WEBVTT - Nvidia and Geopolitics Impact on Markets

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<v Speaker 1>Bloomberg Audio Studios, podcasts, radio news.

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<v Speaker 2>This is the Bloomberg Surveillance Podcast. Catch us live weekdays

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<v Speaker 2>get your podcasts, or watch us live on youtubejacent Draw.

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<v Speaker 3>Joins us right now. He's a the UBS.

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<v Speaker 4>He's out of America's tactical asset worst worst job at UPS.

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<v Speaker 3>It's like being in charge with Frank.

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<v Speaker 4>What are you tactically asset allocating into twenty twenty five?

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<v Speaker 4>With everything that's going on, you just pull aside and

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<v Speaker 4>stay in cash.

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<v Speaker 5>I know, we actually upgraded equities about a month ago,

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<v Speaker 5>make them attractive. Still kind of have confidence in the macro.

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<v Speaker 5>Look at that hasn't changed with the election. It's added

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<v Speaker 5>some volatility the outcome certainly to the potential paths the

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<v Speaker 5>by lunch we think this is bull market in equities

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<v Speaker 5>keep going higher.

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<v Speaker 6>So, I mean, what changed for you a two weeks

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<v Speaker 6>ago election day? Did anything change for you guys as

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<v Speaker 6>you think about allocating assets, stocks, bonds, alternatives, US non US?

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<v Speaker 3>Has anything change for you guys?

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<v Speaker 5>Not fundamentally so the way I would kind of describe

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<v Speaker 5>it is that our expected value stayed the same. The

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<v Speaker 5>variants and distribution probably got wider of different outcomes. So

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<v Speaker 5>certain positions that we've kind of recommended, like gold, which

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<v Speaker 5>has actually pulled back that somebody thought as a hedging,

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<v Speaker 5>that's actually become more attractive since that time. The view

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<v Speaker 5>on rates ultimate kind of drifting lower. That has not

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<v Speaker 5>really changed. I think we probably got a little more

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<v Speaker 5>confidence in certain areas of equities, like financials, you know,

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<v Speaker 5>potentially benefiting from deregulation. But I think overall the core

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<v Speaker 5>thesis has not really changed.

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<v Speaker 6>Did you take your expectation for earnings higher thinking that

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<v Speaker 6>taxes are coming down potentially under a Trump administration.

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<v Speaker 5>We have not adjusted our earnings ultimately. Look, we think

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<v Speaker 5>they'll extend their personal tax cuts. I think anything beyond

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<v Speaker 5>extension of existing tax cuts that seems I would make

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<v Speaker 5>that the base case. You know, just there's not a

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<v Speaker 5>lot of fiscal scope for further expansion for the task cuts.

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<v Speaker 5>That's our view at this point.

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<v Speaker 6>You don't think they'll be further very.

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<v Speaker 5>Like something along the lines of reinstituting the sort of

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<v Speaker 5>accelerated depreciation, you know, things that were actually discussed earlier

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<v Speaker 5>this year and part of the budget deal, but not

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<v Speaker 5>further taking down of tax cuts, not as the base case.

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<v Speaker 6>How do you think about the allocation stocks, bonds, alternatives.

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<v Speaker 6>I think Tom and I grew up in a sixty

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<v Speaker 6>forty era. You know that's not a thing anymore, is it.

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<v Speaker 5>Well, it's not a bad starting point, you know. The

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<v Speaker 5>question is like, how do you improve on that? And

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<v Speaker 5>I think that the key thing about the sixty to

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<v Speaker 5>forty what works so well for you know, decades, a

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<v Speaker 5>couple of decades for sure, is that stocks and bonds

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<v Speaker 5>had have negative correlation, and that was because I think

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<v Speaker 5>part inflation was contained. When inflation is contained, it can

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<v Speaker 5>sort of have that negative correlation when you're worried about inflation.

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<v Speaker 5>Think about what happened in twenty twenty two, stocks and

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<v Speaker 5>bonds sell off, inflation will be I think upside risks

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<v Speaker 5>kind of going forward. In that case, you need other

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<v Speaker 5>things to diversify your portfolio. So it's like you know,

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<v Speaker 5>the forty thirty thirty. I'm thinking more along the lines

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<v Speaker 5>of adding alternatives, especially for clients who have, like you know,

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<v Speaker 5>larger portfolios can move into those asset classes.

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<v Speaker 4>Jackson Yale University. There's always a fun This is Bob

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<v Speaker 4>Schuler's legacy. There's always a fundamental of finance over your economics.

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<v Speaker 3>The what's the.

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<v Speaker 4>Track record of rebalancing and asset allocation in recent years?

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<v Speaker 4>Granted we've had three once in a lifetime events, but

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<v Speaker 4>do you still have a theoretical confidence in rebalancing or

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<v Speaker 4>do you tilt more to a longer buy an old strategy.

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<v Speaker 5>Well, rebalancing and on a regular basis, that is becomes

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<v Speaker 5>a little bit more systematic. I think that the deity

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<v Speaker 5>would show that actually adds value. There's a rebalancing alpha.

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<v Speaker 3>So to speak.

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<v Speaker 5>I think where the tricky part comes in is that

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<v Speaker 5>markets move very fast, and now they move very quickly.

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<v Speaker 5>I think about what happened in the summer when the

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<v Speaker 5>S and P was down almost ten percent in about

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<v Speaker 5>two and a half weeks, and then it almost fully

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<v Speaker 5>recovered in two weeks. So the question is like do

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<v Speaker 5>you rebalance then and not rebalunance? And that's kind of

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<v Speaker 5>complicated matters, But conception and rebalancing is adds value over time.

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<v Speaker 6>You guys have a S and P price target of

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<v Speaker 6>sixty six hundred. Tom likes these round numbers. By December

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<v Speaker 6>twenty twenty five. Is that earning's driven, multiple driven a

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<v Speaker 6>little bit of both.

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<v Speaker 5>Mostly earning is driven because I think about where we

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<v Speaker 5>are right now, it's roughly six thousand. If we open

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<v Speaker 5>up and where the futures are ten percent upside, we're

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<v Speaker 5>looking at eight nine percent earnings grow, so and the

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<v Speaker 5>risk I th thing is probably that number might get,

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<v Speaker 5>you know, taken higher. Multiple Basically stay is kind of

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<v Speaker 5>constant to give or take a little bit. So it's

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<v Speaker 5>really mostly an orning story for next year.

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<v Speaker 6>Are there sectors that screen better for you? Guys here

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<v Speaker 6>at this point, like a lot of folks are saying,

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<v Speaker 6>if we're going to get some of this proeconomic agenda,

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<v Speaker 6>maybe some deregulation, maybe in taxes, I want to be

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<v Speaker 6>in cyclicals. Do you kind of fall to that camp?

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<v Speaker 6>Are you saying, I'm just sticking with my technology names.

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<v Speaker 5>Well, we like technology. We still think that's a secular story, right,

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<v Speaker 5>It's kind of agnostic to some extent of the policy.

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<v Speaker 5>He'll come that's what makes it attractive on the cyclical front.

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<v Speaker 5>I think financials that's one of our attractive sectors. And

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<v Speaker 5>you can kind of kind of multiple reasons why, you know,

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<v Speaker 5>deregulation or less sort of owner's regulation that will kind

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<v Speaker 5>of come down the pipeline, likely pick up in corporate

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<v Speaker 5>activity M and A. I pos against certainly the big

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<v Speaker 5>money center banks. You definitely should benefit from that. And

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<v Speaker 5>ultimately we still think the Fed's going to be cutting rates,

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<v Speaker 5>and you know, as rates comes down, that should benefit

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<v Speaker 5>financials as a kind of curve resteep. And so there's

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<v Speaker 5>cynical drivers, there's structural drivers that I think you know

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<v Speaker 5>financials and among the cyclical space.

