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Speaker 1: Bloomberg Audio Studios, podcasts, radio news. You're listening to the

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Speaker 1: Bloomberg Intelligence podcast. Catch us live weekdays at ten am

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Speaker 1: Eastern on Apple Car, playing Android Auto with the Bloomberg

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Speaker 1: Business app. Listen on demand wherever you get your podcasts,

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Speaker 1: or watch us live on YouTube.

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Speaker 2: Let's stay on the Ebie discussion. We can do that

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Speaker 2: with Steve Mann at Global Autos and Industrials research Channels

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Speaker 2: for Bloomberg Intelligence joining us from Princeton, Jersey. We got

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Speaker 2: a little camera down there, so that is an incentive

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Speaker 2: for the folks to stay in Princeton. I guess Steve Man.

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Speaker 2: Don't get me started. Steve Man joins us though, Hey, Steve,

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Speaker 2: talk to us about I guess President Biden. We're talking

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Speaker 2: about tariffs on China evs and other strategic sectors. What's

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Speaker 2: going on there.

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Speaker 3: It's not surprise that they're going to do that. In

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Speaker 3: terms of the impact on the Chinese automaker, obviously nothing

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Speaker 3: because they don't sell any vehicles to the US at

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Speaker 3: the moment. They have no plans to, you know, byd

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Speaker 3: actually explicitly said they have no plans to actually sell

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Speaker 3: cars in the US at the moment. The only impact

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Speaker 3: is really on the battery side. But I think what

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Speaker 3: the government is trying to do is really to protect

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Speaker 3: a lot of investments that's already been made to onshore

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Speaker 3: EV battery production. You know, through the Inflation Reduction Act,

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Speaker 3: there's over one hundred billion dollars already spent on on

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Speaker 3: shoring EV battery and other supply chain components for evs

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Speaker 3: into the US. So I think, I think for automakers

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Speaker 3: there's no impact, but it's it's it seems like it's

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Speaker 3: more about posturing for for for the election later this year.

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Speaker 4: I have so many thoughts and feelings on this, but

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Speaker 4: I do think the timing is so interesting. So we

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Speaker 4: break the story on the day that Zeker is going

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Speaker 4: public in the US market. I don't know that that

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Speaker 4: feels you can buy stock of a China EV's company,

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Speaker 4: but you can't buy the China evy car.

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Speaker 5: Uh. Is that weird to you?

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Speaker 3: Uh, It's probably a good option to have, I think.

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Speaker 6: Uh.

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Speaker 3: China EV sales is still growing relatively strong. I mean,

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Speaker 3: it's slower this year than last year, but it's it's

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Speaker 3: you know, it's it's no different than what Neil has done,

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Speaker 3: what x Punk and Lee Auto has done. All these

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Speaker 3: companies have listed in the US. I think for the

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Speaker 3: most part, the purpose of this listing is really to

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Speaker 3: build the brand Cachet. You know, they can actually say

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Speaker 3: that we're a US listed company that goes really well

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Speaker 3: with a lot of the Chinese consumer. So if you

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Speaker 3: look at some of the sales Neil sales, Lee Auto

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Speaker 3: and Xpunk sales after they listed in the US, they

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Speaker 3: actually jumped uh in their in their home home turf.

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Speaker 2: I have to backtrack. I mean, you know, Alex is

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Speaker 2: way ahead of me on this. Can you tell me

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Speaker 2: what Zeker is for the It's the first time in

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Speaker 2: my life I've heard about the Zeker. It's a Chinese

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Speaker 2: manufactured electric car. Correct, that's right.

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Speaker 3: It's a premium evy maker. It actually comes competes with

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Speaker 3: the Tesla and the Neil Neils and they sell around

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Speaker 3: on average around the thirty seven forty dollars US dollars

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Speaker 3: per vehicle. It's part of Jili, the parent company of

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Speaker 3: the listed Jili Auto one seven five HK, So this

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Speaker 3: is part of the Jili. It's you know, a lot

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Speaker 3: of the actually a lot of the technology that Zeker

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Speaker 3: has is also part of Vovo they're head of R

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Speaker 3: and D is actually in Gothenburg, Sweden.

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Speaker 2: All right, Just for the IPO folks out there, this

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Speaker 2: is a company. They priced their IPO last night at

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Speaker 2: twenty one dollars a share. We just got a spread

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Speaker 2: just popped up on my terminal. Bid twenty three, ask

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Speaker 2: is twenty five has not opened yet, that's where the

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Speaker 2: bid ass spread is. Won't keep you up to date

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Speaker 2: on that throughout the day. So again, a cool new

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Speaker 2: IPO out there, which is good Goldman Sachs, Morgan Stanley,

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Speaker 2: Bank of America and China International Capital Corporate the underrator,

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Speaker 2: So some big time name supporting this company.

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Speaker 4: What's interesting is that Glee, the parent company, said it's

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Speaker 4: indicated it will subscribe to more than ninety percent of

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Speaker 4: the stock.

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Speaker 5: So yeah, so clearly that's still.

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Speaker 4: Wariness that exists in the EV market when that takes place.

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Speaker 5: Here is my deep question. It's not very deep, by

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Speaker 5: the way. If your goal is to.

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Speaker 4: Decarbonize the world as fast as possible, why would you

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Speaker 4: not flood the US market with the EV with cheap

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Speaker 4: China EV cars. And if that's not the goal, and

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Speaker 4: you want to green the economy, but also do it

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Speaker 4: profitably for US companies.

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Speaker 5: That's okay. Just let's say that. I mean, is that?

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Speaker 5: Can I say that out loud? Can I say the

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Speaker 5: quiet part out loud? Steve? Does this make sense to you?

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Speaker 7: Yeah?

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Speaker 3: I mean I think you have a very legitimate question.

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Speaker 3: But I think you know, with with the geop uh

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Speaker 3: wrangling between the two countries, uh, you know that that

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Speaker 3: that so you know this comes to play, right, you know,

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Speaker 3: do we flood the US? Do we protect the US industry?

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Speaker 3: I think I think especially during this year when there's

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Speaker 3: a lot of you know, to and fro on related

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Speaker 3: to the election, I think there's gonna be more and

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Speaker 3: more discussion around you know, do we let you know,

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Speaker 3: Chinese products come in for example, Evy batteries and solar panels.

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Speaker 3: So it's a very legitimate question. I think the government

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Speaker 3: will have to answer that for you.

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Speaker 2: All right, Steve, thanks so much for joining US. Steve

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Speaker 2: Man Global Autos and Industrials Research Channels for Bloomberg Intelligence.

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Speaker 2: You know, Alex I was up in Boston yesterday meeting

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Speaker 2: with some of the really smart folks at BCG Boston

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Speaker 2: Consulting Group, and one of the folks I spoke to

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Speaker 2: was Ahead of their EVY consulting practice, and he acknowledged

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Speaker 2: that the industry you know, probably put out some inferior products,

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Speaker 2: didn't price them. Well, there's a lot of you know,

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Speaker 2: the kind of a bumpy start to send the ev business.

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Speaker 2: But he says that being said, a lot of good

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Speaker 2: product is coming. Oh yeah, they're figuring out and they

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Speaker 2: will lower their cost structure. And he thinks ultimately, you know,

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Speaker 2: within a reasonable period of time, whether that's five years,

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Speaker 2: ten years, he thinks forty percent of the cars in

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Speaker 2: the US will be EV's. Oh interesting, So that's and

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Speaker 2: I've heard that number a little bit more frequently recently

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Speaker 2: because people are trying to make the sense we could

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Speaker 2: ever beat a one hundred percent EVS And I think

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Speaker 2: ultimately some people would like you to believe that, but

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Speaker 2: that may or may not happen. But I kind of

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Speaker 2: where they're coming from. They're kind of thinking about forty

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Speaker 2: percent within that you know, kind of ten ten year time.

