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Speaker 1: Bloomberg Audio Studios, podcasts, radio news.

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Speaker 2: This is the Bloomberg Surveillance Podcast. Catch us live weekdays

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Speaker 2: at seven am Eastern on Apple car Player, Android Auto

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Speaker 2: with the Bloomberg Business App. Listen on demand wherever you

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Speaker 2: get your podcasts, or watch us live on youtubejacent Draw.

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Speaker 3: Joins us right now. He's a the UBS.

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Speaker 4: He's out of America's tactical asset worst worst job at UPS.

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Speaker 3: It's like being in charge with Frank.

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Speaker 4: What are you tactically asset allocating into twenty twenty five?

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Speaker 4: With everything that's going on, you just pull aside and

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Speaker 4: stay in cash.

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Speaker 5: I know, we actually upgraded equities about a month ago,

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Speaker 5: make them attractive. Still kind of have confidence in the macro.

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Speaker 5: Look at that hasn't changed with the election. It's added

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Speaker 5: some volatility the outcome certainly to the potential paths the

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Speaker 5: by lunch we think this is bull market in equities

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Speaker 5: keep going higher.

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Speaker 6: So, I mean, what changed for you a two weeks

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Speaker 6: ago election day? Did anything change for you guys as

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Speaker 6: you think about allocating assets, stocks, bonds, alternatives, US non US?

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Speaker 3: Has anything change for you guys?

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Speaker 5: Not fundamentally so the way I would kind of describe

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Speaker 5: it is that our expected value stayed the same. The

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Speaker 5: variants and distribution probably got wider of different outcomes. So

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Speaker 5: certain positions that we've kind of recommended, like gold, which

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Speaker 5: has actually pulled back that somebody thought as a hedging,

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Speaker 5: that's actually become more attractive since that time. The view

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Speaker 5: on rates ultimate kind of drifting lower. That has not

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Speaker 5: really changed. I think we probably got a little more

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Speaker 5: confidence in certain areas of equities, like financials, you know,

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Speaker 5: potentially benefiting from deregulation. But I think overall the core

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Speaker 5: thesis has not really changed.

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Speaker 6: Did you take your expectation for earnings higher thinking that

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Speaker 6: taxes are coming down potentially under a Trump administration.

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Speaker 5: We have not adjusted our earnings ultimately. Look, we think

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Speaker 5: they'll extend their personal tax cuts. I think anything beyond

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Speaker 5: extension of existing tax cuts that seems I would make

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Speaker 5: that the base case. You know, just there's not a

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Speaker 5: lot of fiscal scope for further expansion for the task cuts.

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Speaker 5: That's our view at this point.

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Speaker 6: You don't think they'll be further very.

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Speaker 5: Like something along the lines of reinstituting the sort of

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Speaker 5: accelerated depreciation, you know, things that were actually discussed earlier

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Speaker 5: this year and part of the budget deal, but not

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Speaker 5: further taking down of tax cuts, not as the base case.

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Speaker 6: How do you think about the allocation stocks, bonds, alternatives.

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Speaker 6: I think Tom and I grew up in a sixty

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Speaker 6: forty era. You know that's not a thing anymore, is it.

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Speaker 5: Well, it's not a bad starting point, you know. The

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Speaker 5: question is like, how do you improve on that? And

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Speaker 5: I think that the key thing about the sixty to

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Speaker 5: forty what works so well for you know, decades, a

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Speaker 5: couple of decades for sure, is that stocks and bonds

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Speaker 5: had have negative correlation, and that was because I think

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Speaker 5: part inflation was contained. When inflation is contained, it can

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Speaker 5: sort of have that negative correlation when you're worried about inflation.

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Speaker 5: Think about what happened in twenty twenty two, stocks and

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Speaker 5: bonds sell off, inflation will be I think upside risks

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Speaker 5: kind of going forward. In that case, you need other

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Speaker 5: things to diversify your portfolio. So it's like you know,

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Speaker 5: the forty thirty thirty. I'm thinking more along the lines

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Speaker 5: of adding alternatives, especially for clients who have, like you know,

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Speaker 5: larger portfolios can move into those asset classes.

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Speaker 4: Jackson Yale University. There's always a fun This is Bob

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Speaker 4: Schuler's legacy. There's always a fundamental of finance over your economics.

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Speaker 3: The what's the.

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Speaker 4: Track record of rebalancing and asset allocation in recent years?

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Speaker 4: Granted we've had three once in a lifetime events, but

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Speaker 4: do you still have a theoretical confidence in rebalancing or

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Speaker 4: do you tilt more to a longer buy an old strategy.

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Speaker 5: Well, rebalancing and on a regular basis, that is becomes

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Speaker 5: a little bit more systematic. I think that the deity

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Speaker 5: would show that actually adds value. There's a rebalancing alpha.

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Speaker 3: So to speak.

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Speaker 5: I think where the tricky part comes in is that

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Speaker 5: markets move very fast, and now they move very quickly.

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Speaker 5: I think about what happened in the summer when the

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Speaker 5: S and P was down almost ten percent in about

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Speaker 5: two and a half weeks, and then it almost fully

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Speaker 5: recovered in two weeks. So the question is like do

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Speaker 5: you rebalance then and not rebalunance? And that's kind of

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Speaker 5: complicated matters, But conception and rebalancing is adds value over time.

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Speaker 6: You guys have a S and P price target of

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Speaker 6: sixty six hundred. Tom likes these round numbers. By December

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Speaker 6: twenty twenty five. Is that earning's driven, multiple driven a

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Speaker 6: little bit of both.

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Speaker 5: Mostly earning is driven because I think about where we

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Speaker 5: are right now, it's roughly six thousand. If we open

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Speaker 5: up and where the futures are ten percent upside, we're

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Speaker 5: looking at eight nine percent earnings grow, so and the

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Speaker 5: risk I th thing is probably that number might get,

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Speaker 5: you know, taken higher. Multiple Basically stay is kind of

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Speaker 5: constant to give or take a little bit. So it's

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Speaker 5: really mostly an orning story for next year.

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Speaker 6: Are there sectors that screen better for you? Guys here

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Speaker 6: at this point, like a lot of folks are saying,

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Speaker 6: if we're going to get some of this proeconomic agenda,

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Speaker 6: maybe some deregulation, maybe in taxes, I want to be

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Speaker 6: in cyclicals. Do you kind of fall to that camp?

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Speaker 6: Are you saying, I'm just sticking with my technology names.

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Speaker 5: Well, we like technology. We still think that's a secular story, right,

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Speaker 5: It's kind of agnostic to some extent of the policy.

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Speaker 5: He'll come that's what makes it attractive on the cyclical front.