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<v Speaker 3>That's what we like.

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<v Speaker 4>Right when you did your PhD at Yale, you did

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<v Speaker 4>it on IPOs. Paul and I were live in lunches.

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<v Speaker 4>There'd be three lunches in one day. There was this

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<v Speaker 4>eff vescence on the street which you wrote about authoritatively.

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<v Speaker 3>It yeah, okay, great.

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<v Speaker 4>Do we want to go back to that millu or

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<v Speaker 4>are we lucky that we're here where Paul's going to

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<v Speaker 4>one lunch every ninety days?

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<v Speaker 5>Well, I can't speak to punch of Paul's lunch preferences.

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<v Speaker 5>While you were at doing the three lunches, I was

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<v Speaker 5>in the bowels of Barneaky Library kind of working the

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<v Speaker 5>matter is.

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<v Speaker 3>And we a guy in from Black Rock celebrity earlier.

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<v Speaker 4>We're now at a point where there's four phone calls

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<v Speaker 4>made on an IPO, do you want it or not?

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<v Speaker 3>Yes or no.

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<v Speaker 5>So I don't think we're going to go back to

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<v Speaker 5>what it was in the nineties. I mean, there's just

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<v Speaker 5>regulation ngels that that will sift you all that. But

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<v Speaker 5>also like you know, sorry benz Oxley, like there's there's

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<v Speaker 5>fewer companies that want to go public. We did see

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<v Speaker 5>frothiness back in the second half of twenty twenty, in

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<v Speaker 5>the first half twenty one the spacmania, So there's definitely

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<v Speaker 5>that is possible to kind of come back. But I

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<v Speaker 5>think the closest we've seen for IPOs is you go

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<v Speaker 5>back pre pandemic when Uber went public. Airbnb there was

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<v Speaker 5>appeared in eighteen nineteen. There's a little bit of that,

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<v Speaker 5>but it was at a much more modest scale. We

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<v Speaker 5>weren't seeing stock prices go up seven hundred percent on

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<v Speaker 5>the first day. I mean that those days I think

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<v Speaker 5>are gone. We could see them go up sixty percent,

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<v Speaker 5>seventy percent, but not that level of frothiness before.

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<v Speaker 6>All right, the PhD I from economics from Yale. I

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<v Speaker 6>don't know why you would ever do that. The undergraduate

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<v Speaker 6>tom He goes to the University of Manitoba. So I

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<v Speaker 6>have to google maps Manitoba. That's how ignorant I am

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<v Speaker 6>of Canada, the geography of Canada. If you're not in Winnipeg,

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<v Speaker 6>there's nowhere to be in that province. I mean, right,

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<v Speaker 6>I mean it is. I mean I'm looking at this

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<v Speaker 6>map here. If you're not in Winnipeg, where are you.

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<v Speaker 5>Well, Winnipeg is the big city's table city. The next

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<v Speaker 5>biggest city is going to be more like about fifty thousand.

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<v Speaker 5>But it's a lot of beautiful wilderness if you love

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<v Speaker 5>to fish. Fantastic for fishing. Fantastic for lakes.

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<v Speaker 6>Hudson Bay up on the Upper Head.

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<v Speaker 3>How low of cold?

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<v Speaker 4>The record cold I have is twenty one degrees below

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<v Speaker 4>zero Plymouth, New Hampshire.

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<v Speaker 3>How cold did it ever get in Winnipeg where you

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<v Speaker 3>were up there?

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<v Speaker 5>I mean the coldest days will be below minus thirty celsius.

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<v Speaker 5>So look, I'm around minus twenty five with a wind,

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<v Speaker 5>so the windshill is worse.

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<v Speaker 3>Is anybody going to beat the Winnipeg Jets this year?

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<v Speaker 3>That's all we want to know.

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<v Speaker 5>I'm your fingers crossing, looking really good. But there's you know,

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<v Speaker 5>Florida is a tough team. The Rangers are our tough teams.

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<v Speaker 3>The Rangers, we can always lose it. About the third

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<v Speaker 3>week of March to go to like the Maple leaves.

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<v Speaker 6>Tell us still remote from Winnipeg in January, We'll get

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<v Speaker 6>Lisa Matato.

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<v Speaker 5>I will join you if you get that in Winnipeg.

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<v Speaker 4>Promise if we go to Winnipeg, Dreo will be the

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<v Speaker 4>andrel Thank you so much, really important conversation on asset

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<v Speaker 4>allocation and reb Sen.

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<v Speaker 3>There he is with UBS.

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<v Speaker 2>You're listening to the Bloomberg Surveillance Podcast. Catch us live

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<v Speaker 2>weekday afternoons from seven to ten am. Easter Listen on

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<v Speaker 4>Wendy Schiller, as the professor from Brown University, joins us.

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<v Speaker 4>I'm listening to Nicki Haley Wendy Schill yesterday and as

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<v Speaker 4>far as I'm concerned, Lisa, don't let the mess Scarra

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<v Speaker 4>run lissus crying, Professor Schiller. The campaign for twenty twenty

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<v Speaker 4>eight did it start last night? With Nicki Haley going

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<v Speaker 4>after irf K Junior and Tulsi Gabbard.

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<v Speaker 7>Well, it's certainly an indication that she's not going anywhere,

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<v Speaker 7>and that she feels like she got some votes when

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<v Speaker 7>she ran against Trump in the primary. She has a constituency,

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<v Speaker 7>and she's trying to position herself as viable. It's a

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<v Speaker 7>long time to work, you know, the next couple of years.

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<v Speaker 7>But you know the first eight months of this administration

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<v Speaker 7>may be successful and accomplishing some of the President elect's

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<v Speaker 7>stated goals, but after that, you know, parties fall apart

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<v Speaker 7>a little bit.

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<v Speaker 5>Majority is of hert.

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<v Speaker 7>To sustain and so she's positioning herself with somebody who

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<v Speaker 7>looks viable very early on.

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<v Speaker 4>So DNI is intelligence and this is the former governor

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<v Speaker 4>of the Carolinas. Quote d and I is not a

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<v Speaker 4>place for a Russian, Iranian, Syrian, Chinese sympathizer when they

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<v Speaker 4>get in front of the Senate or the House, or

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<v Speaker 4>just they're working in the machinery. Wendy Schiller of your Washington,

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<v Speaker 4>how's that going to play with the middle of the

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<v Speaker 4>Road America.

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<v Speaker 7>Well, I mean, I think the calculation that Republican senators

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<v Speaker 7>have to make is which of these nominees and their

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<v Speaker 7>issues and.

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<v Speaker 8>We know we have sexual assault allegations.

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<v Speaker 7>You know, we have child sex trafficking, drug use, we

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<v Speaker 7>have sympathizing with her on in Russia. We have a

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<v Speaker 7>whole list of things which will catch fire, which will

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<v Speaker 7>actually resonate in their voting constituents community. You know, which

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<v Speaker 7>will voters care about right now? Voters sent a signal

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<v Speaker 7>saying fix inflation, you know, fix our cost of living

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<v Speaker 7>and make sure that we have a future in.

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<v Speaker 8>Terms of economic growth. That's what they've said. Will they

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<v Speaker 8>care about this now? Probably not?

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<v Speaker 7>Could they care about it later if these people get confirmed.

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<v Speaker 3>Possibly surveillance correction, it's an important one.

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<v Speaker 4>It's tassa gabbert o gabert I'm mispronounced it because I'm

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<v Speaker 4>an idiot. I wasn't trying to be Snyder or something.

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<v Speaker 4>I don't want people to read anything in them.

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<v Speaker 3>Not excuse me, yelled at me.