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Speaker 1: I want to think about the implications for electricity production,

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Speaker 1: the grid and balancing the grid.

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Speaker 5: You were talking to the right people, Yes, they need

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Speaker 5: more of it.

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Speaker 4: And you want to know why utilities are some of

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Speaker 4: the best performing stocks in the SMP this year.

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Speaker 5: I mean they're defensive names. Are they defensive anymore?

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Speaker 4: They're growth stocks now they have actual growth in their

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Speaker 4: business because of stuff like EV's.

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Speaker 5: I have so many more thoughts.

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Speaker 1: You're listening to the Bloomberg Intelligence Podcast to catch us

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Speaker 1: live weekdays at ten am Eastern on applecar Play and

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Speaker 1: Androud Auto with a Bloomberg Business Act. You can also

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Speaker 1: listen live on Amazon Alexa from our flagship New York

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Speaker 1: station Just Say Alexa playing Bloomberg eleven thirty.

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Speaker 4: Joanne Shu joins us. She's University of Michigan Surveys of

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Speaker 4: Consumers Director.

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Speaker 5: This is her numbers. She does the data.

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Speaker 4: Joanne, can you just walk us through that decline of

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Speaker 4: US consumer sentiment right at a six month low?

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Speaker 5: Now? What let us here?

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Speaker 7: So it's not just the worst of both worlds. I

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Speaker 7: would say it's the worst on a number. It's worse

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Speaker 7: on a number of dimensions. Not only do consumers expect

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Speaker 7: inflation to rise a bit in the year ahead, but

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Speaker 7: they are also expecting unemployment to worsen, and they're also

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Speaker 7: expecting interest rates to rise, and so these are all

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Speaker 7: things that had kind of been in a holding pattern

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Speaker 7: for most of twenty twenty four, and suddenly in the

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Speaker 7: month of May, consumers are seeing a deterioration on all

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Speaker 7: of these dimensions.

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Speaker 2: So JO just like to call out the magnitude here again,

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Speaker 2: the consensus was seventy six point two for the University

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Speaker 2: Mission Sentiment Indicator seventy seven point two. Last period, it

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Speaker 2: came in its sixty seven point four and on an

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Speaker 2: order of magnitude, I don't remember seeing those types of

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Speaker 2: variances in the past. How unusual is this may reading

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Speaker 2: to you.

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Speaker 7: This is a statistically significant to decrease, but I wouldn't

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Speaker 7: call it a plummeting or a plunge. It definitely feels

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Speaker 7: like that because the last four months, you know, since January,

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Speaker 7: we've been only seeing little wiggles of one two points

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Speaker 7: per month. Essentially, we've had no change for four months,

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Speaker 7: and prior to that we had two months of really

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Speaker 7: strong surges. So this is the first real downward movement

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Speaker 7: we've seen in quite a bit of time. So I

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Speaker 7: think it is jarring to people who watch the data,

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Speaker 7: but it's not a plunge. It is a decline. It

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Speaker 7: is a signific can decline, but it's not a plunge.

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Speaker 4: You mentioned that those surveyed are worried about interest rates,

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Speaker 4: and they're worried the interest rates are going to rise.

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Speaker 4: Am I hearing that correctly, not that the Fed is

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Speaker 4: going to keep them steady or not cut, but they

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Speaker 4: expect higher interest rates.

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Speaker 7: Well, more people expect higher interest rates than last month.

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Speaker 7: Fewer people expect interest rates to fall, so I believe

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Speaker 7: about only about a quarter of consumers expect interest rates

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Speaker 7: to fall in the year ahead. So overall, consumer expectations

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Speaker 7: over interest rates are worse than they were last month.

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Speaker 2: Joanne, thank you so much for joining us. Really appreciate

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Speaker 2: you hopping on there. Joinshew, Surveys of Consumers Director for

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Speaker 2: the University of Michigan.

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Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live

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Speaker 1: weekdays at ten am Eastern on Apple, card Play and

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Speaker 1: Endroid Auto with the Bloomberg Business app. Listen on demand

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Speaker 1: wherever you get your podcasts, or watch us live on YouTube.

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Speaker 4: You're having S and P just up by two tens

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Speaker 4: of one percent, so we're definitely off the highs, but still.

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Speaker 4: Kim Forrest, founder and CIO Booth of Capital Partners joined us. Now,

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Speaker 4: Kim help me understand what's happening survey data, ism services,

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Speaker 4: and if I be youmish pointing to things that are

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Speaker 4: not that good. Equity market still near record highs, corporate profits,

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Speaker 4: high earnings holding up really really well.

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Speaker 5: What gives.

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Speaker 6: Well?

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Speaker 8: I would say that the investors are looking past this

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Speaker 8: information and they are planning for a longer time period

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Speaker 8: than the consumer.

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Speaker 6: We'll put it that way.

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Speaker 5: But does that mean that? So how do you trade

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Speaker 5: on that?

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Speaker 4: Like, do you trust the underlying data and say, look,

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Speaker 4: I mean the survey data and say, okay, maybe we're

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Speaker 4: going to hit some speed bumps, I mean, protect myself

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Speaker 4: for that.

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Speaker 5: Or do you say it.

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Speaker 4: Doesn't matter because these themes, even momentum and effect, a rotation.

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Speaker 5: Or AI, all of that just going to outweigh it.

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Speaker 6: I think it's a little bit of both.

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Speaker 8: First, I think that the con zoomer once again is

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Speaker 8: being shocked by things they do pretty much at least

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Speaker 8: once a week, if not more often, and that's fill

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Speaker 8: up their car or truck with gasoline. And the second

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Speaker 8: thing they do is go to the grocery store or

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Speaker 8: a restaurant and prices are just crazy. So everybody has

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Speaker 8: recency bias, and I believe strongly that food prices in

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Speaker 8: particular and gas are just you know, demoralizing.

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Speaker 6: Have you gone to the grocery store lately. I have?

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Speaker 6: It is you know, it's just it's crazy.

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Speaker 8: You just you can't believe the basket of groceries costs

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Speaker 8: that much, right, That's that's the first thing. And I

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Speaker 8: do think that we have great expectation, probably deservedly so,

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Speaker 8: although not on what I think Wall Street's timeline is

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Speaker 8: for AI to increase productivity. It's been a decade or

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Speaker 8: more since we've had something, a technology that we think

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Speaker 8: could deliver productivity, productivity in.

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Speaker 6: A meaningful way.

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Speaker 8: And I think that's what has captured investors imagination around AI.

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Speaker 2: And Kim, I was up at the Boston visiting the

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Speaker 2: Boston Consulting Group yesterday, and the senior consultants that are

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Speaker 2: you ask them what are your CEO clients asking you about?

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Speaker 2: And to a person AI, I mean, it's it's just amazing.

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Speaker 2: So Kim, as an investor, and as you talk to

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Speaker 2: your clients and as you talk to other money managers,

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Speaker 2: how do you suggest people really get a fundamental exposure

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Speaker 2: to AI? Did they just run out and hold their

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Speaker 2: nose of buying in video or how do you recommend

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Speaker 2: the investors kind of get some exposure here.

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Speaker 8: Well, there are going to be people that want to

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Speaker 8: own in Vidio because again I love the concept of

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Speaker 8: recency bias, like it's gone up a lot, so it

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Speaker 8: should keep going up, you know, forever and ever. And

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Speaker 8: I think that's true to some extent. But we are

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Speaker 8: probably probably going to hit a pothole where they miss

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Speaker 8: expectations and that's just because of the and I'm getting

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Speaker 8: in the weeds here, but go with me. The data

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Speaker 8: center build out, I don't care if it's for regular

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Speaker 8: data centers or AI. You reach a point at which

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Speaker 8: you stop spending and you use the technology you have

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Speaker 8: in a building for a while, and then you add more.