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Speaker 5: I think financials that's one of our attractive sectors. And

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Speaker 5: you can kind of kind of multiple reasons why, you know,

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Speaker 5: deregulation or less sort of owner's regulation that will kind

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Speaker 5: of come down the pipeline, likely pick up in corporate

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Speaker 5: activity M and A. I pos against certainly the big

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Speaker 5: money center banks. You definitely should benefit from that. And

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Speaker 5: ultimately we still think the Fed's going to be cutting rates,

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Speaker 5: and you know, as rates comes down, that should benefit

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Speaker 5: financials as a kind of curve resteep. And so there's

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Speaker 5: cynical drivers, there's structural drivers that I think you know

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Speaker 5: financials and among the cyclical space.

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Speaker 3: That's what we like.

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Speaker 4: Right when you did your PhD at Yale, you did

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Speaker 4: it on IPOs. Paul and I were live in lunches.

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Speaker 4: There'd be three lunches in one day. There was this

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Speaker 4: eff vescence on the street which you wrote about authoritatively.

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Speaker 3: It yeah, okay, great.

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Speaker 4: Do we want to go back to that millu or

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Speaker 4: are we lucky that we're here where Paul's going to

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Speaker 4: one lunch every ninety days?

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Speaker 5: Well, I can't speak to punch of Paul's lunch preferences.

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Speaker 5: While you were at doing the three lunches, I was

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Speaker 5: in the bowels of Barneaky Library kind of working the

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Speaker 5: matter is.

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Speaker 3: And we a guy in from Black Rock celebrity earlier.

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Speaker 4: We're now at a point where there's four phone calls

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Speaker 4: made on an IPO, do you want it or not?

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Speaker 3: Yes or no.

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Speaker 5: So I don't think we're going to go back to

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Speaker 5: what it was in the nineties. I mean, there's just

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Speaker 5: regulation ngels that that will sift you all that. But

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Speaker 5: also like you know, sorry benz Oxley, like there's there's

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Speaker 5: fewer companies that want to go public. We did see

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Speaker 5: frothiness back in the second half of twenty twenty, in

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Speaker 5: the first half twenty one the spacmania, So there's definitely

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Speaker 5: that is possible to kind of come back. But I

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Speaker 5: think the closest we've seen for IPOs is you go

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Speaker 5: back pre pandemic when Uber went public. Airbnb there was

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Speaker 5: appeared in eighteen nineteen. There's a little bit of that,

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Speaker 5: but it was at a much more modest scale. We

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Speaker 5: weren't seeing stock prices go up seven hundred percent on

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Speaker 5: the first day. I mean that those days I think

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Speaker 5: are gone. We could see them go up sixty percent,

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Speaker 5: seventy percent, but not that level of frothiness before.

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Speaker 6: All right, the PhD I from economics from Yale. I

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Speaker 6: don't know why you would ever do that. The undergraduate

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Speaker 6: tom He goes to the University of Manitoba. So I

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Speaker 6: have to google maps Manitoba. That's how ignorant I am

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Speaker 6: of Canada, the geography of Canada. If you're not in Winnipeg,

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Speaker 6: there's nowhere to be in that province. I mean, right,

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Speaker 6: I mean it is. I mean I'm looking at this

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Speaker 6: map here. If you're not in Winnipeg, where are you.

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Speaker 5: Well, Winnipeg is the big city's table city. The next

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Speaker 5: biggest city is going to be more like about fifty thousand.

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Speaker 5: But it's a lot of beautiful wilderness if you love

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Speaker 5: to fish. Fantastic for fishing. Fantastic for lakes.

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Speaker 6: Hudson Bay up on the Upper Head.

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Speaker 3: How low of cold?

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Speaker 4: The record cold I have is twenty one degrees below

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Speaker 4: zero Plymouth, New Hampshire.

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Speaker 3: How cold did it ever get in Winnipeg where you

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Speaker 3: were up there?

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Speaker 5: I mean the coldest days will be below minus thirty celsius.

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Speaker 5: So look, I'm around minus twenty five with a wind,

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Speaker 5: so the windshill is worse.

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Speaker 3: Is anybody going to beat the Winnipeg Jets this year?

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Speaker 3: That's all we want to know.

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Speaker 5: I'm your fingers crossing, looking really good. But there's you know,

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Speaker 5: Florida is a tough team. The Rangers are our tough teams.

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Speaker 3: The Rangers, we can always lose it. About the third

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Speaker 3: week of March to go to like the Maple leaves.

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Speaker 6: Tell us still remote from Winnipeg in January, We'll get

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Speaker 6: Lisa Matato.

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Speaker 5: I will join you if you get that in Winnipeg.

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Speaker 4: Promise if we go to Winnipeg, Dreo will be the

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Speaker 4: andrel Thank you so much, really important conversation on asset

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Speaker 4: allocation and reb Sen.

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Speaker 3: There he is with UBS.

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Speaker 2: You're listening to the Bloomberg Surveillance Podcast. Catch us live

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Speaker 2: weekday afternoons from seven to ten am. Easter Listen on

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Speaker 2: Apple car Play and Android Auto with a Bloomberg Business app,

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Speaker 2: or watch US live on YouTube.

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Speaker 4: Wendy Schiller, as the professor from Brown University, joins us.

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Speaker 4: I'm listening to Nicki Haley Wendy Schill yesterday and as

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Speaker 4: far as I'm concerned, Lisa, don't let the mess Scarra

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Speaker 4: run lissus crying, Professor Schiller. The campaign for twenty twenty

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Speaker 4: eight did it start last night? With Nicki Haley going

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Speaker 4: after irf K Junior and Tulsi Gabbard.

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Speaker 7: Well, it's certainly an indication that she's not going anywhere,

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Speaker 7: and that she feels like she got some votes when

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Speaker 7: she ran against Trump in the primary. She has a constituency,

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Speaker 7: and she's trying to position herself as viable. It's a

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Speaker 7: long time to work, you know, the next couple of years.

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Speaker 7: But you know the first eight months of this administration

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Speaker 7: may be successful and accomplishing some of the President elect's

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Speaker 7: stated goals, but after that, you know, parties fall apart

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Speaker 7: a little bit.

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Speaker 5: Majority is of hert.

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Speaker 7: To sustain and so she's positioning herself with somebody who

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Speaker 7: looks viable very early on.

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Speaker 4: So DNI is intelligence and this is the former governor

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Speaker 4: of the Carolinas. Quote d and I is not a

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Speaker 4: place for a Russian, Iranian, Syrian, Chinese sympathizer when they

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Speaker 4: get in front of the Senate or the House, or

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Speaker 4: just they're working in the machinery. Wendy Schiller of your Washington,

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Speaker 4: how's that going to play with the middle of the

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Speaker 4: Road America.

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Speaker 7: Well, I mean, I think the calculation that Republican senators

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Speaker 7: have to make is which of these nominees and their

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Speaker 7: issues and.

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Speaker 8: We know we have sexual assault allegations.