0:10:33.720 --> 0:10:34.040
<v Speaker 5>She did.

0:10:34.080 --> 0:10:37.080
<v Speaker 6>Well, she's she's on top of these things. So, Professor

0:10:37.080 --> 0:10:40.160
<v Speaker 6>Schiller here, give us a sense of how you think

0:10:40.280 --> 0:10:43.040
<v Speaker 6>this Republican Senate is going to view some of these

0:10:43.080 --> 0:10:45.120
<v Speaker 6>nominations here, particularly some of the ones that have been

0:10:45.360 --> 0:10:48.440
<v Speaker 6>flagged as maybe troublesome, such as you know, mister Gates

0:10:48.440 --> 0:10:50.360
<v Speaker 6>and uh, miss Gabbard.

0:10:52.120 --> 0:10:55.240
<v Speaker 7>Well, we're obviously seeing some attempts by the you know,

0:10:55.280 --> 0:10:59.520
<v Speaker 7>the administration elect to you know, get Gates through. It's

0:10:59.559 --> 0:11:03.440
<v Speaker 7>typical to have nominees meet with senators. I'm just looking

0:11:03.800 --> 0:11:07.600
<v Speaker 7>you know, for Lisa Rakowski, Susan Collins, you know, particularly

0:11:07.600 --> 0:11:08.960
<v Speaker 7>for Headset and Gates.

0:11:09.120 --> 0:11:11.319
<v Speaker 8>You know, what happens? Do they vote for them?

0:11:11.600 --> 0:11:13.640
<v Speaker 7>And if there are cracks in the coalition and they

0:11:13.640 --> 0:11:17.040
<v Speaker 7>get through, will those cracks grow as each nominee is

0:11:17.120 --> 0:11:20.000
<v Speaker 7>voted on? So which of the most problematic nominees do

0:11:20.040 --> 0:11:20.800
<v Speaker 7>you put first?

0:11:21.240 --> 0:11:21.959
<v Speaker 8>That's going to be.

0:11:21.960 --> 0:11:25.200
<v Speaker 7>John Thune's problem, right and his challenge which one does

0:11:25.200 --> 0:11:27.920
<v Speaker 7>he get through first? If the most problematic one gets

0:11:27.920 --> 0:11:30.400
<v Speaker 7>through first, then you don't have a lot of big problems.

0:11:30.640 --> 0:11:32.000
<v Speaker 7>But you know, I'm going to be looking at those

0:11:32.000 --> 0:11:33.920
<v Speaker 7>two senators and senators who are.

0:11:33.720 --> 0:11:35.880
<v Speaker 8>Thinking of running for reelection in twenty twenty six.

0:11:36.200 --> 0:11:38.800
<v Speaker 7>And because Kamala Hawers lost, there seems to be some

0:11:38.840 --> 0:11:42.000
<v Speaker 7>impression that the female vote isn't going to respond to

0:11:42.040 --> 0:11:43.040
<v Speaker 7>these kinds of things.

0:11:43.320 --> 0:11:46.480
<v Speaker 8>No, women are like everybody else. They prioritize what they

0:11:46.559 --> 0:11:49.520
<v Speaker 8>worry about. In this case, it was budgets. It was schooling.

0:11:49.760 --> 0:11:51.679
<v Speaker 7>It was all sorts of things that hit close to home,

0:11:51.960 --> 0:11:55.480
<v Speaker 7>but behavior still might matter. And so this is the

0:11:55.520 --> 0:11:58.120
<v Speaker 7>calculation that Republican senators have to make, which are thin

0:11:58.200 --> 0:12:00.920
<v Speaker 7>on some of these nominations. Telsey gab You know, Nikki

0:12:00.960 --> 0:12:03.560
<v Speaker 7>Haley was former ambassador of the UN. She does know

0:12:03.640 --> 0:12:07.439
<v Speaker 7>the international community, and I think if she's raising alarms,

0:12:07.520 --> 0:12:09.440
<v Speaker 7>then I think some Republican senators.

0:12:09.120 --> 0:12:09.720
<v Speaker 8>May be listening.

0:12:10.880 --> 0:12:14.680
<v Speaker 4>Professor Schiller is just all this a precursor for every

0:12:14.720 --> 0:12:18.840
<v Speaker 4>single Minutia decision, not Minuchin Minusia decision.

0:12:19.040 --> 0:12:21.440
<v Speaker 3>Like it's Latin. You gotta do that with your Brown University.

0:12:22.000 --> 0:12:25.199
<v Speaker 4>Every single minusia decision, Wendy's going to be made out

0:12:25.200 --> 0:12:27.160
<v Speaker 4>of sixteen hundred Pennsylvania Avenue.

0:12:28.640 --> 0:12:29.800
<v Speaker 8>Oh, it's very difficult to do.

0:12:29.920 --> 0:12:33.000
<v Speaker 7>I keep reciting this with you about Ronald Reagan, right

0:12:33.040 --> 0:12:35.439
<v Speaker 7>he said, I would you know, issue a decision or

0:12:35.679 --> 0:12:37.280
<v Speaker 7>a request. It would take a week to get a

0:12:37.320 --> 0:12:40.320
<v Speaker 7>response in the White House. The federal government is massive,

0:12:40.679 --> 0:12:44.080
<v Speaker 7>you know, to move it quickly is very difficult to do,

0:12:44.280 --> 0:12:47.280
<v Speaker 7>even for the Trump administration. But the question of Congress,

0:12:47.440 --> 0:12:50.240
<v Speaker 7>with that very slim majority in the House, we have

0:12:50.320 --> 0:12:53.199
<v Speaker 7>to see what happens when they start really making cuts

0:12:53.559 --> 0:12:57.320
<v Speaker 7>to programs and people that are in Republican districts. And

0:12:57.360 --> 0:12:59.480
<v Speaker 7>there are a lot of federal employees who live in

0:12:59.520 --> 0:13:02.720
<v Speaker 7>Republican dominated districts and Republican states, and if they have

0:13:02.800 --> 0:13:06.000
<v Speaker 7>a life change or their their job goes, then you know,

0:13:06.160 --> 0:13:07.240
<v Speaker 7>Trump will hear about it.

0:13:07.559 --> 0:13:09.320
<v Speaker 8>How that DAMA plays out, We're going to see it

0:13:09.320 --> 0:13:10.080
<v Speaker 8>probably in the summer.

0:13:10.160 --> 0:13:13.640
<v Speaker 4>Philip Bump killing it in the post yesterday. On the mandate,

0:13:14.200 --> 0:13:18.720
<v Speaker 4>I went nuts yesterday with David Melpass, professor Schiller, Trumps supporter,

0:13:19.080 --> 0:13:20.040
<v Speaker 4>when he said this was.

0:13:19.960 --> 0:13:21.080
<v Speaker 3>A mandate election.

0:13:21.760 --> 0:13:25.920
<v Speaker 4>You're expert at this, back to eighteen eighty three, if

0:13:25.960 --> 0:13:27.800
<v Speaker 4>not back to Adams Jefferson.

0:13:28.440 --> 0:13:32.520
<v Speaker 3>Was that a mandate election? Professor, Well, it's tough.

0:13:32.520 --> 0:13:35.480
<v Speaker 7>I mean, I think by my calculations, he's just under

0:13:35.520 --> 0:13:37.920
<v Speaker 7>fifty percent. That's what I think we have right now

0:13:37.960 --> 0:13:41.079
<v Speaker 7>in terms of the vote tallies. So under fifty percent

0:13:41.240 --> 0:13:43.480
<v Speaker 7>is typically not thought to be a mandate. It would

0:13:43.480 --> 0:13:46.679
<v Speaker 7>have been a stronger argument had the House Republicans picked

0:13:46.760 --> 0:13:49.280
<v Speaker 7>up any number of seats, but it looks like they'll

0:13:49.280 --> 0:13:52.000
<v Speaker 7>have the same exact vote majority that they had in

0:13:52.080 --> 0:13:55.280
<v Speaker 7>last session. That does not suggest, as I thought earlier

0:13:55.280 --> 0:13:57.560
<v Speaker 7>that Trump's cotails would be long and strong.