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Speaker 8: So we're at the beginning of the build out of AI,

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Speaker 8: so everybody wants to own in Nvidia. I think AMD

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Speaker 8: and Intel are fine companies that are going to be

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Speaker 8: able to participate in this, and they are going to

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Speaker 8: push in Vidia to probably sell their product at a

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Speaker 8: somewhat lower margin. And I think that both Intel a

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Speaker 8: AMD can play in this space. So those are some ideas.

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Speaker 8: I also think people should look further and keep their

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Speaker 8: eye out for good software that is going to answer

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Speaker 8: some problems. And what I mean by that is that

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Speaker 8: it's not entertainment, only that you know, those CEOs would

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Speaker 8: want to buy it to enhance their productivity, and that

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Speaker 8: I think is the surest way to having a happy

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Speaker 8: portfolio in five to seven years.

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Speaker 4: So huh, a new thing in five to seven years.

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Speaker 4: And I'm going to ask you about the next two

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Speaker 4: and a half weeks. But we do get in video earnings.

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Speaker 4: On May twenty second, I was talking to immobile servemen

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Speaker 4: of RBC. She runs all their derivative strategy, and she

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Speaker 4: was saying, this is the first time that they haven't

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Speaker 4: seen a lot of sentiment upside going into nvideo. There's

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Speaker 4: not the exuberants that we're used to seeing. What do

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Speaker 4: you think the mark how do you think the market's

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Speaker 4: going to react and take to in video's earnings?

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Speaker 6: Well, they're going.

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Speaker 8: To do it Wall Street does, which is overreact in

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Speaker 8: the short term whichever way that is, and then get

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Speaker 8: it right in the long term. I mean, I hate

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Speaker 8: to be so perfunctory about it, but that is the

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Speaker 8: last twenty five years of my life. Is you know,

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Speaker 8: you get that if it is higher than expected. You're

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Speaker 8: going to get a huge sugar rush upwards. It's a

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Speaker 8: little bit you're going to get a sell off because

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Speaker 8: there are people that are expecting this thing to just

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Speaker 8: keep marching up straight, you know that forty five degree

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Speaker 8: angle to the right.

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Speaker 6: Right.

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Speaker 8: But I don't think you should give up hope. This

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Speaker 8: is just what markets do in the short term.

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Speaker 4: So you mentioned AMD and Intel, what else? What other

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Speaker 4: stocks you like to play this trend? You mentioned Synopsis

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Speaker 4: SNPs is the ticker.

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Speaker 5: What's that company?

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Speaker 8: Well, it's for people that design chips and we're going

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Speaker 8: to need not just the AI chips, but to get

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Speaker 8: real productivity, we're going to have to have armies of

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Speaker 8: Internet of things and those will be robots, those will

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Speaker 8: be sensors, and they're all going to be talking to

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Speaker 8: back to the main computer and have the main computer

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Speaker 8: tell them what to.

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Speaker 6: Do, or at least gather that information. So there are

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Speaker 6: going to have to.

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Speaker 8: Be more chips made and improvements to chips made. So

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Speaker 8: that is how Synopsids benefits is from companies having to

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Speaker 8: buy additional licenses or additional capabilities to its products. So

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Speaker 8: I'm a strong believer. There's another company called Cadence. They

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Speaker 8: are also in the same area, but we like Synopsis

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Speaker 8: because of its breadth of product offerings.

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Speaker 4: What about What do you make of the consumer space?

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Speaker 4: Do you like anything in the consumer space? The read

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Speaker 4: through from earnings has been really confusing, like Starbucks and

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Speaker 4: McDonald's warning on the consumer, but then Dutch Bros. Just

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Speaker 4: crushed it yesterday.

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Speaker 5: What are we learning?

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Speaker 6: We're learning we.

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Speaker 8: Have a very picky consumer, as we should, right, I mean,

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Speaker 8: I'm a picky consumer. I'm thinking you're a picky consumer.

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Speaker 8: I might get an eye roll on this one, but

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Speaker 8: I really love companies like Urban Outfitters that know that

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Speaker 8: they can satisfy this really picky base of a customer set.

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Speaker 8: They're not trying to sell product to everybody. They know

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Speaker 8: who their consumer is and they have been through the

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Speaker 8: decades able to delight and surprise this consumer, to get

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Speaker 8: her to open her wallet. And it is a her

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Speaker 8: in their case, to get to open the wallet and

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Speaker 8: to spend for the new looks. And I think you

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Speaker 8: need to know you don't want to you don't want

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Speaker 8: to buy mass marketing. You want to buy narrow, niche

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Speaker 8: kind of products that companies that do it well.

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Speaker 4: So interesting it's like a very different story than just

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Speaker 4: even a few years ago.

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Speaker 5: Right, So what other? Yeah, go ahead.

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Speaker 6: Yeah.

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Speaker 8: I think that they do well in any kind of

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Speaker 8: market because they have this small consume, small consumer base

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Speaker 8: that has money and they want to look fresh and new,

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Speaker 8: and that is what their secret is. They keep inventing

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Speaker 8: and finding those designers and finding those shapes we don't

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Speaker 8: have in our closet.

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Speaker 5: Do you do you shop at Urban Outfitters?

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Speaker 6: Not really.

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Speaker 8: I'm kind of a hipnie, but not that much of

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Speaker 8: a hippie, but I get what they.

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Speaker 6: Do, you know.

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Speaker 5: Yeah, I know.

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Speaker 9: I was like, you're extending this into a fashion discussion,

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Speaker 9: you know, Yeah, and.

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Speaker 6: Yeah, we do that.

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Speaker 5: We do that.

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Speaker 4: I've known Kim for a long time and inevitably the

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Speaker 4: end of our interview always goes to fashion. We've had

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Speaker 4: this like hope and dream that one day we'll actually

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Speaker 4: go shopping in New York together, but that has yet

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Speaker 4: to materialize.

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Speaker 6: Yes, Oh my gosh, I can't wait.

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Speaker 4: Come on, I'm across two from Bloomingdale's. We' hit the

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Speaker 4: sales sample stores. Okay, Kim, I'll let you go get

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Speaker 4: back to your real job. Kim Forrest, founder and CIO

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Speaker 4: a Boca Capital Partners standing by.

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Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live

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Speaker 1: weekdays at ten am Eastern on Apple car Play and

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Speaker 1: Android Auto with a Bloomberg Business Act. You can also

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Speaker 1: Just say Alexa play Bloomberg eleven thirty.

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Speaker 4: Here's super interesting report that came out earlier in the week.

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Speaker 4: You know the story, right, the great resignation. People left

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Speaker 4: jobs during COVID, they got new jobs. Worker satisfaction isn't great,

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Speaker 4: particularly among women, and yet is the grass greener. It's

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Speaker 4: something that if you ever contemplated leaving your job for

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Speaker 4: something else, you constantly think about, are there problems that

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Speaker 4: are at every workplace? Or am I the problem and

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Speaker 4: I'm bringing it with me? Well, now we may have

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Speaker 4: some answers to this. Alan Swire is principal researcher of

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Speaker 4: Human capital over at the Conference Board, and they had

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Speaker 4: a survey out that said the workers who jumped CHIP

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Speaker 4: during COVID are now regretting it. Alan, thanks for coming.

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Speaker 4: This is great. I love this story. Can you walk

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Speaker 4: us through your findings?

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Speaker 2: Yeah?

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Speaker 10: Sure, So that one specifically was a big surprise to

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Speaker 10: us because just last year when we ran the survey

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Speaker 10: or twenty two, I should say, it was completely opposite

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Speaker 10: to what we found this time. Measure of job satisfaction

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Speaker 10: was up amongst those who switched jobs, but this year,

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Speaker 10: overall job satisfaction was down by over five percentage points,

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Speaker 10: and it was down across most of the twenty six

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Speaker 10: components of satisfaction that we measure, so a big swing.

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Speaker 10: We think we agree with your summary that you know,

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Speaker 10: sometimes the grass isn't necessarily greener on the other side,

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Speaker 10: and could be for a range of factors that this

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Speaker 10: is happening, But we definitely see that this year those

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Speaker 10: who have switched jobs during the pandemic are less satisfied

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Speaker 10: than those who stayed.

