2>Do we know what was the primary reason people did

0:20:40.480 --> 0:20:43.600
<v Speaker 2>switch jobs during the pandemic was any different than other times,

0:20:43.600 --> 0:20:46.119
<v Speaker 2>which is, you know, maybe higher pay or just a

0:20:46.119 --> 0:20:46.959
<v Speaker 2>better opportunity.

0:20:48.119 --> 0:20:52.199
<v Speaker 10>Yeah, there were really pay increases during the pandemic that

0:20:52.240 --> 0:20:55.840
<v Speaker 10>we hadn't seen for well over a decade's that's fairly

0:20:55.880 --> 0:20:59.359
<v Speaker 10>well documented too, that some workers were receiving twenty thirty

0:20:59.359 --> 0:21:04.000
<v Speaker 10>percent better offers just for switching jobs. And we think

0:21:04.040 --> 0:21:07.159
<v Speaker 10>that you know, maybe some people took those offers without

0:21:07.400 --> 0:21:10.480
<v Speaker 10>really thinking, you know, about the other elements that lead

0:21:10.520 --> 0:21:13.080
<v Speaker 10>to happiness at work and contentment at work, like who

0:21:13.080 --> 0:21:15.879
<v Speaker 10>you work with and whether the organization is going to

0:21:15.920 --> 0:21:20.040
<v Speaker 10>invest in your career, career growth and many other things.

0:21:20.280 --> 0:21:23.280
<v Speaker 10>So they went for the money maybe, and now they're

0:21:23.320 --> 0:21:24.320
<v Speaker 10>starting to regret it.

0:21:24.720 --> 0:21:27.680
<v Speaker 4>I mean, it's so true, like the older I get,

0:21:27.720 --> 0:21:31.119
<v Speaker 4>the more that I try and really distill down what

0:21:31.320 --> 0:21:34.080
<v Speaker 4>makes me happy day to day, and it's so helpful.

0:21:34.080 --> 0:21:35.399
<v Speaker 4>I mean, it took me a long time to figure

0:21:35.400 --> 0:21:37.240
<v Speaker 4>that out, but it's so helpful and understanding it and

0:21:37.280 --> 0:21:40.120
<v Speaker 4>sometimes staying put is the answer. You had a great

0:21:40.119 --> 0:21:42.240
<v Speaker 4>point out that says once an employee hits a three

0:21:42.359 --> 0:21:45.360
<v Speaker 4>year mark, satisfaction increases substantially.

0:21:45.440 --> 0:21:46.120
<v Speaker 5>Talk us through that.

0:21:47.280 --> 0:21:49.920
<v Speaker 10>Yeah, that's also a difference from what we've seen, not

0:21:49.960 --> 0:21:52.119
<v Speaker 10>only in our own research, but there's a good amount

0:21:52.160 --> 0:21:54.920
<v Speaker 10>of research out there that shows that usually it's people

0:21:54.920 --> 0:21:57.840
<v Speaker 10>who have are shortened their tendon they start a new job,

0:21:57.880 --> 0:22:02.520
<v Speaker 10>they're happy, there's a honeymoon period, and their engagement and

0:22:02.560 --> 0:22:06.600
<v Speaker 10>their satisfaction levels are usually higher than people with more tenure.

0:22:06.640 --> 0:22:10.200
<v Speaker 10>But this year, in our satisfaction survey, we saw the opposite,

0:22:10.320 --> 0:22:13.280
<v Speaker 10>and that goes with the finding that job switchers are

0:22:13.359 --> 0:22:16.280
<v Speaker 10>less satisfying than job stayers, maybe for some of the

0:22:16.320 --> 0:22:19.320
<v Speaker 10>same reasons. Those who switch jobs. They're the people who

0:22:19.320 --> 0:22:22.080
<v Speaker 10>are in the positions with less than three years tenure,

0:22:22.640 --> 0:22:25.600
<v Speaker 10>and they're seeing that, yeah, things aren't quite as good

0:22:25.640 --> 0:22:29.840
<v Speaker 10>as they might have hoped, even if their wages are

0:22:29.920 --> 0:22:34.360
<v Speaker 10>higher than if they had stayed. So that's really good

0:22:34.400 --> 0:22:39.119
<v Speaker 10>reinforcement that those two elements that we examine in this

0:22:39.240 --> 0:22:41.520
<v Speaker 10>report really compliment each other.

0:22:42.000 --> 0:22:44.160
<v Speaker 2>And Alan your survey kind of goes to an issue

0:22:44.200 --> 0:22:46.240
<v Speaker 2>Alex and I were just talking about today. The least

0:22:46.280 --> 0:22:50.879
<v Speaker 2>satisfied group is fully on site workers. The hybrid model

0:22:51.119 --> 0:22:54.919
<v Speaker 2>wins the day. I guess the hybrid model that is

0:22:55.080 --> 0:22:57.439
<v Speaker 2>the new normal for the foreseeable future, isn't it.

0:22:58.560 --> 0:23:01.800
<v Speaker 10>Yeah, I think it is. I think employers or most

0:23:01.800 --> 0:23:04.800
<v Speaker 10>employers that not all see the benefits of the hybrid model.