0:13:57.720 --> 0:14:00.720
<v Speaker 8>I'm wrong about that. They were not that long and strong.

0:14:01.040 --> 0:14:03.760
<v Speaker 8>And that's the big tests usually with a mandate.

0:14:04.040 --> 0:14:06.240
<v Speaker 7>And then you know, getting people back in the office

0:14:06.320 --> 0:14:08.720
<v Speaker 7>is something private sector has been wanting to do. It

0:14:08.800 --> 0:14:10.480
<v Speaker 7>is true that, you know, you could have expected Trump

0:14:10.480 --> 0:14:13.440
<v Speaker 7>to say everybody come back to work. That's something the musk,

0:14:13.720 --> 0:14:16.040
<v Speaker 7>you know musk, they're they're right about that in the

0:14:16.080 --> 0:14:18.960
<v Speaker 7>sense of that was always going to happen. And so

0:14:19.160 --> 0:14:21.400
<v Speaker 7>I think that's something that will be really interesting to

0:14:21.440 --> 0:14:23.680
<v Speaker 7>see and its impact on DC, by.

0:14:23.520 --> 0:14:24.400
<v Speaker 8>The way, as a city.

0:14:24.720 --> 0:14:27.400
<v Speaker 7>The irony is that it could resurrect downtown d C,

0:14:27.680 --> 0:14:29.720
<v Speaker 7>which would be something I think most people would agree

0:14:29.920 --> 0:14:30.480
<v Speaker 7>is a good thing.

0:14:30.560 --> 0:14:32.680
<v Speaker 3>Yeah, it feels like it the thing they proposed.

0:14:32.800 --> 0:14:35.440
<v Speaker 7>Not everything they proposed will be met with opposition, and

0:14:35.520 --> 0:14:37.720
<v Speaker 7>not everything will be successful or a failure.

0:14:37.880 --> 0:14:41.160
<v Speaker 4>Twenty seconds. Professor Schiller is a Talban center at Brown,

0:14:41.240 --> 0:14:43.520
<v Speaker 4>going to five days a week in the office.

0:14:45.960 --> 0:14:47.000
<v Speaker 8>We are excited.

0:14:47.320 --> 0:14:49.680
<v Speaker 7>We're launching a school public policy ju Live for us,

0:14:49.680 --> 0:14:52.440
<v Speaker 7>so we are excited to really be all of us

0:14:52.480 --> 0:14:53.120
<v Speaker 7>on our game.

0:14:53.240 --> 0:14:53.920
<v Speaker 8>That's my answer.

0:14:53.920 --> 0:14:54.160
<v Speaker 7>There.

0:14:54.320 --> 0:14:57.240
<v Speaker 4>There was a sophisticate the General Counsel pass that at

0:14:57.720 --> 0:15:00.640
<v Speaker 4>Brown University. Wendy Shower, thank you so much, particularly those

0:15:00.680 --> 0:15:05.000
<v Speaker 4>comments on our constitution and our civics that is out there.

0:15:05.360 --> 0:15:09.680
<v Speaker 2>This is the Bloomberg Surveillance Podcast. Listen live each weekday

0:15:09.760 --> 0:15:12.960
<v Speaker 2>starting at seven am Eastern on applecar Play and Android

0:15:12.960 --> 0:15:15.840
<v Speaker 2>Auto with the Bloomberg Business app. You can also listen

0:15:15.960 --> 0:15:19.040
<v Speaker 2>live on Amazon Alexa from our flagship New York station

0:15:19.440 --> 0:15:23.000
<v Speaker 2>Just Say Alexa playing Bloomberg eleven thirty joining us.

0:15:22.920 --> 0:15:25.960
<v Speaker 4>Now the conversation of the day and technology. We had

0:15:26.000 --> 0:15:28.800
<v Speaker 4>Dan Ey's earlier with us from his Sikorski Gene Munster

0:15:29.840 --> 0:15:33.480
<v Speaker 4>joins us now really pleased with that of course, with Deepwater.

0:15:33.040 --> 0:15:36.200
<v Speaker 3>Asset management, Jane, What is the moment we're in?

0:15:36.320 --> 0:15:40.680
<v Speaker 4>Paul Swede and I did important AI seminars for aniog

0:15:40.720 --> 0:15:44.800
<v Speaker 4>Run and Man Deep sing yesterday with Adobe with Salesforce,

0:15:45.280 --> 0:15:49.280
<v Speaker 4>there's Nvidia earnings in the rest. You've been doing this forever.

0:15:49.840 --> 0:15:53.360
<v Speaker 4>What moment are we in in the next year in

0:15:53.480 --> 0:15:54.880
<v Speaker 4>American technology?

0:15:57.160 --> 0:16:00.760
<v Speaker 9>We're in what is going to be the more I

0:16:00.760 --> 0:16:04.600
<v Speaker 9>think exciting year and innovation over the last fifty years.

0:16:04.880 --> 0:16:08.480
<v Speaker 9>And it's hard to really put a language on it

0:16:08.520 --> 0:16:14.560
<v Speaker 9>to capture what the concept of infinite intelligence or close

0:16:14.600 --> 0:16:18.800
<v Speaker 9>to infinite intelligence at almost no cost means for humanity.

0:16:19.320 --> 0:16:22.200
<v Speaker 9>And so as someone who has been around for the

0:16:22.320 --> 0:16:26.200
<v Speaker 9>Internet and saw the boom and bust of that, I

0:16:26.240 --> 0:16:29.600
<v Speaker 9>think this is going to be more significant. And I

0:16:29.640 --> 0:16:32.400
<v Speaker 9>think that next year, if you think about the markets,

0:16:33.240 --> 0:16:36.040
<v Speaker 9>I've been talking about this three to five year bull market,

0:16:36.160 --> 0:16:38.720
<v Speaker 9>and so I started talking about that about a year ago,

0:16:39.000 --> 0:16:40.840
<v Speaker 9>So we're going to say we got a two to.

0:16:40.840 --> 0:16:41.680
<v Speaker 10>Four more years.

0:16:41.680 --> 0:16:45.160
<v Speaker 9>It's going to end in a spectacular bursting of the bubble.

0:16:45.240 --> 0:16:49.000
<v Speaker 9>But I think next year, from just a broader market perspective,

0:16:49.000 --> 0:16:51.560
<v Speaker 9>I think we're going to see another incredible year. And

0:16:51.640 --> 0:16:55.840
<v Speaker 9>I think that it's driven by substance around AI going

0:16:55.920 --> 0:16:59.480
<v Speaker 9>from a build out to starting to move to more

0:16:59.520 --> 0:17:01.240
<v Speaker 9>application since we saw a little bit of that with

0:17:01.240 --> 0:17:04.720
<v Speaker 9>Snowflake today. Of course we've seen most of the excitement

0:17:04.800 --> 0:17:07.919
<v Speaker 9>around the hardware, including Nvidia, but I think we're going

0:17:07.960 --> 0:17:10.960
<v Speaker 9>to start to shift to slowly see more performance in

0:17:11.080 --> 0:17:16.199
<v Speaker 9>software specifically. But just I'm just really excited to be

0:17:16.359 --> 0:17:17.920
<v Speaker 9>alive at this time in life.

0:17:17.920 --> 0:17:19.560
<v Speaker 10>It's just a really special moment.