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Speaker 7: You know, we have child sex trafficking, drug use, we

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Speaker 7: have sympathizing with her on in Russia. We have a

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Speaker 7: whole list of things which will catch fire, which will

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Speaker 7: actually resonate in their voting constituents community. You know, which

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Speaker 7: will voters care about right now? Voters sent a signal

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Speaker 7: saying fix inflation, you know, fix our cost of living

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Speaker 7: and make sure that we have a future in.

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Speaker 8: Terms of economic growth. That's what they've said. Will they

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Speaker 8: care about this now? Probably not?

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Speaker 7: Could they care about it later if these people get confirmed.

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Speaker 3: Possibly surveillance correction, it's an important one.

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Speaker 4: It's tassa gabbert o gabert I'm mispronounced it because I'm

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Speaker 4: an idiot. I wasn't trying to be Snyder or something.

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Speaker 4: I don't want people to read anything in them.

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Speaker 3: Not excuse me, yelled at me.

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Speaker 5: She did.

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Speaker 6: Well, she's she's on top of these things. So, Professor

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Speaker 6: Schiller here, give us a sense of how you think

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Speaker 6: this Republican Senate is going to view some of these

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Speaker 6: nominations here, particularly some of the ones that have been

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Speaker 6: flagged as maybe troublesome, such as you know, mister Gates

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Speaker 6: and uh, miss Gabbard.

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Speaker 7: Well, we're obviously seeing some attempts by the you know,

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Speaker 7: the administration elect to you know, get Gates through. It's

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Speaker 7: typical to have nominees meet with senators. I'm just looking

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Speaker 7: you know, for Lisa Rakowski, Susan Collins, you know, particularly

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Speaker 7: for Headset and Gates.

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Speaker 8: You know, what happens? Do they vote for them?

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Speaker 7: And if there are cracks in the coalition and they

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Speaker 7: get through, will those cracks grow as each nominee is

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Speaker 7: voted on? So which of the most problematic nominees do

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Speaker 7: you put first?

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Speaker 8: That's going to be.

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Speaker 7: John Thune's problem, right and his challenge which one does

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Speaker 7: he get through first? If the most problematic one gets

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Speaker 7: through first, then you don't have a lot of big problems.

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Speaker 7: But you know, I'm going to be looking at those

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Speaker 7: two senators and senators who are.

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Speaker 8: Thinking of running for reelection in twenty twenty six.

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Speaker 7: And because Kamala Hawers lost, there seems to be some

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Speaker 7: impression that the female vote isn't going to respond to

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Speaker 7: these kinds of things.

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Speaker 8: No, women are like everybody else. They prioritize what they

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Speaker 8: worry about. In this case, it was budgets. It was schooling.

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Speaker 7: It was all sorts of things that hit close to home,

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Speaker 7: but behavior still might matter. And so this is the

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Speaker 7: calculation that Republican senators have to make, which are thin

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Speaker 7: on some of these nominations. Telsey gab You know, Nikki

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Speaker 7: Haley was former ambassador of the UN. She does know

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Speaker 7: the international community, and I think if she's raising alarms,

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Speaker 7: then I think some Republican senators.

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Speaker 8: May be listening.

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Speaker 4: Professor Schiller is just all this a precursor for every

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Speaker 4: single Minutia decision, not Minuchin Minusia decision.

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Speaker 3: Like it's Latin. You gotta do that with your Brown University.

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Speaker 4: Every single minusia decision, Wendy's going to be made out

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Speaker 4: of sixteen hundred Pennsylvania Avenue.

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Speaker 8: Oh, it's very difficult to do.

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Speaker 7: I keep reciting this with you about Ronald Reagan, right

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Speaker 7: he said, I would you know, issue a decision or

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Speaker 7: a request. It would take a week to get a

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Speaker 7: response in the White House. The federal government is massive,

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Speaker 7: you know, to move it quickly is very difficult to do,

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Speaker 7: even for the Trump administration. But the question of Congress,

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Speaker 7: with that very slim majority in the House, we have

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Speaker 7: to see what happens when they start really making cuts

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Speaker 7: to programs and people that are in Republican districts. And

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Speaker 7: there are a lot of federal employees who live in

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Speaker 7: Republican dominated districts and Republican states, and if they have

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Speaker 7: a life change or their their job goes, then you know,

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Speaker 7: Trump will hear about it.

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Speaker 8: How that DAMA plays out, We're going to see it

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Speaker 8: probably in the summer.

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Speaker 4: Philip Bump killing it in the post yesterday. On the mandate,

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Speaker 4: I went nuts yesterday with David Melpass, professor Schiller, Trumps supporter,

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Speaker 4: when he said this was.

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Speaker 3: A mandate election.

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Speaker 4: You're expert at this, back to eighteen eighty three, if

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Speaker 4: not back to Adams Jefferson.

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Speaker 3: Was that a mandate election? Professor, Well, it's tough.

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Speaker 7: I mean, I think by my calculations, he's just under

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Speaker 7: fifty percent. That's what I think we have right now

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Speaker 7: in terms of the vote tallies. So under fifty percent

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Speaker 7: is typically not thought to be a mandate. It would

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Speaker 7: have been a stronger argument had the House Republicans picked

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Speaker 7: up any number of seats, but it looks like they'll

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Speaker 7: have the same exact vote majority that they had in

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Speaker 7: last session. That does not suggest, as I thought earlier

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Speaker 7: that Trump's cotails would be long and strong.

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Speaker 8: I'm wrong about that. They were not that long and strong.

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Speaker 8: And that's the big tests usually with a mandate.

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Speaker 7: And then you know, getting people back in the office

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Speaker 7: is something private sector has been wanting to do. It

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Speaker 7: is true that, you know, you could have expected Trump

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Speaker 7: to say everybody come back to work. That's something the musk,

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Speaker 7: you know musk, they're they're right about that in the

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Speaker 7: sense of that was always going to happen. And so

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Speaker 7: I think that's something that will be really interesting to

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Speaker 7: see and its impact on DC, by.

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Speaker 8: The way, as a city.

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Speaker 7: The irony is that it could resurrect downtown d C,

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Speaker 7: which would be something I think most people would agree

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Speaker 7: is a good thing.

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Speaker 3: Yeah, it feels like it the thing they proposed.

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Speaker 7: Not everything they proposed will be met with opposition, and

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Speaker 7: not everything will be successful or a failure.

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Speaker 4: Twenty seconds. Professor Schiller is a Talban center at Brown,

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Speaker 4: going to five days a week in the office.

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Speaker 8: We are excited.

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Speaker 7: We're launching a school public policy ju Live for us,

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Speaker 7: so we are excited to really be all of us

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Speaker 7: on our game.

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Speaker 8: That's my answer.

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Speaker 7: There.

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Speaker 4: There was a sophisticate the General Counsel pass that at

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Speaker 4: Brown University. Wendy Shower, thank you so much, particularly those

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Speaker 4: comments on our constitution and our civics that is out there.