0:23:05.280 --> 0:23:08.160
<v Speaker 10>You get the best of both worlds if it's done right.

0:23:09.600 --> 0:23:11.960
<v Speaker 10>Some of employees who can work from home may be

0:23:11.960 --> 0:23:15.240
<v Speaker 10>more productive at home for some types of work, but

0:23:15.280 --> 0:23:18.639
<v Speaker 10>when it comes to collaboration, building trust in relationships, and

0:23:18.640 --> 0:23:21.919
<v Speaker 10>innovation there at work. For several days a week. So

0:23:22.240 --> 0:23:23.920
<v Speaker 10>if they do it right, they're getting the best of

0:23:23.960 --> 0:23:27.480
<v Speaker 10>both worlds. But what's really interesting is that job as

0:23:27.520 --> 0:23:30.159
<v Speaker 10>that workers see this too. You know, even though we

0:23:30.200 --> 0:23:32.360
<v Speaker 10>see sometimes that workers say I'd love to work five

0:23:32.440 --> 0:23:36.320
<v Speaker 10>days remote, maybe they also see the benefits of being

0:23:36.320 --> 0:23:38.160
<v Speaker 10>with their colleagues two or three days a week.

0:23:38.440 --> 0:23:40.360
<v Speaker 4>There's literally no bone in my body that ever wants

0:23:40.400 --> 0:23:42.280
<v Speaker 4>to work at home. Again, Like the eight weeks during

0:23:42.320 --> 0:23:44.159
<v Speaker 4>COVID with a camera set up my living room was

0:23:44.200 --> 0:23:47.840
<v Speaker 4>more than enough. Are we noticing any distinction in terms

0:23:47.840 --> 0:23:50.520
<v Speaker 4>of the types of jobs we're talking about in terms

0:23:50.520 --> 0:23:54.480
<v Speaker 4>of say, you know, women versus men or any ethnicity.

0:23:56.760 --> 0:24:00.399
<v Speaker 10>We did see big differences with women. For the seventh

0:24:00.440 --> 0:24:04.040
<v Speaker 10>year in a row. Women are significantly less satisfied than men,

0:24:04.160 --> 0:24:08.000
<v Speaker 10>and that gap through between twenty twenty two and twenty

0:24:08.040 --> 0:24:13.080
<v Speaker 10>twenty three. And you know, those the main reasons or

0:24:13.200 --> 0:24:19.240
<v Speaker 10>drivers of that dissatisfaction are around hay, whether that's wages

0:24:19.359 --> 0:24:26.080
<v Speaker 10>or bonuses or even promotions, and also around the flexibility

0:24:26.119 --> 0:24:28.879
<v Speaker 10>of work. So by that I mean some of the

0:24:28.920 --> 0:24:32.959
<v Speaker 10>benefits like vacation and familyly. And it's not too surprising

0:24:33.000 --> 0:24:35.800
<v Speaker 10>because we see that even in twenty twenty four women

0:24:35.840 --> 0:24:38.040
<v Speaker 10>are making eighty two point five cents on the dollar

0:24:38.080 --> 0:24:40.880
<v Speaker 10>for men, and that most of the burden of care,

0:24:41.000 --> 0:24:44.520
<v Speaker 10>whether it's for kids or sea or parents, and most

0:24:44.520 --> 0:24:47.320
<v Speaker 10>of the household work falls on women still, so this

0:24:47.440 --> 0:24:50.040
<v Speaker 10>is really reflected in results. And for the past seven

0:24:50.080 --> 0:24:53.520
<v Speaker 10>years we did not collect data on race. We are

0:24:53.560 --> 0:24:56.800
<v Speaker 10>contemplating doing that race and ethnicity next year.

0:24:57.880 --> 0:25:01.399
<v Speaker 2>Hey, Alan Helmporten, are some of the I don't know,

0:25:01.440 --> 0:25:06.440
<v Speaker 2>the fringe perks, whether it's a foosball table, food, you know,

0:25:06.600 --> 0:25:09.600
<v Speaker 2>coffee machine, there's things that really came to the forefront

0:25:09.680 --> 0:25:12.720
<v Speaker 2>or in the pandemic as employers tried to lure employees

0:25:12.720 --> 0:25:14.200
<v Speaker 2>back to the office. It seems like a lot of

0:25:14.240 --> 0:25:16.199
<v Speaker 2>companies made a lot of investments in that kind of stuff.

0:25:17.160 --> 0:25:20.680
<v Speaker 10>Yeah, I think you're right. We don't measure that specifically.

0:25:20.720 --> 0:25:23.359
<v Speaker 10>We don't have a measure for sort of some of

0:25:23.359 --> 0:25:26.639
<v Speaker 10>those extra perks that you're you're talking about, but we know,

0:25:27.080 --> 0:25:30.320
<v Speaker 10>you know that organizations are doing their their their best

0:25:30.400 --> 0:25:33.720
<v Speaker 10>to lure people to the office, and they're trying not

0:25:33.840 --> 0:25:38.320
<v Speaker 10>to command people to the office with those extra perps.