0:17:19.760 --> 0:17:22.440
<v Speaker 6>Hey, Gene, When I talk to people about AI, I

0:17:22.480 --> 0:17:24.600
<v Speaker 6>often quote you because I remember talking to you maybe

0:17:24.640 --> 0:17:27.359
<v Speaker 6>eighteen months ago on this program, and you put into

0:17:27.440 --> 0:17:30.600
<v Speaker 6>content into a kind of a framework of a companium

0:17:30.640 --> 0:17:33.720
<v Speaker 6>of kind of where you view AI relative to, say,

0:17:33.840 --> 0:17:37.240
<v Speaker 6>the Internet to electricity. Can you just review that for us,

0:17:37.280 --> 0:17:40.440
<v Speaker 6>because it's so powerful to give people a sense of

0:17:40.480 --> 0:17:43.720
<v Speaker 6>how you view the potential impact of AI.

0:17:45.280 --> 0:17:48.960
<v Speaker 9>So it's a zero to one hundred scale, and one

0:17:49.040 --> 0:17:54.000
<v Speaker 9>hundred is the most impactful, most transformative. But at twenty five,

0:17:54.040 --> 0:17:58.399
<v Speaker 9>I would put mobile at about thirty five forty. I

0:17:58.440 --> 0:18:01.720
<v Speaker 9>would have the PC the Internet at fifty. And obviously

0:18:01.760 --> 0:18:03.760
<v Speaker 9>these all build on themselves, but if you look at

0:18:03.760 --> 0:18:06.879
<v Speaker 9>them as a standalone, So the Internet is fifty, I

0:18:06.920 --> 0:18:11.679
<v Speaker 9>think AI is probably ninety and electricity would be one hundred.

0:18:12.080 --> 0:18:15.080
<v Speaker 9>And I think AI would be greater than electricity if

0:18:15.119 --> 0:18:17.720
<v Speaker 9>not for the fact that you need electricity to run

0:18:17.760 --> 0:18:20.960
<v Speaker 9>these machines. Of course, But this is a big deal, Paul,

0:18:21.160 --> 0:18:23.480
<v Speaker 9>And you know there's that's a lot of hype.

0:18:23.480 --> 0:18:24.320
<v Speaker 10>There's a lot of hype in that.

0:18:24.400 --> 0:18:26.479
<v Speaker 9>I think the substance will exceed that hype.

0:18:26.960 --> 0:18:30.400
<v Speaker 6>Yeah, but you're gene when someone like you makes that analysis.

0:18:30.560 --> 0:18:32.920
<v Speaker 6>A lot of people like myself, we pay attention here

0:18:33.040 --> 0:18:35.800
<v Speaker 6>as we try to get our minds around AI. What's

0:18:35.840 --> 0:18:38.320
<v Speaker 6>the next step here? At it feels like we're we're

0:18:38.440 --> 0:18:40.960
<v Speaker 6>so early innings on this, but it feels like we're

0:18:40.960 --> 0:18:42.520
<v Speaker 6>now at the discussion point where.

0:18:42.440 --> 0:18:45.000
<v Speaker 3>The next step is one of your kids needs in Mac.

0:18:45.320 --> 0:18:48.000
<v Speaker 6>It's a new left, new laptop with the AX just

0:18:48.080 --> 0:18:52.240
<v Speaker 6>so you know, that's the next steps this weekend exactly, Geene,

0:18:52.440 --> 0:18:53.919
<v Speaker 6>are we at the point now where we really have

0:18:53.960 --> 0:18:57.399
<v Speaker 6>to make the use case for AI applications? Maybe the

0:18:57.480 --> 0:18:59.480
<v Speaker 6>return on all this investment that we've seen.

0:19:00.720 --> 0:19:02.199
<v Speaker 9>I don't think we're at that point. I think we're

0:19:02.240 --> 0:19:04.120
<v Speaker 9>going to start to see some of that next year.

0:19:04.119 --> 0:19:07.240
<v Speaker 9>And that has been the hesitancy around this whole AI

0:19:07.359 --> 0:19:10.800
<v Speaker 9>trade is that the I think the skeptics would say

0:19:10.840 --> 0:19:14.320
<v Speaker 9>that we just really haven't seen those incredible bread taking

0:19:15.200 --> 0:19:17.639
<v Speaker 9>use cases yet, but they will ultimately come. And I

0:19:17.640 --> 0:19:20.120
<v Speaker 9>think that what we saw with the Nvidia earnings last

0:19:20.200 --> 0:19:23.520
<v Speaker 9>night and specifically this demand, I mean, this is incredible.

0:19:23.520 --> 0:19:25.560
<v Speaker 9>I've never seen a story like this. They did twenty

0:19:25.560 --> 0:19:28.840
<v Speaker 9>one billion in revenue in two thousand. Calendar twenty two,

0:19:29.160 --> 0:19:30.879
<v Speaker 9>they'll do call it one hundred and forty billion in

0:19:30.960 --> 0:19:34.040
<v Speaker 9>revenue next year. There's nothing been anything like this, And

0:19:34.080 --> 0:19:36.639
<v Speaker 9>when you see that, what they're doing is they're building

0:19:36.720 --> 0:19:40.040
<v Speaker 9>the infrastructure. Was still largely in this building infrastructure phase, Paul.

0:19:40.160 --> 0:19:44.080
<v Speaker 9>But some of these breathtaking applications that will change basically

0:19:44.160 --> 0:19:45.600
<v Speaker 9>all forms of our life. I think we're going to

0:19:45.640 --> 0:19:48.280
<v Speaker 9>start to see those a little bit later next year.

0:19:48.760 --> 0:19:52.080
<v Speaker 9>Specifically to this concept of agentic AI came up a

0:19:52.119 --> 0:19:54.919
<v Speaker 9>lot on the Nvidia call last night. But this idea

0:19:54.960 --> 0:19:57.320
<v Speaker 9>of these agents going out and actually getting work done

0:19:57.320 --> 0:19:58.720
<v Speaker 9>for us, I think that's going to be the big

0:19:58.800 --> 0:20:00.840
<v Speaker 9>change how we're all going to interact with these eight.

0:20:01.560 --> 0:20:04.480
<v Speaker 4>First single work gene Munster with us here right now,

0:20:04.480 --> 0:20:06.679
<v Speaker 4>folks thrill these with us, Danizer with US earlier as

0:20:06.720 --> 0:20:10.040
<v Speaker 4>we look at technology in America, Munster with deep water

0:20:10.400 --> 0:20:13.920
<v Speaker 4>asset management, Gene Muster. I look at the Apple A

0:20:14.080 --> 0:20:16.200
<v Speaker 4>and R used to be part of the cellside racket.

0:20:16.280 --> 0:20:20.760
<v Speaker 4>Loved to Piper Jeff for years ago in Minnesota. But Gina,

0:20:20.800 --> 0:20:22.879
<v Speaker 4>I'm looking at Okay, I got forty buys, but the

0:20:22.920 --> 0:20:23.400
<v Speaker 4>fact is.

0:20:23.320 --> 0:20:26.359
<v Speaker 3>They got eighteen holds. I got Apple in a trading

0:20:26.440 --> 0:20:29.880
<v Speaker 3>range since June. It's butchers up now near new highs.

0:20:30.400 --> 0:20:33.480
<v Speaker 4>I assume Gene Munster thinks Apple's going to break out,

0:20:34.160 --> 0:20:35.800
<v Speaker 4>How is it going to break out?

0:20:35.880 --> 0:20:38.520
<v Speaker 3>What will be the catalyst at the top of the

0:20:38.560 --> 0:20:40.760
<v Speaker 3>income statement or on downward.