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Speaker 2: This is the Bloomberg Surveillance Podcast. Listen live each weekday

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Speaker 2: starting at seven am Eastern on applecar Play and Android

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Speaker 2: Auto with the Bloomberg Business app. You can also listen

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Speaker 2: live on Amazon Alexa from our flagship New York station

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Speaker 2: Just Say Alexa playing Bloomberg eleven thirty joining us.

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Speaker 4: Now the conversation of the day and technology. We had

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Speaker 4: Dan Ey's earlier with us from his Sikorski Gene Munster

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Speaker 4: joins us now really pleased with that of course, with Deepwater.

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Speaker 3: Asset management, Jane, What is the moment we're in?

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Speaker 4: Paul Swede and I did important AI seminars for aniog

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Speaker 4: Run and Man Deep sing yesterday with Adobe with Salesforce,

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Speaker 4: there's Nvidia earnings in the rest. You've been doing this forever.

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Speaker 4: What moment are we in in the next year in

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Speaker 4: American technology?

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Speaker 9: We're in what is going to be the more I

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Speaker 9: think exciting year and innovation over the last fifty years.

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Speaker 9: And it's hard to really put a language on it

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Speaker 9: to capture what the concept of infinite intelligence or close

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Speaker 9: to infinite intelligence at almost no cost means for humanity.

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Speaker 9: And so as someone who has been around for the

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Speaker 9: Internet and saw the boom and bust of that, I

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Speaker 9: think this is going to be more significant. And I

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Speaker 9: think that next year, if you think about the markets,

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Speaker 9: I've been talking about this three to five year bull market,

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Speaker 9: and so I started talking about that about a year ago,

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Speaker 9: So we're going to say we got a two to.

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Speaker 10: Four more years.

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Speaker 9: It's going to end in a spectacular bursting of the bubble.

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Speaker 9: But I think next year, from just a broader market perspective,

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Speaker 9: I think we're going to see another incredible year. And

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Speaker 9: I think that it's driven by substance around AI going

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Speaker 9: from a build out to starting to move to more

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Speaker 9: application since we saw a little bit of that with

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Speaker 9: Snowflake today. Of course we've seen most of the excitement

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Speaker 9: around the hardware, including Nvidia, but I think we're going

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Speaker 9: to start to shift to slowly see more performance in

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Speaker 9: software specifically. But just I'm just really excited to be

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Speaker 9: alive at this time in life.

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Speaker 10: It's just a really special moment.

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Speaker 6: Hey, Gene, When I talk to people about AI, I

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Speaker 6: often quote you because I remember talking to you maybe

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Speaker 6: eighteen months ago on this program, and you put into

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Speaker 6: content into a kind of a framework of a companium

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Speaker 6: of kind of where you view AI relative to, say,

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Speaker 6: the Internet to electricity. Can you just review that for us,

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Speaker 6: because it's so powerful to give people a sense of

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Speaker 6: how you view the potential impact of AI.

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Speaker 9: So it's a zero to one hundred scale, and one

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Speaker 9: hundred is the most impactful, most transformative. But at twenty five,

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Speaker 9: I would put mobile at about thirty five forty. I

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Speaker 9: would have the PC the Internet at fifty. And obviously

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Speaker 9: these all build on themselves, but if you look at

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Speaker 9: them as a standalone, So the Internet is fifty, I

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Speaker 9: think AI is probably ninety and electricity would be one hundred.

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Speaker 9: And I think AI would be greater than electricity if

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Speaker 9: not for the fact that you need electricity to run

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Speaker 9: these machines. Of course, But this is a big deal, Paul,

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Speaker 9: And you know there's that's a lot of hype.

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Speaker 10: There's a lot of hype in that.

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Speaker 9: I think the substance will exceed that hype.

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Speaker 6: Yeah, but you're gene when someone like you makes that analysis.

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Speaker 6: A lot of people like myself, we pay attention here

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Speaker 6: as we try to get our minds around AI. What's

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Speaker 6: the next step here? At it feels like we're we're

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Speaker 6: so early innings on this, but it feels like we're

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Speaker 6: now at the discussion point where.

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Speaker 3: The next step is one of your kids needs in Mac.

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Speaker 6: It's a new left, new laptop with the AX just

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Speaker 6: so you know, that's the next steps this weekend exactly, Geene,

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Speaker 6: are we at the point now where we really have

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Speaker 6: to make the use case for AI applications? Maybe the

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Speaker 6: return on all this investment that we've seen.

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Speaker 9: I don't think we're at that point. I think we're

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Speaker 9: going to start to see some of that next year.

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Speaker 9: And that has been the hesitancy around this whole AI

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Speaker 9: trade is that the I think the skeptics would say

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Speaker 9: that we just really haven't seen those incredible bread taking

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Speaker 9: use cases yet, but they will ultimately come. And I

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Speaker 9: think that what we saw with the Nvidia earnings last

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Speaker 9: night and specifically this demand, I mean, this is incredible.

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Speaker 9: I've never seen a story like this. They did twenty

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Speaker 9: one billion in revenue in two thousand. Calendar twenty two,

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Speaker 9: they'll do call it one hundred and forty billion in

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Speaker 9: revenue next year. There's nothing been anything like this, And

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Speaker 9: when you see that, what they're doing is they're building

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Speaker 9: the infrastructure. Was still largely in this building infrastructure phase, Paul.

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Speaker 9: But some of these breathtaking applications that will change basically

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Speaker 9: all forms of our life. I think we're going to

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Speaker 9: start to see those a little bit later next year.

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Speaker 9: Specifically to this concept of agentic AI came up a

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Speaker 9: lot on the Nvidia call last night. But this idea

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Speaker 9: of these agents going out and actually getting work done

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Speaker 9: for us, I think that's going to be the big

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Speaker 9: change how we're all going to interact with these eight.

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Speaker 4: First single work gene Munster with us here right now,

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Speaker 4: folks thrill these with us, Danizer with US earlier as

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Speaker 4: we look at technology in America, Munster with deep water

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Speaker 4: asset management, Gene Muster. I look at the Apple A

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Speaker 4: and R used to be part of the cellside racket.

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Speaker 4: Loved to Piper Jeff for years ago in Minnesota. But Gina,

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Speaker 4: I'm looking at Okay, I got forty buys, but the

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Speaker 4: fact is.

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Speaker 3: They got eighteen holds. I got Apple in a trading

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Speaker 3: range since June. It's butchers up now near new highs.

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Speaker 4: I assume Gene Munster thinks Apple's going to break out,

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Speaker 4: How is it going to break out?

438
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Speaker 3: What will be the catalyst at the top of the

439
00:20:38,560 --> 00:20:40,760
Speaker 3: income statement or on downward.