0:25:38.760 --> 0:25:41.639
<v Speaker 10>And probably it's a good idea, whether it has as

0:25:41.720 --> 0:25:44.800
<v Speaker 10>much impact as a good health plan, probably not, or

0:25:44.880 --> 0:25:48.240
<v Speaker 10>even a good strong you know, talent culture where people

0:25:48.280 --> 0:25:52.440
<v Speaker 10>collaborate and respect each other. They're probably not, but some

0:25:52.480 --> 0:25:55.720
<v Speaker 10>of those things definitely helped to entice people back to

0:25:55.760 --> 0:25:56.440
<v Speaker 10>the office.

0:25:56.880 --> 0:25:59.520
<v Speaker 4>Just giving my coffee, you know, like I definitely don't care,

0:25:59.640 --> 0:26:02.440
<v Speaker 4>Slash want a foosball table. Alan, thanks a lot, really

0:26:02.480 --> 0:26:04.960
<v Speaker 4>interesting stuff. Great to sort of break all of that down,

0:26:05.160 --> 0:26:09.480
<v Speaker 4>Alan Swire, principal researcher human Capital at the conference board,

0:26:09.560 --> 0:26:12.560
<v Speaker 4>joining us from Savannah, Georgia. I mean, none of this

0:26:12.640 --> 0:26:15.359
<v Speaker 4>surprises me, But I also wonder if our tolerance is

0:26:15.480 --> 0:26:20.240
<v Speaker 4>changing what work life balance means in a very different way.

0:26:20.400 --> 0:26:23.159
<v Speaker 2>Yes, I think I think that has changed for a

0:26:23.240 --> 0:26:26.359
<v Speaker 2>lot of people, and they express in different ways, you know,

0:26:26.440 --> 0:26:30.960
<v Speaker 2>leaving the workforce the only working places that have hybrid

0:26:31.080 --> 0:26:33.480
<v Speaker 2>all that kind of stuff. So yeah, then there are

0:26:33.480 --> 0:26:35.560
<v Speaker 2>the special people like us who just come back every

0:26:35.600 --> 0:26:36.160
<v Speaker 2>day of the week.

0:26:36.320 --> 0:26:36.520
<v Speaker 5>Yeah.

0:26:36.520 --> 0:26:38.520
<v Speaker 4>And to be fair, like my husband does most of

0:26:38.520 --> 0:26:40.600
<v Speaker 4>the childcare and he always has, so I just want

0:26:40.640 --> 0:26:42.720
<v Speaker 4>to shout out that I'm not one of those women

0:26:42.720 --> 0:26:43.960
<v Speaker 4>that have to do more of the childcare.

0:26:44.000 --> 0:26:44.800
<v Speaker 5>We love the husband.

0:26:46.320 --> 0:26:50.200
<v Speaker 1>You're listening to the Bloomberg Intelligence Podcast catch us live

0:26:50.280 --> 0:26:53.200
<v Speaker 1>weekdays at ten am Eastern on applecar.

0:26:52.840 --> 0:26:54.400
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0:26:54.359 --> 0:26:57.199
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0:26:57.280 --> 0:27:00.560
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0:27:00.600 --> 0:27:02.920
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0:27:05.000 --> 0:27:09.159
<v Speaker 2>All right, let's turn a conversation to AI. Yesterday I

0:27:09.160 --> 0:27:13.240
<v Speaker 2>attended the Boston Consulting Group EDGE Expo in Boston. Had

0:27:13.280 --> 0:27:15.440
<v Speaker 2>a lot of great conversations with the folks at BCG.

0:27:15.520 --> 0:27:18.720
<v Speaker 2>A lot are really smart people thinking about some important

0:27:18.720 --> 0:27:21.320
<v Speaker 2>issues for their clients. I first book with Steve Mills,

0:27:21.440 --> 0:27:25.359
<v Speaker 2>chief AI ethics Officer. He focuses on the risks for

0:27:25.520 --> 0:27:29.040
<v Speaker 2>businesses and forging ahead with AI without considering how to

0:27:29.160 --> 0:27:32.320
<v Speaker 2>do that in a responsible ethical manner. I began by

0:27:32.359 --> 0:27:34.480
<v Speaker 2>asking him to talk about his role and how he

0:27:34.520 --> 0:27:37.200
<v Speaker 2>thinks it about AI from an ethics perspective. Let's take

0:27:37.200 --> 0:27:38.080
<v Speaker 2>a lism the.

0:27:38.040 --> 0:27:40.280
<v Speaker 9>Way we think about it, and particularly as you start

0:27:40.320 --> 0:27:44.600
<v Speaker 9>talking about generative AI. A responsible generative AI system needs

0:27:44.640 --> 0:27:47.200
<v Speaker 9>to be proficient, meaning it does the thing.

0:27:47.080 --> 0:27:48.560
<v Speaker 11>We want well, okay, you know.