0:20:42.359 --> 0:20:45.120
<v Speaker 9>It's going to be at the top, and it's it's

0:20:45.160 --> 0:20:47.879
<v Speaker 9>going to be either later this fiscal year, kind of

0:20:48.160 --> 0:20:50.560
<v Speaker 9>the June quarter ish of next year, I think is

0:20:50.600 --> 0:20:54.679
<v Speaker 9>a potential breakout or in fiscal twenty six. And I

0:20:54.680 --> 0:20:56.960
<v Speaker 9>think it's inevitable that this happens. And to put some

0:20:57.040 --> 0:20:59.119
<v Speaker 9>numbers around it, what that breakout is is that the

0:20:59.119 --> 0:21:02.800
<v Speaker 9>streets looking for or iPhone growth in fiscal twenty five

0:21:03.000 --> 0:21:06.360
<v Speaker 9>to be around six percent and for fiscal twenty six

0:21:06.440 --> 0:21:08.359
<v Speaker 9>a pretty similar number. I think that that can be

0:21:08.480 --> 0:21:12.800
<v Speaker 9>ten to fifteen percent. And ultimately all this talk about AI,

0:21:13.400 --> 0:21:17.280
<v Speaker 9>the vast majority of people still don't use AI. And

0:21:18.080 --> 0:21:20.240
<v Speaker 9>if you look at the number of chat GPT on

0:21:20.280 --> 0:21:22.600
<v Speaker 9>a daily basis is probably about one hundred and fifty million,

0:21:22.640 --> 0:21:25.480
<v Speaker 9>two hundred million people. If you look at metas products,

0:21:25.520 --> 0:21:29.560
<v Speaker 9>that's about three point two billion Instagram and so this

0:21:29.600 --> 0:21:31.600
<v Speaker 9>is still a fraction of the use. But what is

0:21:31.800 --> 0:21:34.959
<v Speaker 9>special about what Apple is doing is taking these tools

0:21:34.960 --> 0:21:38.320
<v Speaker 9>and just making them accessible to everyone. And I think that, well, today,

0:21:38.359 --> 0:21:39.840
<v Speaker 9>those features really.

0:21:39.680 --> 0:21:41.399
<v Speaker 10>Don't light up your life.

0:21:41.440 --> 0:21:43.520
<v Speaker 9>I think that as they roll more of them out,

0:21:43.560 --> 0:21:45.240
<v Speaker 9>it will become more clear to people that they have

0:21:45.280 --> 0:21:46.000
<v Speaker 9>to upgrade.

0:21:46.440 --> 0:21:48.920
<v Speaker 6>If I were to put together kind of an ETF

0:21:49.000 --> 0:21:51.800
<v Speaker 6>for AI, because I'm trying to get some exposure to AI,

0:21:51.880 --> 0:21:54.880
<v Speaker 6>but maybe I feel like I've missed the Nvidia trade.

0:21:55.160 --> 0:21:58.879
<v Speaker 6>Are you asking for one of your kids emailed in

0:21:58.960 --> 0:22:01.200
<v Speaker 6>for one of your kids need a basket of AI?

0:22:01.280 --> 0:22:04.680
<v Speaker 6>How else are you thinking about kind of getting exposure

0:22:04.720 --> 0:22:08.560
<v Speaker 6>to Again, this is really seminal shift in technology.

0:22:09.440 --> 0:22:12.479
<v Speaker 9>Paul, I'd be remiss not to mention we have an

0:22:12.480 --> 0:22:16.679
<v Speaker 9>ETF the tickers LOUP. It is frontier tech and it

0:22:16.800 --> 0:22:22.000
<v Speaker 9>basically gives exposure to AI themes that are outside of

0:22:22.040 --> 0:22:25.200
<v Speaker 9>the megacap So if you're looking for some smaller companies

0:22:25.280 --> 0:22:27.720
<v Speaker 9>like Verted, for example, that does cooling, it was mentioned

0:22:27.760 --> 0:22:29.760
<v Speaker 9>on the call, these are the sub kind of two

0:22:29.840 --> 0:22:32.680
<v Speaker 9>hundred billion dollars, So I have to mention that LOUP.

0:22:32.880 --> 0:22:36.320
<v Speaker 9>But I would say that more broadly, I still think

0:22:36.359 --> 0:22:38.639
<v Speaker 9>we're in a period where the hardware piece.

0:22:38.720 --> 0:22:40.080
<v Speaker 10>This is a contrarian view.

0:22:40.160 --> 0:22:42.800
<v Speaker 9>I think the hardware trade is going to continue to

0:22:42.840 --> 0:22:46.600
<v Speaker 9>be stronger than expected over the next several quarters, and

0:22:46.640 --> 0:22:49.400
<v Speaker 9>then as I mentioned, kind of exiting end of next year.

0:22:49.400 --> 0:22:51.480
<v Speaker 9>I think, you know what we're seeing with Snowflake. I

0:22:51.480 --> 0:22:53.040
<v Speaker 9>think you're going to see more and more of that

0:22:53.080 --> 0:22:55.800
<v Speaker 9>in software land kind of later next year. But that's

0:22:55.840 --> 0:22:57.679
<v Speaker 9>kind of how I think about it, still in the

0:22:57.720 --> 0:23:00.520
<v Speaker 9>hardware piece and then moving to software later on.

0:23:00.760 --> 0:23:06.399
<v Speaker 4>Okay, lup up to thirty five percent, three five up,

0:23:06.480 --> 0:23:08.120
<v Speaker 4>thirty five percent.

0:23:07.920 --> 0:23:11.520
<v Speaker 3>One year trail. Robinhood's you're number one holding?

0:23:12.600 --> 0:23:13.000
<v Speaker 5>How do you have?

0:23:13.119 --> 0:23:15.960
<v Speaker 3>Robinhood is part of AI.

0:23:17.160 --> 0:23:21.040
<v Speaker 9>So what they're using is as part of how they

0:23:21.200 --> 0:23:26.400
<v Speaker 9>assess risk with customers, they use AI. They also use

0:23:26.440 --> 0:23:30.720
<v Speaker 9>AI as a means to go and generate new customers

0:23:30.760 --> 0:23:34.560
<v Speaker 9>so for doing outreach. So they've been pretty they've been

0:23:34.600 --> 0:23:37.600
<v Speaker 9>proactive at doing that. So it's companies like Robinhood is.

0:23:37.760 --> 0:23:40.879
<v Speaker 9>Another company that we looked at recently to add to

0:23:40.880 --> 0:23:44.000
<v Speaker 9>this was like Crispin Green. It sounds crazy, but they

0:23:44.000 --> 0:23:48.320
<v Speaker 9>have a big robotics initiative going on. So it is

0:23:48.359 --> 0:23:50.959
<v Speaker 9>these companies that are studying to implement AI kind of

0:23:51.040 --> 0:23:51.640
<v Speaker 9>under the hood.

0:23:52.080 --> 0:23:55.959
<v Speaker 6>Hey, Gene Snowflake, I am looking at Snowflake stocks up

0:23:55.960 --> 0:23:58.920
<v Speaker 6>twenty five percent. Can you tell us what Snowflake does

0:23:58.960 --> 0:24:02.080
<v Speaker 6>and why the stock is up so much today?

0:24:02.200 --> 0:24:03.440
<v Speaker 10>So what Snowflake does.

0:24:03.640 --> 0:24:07.720
<v Speaker 9>It's called a data warehouse, which means that you take data.

0:24:07.920 --> 0:24:12.119
<v Speaker 9>It could be anything from structured data inside of a business.