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Speaker 2: Do we know what was the primary reason people did

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Speaker 2: switch jobs during the pandemic was any different than other times,

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Speaker 2: which is, you know, maybe higher pay or just a

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Speaker 2: better opportunity.

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Speaker 10: Yeah, there were really pay increases during the pandemic that

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Speaker 10: we hadn't seen for well over a decade's that's fairly

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Speaker 10: well documented too, that some workers were receiving twenty thirty

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Speaker 10: percent better offers just for switching jobs. And we think

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Speaker 10: that you know, maybe some people took those offers without

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Speaker 10: really thinking, you know, about the other elements that lead

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Speaker 10: to happiness at work and contentment at work, like who

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Speaker 10: you work with and whether the organization is going to

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Speaker 10: invest in your career, career growth and many other things.

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Speaker 10: So they went for the money maybe, and now they're

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Speaker 10: starting to regret it.

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Speaker 4: I mean, it's so true, like the older I get,

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Speaker 4: the more that I try and really distill down what

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Speaker 4: makes me happy day to day, and it's so helpful.

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Speaker 4: I mean, it took me a long time to figure

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Speaker 4: that out, but it's so helpful and understanding it and

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Speaker 4: sometimes staying put is the answer. You had a great

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Speaker 4: point out that says once an employee hits a three

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Speaker 4: year mark, satisfaction increases substantially.

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Speaker 5: Talk us through that.

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Speaker 10: Yeah, that's also a difference from what we've seen, not

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Speaker 10: only in our own research, but there's a good amount

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Speaker 10: of research out there that shows that usually it's people

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Speaker 10: who have are shortened their tendon they start a new job,

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Speaker 10: they're happy, there's a honeymoon period, and their engagement and

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Speaker 10: their satisfaction levels are usually higher than people with more tenure.

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Speaker 10: But this year, in our satisfaction survey, we saw the opposite,

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Speaker 10: and that goes with the finding that job switchers are

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Speaker 10: less satisfying than job stayers, maybe for some of the

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Speaker 10: same reasons. Those who switch jobs. They're the people who

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Speaker 10: are in the positions with less than three years tenure,

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Speaker 10: and they're seeing that, yeah, things aren't quite as good

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Speaker 10: as they might have hoped, even if their wages are

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Speaker 10: higher than if they had stayed. So that's really good

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Speaker 10: reinforcement that those two elements that we examine in this

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Speaker 10: report really compliment each other.

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Speaker 2: And Alan your survey kind of goes to an issue

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Speaker 2: Alex and I were just talking about today. The least

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Speaker 2: satisfied group is fully on site workers. The hybrid model

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Speaker 2: wins the day. I guess the hybrid model that is

442
00:22:55,080 --> 00:22:57,439
Speaker 2: the new normal for the foreseeable future, isn't it.

443
00:22:58,560 --> 00:23:01,800
Speaker 10: Yeah, I think it is. I think employers or most

444
00:23:01,800 --> 00:23:04,800
Speaker 10: employers that not all see the benefits of the hybrid model.

445
00:23:05,280 --> 00:23:08,160
Speaker 10: You get the best of both worlds if it's done right.

446
00:23:09,600 --> 00:23:11,960
Speaker 10: Some of employees who can work from home may be

447
00:23:11,960 --> 00:23:15,240
Speaker 10: more productive at home for some types of work, but

448
00:23:15,280 --> 00:23:18,639
Speaker 10: when it comes to collaboration, building trust in relationships, and

449
00:23:18,640 --> 00:23:21,919
Speaker 10: innovation there at work. For several days a week. So

450
00:23:22,240 --> 00:23:23,920
Speaker 10: if they do it right, they're getting the best of

451
00:23:23,960 --> 00:23:27,480
Speaker 10: both worlds. But what's really interesting is that job as

452
00:23:27,520 --> 00:23:30,159
Speaker 10: that workers see this too. You know, even though we

453
00:23:30,200 --> 00:23:32,360
Speaker 10: see sometimes that workers say I'd love to work five

454
00:23:32,440 --> 00:23:36,320
Speaker 10: days remote, maybe they also see the benefits of being

455
00:23:36,320 --> 00:23:38,160
Speaker 10: with their colleagues two or three days a week.

456
00:23:38,440 --> 00:23:40,360
Speaker 4: There's literally no bone in my body that ever wants

457
00:23:40,400 --> 00:23:42,280
Speaker 4: to work at home. Again, Like the eight weeks during

458
00:23:42,320 --> 00:23:44,159
Speaker 4: COVID with a camera set up my living room was

459
00:23:44,200 --> 00:23:47,840
Speaker 4: more than enough. Are we noticing any distinction in terms

460
00:23:47,840 --> 00:23:50,520
Speaker 4: of the types of jobs we're talking about in terms

461
00:23:50,520 --> 00:23:54,480
Speaker 4: of say, you know, women versus men or any ethnicity.

462
00:23:56,760 --> 00:24:00,399
Speaker 10: We did see big differences with women. For the seventh

463
00:24:00,440 --> 00:24:04,040
Speaker 10: year in a row. Women are significantly less satisfied than men,

464
00:24:04,160 --> 00:24:08,000
Speaker 10: and that gap through between twenty twenty two and twenty

465
00:24:08,040 --> 00:24:13,080
Speaker 10: twenty three. And you know, those the main reasons or

466
00:24:13,200 --> 00:24:19,240
Speaker 10: drivers of that dissatisfaction are around hay, whether that's wages

467
00:24:19,359 --> 00:24:26,080
Speaker 10: or bonuses or even promotions, and also around the flexibility

468
00:24:26,119 --> 00:24:28,879
Speaker 10: of work. So by that I mean some of the

469
00:24:28,920 --> 00:24:32,959
Speaker 10: benefits like vacation and familyly. And it's not too surprising

470
00:24:33,000 --> 00:24:35,800
Speaker 10: because we see that even in twenty twenty four women

471
00:24:35,840 --> 00:24:38,040
Speaker 10: are making eighty two point five cents on the dollar

472
00:24:38,080 --> 00:24:40,880
Speaker 10: for men, and that most of the burden of care,

473
00:24:41,000 --> 00:24:44,520
Speaker 10: whether it's for kids or sea or parents, and most

474
00:24:44,520 --> 00:24:47,320
Speaker 10: of the household work falls on women still, so this

475
00:24:47,440 --> 00:24:50,040
Speaker 10: is really reflected in results. And for the past seven

476
00:24:50,080 --> 00:24:53,520
Speaker 10: years we did not collect data on race. We are

477
00:24:53,560 --> 00:24:56,800
Speaker 10: contemplating doing that race and ethnicity next year.

478
00:24:57,880 --> 00:25:01,399
Speaker 2: Hey, Alan Helmporten, are some of the I don't know,

479
00:25:01,440 --> 00:25:06,440
Speaker 2: the fringe perks, whether it's a foosball table, food, you know,

480
00:25:06,600 --> 00:25:09,600
Speaker 2: coffee machine, there's things that really came to the forefront

481
00:25:09,680 --> 00:25:12,720
Speaker 2: or in the pandemic as employers tried to lure employees

482
00:25:12,720 --> 00:25:14,200
Speaker 2: back to the office. It seems like a lot of

483
00:25:14,240 --> 00:25:16,199
Speaker 2: companies made a lot of investments in that kind of stuff.

484
00:25:17,160 --> 00:25:20,680
Speaker 10: Yeah, I think you're right. We don't measure that specifically.