0:27:48.640 --> 0:27:51.080
<v Speaker 9>So if it's a question answer system, it can actually

0:27:51.080 --> 0:27:53.840
<v Speaker 9>accurately answer questions, and that's key to driving value with

0:27:53.920 --> 0:27:56.320
<v Speaker 9>these systems. That's the first piece. It needs to be

0:27:56.359 --> 0:27:59.120
<v Speaker 9>safe and equitable. So these are the things like bias

0:27:59.200 --> 0:28:03.000
<v Speaker 9>and you know, harmful language and the like. It needs

0:28:03.040 --> 0:28:05.320
<v Speaker 9>to be secure, and then it needs to be compliant

0:28:05.359 --> 0:28:08.040
<v Speaker 9>with laws and regulations. And so as we're building AI

0:28:08.160 --> 0:28:10.520
<v Speaker 9>on behalf of our clients, as we're helping clients navigate

0:28:10.560 --> 0:28:12.800
<v Speaker 9>these issues, those are the things we're trying to guard against.

0:28:12.880 --> 0:28:15.919
<v Speaker 2>What are the some of the common risks that are

0:28:15.960 --> 0:28:19.680
<v Speaker 2>really out there for implementing AI across a product, across

0:28:19.680 --> 0:28:22.480
<v Speaker 2>the service that you've seen so far, because it feels

0:28:22.600 --> 0:28:24.760
<v Speaker 2>like we're still in the very very early innings of

0:28:25.119 --> 0:28:26.240
<v Speaker 2>this AI discussion.

0:28:26.920 --> 0:28:29.679
<v Speaker 9>Yeah, I mean the most common things, particularly you know,

0:28:29.800 --> 0:28:32.840
<v Speaker 9>we see a lot of chatbots for example, it is.

0:28:32.840 --> 0:28:33.520
<v Speaker 11>Things like.

0:28:35.040 --> 0:28:39.040
<v Speaker 9>Biased language or you know, sexist language in a subtle way.

0:28:39.080 --> 0:28:41.240
<v Speaker 9>Typically it's not over, but it can come across in

0:28:41.280 --> 0:28:46.160
<v Speaker 9>a subtle way. Very security flaws. You know, when you

0:28:47.400 --> 0:28:50.120
<v Speaker 9>issue a certain prompt to the system, it reveals sensitive data.

0:28:50.240 --> 0:28:54.040
<v Speaker 9>We've seen examples of you know, people being able through

0:28:54.040 --> 0:28:57.760
<v Speaker 9>smart prompting of the system manipulate it to offer products

0:28:57.800 --> 0:29:01.960
<v Speaker 9>for you know, next to nothing, basically, you know, inaccurate

0:29:02.080 --> 0:29:04.440
<v Speaker 9>you know answers or you know, people refer to to

0:29:04.480 --> 0:29:08.000
<v Speaker 9>offen its hallucinations. But you know, again like basically the

0:29:08.080 --> 0:29:10.400
<v Speaker 9>wrong answer coming out of the system that the company

0:29:10.440 --> 0:29:12.520
<v Speaker 9>is then held viable for after the fact. So these

0:29:12.520 --> 0:29:14.640
<v Speaker 9>are all things I'm sort of picking from the headlines

0:29:14.640 --> 0:29:15.200
<v Speaker 9>that we've seen.

0:29:15.520 --> 0:29:18.240
<v Speaker 2>It seems to me that before you guys walk into

0:29:18.240 --> 0:29:20.920
<v Speaker 2>my office, I think I have to have a commitment

0:29:20.960 --> 0:29:23.520
<v Speaker 2>to do this thing ethically before I spend dollar one.

0:29:23.880 --> 0:29:25.080
<v Speaker 2>Is that the kind of the message you want to

0:29:25.080 --> 0:29:26.760
<v Speaker 2>get across to these people, Like, listen, guys, you got

0:29:26.800 --> 0:29:29.560
<v Speaker 2>to buy into this, implement this because this is a

0:29:29.600 --> 0:29:32.240
<v Speaker 2>powerful tool, maybe more powerful than maybe we even know

0:29:32.280 --> 0:29:32.800
<v Speaker 2>at this point.

0:29:33.040 --> 0:29:34.160
<v Speaker 11>Yeah, that's exactly right.

0:29:34.200 --> 0:29:38.480
<v Speaker 9>I always say you cannot deploy and scale GENAI without

0:29:38.640 --> 0:29:42.240
<v Speaker 9>responsible AI. And it kind of comes in two as

0:29:42.280 --> 0:29:46.680
<v Speaker 9>one is, organizations are realizing there's these risks, there's there's regulation,

0:29:47.760 --> 0:29:50.680
<v Speaker 9>you need this in place to minimize those risks so

0:29:50.720 --> 0:29:53.560
<v Speaker 9>you can do it confidently. At the same time, we

0:29:53.640 --> 0:29:56.840
<v Speaker 9>see it as a source of value both directly and indirectly.

0:29:56.920 --> 0:30:00.000
<v Speaker 9>What I mean by that is companies with mature respond

0:30:00.200 --> 0:30:02.360
<v Speaker 9>AI programs, and this came from research we did with MIT,

0:30:03.360 --> 0:30:06.920
<v Speaker 9>if they have a mature responsibili program, they report higher

0:30:06.960 --> 0:30:11.000
<v Speaker 9>customer engagement, better customer retention, higher customer trust, better long

0:30:11.080 --> 0:30:13.600
<v Speaker 9>term profitabilities. That's sort of the direct piece, and faster

0:30:13.640 --> 0:30:14.280
<v Speaker 9>innovation too.