440
00:20:42,359 --> 00:20:45,120
Speaker 9: It's going to be at the top, and it's it's

441
00:20:45,160 --> 00:20:47,879
Speaker 9: going to be either later this fiscal year, kind of

442
00:20:48,160 --> 00:20:50,560
Speaker 9: the June quarter ish of next year, I think is

443
00:20:50,600 --> 00:20:54,679
Speaker 9: a potential breakout or in fiscal twenty six. And I

444
00:20:54,680 --> 00:20:56,960
Speaker 9: think it's inevitable that this happens. And to put some

445
00:20:57,040 --> 00:20:59,119
Speaker 9: numbers around it, what that breakout is is that the

446
00:20:59,119 --> 00:21:02,800
Speaker 9: streets looking for or iPhone growth in fiscal twenty five

447
00:21:03,000 --> 00:21:06,360
Speaker 9: to be around six percent and for fiscal twenty six

448
00:21:06,440 --> 00:21:08,359
Speaker 9: a pretty similar number. I think that that can be

449
00:21:08,480 --> 00:21:12,800
Speaker 9: ten to fifteen percent. And ultimately all this talk about AI,

450
00:21:13,400 --> 00:21:17,280
Speaker 9: the vast majority of people still don't use AI. And

451
00:21:18,080 --> 00:21:20,240
Speaker 9: if you look at the number of chat GPT on

452
00:21:20,280 --> 00:21:22,600
Speaker 9: a daily basis is probably about one hundred and fifty million,

453
00:21:22,640 --> 00:21:25,480
Speaker 9: two hundred million people. If you look at metas products,

454
00:21:25,520 --> 00:21:29,560
Speaker 9: that's about three point two billion Instagram and so this

455
00:21:29,600 --> 00:21:31,600
Speaker 9: is still a fraction of the use. But what is

456
00:21:31,800 --> 00:21:34,959
Speaker 9: special about what Apple is doing is taking these tools

457
00:21:34,960 --> 00:21:38,320
Speaker 9: and just making them accessible to everyone. And I think that, well, today,

458
00:21:38,359 --> 00:21:39,840
Speaker 9: those features really.

459
00:21:39,680 --> 00:21:41,399
Speaker 10: Don't light up your life.

460
00:21:41,440 --> 00:21:43,520
Speaker 9: I think that as they roll more of them out,

461
00:21:43,560 --> 00:21:45,240
Speaker 9: it will become more clear to people that they have

462
00:21:45,280 --> 00:21:46,000
Speaker 9: to upgrade.

463
00:21:46,440 --> 00:21:48,920
Speaker 6: If I were to put together kind of an ETF

464
00:21:49,000 --> 00:21:51,800
Speaker 6: for AI, because I'm trying to get some exposure to AI,

465
00:21:51,880 --> 00:21:54,880
Speaker 6: but maybe I feel like I've missed the Nvidia trade.

466
00:21:55,160 --> 00:21:58,879
Speaker 6: Are you asking for one of your kids emailed in

467
00:21:58,960 --> 00:22:01,200
Speaker 6: for one of your kids need a basket of AI?

468
00:22:01,280 --> 00:22:04,680
Speaker 6: How else are you thinking about kind of getting exposure

469
00:22:04,720 --> 00:22:08,560
Speaker 6: to Again, this is really seminal shift in technology.

470
00:22:09,440 --> 00:22:12,479
Speaker 9: Paul, I'd be remiss not to mention we have an

471
00:22:12,480 --> 00:22:16,679
Speaker 9: ETF the tickers LOUP. It is frontier tech and it

472
00:22:16,800 --> 00:22:22,000
Speaker 9: basically gives exposure to AI themes that are outside of

473
00:22:22,040 --> 00:22:25,200
Speaker 9: the megacap So if you're looking for some smaller companies

474
00:22:25,280 --> 00:22:27,720
Speaker 9: like Verted, for example, that does cooling, it was mentioned

475
00:22:27,760 --> 00:22:29,760
Speaker 9: on the call, these are the sub kind of two

476
00:22:29,840 --> 00:22:32,680
Speaker 9: hundred billion dollars, So I have to mention that LOUP.

477
00:22:32,880 --> 00:22:36,320
Speaker 9: But I would say that more broadly, I still think

478
00:22:36,359 --> 00:22:38,639
Speaker 9: we're in a period where the hardware piece.

479
00:22:38,720 --> 00:22:40,080
Speaker 10: This is a contrarian view.

480
00:22:40,160 --> 00:22:42,800
Speaker 9: I think the hardware trade is going to continue to

481
00:22:42,840 --> 00:22:46,600
Speaker 9: be stronger than expected over the next several quarters, and

482
00:22:46,640 --> 00:22:49,400
Speaker 9: then as I mentioned, kind of exiting end of next year.

483
00:22:49,400 --> 00:22:51,480
Speaker 9: I think, you know what we're seeing with Snowflake. I

484
00:22:51,480 --> 00:22:53,040
Speaker 9: think you're going to see more and more of that

485
00:22:53,080 --> 00:22:55,800
Speaker 9: in software land kind of later next year. But that's

486
00:22:55,840 --> 00:22:57,679
Speaker 9: kind of how I think about it, still in the

487
00:22:57,720 --> 00:23:00,520
Speaker 9: hardware piece and then moving to software later on.

488
00:23:00,760 --> 00:23:06,399
Speaker 4: Okay, lup up to thirty five percent, three five up,

489
00:23:06,480 --> 00:23:08,120
Speaker 4: thirty five percent.

490
00:23:07,920 --> 00:23:11,520
Speaker 3: One year trail. Robinhood's you're number one holding?

491
00:23:12,600 --> 00:23:13,000
Speaker 5: How do you have?

492
00:23:13,119 --> 00:23:15,960
Speaker 3: Robinhood is part of AI.

493
00:23:17,160 --> 00:23:21,040
Speaker 9: So what they're using is as part of how they

494
00:23:21,200 --> 00:23:26,400
Speaker 9: assess risk with customers, they use AI. They also use

495
00:23:26,440 --> 00:23:30,720
Speaker 9: AI as a means to go and generate new customers

496
00:23:30,760 --> 00:23:34,560
Speaker 9: so for doing outreach. So they've been pretty they've been

497
00:23:34,600 --> 00:23:37,600
Speaker 9: proactive at doing that. So it's companies like Robinhood is.

498
00:23:37,760 --> 00:23:40,879
Speaker 9: Another company that we looked at recently to add to

499
00:23:40,880 --> 00:23:44,000
Speaker 9: this was like Crispin Green. It sounds crazy, but they

500
00:23:44,000 --> 00:23:48,320
Speaker 9: have a big robotics initiative going on. So it is

501
00:23:48,359 --> 00:23:50,959
Speaker 9: these companies that are studying to implement AI kind of

502
00:23:51,040 --> 00:23:51,640
Speaker 9: under the hood.

503
00:23:52,080 --> 00:23:55,959
Speaker 6: Hey, Gene Snowflake, I am looking at Snowflake stocks up

504
00:23:55,960 --> 00:23:58,920
Speaker 6: twenty five percent. Can you tell us what Snowflake does

505
00:23:58,960 --> 00:24:02,080
Speaker 6: and why the stock is up so much today?