485
00:25:20,720 --> 00:25:23,359
Speaker 10: We don't have a measure for sort of some of

486
00:25:23,359 --> 00:25:26,639
Speaker 10: those extra perks that you're you're talking about, but we know,

487
00:25:27,080 --> 00:25:30,320
Speaker 10: you know that organizations are doing their their their best

488
00:25:30,400 --> 00:25:33,720
Speaker 10: to lure people to the office, and they're trying not

489
00:25:33,840 --> 00:25:38,320
Speaker 10: to command people to the office with those extra perps.

490
00:25:38,760 --> 00:25:41,639
Speaker 10: And probably it's a good idea, whether it has as

491
00:25:41,720 --> 00:25:44,800
Speaker 10: much impact as a good health plan, probably not, or

492
00:25:44,880 --> 00:25:48,240
Speaker 10: even a good strong you know, talent culture where people

493
00:25:48,280 --> 00:25:52,440
Speaker 10: collaborate and respect each other. They're probably not, but some

494
00:25:52,480 --> 00:25:55,720
Speaker 10: of those things definitely helped to entice people back to

495
00:25:55,760 --> 00:25:56,440
Speaker 10: the office.

496
00:25:56,880 --> 00:25:59,520
Speaker 4: Just giving my coffee, you know, like I definitely don't care,

497
00:25:59,640 --> 00:26:02,440
Speaker 4: Slash want a foosball table. Alan, thanks a lot, really

498
00:26:02,480 --> 00:26:04,960
Speaker 4: interesting stuff. Great to sort of break all of that down,

499
00:26:05,160 --> 00:26:09,480
Speaker 4: Alan Swire, principal researcher human Capital at the conference board,

500
00:26:09,560 --> 00:26:12,560
Speaker 4: joining us from Savannah, Georgia. I mean, none of this

501
00:26:12,640 --> 00:26:15,359
Speaker 4: surprises me, But I also wonder if our tolerance is

502
00:26:15,480 --> 00:26:20,240
Speaker 4: changing what work life balance means in a very different way.

503
00:26:20,400 --> 00:26:23,159
Speaker 2: Yes, I think I think that has changed for a

504
00:26:23,240 --> 00:26:26,359
Speaker 2: lot of people, and they express in different ways, you know,

505
00:26:26,440 --> 00:26:30,960
Speaker 2: leaving the workforce the only working places that have hybrid

506
00:26:31,080 --> 00:26:33,480
Speaker 2: all that kind of stuff. So yeah, then there are

507
00:26:33,480 --> 00:26:35,560
Speaker 2: the special people like us who just come back every

508
00:26:35,600 --> 00:26:36,160
Speaker 2: day of the week.

509
00:26:36,320 --> 00:26:36,520
Speaker 5: Yeah.

510
00:26:36,520 --> 00:26:38,520
Speaker 4: And to be fair, like my husband does most of

511
00:26:38,520 --> 00:26:40,600
Speaker 4: the childcare and he always has, so I just want

512
00:26:40,640 --> 00:26:42,720
Speaker 4: to shout out that I'm not one of those women

513
00:26:42,720 --> 00:26:43,960
Speaker 4: that have to do more of the childcare.

514
00:26:44,000 --> 00:26:44,800
Speaker 5: We love the husband.

515
00:26:46,320 --> 00:26:50,200
Speaker 1: You're listening to the Bloomberg Intelligence Podcast catch us live

516
00:26:50,280 --> 00:26:53,200
Speaker 1: weekdays at ten am Eastern on applecar.

517
00:26:52,840 --> 00:26:54,400
Speaker 10: Play and Android Auto with.

518
00:26:54,359 --> 00:26:57,199
Speaker 1: The Bloomberg Business app. You can also listen live on

519
00:26:57,280 --> 00:27:00,560
Speaker 1: Amazon Alexa from our flagship New York station just Say

520
00:27:00,600 --> 00:27:02,920
Speaker 1: Alexa playing Bloomberg eleven thirty.

521
00:27:05,000 --> 00:27:09,159
Speaker 2: All right, let's turn a conversation to AI. Yesterday I

522
00:27:09,160 --> 00:27:13,240
Speaker 2: attended the Boston Consulting Group EDGE Expo in Boston. Had

523
00:27:13,280 --> 00:27:15,440
Speaker 2: a lot of great conversations with the folks at BCG.

524
00:27:15,520 --> 00:27:18,720
Speaker 2: A lot are really smart people thinking about some important

525
00:27:18,720 --> 00:27:21,320
Speaker 2: issues for their clients. I first book with Steve Mills,

526
00:27:21,440 --> 00:27:25,359
Speaker 2: chief AI ethics Officer. He focuses on the risks for

527
00:27:25,520 --> 00:27:29,040
Speaker 2: businesses and forging ahead with AI without considering how to

528
00:27:29,160 --> 00:27:32,320
Speaker 2: do that in a responsible ethical manner. I began by

529
00:27:32,359 --> 00:27:34,480
Speaker 2: asking him to talk about his role and how he

530
00:27:34,520 --> 00:27:37,200
Speaker 2: thinks it about AI from an ethics perspective. Let's take

531
00:27:37,200 --> 00:27:38,080
Speaker 2: a lism the.

532
00:27:38,040 --> 00:27:40,280
Speaker 9: Way we think about it, and particularly as you start

533
00:27:40,320 --> 00:27:44,600
Speaker 9: talking about generative AI. A responsible generative AI system needs

534
00:27:44,640 --> 00:27:47,200
Speaker 9: to be proficient, meaning it does the thing.

535
00:27:47,080 --> 00:27:48,560
Speaker 11: We want well, okay, you know.

536
00:27:48,640 --> 00:27:51,080
Speaker 9: So if it's a question answer system, it can actually

537
00:27:51,080 --> 00:27:53,840
Speaker 9: accurately answer questions, and that's key to driving value with

538
00:27:53,920 --> 00:27:56,320
Speaker 9: these systems. That's the first piece. It needs to be

539
00:27:56,359 --> 00:27:59,120
Speaker 9: safe and equitable. So these are the things like bias

540
00:27:59,200 --> 00:28:03,000
Speaker 9: and you know, harmful language and the like. It needs

541
00:28:03,040 --> 00:28:05,320
Speaker 9: to be secure, and then it needs to be compliant

542
00:28:05,359 --> 00:28:08,040
Speaker 9: with laws and regulations. And so as we're building AI

543
00:28:08,160 --> 00:28:10,520
Speaker 9: on behalf of our clients, as we're helping clients navigate

544
00:28:10,560 --> 00:28:12,800
Speaker 9: these issues, those are the things we're trying to guard against.

545
00:28:12,880 --> 00:28:15,919
Speaker 2: What are the some of the common risks that are

546
00:28:15,960 --> 00:28:19,680
Speaker 2: really out there for implementing AI across a product, across

547
00:28:19,680 --> 00:28:22,480
Speaker 2: the service that you've seen so far, because it feels

548
00:28:22,600 --> 00:28:24,760
Speaker 2: like we're still in the very very early innings of

549
00:28:25,119 --> 00:28:26,240
Speaker 2: this AI discussion.

550
00:28:26,920 --> 00:28:29,679
Speaker 9: Yeah, I mean the most common things, particularly you know,

551
00:28:29,800 --> 00:28:32,840
Speaker 9: we see a lot of chatbots for example, it is.

552
00:28:32,840 --> 00:28:33,520
Speaker 11: Things like.

553
00:28:35,040 --> 00:28:39,040
Speaker 9: Biased language or you know, sexist language in a subtle way.

554
00:28:39,080 --> 00:28:41,240
Speaker 9: Typically it's not over, but it can come across in

555
00:28:41,280 --> 00:28:46,160
Speaker 9: a subtle way. Very security flaws. You know, when you

556
00:28:47,400 --> 00:28:50,120
Speaker 9: issue a certain prompt to the system, it reveals sensitive data.