0:30:14.320 --> 0:30:15.400
<v Speaker 11>By the way.

0:30:15.480 --> 0:30:18.840
<v Speaker 9>The indirect piece is they also report they get more

0:30:18.960 --> 0:30:21.560
<v Speaker 9>value from their AI investment, which I think is really

0:30:21.640 --> 0:30:24.640
<v Speaker 9>interesting and the intuition there is many of the things

0:30:24.680 --> 0:30:27.720
<v Speaker 9>you do to build product responsibly just makes better product

0:30:27.800 --> 0:30:30.160
<v Speaker 9>in the first place, and so it's not really that

0:30:30.280 --> 0:30:32.160
<v Speaker 9>much of a surprise that if you do those things

0:30:32.480 --> 0:30:33.320
<v Speaker 9>you build better product.

0:30:33.360 --> 0:30:35.680
<v Speaker 11>Do you get more value from your AI? Are there

0:30:36.200 --> 0:30:36.920
<v Speaker 11>in your experience?

0:30:36.960 --> 0:30:39.479
<v Speaker 2>Have you had companies that do better than others that

0:30:39.520 --> 0:30:42.400
<v Speaker 2>make a bigger commitment or what's kind of like a

0:30:42.400 --> 0:30:45.360
<v Speaker 2>best use case from your perspective, what's your not your pitch,

0:30:45.400 --> 0:30:48.040
<v Speaker 2>but what's your best idea best practice when you go

0:30:48.040 --> 0:30:48.880
<v Speaker 2>into talk to clients.

0:30:49.960 --> 0:30:51.920
<v Speaker 9>Well, I mean we always talk about, you know, what

0:30:51.960 --> 0:30:54.800
<v Speaker 9>we see as a mature responsible AI program that you know,

0:30:54.880 --> 0:30:58.440
<v Speaker 9>at its heart, there's there's basically a strategy that's really

0:30:58.480 --> 0:31:02.440
<v Speaker 9>bridging between corporate value and the AI strategy the company itself,

0:31:02.520 --> 0:31:04.200
<v Speaker 9>and this is a means to bring those two things

0:31:04.240 --> 0:31:07.720
<v Speaker 9>together and then there's you know, the governance, the processes,

0:31:07.800 --> 0:31:11.520
<v Speaker 9>all the tools, but ultimately you want this underpinned by

0:31:11.520 --> 0:31:12.800
<v Speaker 9>a culture of responsibility.

0:31:13.400 --> 0:31:15.160
<v Speaker 11>Who checks that?

0:31:15.560 --> 0:31:19.600
<v Speaker 2>Like I'm wondering, like are auditors being trained now not

0:31:19.800 --> 0:31:23.920
<v Speaker 2>wanted to look at financial statements but also and controls

0:31:24.440 --> 0:31:27.440
<v Speaker 2>and so on and so forth, but also you know

0:31:27.600 --> 0:31:31.960
<v Speaker 2>data really Like Bloomberg, we take data security extraordinarily seriously

0:31:32.000 --> 0:31:34.280
<v Speaker 2>as a data driven company. I would think that almost

0:31:34.360 --> 0:31:38.360
<v Speaker 2>any company now that is employing some level of AI.

0:31:39.440 --> 0:31:40.280
<v Speaker 11>There's going to be risk.

0:31:40.960 --> 0:31:41.760
<v Speaker 2>Who checks on that?

0:31:42.480 --> 0:31:46.040
<v Speaker 9>Yeah, it's a great question, and we're honestly extremely nascent

0:31:46.160 --> 0:31:49.480
<v Speaker 9>in this space. There are no standards yet for AI.

0:31:50.320 --> 0:31:52.960
<v Speaker 9>Therefore there's really no audit. Now You've got folks like

0:31:52.960 --> 0:31:55.720
<v Speaker 9>the Responsible AI Institute, which is a nonprofit that has

0:31:55.760 --> 0:31:59.440
<v Speaker 9>created a certification standard which some companies are following. I

0:31:59.480 --> 0:32:02.840
<v Speaker 9>think this is to change extremely rapidly. The EU just

0:32:02.840 --> 0:32:06.160
<v Speaker 9>past the AI Act. Very soon weel see standards associate

0:32:06.200 --> 0:32:07.520
<v Speaker 9>with that, and so I think we will start to

0:32:07.520 --> 0:32:10.520
<v Speaker 9>see this really come to the fore very very quickly.

0:32:10.560 --> 0:32:13.360
<v Speaker 2>It's interesting mention Europe because we've seen just with technology

0:32:13.400 --> 0:32:16.840
<v Speaker 2>in general Europe and privacy, for example, data privacy Europe

0:32:16.880 --> 0:32:19.520
<v Speaker 2>ahead of the US, And I would argue, you know,

0:32:19.520 --> 0:32:20.959
<v Speaker 2>if you go all the way back to the eighties

0:32:21.080 --> 0:32:23.840
<v Speaker 2>and early nineties with Microsoft in terms of just dominance

0:32:23.840 --> 0:32:27.600
<v Speaker 2>of US technology, do you expect other parts of the

0:32:27.600 --> 0:32:30.360
<v Speaker 2>world to maybe be a little bit more out front

0:32:30.360 --> 0:32:32.920
<v Speaker 2>on some of the regulations of this new technology visa

0:32:32.960 --> 0:32:36.400
<v Speaker 2>be the US or because I know, BCG's a global business,

0:32:36.600 --> 0:32:37.280
<v Speaker 2>you see everybody.