506
00:24:02,200 --> 00:24:03,440
Speaker 10: So what Snowflake does.

507
00:24:03,640 --> 00:24:07,720
Speaker 9: It's called a data warehouse, which means that you take data.

508
00:24:07,920 --> 00:24:12,119
Speaker 9: It could be anything from structured data inside of a business.

509
00:24:12,119 --> 00:24:15,359
Speaker 9: Structured data would be data that is in like an

510
00:24:15,359 --> 00:24:19,440
Speaker 9: Oracle database, and it warehouses it so AI models can

511
00:24:19,480 --> 00:24:22,679
Speaker 9: train on it. It basically makes data more accessible and

512
00:24:22,840 --> 00:24:27,400
Speaker 9: usable for training. So that is what they're benefiting from

513
00:24:27,600 --> 00:24:30,879
Speaker 9: is just more training and more enterprises starting to use this.

514
00:24:31,160 --> 00:24:33,840
Speaker 9: Their biggest competitor where they've been losing share, the reason

515
00:24:33,880 --> 00:24:37,680
Speaker 9: why the stock has not done well before today. It's

516
00:24:37,720 --> 00:24:40,360
Speaker 9: called data Bricks, and they have a data lake where

517
00:24:40,400 --> 00:24:43,240
Speaker 9: you can just throw in PDF and Excel documents and

518
00:24:43,920 --> 00:24:46,120
Speaker 9: just throw a bunch of data into this data lake

519
00:24:46,119 --> 00:24:47,679
Speaker 9: and then it organizes it for you.

520
00:24:47,720 --> 00:24:49,640
Speaker 10: But that's what's going on with Snowflake.

521
00:24:49,840 --> 00:24:50,040
Speaker 3: Jane.

522
00:24:50,080 --> 00:24:53,880
Speaker 4: First question to Eli Greenfield yesterday at Adobe is all

523
00:24:53,880 --> 00:24:56,320
Speaker 4: this fancy gene monster data ives talk.

524
00:24:56,920 --> 00:24:59,040
Speaker 3: Is it going to take our jobs away from us?

525
00:25:02,320 --> 00:25:02,480
Speaker 10: Well?

526
00:25:02,680 --> 00:25:05,080
Speaker 9: As an asset manager, I think that that's a very

527
00:25:05,119 --> 00:25:08,600
Speaker 9: real potential. I think as an ANALYSTI it's a little different.

528
00:25:08,640 --> 00:25:10,920
Speaker 9: At deep Water, we launched an affiliate company a few

529
00:25:10,960 --> 00:25:15,560
Speaker 9: months ago called Intelligent Alpha, and it basically is machines picking.

530
00:25:16,040 --> 00:25:19,280
Speaker 9: It's just machines picking stocks, just an et F L

531
00:25:19,320 --> 00:25:23,920
Speaker 9: I v R livermore. But I think it is. It's

532
00:25:23,920 --> 00:25:28,879
Speaker 9: something I just mentioned that in context too. It's one

533
00:25:28,920 --> 00:25:30,480
Speaker 9: of two things is going to happen. Either it's going

534
00:25:30,520 --> 00:25:33,640
Speaker 9: to take our jobs. As an asset manager. I would

535
00:25:33,680 --> 00:25:36,920
Speaker 9: say for you and Paul and your team, you need

536
00:25:37,000 --> 00:25:41,160
Speaker 9: not worry. One of the things that AI won't take

537
00:25:41,200 --> 00:25:45,520
Speaker 9: away is community and empathy. And that's things definitionly that

538
00:25:45,600 --> 00:25:47,160
Speaker 9: can't be done. And I think that's what you guys

539
00:25:47,200 --> 00:25:47,800
Speaker 9: bring every day.

540
00:25:48,080 --> 00:25:50,879
Speaker 3: Well, we have community, do we have empathy? Lisa, I

541
00:25:50,880 --> 00:25:51,680
Speaker 3: don't you know.

542
00:25:51,920 --> 00:25:52,560
Speaker 1: That's a stretch.

543
00:25:52,880 --> 00:25:56,720
Speaker 4: You know, it's like pushing the envelope here a little

544
00:25:56,720 --> 00:25:58,920
Speaker 4: to its gene monster hugely valuable.

545
00:25:58,920 --> 00:25:59,919
Speaker 3: We get a huge signs.

546
00:26:06,480 --> 00:26:10,760
Speaker 2: This is the Bloomberg Surveillance Podcast. Listen live each weekday

547
00:26:10,880 --> 00:26:14,439
Speaker 2: starting at seven am Eastern on Applecarplay and Android Auto

548
00:26:14,480 --> 00:26:17,240
Speaker 2: with the Bloomberg Business app. You can also watch us

549
00:26:17,359 --> 00:26:21,359
Speaker 2: live every weekday on YouTube and always on the Bloomberg terminal.

550
00:26:21,600 --> 00:26:22,600
Speaker 3: You better not have Chris.

551
00:26:22,600 --> 00:26:25,240
Speaker 4: Sale on this, Lisa, I'll tell you that your daily

552
00:26:25,280 --> 00:26:29,080
Speaker 4: look at the front pages of Lisa Mateo hour anticipated

553
00:26:29,320 --> 00:26:30,040
Speaker 4: Lisa Mateo.

554
00:26:30,280 --> 00:26:32,760
Speaker 1: Okay, it's the end of the year, right, Accounting departments

555
00:26:32,840 --> 00:26:35,480
Speaker 1: racing to close the books, so they have all these

556
00:26:35,520 --> 00:26:37,600
Speaker 1: expenses to go through, and they got to get them through.

557
00:26:37,640 --> 00:26:39,439
Speaker 1: So now what they're finding out is a lot of

558
00:26:39,480 --> 00:26:42,960
Speaker 1: workers are pushing the limits with those company credit cards.

559
00:26:43,000 --> 00:26:45,840
Speaker 1: They're finding things, they're telling them Wester Journal. People are

560
00:26:45,840 --> 00:26:50,200
Speaker 1: buying things like Christmas gifts, jet skis, cruises, plastic surgery.

561
00:26:50,640 --> 00:26:54,080
Speaker 1: They're even charging their monthly mortgage payment because they're disguising

562
00:26:54,359 --> 00:26:58,239
Speaker 1: the lender as a business vendor. So this is kind

563
00:26:58,280 --> 00:27:00,680
Speaker 1: of coming under screwtin now you end of the year,

564
00:27:00,760 --> 00:27:02,240
Speaker 1: like people are under the guns that they're trying to

565
00:27:02,320 --> 00:27:02,800
Speaker 1: rush these things.

566
00:27:04,040 --> 00:27:06,760
Speaker 6: Would not allow this, I don't think here at Bloomberg

567
00:27:07,000 --> 00:27:09,720
Speaker 6: he would not. All right, interesting, I mean, I know

568
00:27:09,760 --> 00:27:11,600
Speaker 6: people got like some of the spending accounts and they

569
00:27:11,600 --> 00:27:13,639
Speaker 6: gotta flush the money before they lose it and all

570
00:27:13,640 --> 00:27:14,200
Speaker 6: that kind of stuff.