0:24:12.119 --> 0:24:15.359
<v Speaker 9>Structured data would be data that is in like an

0:24:15.359 --> 0:24:19.440
<v Speaker 9>Oracle database, and it warehouses it so AI models can

0:24:19.480 --> 0:24:22.679
<v Speaker 9>train on it. It basically makes data more accessible and

0:24:22.840 --> 0:24:27.400
<v Speaker 9>usable for training. So that is what they're benefiting from

0:24:27.600 --> 0:24:30.879
<v Speaker 9>is just more training and more enterprises starting to use this.

0:24:31.160 --> 0:24:33.840
<v Speaker 9>Their biggest competitor where they've been losing share, the reason

0:24:33.880 --> 0:24:37.680
<v Speaker 9>why the stock has not done well before today. It's

0:24:37.720 --> 0:24:40.360
<v Speaker 9>called data Bricks, and they have a data lake where

0:24:40.400 --> 0:24:43.240
<v Speaker 9>you can just throw in PDF and Excel documents and

0:24:43.920 --> 0:24:46.120
<v Speaker 9>just throw a bunch of data into this data lake

0:24:46.119 --> 0:24:47.679
<v Speaker 9>and then it organizes it for you.

0:24:47.720 --> 0:24:49.640
<v Speaker 10>But that's what's going on with Snowflake.

0:24:49.840 --> 0:24:50.040
<v Speaker 3>Jane.

0:24:50.080 --> 0:24:53.880
<v Speaker 4>First question to Eli Greenfield yesterday at Adobe is all

0:24:53.880 --> 0:24:56.320
<v Speaker 4>this fancy gene monster data ives talk.

0:24:56.920 --> 0:24:59.040
<v Speaker 3>Is it going to take our jobs away from us?

0:25:02.320 --> 0:25:02.480
<v Speaker 10>Well?

0:25:02.680 --> 0:25:05.080
<v Speaker 9>As an asset manager, I think that that's a very

0:25:05.119 --> 0:25:08.600
<v Speaker 9>real potential. I think as an ANALYSTI it's a little different.

0:25:08.640 --> 0:25:10.920
<v Speaker 9>At deep Water, we launched an affiliate company a few

0:25:10.960 --> 0:25:15.560
<v Speaker 9>months ago called Intelligent Alpha, and it basically is machines picking.

0:25:16.040 --> 0:25:19.280
<v Speaker 9>It's just machines picking stocks, just an et F L

0:25:19.320 --> 0:25:23.920
<v Speaker 9>I v R livermore. But I think it is. It's

0:25:23.920 --> 0:25:28.879
<v Speaker 9>something I just mentioned that in context too. It's one

0:25:28.920 --> 0:25:30.480
<v Speaker 9>of two things is going to happen. Either it's going

0:25:30.520 --> 0:25:33.640
<v Speaker 9>to take our jobs. As an asset manager. I would

0:25:33.680 --> 0:25:36.920
<v Speaker 9>say for you and Paul and your team, you need

0:25:37.000 --> 0:25:41.160
<v Speaker 9>not worry. One of the things that AI won't take

0:25:41.200 --> 0:25:45.520
<v Speaker 9>away is community and empathy. And that's things definitionly that

0:25:45.600 --> 0:25:47.160
<v Speaker 9>can't be done. And I think that's what you guys

0:25:47.200 --> 0:25:47.800
<v Speaker 9>bring every day.

0:25:48.080 --> 0:25:50.879
<v Speaker 3>Well, we have community, do we have empathy? Lisa, I

0:25:50.880 --> 0:25:51.680
<v Speaker 3>don't you know.

0:25:51.920 --> 0:25:52.560
<v Speaker 1>That's a stretch.

0:25:52.880 --> 0:25:56.720
<v Speaker 4>You know, it's like pushing the envelope here a little

0:25:56.720 --> 0:25:58.920
<v Speaker 4>to its gene monster hugely valuable.

0:25:58.920 --> 0:25:59.919
<v Speaker 3>We get a huge signs.

0:26:06.480 --> 0:26:10.760
<v Speaker 2>This is the Bloomberg Surveillance Podcast. Listen live each weekday

0:26:10.880 --> 0:26:14.439
<v Speaker 2>starting at seven am Eastern on Applecarplay and Android Auto

0:26:14.480 --> 0:26:17.240
<v Speaker 2>with the Bloomberg Business app. You can also watch us

0:26:17.359 --> 0:26:21.359
<v Speaker 2>live every weekday on YouTube and always on the Bloomberg terminal.

0:26:21.600 --> 0:26:22.600
<v Speaker 3>You better not have Chris.

0:26:22.600 --> 0:26:25.240
<v Speaker 4>Sale on this, Lisa, I'll tell you that your daily

0:26:25.280 --> 0:26:29.080
<v Speaker 4>look at the front pages of Lisa Mateo hour anticipated

0:26:29.320 --> 0:26:30.040
<v Speaker 4>Lisa Mateo.

0:26:30.280 --> 0:26:32.760
<v Speaker 1>Okay, it's the end of the year, right, Accounting departments

0:26:32.840 --> 0:26:35.480
<v Speaker 1>racing to close the books, so they have all these

0:26:35.520 --> 0:26:37.600
<v Speaker 1>expenses to go through, and they got to get them through.

0:26:37.640 --> 0:26:39.439
<v Speaker 1>So now what they're finding out is a lot of

0:26:39.480 --> 0:26:42.960
<v Speaker 1>workers are pushing the limits with those company credit cards.

0:26:43.000 --> 0:26:45.840
<v Speaker 1>They're finding things, they're telling them Wester Journal. People are

0:26:45.840 --> 0:26:50.200
<v Speaker 1>buying things like Christmas gifts, jet skis, cruises, plastic surgery.

0:26:50.640 --> 0:26:54.080
<v Speaker 1>They're even charging their monthly mortgage payment because they're disguising

0:26:54.359 --> 0:26:58.239
<v Speaker 1>the lender as a business vendor. So this is kind

0:26:58.280 --> 0:27:00.680
<v Speaker 1>of coming under screwtin now you end of the year,

0:27:00.760 --> 0:27:02.240
<v Speaker 1>like people are under the guns that they're trying to

0:27:02.320 --> 0:27:02.800
<v Speaker 1>rush these things.

0:27:04.040 --> 0:27:06.760
<v Speaker 6>Would not allow this, I don't think here at Bloomberg

0:27:07.000 --> 0:27:09.720
<v Speaker 6>he would not. All right, interesting, I mean, I know

0:27:09.760 --> 0:27:11.600
<v Speaker 6>people got like some of the spending accounts and they

0:27:11.600 --> 0:27:13.639
<v Speaker 6>gotta flush the money before they lose it and all

0:27:13.640 --> 0:27:14.200
<v Speaker 6>that kind of stuff.

0:27:14.320 --> 0:27:16.159
<v Speaker 4>I had a beverage of my choice ones for the

0:27:16.240 --> 0:27:19.720
<v Speaker 4>evil Laura, she's in charge of all of our auditing,

0:27:19.760 --> 0:27:22.280
<v Speaker 4>and she told me a couple of stories own.

0:27:22.200 --> 0:27:27.040
<v Speaker 3>And Laura told me, you know, she said, time you competing. Yeah, exactly,

0:27:27.280 --> 0:27:28.480
<v Speaker 3>next it's on the hand.

0:27:28.600 --> 0:27:31.360
<v Speaker 4>No, seriously, it's and you look, we're like everybody else.

0:27:31.400 --> 0:27:34.000
<v Speaker 4>We monitor it is, you know, we do, we do,

0:27:34.160 --> 0:27:34.359
<v Speaker 4>we do.