0:32:37.440 --> 0:32:39.760
<v Speaker 9>Yeah, I mean, we're starting to see a patchwork or regulation.

0:32:40.120 --> 0:32:43.040
<v Speaker 9>The EU, I would say, is the first place we're

0:32:43.080 --> 0:32:47.520
<v Speaker 9>seeing comprehensive AI specific regulation. But in the US the

0:32:47.880 --> 0:32:51.760
<v Speaker 9>administration has directed the executive branch enforcement agencies to take

0:32:51.800 --> 0:32:54.280
<v Speaker 9>existing regulations and apply them to AI, and so we're

0:32:54.280 --> 0:32:58.920
<v Speaker 9>seeing that with Consumer Financial Protection, Fair Housing, FDA.

0:32:59.080 --> 0:32:59.280
<v Speaker 11>You know.

0:32:59.440 --> 0:33:02.880
<v Speaker 9>So while we in the US don't have the AI

0:33:02.880 --> 0:33:06.480
<v Speaker 9>specific regulation, there is certainly regulation applied to AI right now.

0:33:06.520 --> 0:33:09.680
<v Speaker 9>So this is the situation we're in, in this odd

0:33:09.720 --> 0:33:13.000
<v Speaker 9>patchwork emerging and US states honestly for passing regulation states.

0:33:13.080 --> 0:33:16.360
<v Speaker 2>Okay, yeah, interesting, what are are there certain industries today

0:33:16.400 --> 0:33:18.760
<v Speaker 2>here again early stages of AI, that seem to be

0:33:18.840 --> 0:33:23.080
<v Speaker 2>more open to making the investments in AI, making the

0:33:23.120 --> 0:33:27.280
<v Speaker 2>commitment to AI. I would think technology companies probably defined

0:33:27.360 --> 0:33:30.240
<v Speaker 2>as opposed to I don't know a manufacturer company in

0:33:30.280 --> 0:33:32.720
<v Speaker 2>the Midwest that says this doesn't really apply to me.

0:33:33.040 --> 0:33:34.320
<v Speaker 11>What are your discussions?

0:33:34.320 --> 0:33:34.760
<v Speaker 2>How do they go?

0:33:34.960 --> 0:33:35.160
<v Speaker 6>Yeah?

0:33:35.160 --> 0:33:38.920
<v Speaker 9>I mean I think big technology companies are absolutely at

0:33:38.920 --> 0:33:42.000
<v Speaker 9>the foror of this space. You know, from years ago

0:33:42.080 --> 0:33:45.840
<v Speaker 9>they were working on these topics. The other place we're

0:33:45.880 --> 0:33:49.200
<v Speaker 9>seeing a lot of maturity is the highly regulated industry

0:33:49.240 --> 0:33:54.000
<v Speaker 9>in healthcare, insurance finance. Not surprising in that they've historically

0:33:54.000 --> 0:33:58.080
<v Speaker 9>had model risk management functions. You know, they've got regulators

0:33:58.080 --> 0:34:00.280
<v Speaker 9>looking at them, and so they've been very proactive. I

0:34:00.280 --> 0:34:05.120
<v Speaker 9>think other industries, there are absolutely very mature companies in

0:34:05.160 --> 0:34:06.520
<v Speaker 9>every industry, but I think if.

0:34:06.360 --> 0:34:10.040
<v Speaker 11>We're talking broad brushstrokes, others are lagging behind. I was

0:34:10.080 --> 0:34:13.000
<v Speaker 11>just talking and mentioned to somebody else. AI seem to

0:34:13.040 --> 0:34:14.160
<v Speaker 11>have come out of nowhere.

0:34:14.680 --> 0:34:17.080
<v Speaker 2>And how do you think about the evolution of AI?

0:34:17.200 --> 0:34:20.040
<v Speaker 2>Is this just the big data discussion we had five

0:34:20.120 --> 0:34:22.759
<v Speaker 2>years ago or is it just the commitment we made

0:34:22.760 --> 0:34:25.480
<v Speaker 2>in the eighties and nineties that oh boy, everything's going digital,

0:34:25.480 --> 0:34:28.239
<v Speaker 2>we better start spending more on it. How do you

0:34:28.239 --> 0:34:30.359
<v Speaker 2>guys think about the evolution of AI? Because I could

0:34:30.400 --> 0:34:33.000
<v Speaker 2>see a CEO or a board member saying to you,

0:34:33.360 --> 0:34:36.640
<v Speaker 2>I don't understand it. Haven't we been spending on technology

0:34:36.640 --> 0:34:38.080
<v Speaker 2>for the past twenty five years.