571
00:27:14,320 --> 00:27:16,159
Speaker 4: I had a beverage of my choice ones for the

572
00:27:16,240 --> 00:27:19,720
Speaker 4: evil Laura, she's in charge of all of our auditing,

573
00:27:19,760 --> 00:27:22,280
Speaker 4: and she told me a couple of stories own.

574
00:27:22,200 --> 00:27:27,040
Speaker 3: And Laura told me, you know, she said, time you competing. Yeah, exactly,

575
00:27:27,280 --> 00:27:28,480
Speaker 3: next it's on the hand.

576
00:27:28,600 --> 00:27:31,360
Speaker 4: No, seriously, it's and you look, we're like everybody else.

577
00:27:31,400 --> 00:27:34,000
Speaker 4: We monitor it is, you know, we do, we do,

578
00:27:34,160 --> 00:27:34,359
Speaker 4: we do.

579
00:27:34,960 --> 00:27:37,280
Speaker 1: So yesterday we talked about the Comcast spinoff right of

580
00:27:37,320 --> 00:27:39,679
Speaker 1: the cable channels, and Paul, it's funny because you and

581
00:27:39,760 --> 00:27:42,160
Speaker 1: Alex and I were talking about this. What about these

582
00:27:42,320 --> 00:27:44,720
Speaker 1: MSNBC staffers like they're going to start to be in

583
00:27:44,760 --> 00:27:47,280
Speaker 1: a panic? Well, sources actually telling you The New York Post,

584
00:27:48,160 --> 00:27:51,240
Speaker 1: MSNBC start, Rachel Maddock, Chris Jansen, Kitty Tour. They had

585
00:27:51,240 --> 00:27:54,439
Speaker 1: this tense meeting at thirty Rock led by NBC Universal

586
00:27:54,480 --> 00:27:57,280
Speaker 1: chairman Mark Lazarus, who's going to lead the company, and

587
00:27:57,280 --> 00:28:00,520
Speaker 1: they say staffers, they're fearing layoffs. They just want to know,

588
00:28:00,600 --> 00:28:03,720
Speaker 1: is it going to change the logo, the name, the headquarters.

589
00:28:04,520 --> 00:28:08,240
Speaker 1: Lazarus said that he just wasn't sure, So it wasn't

590
00:28:08,359 --> 00:28:11,320
Speaker 1: like a good sign. But it's just MSNBC. You know,

591
00:28:11,440 --> 00:28:13,800
Speaker 1: since the election, their ratings have been tanking according to

592
00:28:13,920 --> 00:28:17,679
Speaker 1: Nielsen and people you know MSNBCC they use, they cross

593
00:28:17,800 --> 00:28:19,119
Speaker 1: use a lot of reporters.

594
00:28:19,160 --> 00:28:23,400
Speaker 4: There's the financial side which is always interesting, but the

595
00:28:23,520 --> 00:28:27,080
Speaker 4: editorial side will be very very interesting. In the heritage

596
00:28:27,600 --> 00:28:31,840
Speaker 4: going back, pushing thirty years back when it was Microsoft NBC.

597
00:28:32,200 --> 00:28:33,920
Speaker 4: I mean, there's a lot of heritage here.

598
00:28:34,040 --> 00:28:36,840
Speaker 6: Yep, absolutely, I mean, and again you just think kind

599
00:28:36,840 --> 00:28:39,840
Speaker 6: of where the country is these days? Where is MSNBC

600
00:28:40,000 --> 00:28:43,560
Speaker 6: and it's editorial background. Where does that kind of stand up?

601
00:28:43,560 --> 00:28:45,720
Speaker 6: And again it's a standalone company. You can't hide behind

602
00:28:46,160 --> 00:28:48,400
Speaker 6: the compcast, broadband and cable television number.

603
00:28:48,440 --> 00:28:50,600
Speaker 4: Its fascinating and of course we're all looking at this

604
00:28:50,680 --> 00:28:52,880
Speaker 4: folks on the island of Manhattan.

605
00:28:52,880 --> 00:28:55,560
Speaker 3: I mean, it only focuses on three zip codes. Right,

606
00:28:55,600 --> 00:28:58,600
Speaker 3: there's a zip code of Sunset Boulevard and here in

607
00:28:58,680 --> 00:29:00,280
Speaker 3: New York. Next, Li said, all.

608
00:29:00,320 --> 00:29:03,160
Speaker 1: Right, if you're going shopping New York City's fifth Avenue,

609
00:29:03,520 --> 00:29:07,680
Speaker 1: it is no longer the world's most expensive shopping street. Okay,

610
00:29:07,720 --> 00:29:10,480
Speaker 1: this is according to a new list from Cushman and Wakefield.

611
00:29:11,000 --> 00:29:14,280
Speaker 1: The number one is now Milan's let me see if

612
00:29:14,320 --> 00:29:22,360
Speaker 1: I say this right, via Montenna Napoleon. Enough, it has

613
00:29:22,400 --> 00:29:24,640
Speaker 1: the first but the first time a European city topped

614
00:29:24,640 --> 00:29:27,560
Speaker 1: the list. So it gets the rents globally from about

615
00:29:27,560 --> 00:29:30,440
Speaker 1: one hundred and thirty eight luxury shopping districts, so this

616
00:29:30,480 --> 00:29:33,000
Speaker 1: one topped it. The reason why it is so expensive

617
00:29:33,080 --> 00:29:35,600
Speaker 1: is because it's a shorter street than everyone else on

618
00:29:35,640 --> 00:29:39,040
Speaker 1: the list. So it's about two thousand dollars per square foot.

619
00:29:39,080 --> 00:29:41,000
Speaker 1: That's you know a little bit higher eleven percent than

620
00:29:41,000 --> 00:29:43,520
Speaker 1: they paid the year before. So Fifth Avenue came in second,

621
00:29:43,720 --> 00:29:45,200
Speaker 1: and London's New Bond.

622
00:29:45,000 --> 00:29:47,840
Speaker 6: Street that ubon. Okay, that's right. Well Milan, I mean

623
00:29:47,920 --> 00:29:50,640
Speaker 6: one of my favorite hotels in the world, Hotel Pinchy

624
00:29:50,680 --> 00:29:51,640
Speaker 6: Pey Savoy.

625
00:29:51,920 --> 00:29:54,440
Speaker 4: Oh yeah, can I tell you where growing zeros on

626
00:29:54,480 --> 00:29:59,840
Speaker 4: the three three via Monte Napoleon and that would be

627
00:30:00,080 --> 00:30:02,680
Speaker 4: Fendi Milano monten Napoleon.

628
00:30:03,440 --> 00:30:06,600
Speaker 3: And yes, it's two thousand dollars per square foot. Every

629
00:30:06,640 --> 00:30:08,160
Speaker 3: time you walk a square.