557
00:28:50,240 --> 00:28:54,040
Speaker 9: We've seen examples of you know, people being able through

558
00:28:54,040 --> 00:28:57,760
Speaker 9: smart prompting of the system manipulate it to offer products

559
00:28:57,800 --> 00:29:01,960
Speaker 9: for you know, next to nothing, basically, you know, inaccurate

560
00:29:02,080 --> 00:29:04,440
Speaker 9: you know answers or you know, people refer to to

561
00:29:04,480 --> 00:29:08,000
Speaker 9: offen its hallucinations. But you know, again like basically the

562
00:29:08,080 --> 00:29:10,400
Speaker 9: wrong answer coming out of the system that the company

563
00:29:10,440 --> 00:29:12,520
Speaker 9: is then held viable for after the fact. So these

564
00:29:12,520 --> 00:29:14,640
Speaker 9: are all things I'm sort of picking from the headlines

565
00:29:14,640 --> 00:29:15,200
Speaker 9: that we've seen.

566
00:29:15,520 --> 00:29:18,240
Speaker 2: It seems to me that before you guys walk into

567
00:29:18,240 --> 00:29:20,920
Speaker 2: my office, I think I have to have a commitment

568
00:29:20,960 --> 00:29:23,520
Speaker 2: to do this thing ethically before I spend dollar one.

569
00:29:23,880 --> 00:29:25,080
Speaker 2: Is that the kind of the message you want to

570
00:29:25,080 --> 00:29:26,760
Speaker 2: get across to these people, Like, listen, guys, you got

571
00:29:26,800 --> 00:29:29,560
Speaker 2: to buy into this, implement this because this is a

572
00:29:29,600 --> 00:29:32,240
Speaker 2: powerful tool, maybe more powerful than maybe we even know

573
00:29:32,280 --> 00:29:32,800
Speaker 2: at this point.

574
00:29:33,040 --> 00:29:34,160
Speaker 11: Yeah, that's exactly right.

575
00:29:34,200 --> 00:29:38,480
Speaker 9: I always say you cannot deploy and scale GENAI without

576
00:29:38,640 --> 00:29:42,240
Speaker 9: responsible AI. And it kind of comes in two as

577
00:29:42,280 --> 00:29:46,680
Speaker 9: one is, organizations are realizing there's these risks, there's there's regulation,

578
00:29:47,760 --> 00:29:50,680
Speaker 9: you need this in place to minimize those risks so

579
00:29:50,720 --> 00:29:53,560
Speaker 9: you can do it confidently. At the same time, we

580
00:29:53,640 --> 00:29:56,840
Speaker 9: see it as a source of value both directly and indirectly.

581
00:29:56,920 --> 00:30:00,000
Speaker 9: What I mean by that is companies with mature respond

582
00:30:00,200 --> 00:30:02,360
Speaker 9: AI programs, and this came from research we did with MIT,

583
00:30:03,360 --> 00:30:06,920
Speaker 9: if they have a mature responsibili program, they report higher

584
00:30:06,960 --> 00:30:11,000
Speaker 9: customer engagement, better customer retention, higher customer trust, better long

585
00:30:11,080 --> 00:30:13,600
Speaker 9: term profitabilities. That's sort of the direct piece, and faster

586
00:30:13,640 --> 00:30:14,280
Speaker 9: innovation too.

587
00:30:14,320 --> 00:30:15,400
Speaker 11: By the way.

588
00:30:15,480 --> 00:30:18,840
Speaker 9: The indirect piece is they also report they get more

589
00:30:18,960 --> 00:30:21,560
Speaker 9: value from their AI investment, which I think is really

590
00:30:21,640 --> 00:30:24,640
Speaker 9: interesting and the intuition there is many of the things

591
00:30:24,680 --> 00:30:27,720
Speaker 9: you do to build product responsibly just makes better product

592
00:30:27,800 --> 00:30:30,160
Speaker 9: in the first place, and so it's not really that

593
00:30:30,280 --> 00:30:32,160
Speaker 9: much of a surprise that if you do those things

594
00:30:32,480 --> 00:30:33,320
Speaker 9: you build better product.

595
00:30:33,360 --> 00:30:35,680
Speaker 11: Do you get more value from your AI? Are there

596
00:30:36,200 --> 00:30:36,920
Speaker 11: in your experience?

597
00:30:36,960 --> 00:30:39,479
Speaker 2: Have you had companies that do better than others that

598
00:30:39,520 --> 00:30:42,400
Speaker 2: make a bigger commitment or what's kind of like a

599
00:30:42,400 --> 00:30:45,360
Speaker 2: best use case from your perspective, what's your not your pitch,

600
00:30:45,400 --> 00:30:48,040
Speaker 2: but what's your best idea best practice when you go

601
00:30:48,040 --> 00:30:48,880
Speaker 2: into talk to clients.

602
00:30:49,960 --> 00:30:51,920
Speaker 9: Well, I mean we always talk about, you know, what

603
00:30:51,960 --> 00:30:54,800
Speaker 9: we see as a mature responsible AI program that you know,

604
00:30:54,880 --> 00:30:58,440
Speaker 9: at its heart, there's there's basically a strategy that's really

605
00:30:58,480 --> 00:31:02,440
Speaker 9: bridging between corporate value and the AI strategy the company itself,

606
00:31:02,520 --> 00:31:04,200
Speaker 9: and this is a means to bring those two things

607
00:31:04,240 --> 00:31:07,720
Speaker 9: together and then there's you know, the governance, the processes,

608
00:31:07,800 --> 00:31:11,520
Speaker 9: all the tools, but ultimately you want this underpinned by

609
00:31:11,520 --> 00:31:12,800
Speaker 9: a culture of responsibility.

610
00:31:13,400 --> 00:31:15,160
Speaker 11: Who checks that?

611
00:31:15,560 --> 00:31:19,600
Speaker 2: Like I'm wondering, like are auditors being trained now not

612
00:31:19,800 --> 00:31:23,920
Speaker 2: wanted to look at financial statements but also and controls

613
00:31:24,440 --> 00:31:27,440
Speaker 2: and so on and so forth, but also you know

614
00:31:27,600 --> 00:31:31,960
Speaker 2: data really Like Bloomberg, we take data security extraordinarily seriously

615
00:31:32,000 --> 00:31:34,280
Speaker 2: as a data driven company. I would think that almost

616
00:31:34,360 --> 00:31:38,360
Speaker 2: any company now that is employing some level of AI.

617
00:31:39,440 --> 00:31:40,280
Speaker 11: There's going to be risk.

618
00:31:40,960 --> 00:31:41,760
Speaker 2: Who checks on that?

619
00:31:42,480 --> 00:31:46,040
Speaker 9: Yeah, it's a great question, and we're honestly extremely nascent

620
00:31:46,160 --> 00:31:49,480
Speaker 9: in this space. There are no standards yet for AI.

621
00:31:50,320 --> 00:31:52,960
Speaker 9: Therefore there's really no audit. Now You've got folks like

622
00:31:52,960 --> 00:31:55,720
Speaker 9: the Responsible AI Institute, which is a nonprofit that has

623
00:31:55,760 --> 00:31:59,440
Speaker 9: created a certification standard which some companies are following. I

624
00:31:59,480 --> 00:32:02,840
Speaker 9: think this is to change extremely rapidly. The EU just

625
00:32:02,840 --> 00:32:06,160
Speaker 9: past the AI Act. Very soon weel see standards associate

626
00:32:06,200 --> 00:32:07,520
Speaker 9: with that, and so I think we will start to

627
00:32:07,520 --> 00:32:10,520
Speaker 9: see this really come to the fore very very quickly.