0:34:38.640 --> 0:34:42.920
<v Speaker 9>I mean, I really believe that the generative AI explosion

0:34:42.920 --> 0:34:46.320
<v Speaker 9>we've seen is an inflection point. It is something fundamentally different,

0:34:46.920 --> 0:34:49.560
<v Speaker 9>and I don't think we fully know how it will

0:34:49.800 --> 0:34:53.200
<v Speaker 9>impact everything. But I mean, you're seeing the degree of

0:34:53.239 --> 0:34:56.560
<v Speaker 9>hype around it. Yes, there is some hype, but it's

0:34:56.600 --> 0:34:58.719
<v Speaker 9>well founded. I mean, I think we're talking on par

0:34:58.960 --> 0:35:01.920
<v Speaker 9>with the Internet in some ways of how it could

0:35:01.920 --> 0:35:07.319
<v Speaker 9>be transformative inside organizations, And we're still discovering the capabilities, right,

0:35:07.360 --> 0:35:09.759
<v Speaker 9>That's some the unique thing about it, the emerging capabilities

0:35:09.760 --> 0:35:12.839
<v Speaker 9>we see and so each you know, each generation a

0:35:12.840 --> 0:35:15.080
<v Speaker 9>model that's being released is just so much more performant

0:35:15.120 --> 0:35:16.080
<v Speaker 9>and can do so much more.

0:35:16.080 --> 0:35:17.799
<v Speaker 11>And so the question is where where does it end?

0:35:17.800 --> 0:35:20.000
<v Speaker 9>But even with what we have today, there are so

0:35:20.080 --> 0:35:21.919
<v Speaker 9>many applications we haven't explored yet.

0:35:22.080 --> 0:35:25.200
<v Speaker 11>So what's kind of your when you walk into see

0:35:25.239 --> 0:35:26.799
<v Speaker 11>a client? How do you how do you lead off?

0:35:26.840 --> 0:35:29.120
<v Speaker 2>Do you do you just say I'm going to try

0:35:29.120 --> 0:35:30.960
<v Speaker 2>to My goal today is to get you to commit

0:35:31.880 --> 0:35:36.000
<v Speaker 2>or consider committing to an ethical implementation of your tech investment.

0:35:36.040 --> 0:35:37.520
<v Speaker 2>Is that kind of your pitch or what how do

0:35:37.560 --> 0:35:38.720
<v Speaker 2>you walk into when you see a client?

0:35:38.800 --> 0:35:41.239
<v Speaker 9>Yeah, in essence, it's you know, it's the message I

0:35:41.680 --> 0:35:44.160
<v Speaker 9>sort of led with earlier that you cannot scale Jenai

0:35:44.440 --> 0:35:47.759
<v Speaker 9>without doing it in a responsible way. And it's twofold, like, yes,

0:35:47.800 --> 0:35:51.800
<v Speaker 9>there's absolutely a risk mitigation play, both for the company

0:35:51.840 --> 0:35:54.120
<v Speaker 9>but even more importantly for customers, right because you have

0:35:54.160 --> 0:35:56.520
<v Speaker 9>the potential to create an unintended harm to individuals, so

0:35:56.719 --> 0:35:58.960
<v Speaker 9>like full stop, we need to avoid that. But from

0:35:58.960 --> 0:36:02.000
<v Speaker 9>a corporate perspective, there there's certainly risk, but there's also

0:36:02.040 --> 0:36:05.319
<v Speaker 9>this value side. It's not just about mitigating risk, it's

0:36:05.360 --> 0:36:07.759
<v Speaker 9>about making sure you realize the value. And so that's

0:36:07.800 --> 0:36:11.800
<v Speaker 9>always the message because I think the risk is motivating

0:36:11.840 --> 0:36:14.399
<v Speaker 9>to a point. But then it's okay if you want

0:36:14.440 --> 0:36:16.560
<v Speaker 9>to make this investment in AI anyway, if you want

0:36:16.600 --> 0:36:18.520
<v Speaker 9>to see that value, you need to make this investment

0:36:18.520 --> 0:36:19.600
<v Speaker 9>to do it in a responsible way.

0:36:19.640 --> 0:36:23.520
<v Speaker 2>And that I guess is that message getting through?

0:36:23.640 --> 0:36:25.359
<v Speaker 11>Do you think I think it is.

0:36:25.440 --> 0:36:29.920
<v Speaker 9>I think companies are still like there's I would say

0:36:29.960 --> 0:36:33.640
<v Speaker 9>two things we're seeing. One it is the topic of

0:36:33.719 --> 0:36:37.160
<v Speaker 9>responsibile AI has not been elevated senior enough inside organizations.

0:36:37.200 --> 0:36:39.319
<v Speaker 11>Yet I argue it's a c suite issue. It has

0:36:39.360 --> 0:36:40.880
<v Speaker 11>to be. If AI is a c suite issue, this

0:36:40.960 --> 0:36:41.360
<v Speaker 11>needs to be.

0:36:41.800 --> 0:36:44.720
<v Speaker 9>Too often we're seeing it a few layers down inside

0:36:44.760 --> 0:36:48.120
<v Speaker 9>the organization, and it's people who are passionate and brilliant

0:36:48.160 --> 0:36:50.319
<v Speaker 9>and wanted to do good things, but they don't have

0:36:50.360 --> 0:36:52.360
<v Speaker 9>the clout inside the organization really drive the kind of

0:36:52.400 --> 0:36:52.879
<v Speaker 9>change you need.