630
00:30:07,840 --> 00:30:12,720
Speaker 6: Foot it goes k ching, nice, you love me.

631
00:30:13,360 --> 00:30:15,240
Speaker 5: Next, and finally here's our kicker.

632
00:30:15,280 --> 00:30:18,680
Speaker 1: So this piece of artwork a duct tape Banantic. So

633
00:30:18,880 --> 00:30:22,760
Speaker 1: he's six point million dollars at Southerb's in New York.

634
00:30:23,560 --> 00:30:26,680
Speaker 1: Six minutes of fear spitting, and yes we did have it.

635
00:30:26,680 --> 00:30:31,600
Speaker 1: It's called the Comedian, okay. It's a sculpture artist Marizio Catilan.

636
00:30:32,120 --> 00:30:35,240
Speaker 1: The buyers is China born crypto entrepreneur. He's going to

637
00:30:35,280 --> 00:30:37,600
Speaker 1: consider lending it out to people, he said. Even if

638
00:30:37,640 --> 00:30:39,840
Speaker 1: Elon mus wants to take it to the moon, he's

639
00:30:39,840 --> 00:30:40,520
Speaker 1: more than welcome.

640
00:30:40,560 --> 00:30:43,000
Speaker 4: In twenty nineteen, if it was one and a half

641
00:30:43,120 --> 00:30:46,400
Speaker 4: million and they popped the sucker at six million, I mean,

642
00:30:46,760 --> 00:30:47,480
Speaker 4: Tang is our.

643
00:30:47,680 --> 00:30:49,640
Speaker 3: You know, I'm drinking Tang zero this morning.

644
00:30:50,040 --> 00:30:51,880
Speaker 4: But you know, if you were to duct tape and

645
00:30:52,000 --> 00:30:55,440
Speaker 4: orange onto a wall, I mean, that's gotta be worth

646
00:30:55,440 --> 00:30:58,840
Speaker 4: eight hundred dollars and you have done.

647
00:30:58,600 --> 00:31:02,360
Speaker 1: It of our YouTube viewers who can actually.

648
00:31:02,080 --> 00:31:06,520
Speaker 4: Mister full disclosure, folks, mister Bloomberg, with his wonderful philanthropy,

649
00:31:06,960 --> 00:31:11,680
Speaker 4: is expanding out on Madison Avenue to the Gagosian Gallery building,

650
00:31:11,680 --> 00:31:13,760
Speaker 4: which is I gets right across from the Carlisle. It's

651
00:31:13,760 --> 00:31:17,280
Speaker 4: a spectacular building and Mike and the architects are doing

652
00:31:17,280 --> 00:31:20,400
Speaker 4: it justice. And I think it's some of those galleries

653
00:31:21,040 --> 00:31:22,400
Speaker 4: in Gagosian's building.

654
00:31:22,440 --> 00:31:25,480
Speaker 3: I mean, I'm sorry the orange tape to the wall.

655
00:31:26,280 --> 00:31:30,480
Speaker 6: You know, folks on YouTube, we have an orange tape

656
00:31:30,520 --> 00:31:33,320
Speaker 6: to the wall in homage to the banana. What do

657
00:31:33,400 --> 00:31:35,160
Speaker 6: we get for the orange? Do you think it's.

658
00:31:35,000 --> 00:31:37,920
Speaker 3: Called still tank at the end of it. It's a

659
00:31:38,040 --> 00:31:38,720
Speaker 3: still life.

660
00:31:39,240 --> 00:31:40,920
Speaker 1: I just want to know if it's the same banana

661
00:31:40,920 --> 00:31:42,000
Speaker 1: from twenty nineteen.

662
00:31:43,160 --> 00:31:43,640
Speaker 3: Exactly.

663
00:31:43,720 --> 00:31:44,200
Speaker 5: You don't know.

664
00:31:44,280 --> 00:31:45,160
Speaker 8: It doesn't say.

665
00:31:45,440 --> 00:31:47,040
Speaker 3: It's what we call installation.

666
00:31:47,240 --> 00:31:50,520
Speaker 5: If you read art news, Okay, the latest.

667
00:31:50,520 --> 00:31:53,200
Speaker 3: It came from Art Basel, Basil. I don't know my name,

668
00:31:54,680 --> 00:31:57,760
Speaker 3: Damien Goes, Alex Steele. I think Goes. Probably I've never

669
00:31:57,800 --> 00:32:01,360
Speaker 3: gone to Art Basel. No, no, mclone. Yes, he is

670
00:32:01,400 --> 00:32:05,040
Speaker 3: so tasteful when he goes. He wears socks exactly. That's it.

671
00:32:05,040 --> 00:32:05,600
Speaker 5: It's formal.

672
00:32:06,120 --> 00:32:07,880
Speaker 8: Are you done?

673
00:32:07,280 --> 00:32:10,360
Speaker 1: I love your edition with the orange tape to.

674
00:32:10,320 --> 00:32:13,200
Speaker 4: The Okay, I got a live chat here YouTube that

675
00:32:14,200 --> 00:32:17,200
Speaker 4: Wicked is going to flop. I mean, what do you think, Lisa, Oh,

676
00:32:17,320 --> 00:32:20,000
Speaker 4: I don't telegraph butchered it in London.

677
00:32:20,000 --> 00:32:24,160
Speaker 1: Don't Ariana. All all the kids are talking about going.

678
00:32:24,240 --> 00:32:27,400
Speaker 1: They're dressing up, some of them, so yeah, it's an event.

679
00:32:28,040 --> 00:32:31,800
Speaker 3: Like how Lisa Matteo, are you going dressed.

680
00:32:31,560 --> 00:32:33,280
Speaker 8: Like I'm going to Gladiator?

681
00:32:34,240 --> 00:32:36,200
Speaker 6: You're going Gladiator?

682
00:32:37,160 --> 00:32:37,480
Speaker 7: I don't.

683
00:32:37,760 --> 00:32:40,000
Speaker 1: I just love that. Denzel Washington's big.

684
00:32:41,040 --> 00:32:44,200
Speaker 3: Lisa Mateo, thank you so much. Our newspaper segment.

685
00:32:44,320 --> 00:32:48,760
Speaker 2: This is the Bloomberg Surveillance Podcast, available on Apple, Spotify,

686
00:32:48,920 --> 00:32:53,040
Speaker 2: and anywhere else you get your podcasts. Listen live each weekday,

687
00:32:53,120 --> 00:32:56,040
Speaker 2: seven to ten a m. Eastern on Bloomberg dot Com,

688
00:32:56,160 --> 00:32:59,760
Speaker 2: the iHeartRadio app, tune In, and the Bloomberg Business app.

689
00:33:00,080 --> 00:33:03,080
Speaker 2: You can also watch us live every weekday on YouTube

690
00:33:03,360 --> 00:33:05,160
Speaker 2: and always on the Bloomberg terminal