628
00:32:10,560 --> 00:32:13,360
Speaker 2: It's interesting mention Europe because we've seen just with technology

629
00:32:13,400 --> 00:32:16,840
Speaker 2: in general Europe and privacy, for example, data privacy Europe

630
00:32:16,880 --> 00:32:19,520
Speaker 2: ahead of the US, And I would argue, you know,

631
00:32:19,520 --> 00:32:20,959
Speaker 2: if you go all the way back to the eighties

632
00:32:21,080 --> 00:32:23,840
Speaker 2: and early nineties with Microsoft in terms of just dominance

633
00:32:23,840 --> 00:32:27,600
Speaker 2: of US technology, do you expect other parts of the

634
00:32:27,600 --> 00:32:30,360
Speaker 2: world to maybe be a little bit more out front

635
00:32:30,360 --> 00:32:32,920
Speaker 2: on some of the regulations of this new technology visa

636
00:32:32,960 --> 00:32:36,400
Speaker 2: be the US or because I know, BCG's a global business,

637
00:32:36,600 --> 00:32:37,280
Speaker 2: you see everybody.

638
00:32:37,440 --> 00:32:39,760
Speaker 9: Yeah, I mean, we're starting to see a patchwork or regulation.

639
00:32:40,120 --> 00:32:43,040
Speaker 9: The EU, I would say, is the first place we're

640
00:32:43,080 --> 00:32:47,520
Speaker 9: seeing comprehensive AI specific regulation. But in the US the

641
00:32:47,880 --> 00:32:51,760
Speaker 9: administration has directed the executive branch enforcement agencies to take

642
00:32:51,800 --> 00:32:54,280
Speaker 9: existing regulations and apply them to AI, and so we're

643
00:32:54,280 --> 00:32:58,920
Speaker 9: seeing that with Consumer Financial Protection, Fair Housing, FDA.

644
00:32:59,080 --> 00:32:59,280
Speaker 11: You know.

645
00:32:59,440 --> 00:33:02,880
Speaker 9: So while we in the US don't have the AI

646
00:33:02,880 --> 00:33:06,480
Speaker 9: specific regulation, there is certainly regulation applied to AI right now.

647
00:33:06,520 --> 00:33:09,680
Speaker 9: So this is the situation we're in, in this odd

648
00:33:09,720 --> 00:33:13,000
Speaker 9: patchwork emerging and US states honestly for passing regulation states.

649
00:33:13,080 --> 00:33:16,360
Speaker 2: Okay, yeah, interesting, what are are there certain industries today

650
00:33:16,400 --> 00:33:18,760
Speaker 2: here again early stages of AI, that seem to be

651
00:33:18,840 --> 00:33:23,080
Speaker 2: more open to making the investments in AI, making the

652
00:33:23,120 --> 00:33:27,280
Speaker 2: commitment to AI. I would think technology companies probably defined

653
00:33:27,360 --> 00:33:30,240
Speaker 2: as opposed to I don't know a manufacturer company in

654
00:33:30,280 --> 00:33:32,720
Speaker 2: the Midwest that says this doesn't really apply to me.

655
00:33:33,040 --> 00:33:34,320
Speaker 11: What are your discussions?

656
00:33:34,320 --> 00:33:34,760
Speaker 2: How do they go?

657
00:33:34,960 --> 00:33:35,160
Speaker 6: Yeah?

658
00:33:35,160 --> 00:33:38,920
Speaker 9: I mean I think big technology companies are absolutely at

659
00:33:38,920 --> 00:33:42,000
Speaker 9: the foror of this space. You know, from years ago

660
00:33:42,080 --> 00:33:45,840
Speaker 9: they were working on these topics. The other place we're

661
00:33:45,880 --> 00:33:49,200
Speaker 9: seeing a lot of maturity is the highly regulated industry

662
00:33:49,240 --> 00:33:54,000
Speaker 9: in healthcare, insurance finance. Not surprising in that they've historically

663
00:33:54,000 --> 00:33:58,080
Speaker 9: had model risk management functions. You know, they've got regulators

664
00:33:58,080 --> 00:34:00,280
Speaker 9: looking at them, and so they've been very proactive. I

665
00:34:00,280 --> 00:34:05,120
Speaker 9: think other industries, there are absolutely very mature companies in

666
00:34:05,160 --> 00:34:06,520
Speaker 9: every industry, but I think if.

667
00:34:06,360 --> 00:34:10,040
Speaker 11: We're talking broad brushstrokes, others are lagging behind. I was

668
00:34:10,080 --> 00:34:13,000
Speaker 11: just talking and mentioned to somebody else. AI seem to

669
00:34:13,040 --> 00:34:14,160
Speaker 11: have come out of nowhere.

670
00:34:14,680 --> 00:34:17,080
Speaker 2: And how do you think about the evolution of AI?

671
00:34:17,200 --> 00:34:20,040
Speaker 2: Is this just the big data discussion we had five

672
00:34:20,120 --> 00:34:22,759
Speaker 2: years ago or is it just the commitment we made

673
00:34:22,760 --> 00:34:25,480
Speaker 2: in the eighties and nineties that oh boy, everything's going digital,

674
00:34:25,480 --> 00:34:28,239
Speaker 2: we better start spending more on it. How do you

675
00:34:28,239 --> 00:34:30,359
Speaker 2: guys think about the evolution of AI? Because I could

676
00:34:30,400 --> 00:34:33,000
Speaker 2: see a CEO or a board member saying to you,

677
00:34:33,360 --> 00:34:36,640
Speaker 2: I don't understand it. Haven't we been spending on technology

678
00:34:36,640 --> 00:34:38,080
Speaker 2: for the past twenty five years.

679
00:34:38,640 --> 00:34:42,920
Speaker 9: I mean, I really believe that the generative AI explosion

680
00:34:42,920 --> 00:34:46,320
Speaker 9: we've seen is an inflection point. It is something fundamentally different,

681
00:34:46,920 --> 00:34:49,560
Speaker 9: and I don't think we fully know how it will

682
00:34:49,800 --> 00:34:53,200
Speaker 9: impact everything. But I mean, you're seeing the degree of

683
00:34:53,239 --> 00:34:56,560
Speaker 9: hype around it. Yes, there is some hype, but it's

684
00:34:56,600 --> 00:34:58,719
Speaker 9: well founded. I mean, I think we're talking on par

685
00:34:58,960 --> 00:35:01,920
Speaker 9: with the Internet in some ways of how it could

686
00:35:01,920 --> 00:35:07,319
Speaker 9: be transformative inside organizations, And we're still discovering the capabilities, right,

687
00:35:07,360 --> 00:35:09,759
Speaker 9: That's some the unique thing about it, the emerging capabilities

688
00:35:09,760 --> 00:35:12,839
Speaker 9: we see and so each you know, each generation a

689
00:35:12,840 --> 00:35:15,080
Speaker 9: model that's being released is just so much more performant

690
00:35:15,120 --> 00:35:16,080
Speaker 9: and can do so much more.

691
00:35:16,080 --> 00:35:17,799
Speaker 11: And so the question is where where does it end?

692
00:35:17,800 --> 00:35:20,000
Speaker 9: But even with what we have today, there are so

693
00:35:20,080 --> 00:35:21,919
Speaker 9: many applications we haven't explored yet.

694
00:35:22,080 --> 00:35:25,200
Speaker 11: So what's kind of your when you walk into see

695
00:35:25,239 --> 00:35:26,799
Speaker 11: a client? How do you how do you lead off?

696
00:35:26,840 --> 00:35:29,120
Speaker 2: Do you do you just say I'm going to try

697
00:35:29,120 --> 00:35:30,960
Speaker 2: to My goal today is to get you to commit

698
00:35:31,880 --> 00:35:36,000
Speaker 2: or consider committing to an ethical implementation of your tech investment.

699
00:35:36,040 --> 00:35:37,520
Speaker 2: Is that kind of your pitch or what how do

700
00:35:37,560 --> 00:35:38,720
Speaker 2: you walk into when you see a client?