0:36:52.920 --> 0:36:54.520
<v Speaker 11>I mean, I mentioned this is a cultural change. You

0:36:54.520 --> 0:36:55.759
<v Speaker 11>need senior people.

0:36:55.680 --> 0:36:57.680
<v Speaker 9>So that's one, and then two, they're just not putting

0:36:57.680 --> 0:37:01.319
<v Speaker 9>the right right amount of resourcing any right. I think

0:37:01.400 --> 0:37:04.080
<v Speaker 9>this is being seen as you know, smaller dollar we

0:37:04.080 --> 0:37:05.040
<v Speaker 9>can do a few little.

0:37:04.920 --> 0:37:07.640
<v Speaker 11>Things and do it responsibly. It's a much bigger investment.

0:37:07.719 --> 0:37:10.160
<v Speaker 9>And if you think about you brought up privacy earlier

0:37:10.200 --> 0:37:13.560
<v Speaker 9>like GDPR or cyber sure. I mean if you look

0:37:13.600 --> 0:37:15.440
<v Speaker 9>back at some of the data, we're talking tens of

0:37:15.440 --> 0:37:17.840
<v Speaker 9>millions of dollars to get compliant with GDPR. It's the

0:37:17.880 --> 0:37:19.520
<v Speaker 9>same thing here and companies need to be ready for

0:37:19.560 --> 0:37:21.080
<v Speaker 9>that degree of investment.

0:37:21.719 --> 0:37:22.080
<v Speaker 11>All right.

0:37:22.080 --> 0:37:25.239
<v Speaker 2>That was Steve Mills, chief AI ethics officer at the

0:37:25.239 --> 0:37:28.280
<v Speaker 2>Boston Consulting Group. I spoke with them up in Boston yesterday.

0:37:28.480 --> 0:37:31.400
<v Speaker 2>And what was interesting A that they have a chief

0:37:31.440 --> 0:37:34.040
<v Speaker 2>AI ethics officer. Wouldn't have thought at that, didn't even

0:37:34.040 --> 0:37:36.879
<v Speaker 2>think about it. But b they say at BCG, when

0:37:36.880 --> 0:37:38.359
<v Speaker 2>they got to talk to one of their clients about

0:37:38.400 --> 0:37:40.960
<v Speaker 2>AI and making the investment in AI and the commitment

0:37:41.000 --> 0:37:43.920
<v Speaker 2>in AI, they say, listen, we're not going to engage

0:37:43.920 --> 0:37:47.799
<v Speaker 2>with you unless you make the commitment that ethics and

0:37:47.840 --> 0:37:49.640
<v Speaker 2>the management ethics is going to be at a board

0:37:49.800 --> 0:37:52.840
<v Speaker 2>level commitment because if not, there are so many pitfalls

0:37:52.840 --> 0:37:55.000
<v Speaker 2>that we don't even know about. And before you start

0:37:55.080 --> 0:37:57.399
<v Speaker 2>rushing off to make your investments in technology, you better

0:37:57.440 --> 0:38:00.080
<v Speaker 2>recognize that you need to manage the risk here. So

0:38:00.600 --> 0:38:02.759
<v Speaker 2>from their perspective, at least at BCG, that's how they

0:38:02.760 --> 0:38:04.719
<v Speaker 2>try to engage with their clients.

0:38:05.080 --> 0:38:08.080
<v Speaker 5>What is the what's the reaction to that? Did you say, I.

0:38:08.040 --> 0:38:09.719
<v Speaker 2>Think they get it. I think they get it because

0:38:09.719 --> 0:38:11.600
<v Speaker 2>I think he says, they were saying, the clients are

0:38:11.640 --> 0:38:15.840
<v Speaker 2>basically like, you know, we'll spend the money to upgrade

0:38:15.880 --> 0:38:17.760
<v Speaker 2>our tech and do all that kind of stuff because

0:38:17.760 --> 0:38:20.279
<v Speaker 2>we can't be left behind. That's the biggest risk that

0:38:21.000 --> 0:38:24.240
<v Speaker 2>the BCG CEOs say, we can't risk getting left behind.

0:38:24.320 --> 0:38:26.239
<v Speaker 2>So their in tendency is just to spend, spend, spend,

0:38:26.320 --> 0:38:29.440
<v Speaker 2>spend without really thinking about how to do it smartly

0:38:29.560 --> 0:38:32.719
<v Speaker 2>and overtime and do it responsibly. They feel like if

0:38:32.760 --> 0:38:35.480
<v Speaker 2>they don't, their competitors will and they'll be left in

0:38:35.520 --> 0:38:38.640
<v Speaker 2>the dust. So very interesting discussion about AI and how

0:38:38.680 --> 0:38:41.759
<v Speaker 2>it's really involved with all their discussions.

0:38:41.200 --> 0:38:45.920
<v Speaker 4>And apparently they'll be a CEO officer chief, aix ETHX officer.

0:38:46.080 --> 0:38:47.919
<v Speaker 5>That will now be something to think about.

0:38:48.200 --> 0:38:52.680
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