701
00:35:38,800 --> 00:35:41,239
Speaker 9: Yeah, in essence, it's you know, it's the message I

702
00:35:41,680 --> 00:35:44,160
Speaker 9: sort of led with earlier that you cannot scale Jenai

703
00:35:44,440 --> 00:35:47,759
Speaker 9: without doing it in a responsible way. And it's twofold, like, yes,

704
00:35:47,800 --> 00:35:51,800
Speaker 9: there's absolutely a risk mitigation play, both for the company

705
00:35:51,840 --> 00:35:54,120
Speaker 9: but even more importantly for customers, right because you have

706
00:35:54,160 --> 00:35:56,520
Speaker 9: the potential to create an unintended harm to individuals, so

707
00:35:56,719 --> 00:35:58,960
Speaker 9: like full stop, we need to avoid that. But from

708
00:35:58,960 --> 00:36:02,000
Speaker 9: a corporate perspective, there there's certainly risk, but there's also

709
00:36:02,040 --> 00:36:05,319
Speaker 9: this value side. It's not just about mitigating risk, it's

710
00:36:05,360 --> 00:36:07,759
Speaker 9: about making sure you realize the value. And so that's

711
00:36:07,800 --> 00:36:11,800
Speaker 9: always the message because I think the risk is motivating

712
00:36:11,840 --> 00:36:14,399
Speaker 9: to a point. But then it's okay if you want

713
00:36:14,440 --> 00:36:16,560
Speaker 9: to make this investment in AI anyway, if you want

714
00:36:16,600 --> 00:36:18,520
Speaker 9: to see that value, you need to make this investment

715
00:36:18,520 --> 00:36:19,600
Speaker 9: to do it in a responsible way.

716
00:36:19,640 --> 00:36:23,520
Speaker 2: And that I guess is that message getting through?

717
00:36:23,640 --> 00:36:25,359
Speaker 11: Do you think I think it is.

718
00:36:25,440 --> 00:36:29,920
Speaker 9: I think companies are still like there's I would say

719
00:36:29,960 --> 00:36:33,640
Speaker 9: two things we're seeing. One it is the topic of

720
00:36:33,719 --> 00:36:37,160
Speaker 9: responsibile AI has not been elevated senior enough inside organizations.

721
00:36:37,200 --> 00:36:39,319
Speaker 11: Yet I argue it's a c suite issue. It has

722
00:36:39,360 --> 00:36:40,880
Speaker 11: to be. If AI is a c suite issue, this

723
00:36:40,960 --> 00:36:41,360
Speaker 11: needs to be.

724
00:36:41,800 --> 00:36:44,720
Speaker 9: Too often we're seeing it a few layers down inside

725
00:36:44,760 --> 00:36:48,120
Speaker 9: the organization, and it's people who are passionate and brilliant

726
00:36:48,160 --> 00:36:50,319
Speaker 9: and wanted to do good things, but they don't have

727
00:36:50,360 --> 00:36:52,360
Speaker 9: the clout inside the organization really drive the kind of

728
00:36:52,400 --> 00:36:52,879
Speaker 9: change you need.

729
00:36:52,920 --> 00:36:54,520
Speaker 11: I mean, I mentioned this is a cultural change. You

730
00:36:54,520 --> 00:36:55,759
Speaker 11: need senior people.

731
00:36:55,680 --> 00:36:57,680
Speaker 9: So that's one, and then two, they're just not putting

732
00:36:57,680 --> 00:37:01,319
Speaker 9: the right right amount of resourcing any right. I think

733
00:37:01,400 --> 00:37:04,080
Speaker 9: this is being seen as you know, smaller dollar we

734
00:37:04,080 --> 00:37:05,040
Speaker 9: can do a few little.

735
00:37:04,920 --> 00:37:07,640
Speaker 11: Things and do it responsibly. It's a much bigger investment.

736
00:37:07,719 --> 00:37:10,160
Speaker 9: And if you think about you brought up privacy earlier

737
00:37:10,200 --> 00:37:13,560
Speaker 9: like GDPR or cyber sure. I mean if you look

738
00:37:13,600 --> 00:37:15,440
Speaker 9: back at some of the data, we're talking tens of

739
00:37:15,440 --> 00:37:17,840
Speaker 9: millions of dollars to get compliant with GDPR. It's the

740
00:37:17,880 --> 00:37:19,520
Speaker 9: same thing here and companies need to be ready for

741
00:37:19,560 --> 00:37:21,080
Speaker 9: that degree of investment.

742
00:37:21,719 --> 00:37:22,080
Speaker 11: All right.

743
00:37:22,080 --> 00:37:25,239
Speaker 2: That was Steve Mills, chief AI ethics officer at the

744
00:37:25,239 --> 00:37:28,280
Speaker 2: Boston Consulting Group. I spoke with them up in Boston yesterday.

745
00:37:28,480 --> 00:37:31,400
Speaker 2: And what was interesting A that they have a chief

746
00:37:31,440 --> 00:37:34,040
Speaker 2: AI ethics officer. Wouldn't have thought at that, didn't even

747
00:37:34,040 --> 00:37:36,879
Speaker 2: think about it. But b they say at BCG, when

748
00:37:36,880 --> 00:37:38,359
Speaker 2: they got to talk to one of their clients about

749
00:37:38,400 --> 00:37:40,960
Speaker 2: AI and making the investment in AI and the commitment

750
00:37:41,000 --> 00:37:43,920
Speaker 2: in AI, they say, listen, we're not going to engage

751
00:37:43,920 --> 00:37:47,799
Speaker 2: with you unless you make the commitment that ethics and

752
00:37:47,840 --> 00:37:49,640
Speaker 2: the management ethics is going to be at a board

753
00:37:49,800 --> 00:37:52,840
Speaker 2: level commitment because if not, there are so many pitfalls

754
00:37:52,840 --> 00:37:55,000
Speaker 2: that we don't even know about. And before you start

755
00:37:55,080 --> 00:37:57,399
Speaker 2: rushing off to make your investments in technology, you better

756
00:37:57,440 --> 00:38:00,080
Speaker 2: recognize that you need to manage the risk here. So

757
00:38:00,600 --> 00:38:02,759
Speaker 2: from their perspective, at least at BCG, that's how they

758
00:38:02,760 --> 00:38:04,719
Speaker 2: try to engage with their clients.

759
00:38:05,080 --> 00:38:08,080
Speaker 5: What is the what's the reaction to that? Did you say, I.

760
00:38:08,040 --> 00:38:09,719
Speaker 2: Think they get it. I think they get it because

761
00:38:09,719 --> 00:38:11,600
Speaker 2: I think he says, they were saying, the clients are

762
00:38:11,640 --> 00:38:15,840
Speaker 2: basically like, you know, we'll spend the money to upgrade

763
00:38:15,880 --> 00:38:17,760
Speaker 2: our tech and do all that kind of stuff because

764
00:38:17,760 --> 00:38:20,279
Speaker 2: we can't be left behind. That's the biggest risk that

765
00:38:21,000 --> 00:38:24,240
Speaker 2: the BCG CEOs say, we can't risk getting left behind.

766
00:38:24,320 --> 00:38:26,239
Speaker 2: So their in tendency is just to spend, spend, spend,

767
00:38:26,320 --> 00:38:29,440
Speaker 2: spend without really thinking about how to do it smartly

768
00:38:29,560 --> 00:38:32,719
Speaker 2: and overtime and do it responsibly. They feel like if

769
00:38:32,760 --> 00:38:35,480
Speaker 2: they don't, their competitors will and they'll be left in

770
00:38:35,520 --> 00:38:38,640
Speaker 2: the dust. So very interesting discussion about AI and how

771
00:38:38,680 --> 00:38:41,759
Speaker 2: it's really involved with all their discussions.

772
00:38:41,200 --> 00:38:45,920
Speaker 4: And apparently they'll be a CEO officer chief, aix ETHX officer.

773
00:38:46,080 --> 00:38:47,919
Speaker 5: That will now be something to think about.

774
00:38:48,200 --> 00:38:52,680
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775
00:38:52,920 --> 00:38:55,799
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776
00:38:55,920 --> 00:38:59,480
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777
00:39:00,280 --> 00:39:03,200
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778
00:39:03,239 --> 00:39:06,440
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779
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