0:27:34.960 --> 0:27:37.280
<v Speaker 1>So yesterday we talked about the Comcast spinoff right of

0:27:37.320 --> 0:27:39.679
<v Speaker 1>the cable channels, and Paul, it's funny because you and

0:27:39.760 --> 0:27:42.160
<v Speaker 1>Alex and I were talking about this. What about these

0:27:42.320 --> 0:27:44.720
<v Speaker 1>MSNBC staffers like they're going to start to be in

0:27:44.760 --> 0:27:47.280
<v Speaker 1>a panic? Well, sources actually telling you The New York Post,

0:27:48.160 --> 0:27:51.240
<v Speaker 1>MSNBC start, Rachel Maddock, Chris Jansen, Kitty Tour. They had

0:27:51.240 --> 0:27:54.439
<v Speaker 1>this tense meeting at thirty Rock led by NBC Universal

0:27:54.480 --> 0:27:57.280
<v Speaker 1>chairman Mark Lazarus, who's going to lead the company, and

0:27:57.280 --> 0:28:00.520
<v Speaker 1>they say staffers, they're fearing layoffs. They just want to know,

0:28:00.600 --> 0:28:03.720
<v Speaker 1>is it going to change the logo, the name, the headquarters.

0:28:04.520 --> 0:28:08.240
<v Speaker 1>Lazarus said that he just wasn't sure, So it wasn't

0:28:08.359 --> 0:28:11.320
<v Speaker 1>like a good sign. But it's just MSNBC. You know,

0:28:11.440 --> 0:28:13.800
<v Speaker 1>since the election, their ratings have been tanking according to

0:28:13.920 --> 0:28:17.679
<v Speaker 1>Nielsen and people you know MSNBCC they use, they cross

0:28:17.800 --> 0:28:19.119
<v Speaker 1>use a lot of reporters.

0:28:19.160 --> 0:28:23.400
<v Speaker 4>There's the financial side which is always interesting, but the

0:28:23.520 --> 0:28:27.080
<v Speaker 4>editorial side will be very very interesting. In the heritage

0:28:27.600 --> 0:28:31.840
<v Speaker 4>going back, pushing thirty years back when it was Microsoft NBC.

0:28:32.200 --> 0:28:33.920
<v Speaker 4>I mean, there's a lot of heritage here.

0:28:34.040 --> 0:28:36.840
<v Speaker 6>Yep, absolutely, I mean, and again you just think kind

0:28:36.840 --> 0:28:39.840
<v Speaker 6>of where the country is these days? Where is MSNBC

0:28:40.000 --> 0:28:43.560
<v Speaker 6>and it's editorial background. Where does that kind of stand up?

0:28:43.560 --> 0:28:45.720
<v Speaker 6>And again it's a standalone company. You can't hide behind

0:28:46.160 --> 0:28:48.400
<v Speaker 6>the compcast, broadband and cable television number.

0:28:48.440 --> 0:28:50.600
<v Speaker 4>Its fascinating and of course we're all looking at this

0:28:50.680 --> 0:28:52.880
<v Speaker 4>folks on the island of Manhattan.

0:28:52.880 --> 0:28:55.560
<v Speaker 3>I mean, it only focuses on three zip codes. Right,

0:28:55.600 --> 0:28:58.600
<v Speaker 3>there's a zip code of Sunset Boulevard and here in

0:28:58.680 --> 0:29:00.280
<v Speaker 3>New York. Next, Li said, all.

0:29:00.320 --> 0:29:03.160
<v Speaker 1>Right, if you're going shopping New York City's fifth Avenue,

0:29:03.520 --> 0:29:07.680
<v Speaker 1>it is no longer the world's most expensive shopping street. Okay,

0:29:07.720 --> 0:29:10.480
<v Speaker 1>this is according to a new list from Cushman and Wakefield.

0:29:11.000 --> 0:29:14.280
<v Speaker 1>The number one is now Milan's let me see if

0:29:14.320 --> 0:29:22.360
<v Speaker 1>I say this right, via Montenna Napoleon. Enough, it has

0:29:22.400 --> 0:29:24.640
<v Speaker 1>the first but the first time a European city topped

0:29:24.640 --> 0:29:27.560
<v Speaker 1>the list. So it gets the rents globally from about

0:29:27.560 --> 0:29:30.440
<v Speaker 1>one hundred and thirty eight luxury shopping districts, so this

0:29:30.480 --> 0:29:33.000
<v Speaker 1>one topped it. The reason why it is so expensive

0:29:33.080 --> 0:29:35.600
<v Speaker 1>is because it's a shorter street than everyone else on

0:29:35.640 --> 0:29:39.040
<v Speaker 1>the list. So it's about two thousand dollars per square foot.

0:29:39.080 --> 0:29:41.000
<v Speaker 1>That's you know a little bit higher eleven percent than

0:29:41.000 --> 0:29:43.520
<v Speaker 1>they paid the year before. So Fifth Avenue came in second,

0:29:43.720 --> 0:29:45.200
<v Speaker 1>and London's New Bond.

0:29:45.000 --> 0:29:47.840
<v Speaker 6>Street that ubon. Okay, that's right. Well Milan, I mean

0:29:47.920 --> 0:29:50.640
<v Speaker 6>one of my favorite hotels in the world, Hotel Pinchy

0:29:50.680 --> 0:29:51.640
<v Speaker 6>Pey Savoy.

0:29:51.920 --> 0:29:54.440
<v Speaker 4>Oh yeah, can I tell you where growing zeros on

0:29:54.480 --> 0:29:59.840
<v Speaker 4>the three three via Monte Napoleon and that would be

0:30:00.080 --> 0:30:02.680
<v Speaker 4>Fendi Milano monten Napoleon.

0:30:03.440 --> 0:30:06.600
<v Speaker 3>And yes, it's two thousand dollars per square foot. Every

0:30:06.640 --> 0:30:08.160
<v Speaker 3>time you walk a square.

0:30:07.840 --> 0:30:12.720
<v Speaker 6>Foot it goes k ching, nice, you love me.

0:30:13.360 --> 0:30:15.240
<v Speaker 5>Next, and finally here's our kicker.

0:30:15.280 --> 0:30:18.680
<v Speaker 1>So this piece of artwork a duct tape Banantic. So

0:30:18.880 --> 0:30:22.760
<v Speaker 1>he's six point million dollars at Southerb's in New York.

0:30:23.560 --> 0:30:26.680
<v Speaker 1>Six minutes of fear spitting, and yes we did have it.

0:30:26.680 --> 0:30:31.600
<v Speaker 1>It's called the Comedian, okay. It's a sculpture artist Marizio Catilan.

0:30:32.120 --> 0:30:35.240
<v Speaker 1>The buyers is China born crypto entrepreneur. He's going to

0:30:35.280 --> 0:30:37.600
<v Speaker 1>consider lending it out to people, he said. Even if

0:30:37.640 --> 0:30:39.840
<v Speaker 1>Elon mus wants to take it to the moon, he's

0:30:39.840 --> 0:30:40.520
<v Speaker 1>more than welcome.

0:30:40.560 --> 0:30:43.000
<v Speaker 4>In twenty nineteen, if it was one and a half

0:30:43.120 --> 0:30:46.400
<v Speaker 4>million and they popped the sucker at six million, I mean,

0:30:46.760 --> 0:30:47.480
<v Speaker 4>Tang is our.

0:30:47.680 --> 0:30:49.640
<v Speaker 3>You know, I'm drinking Tang zero this morning.

0:30:50.040 --> 0:30:51.880
<v Speaker 4>But you know, if you were to duct tape and

0:30:52.000 --> 0:30:55.440
<v Speaker 4>orange onto a wall, I mean, that's gotta be worth

0:30:55.440 --> 0:30:58.840
<v Speaker 4>eight hundred dollars and you have done.

0:30:58.600 --> 0:31:02.360
<v Speaker 1>It of our YouTube viewers who can actually.

0:31:02.080 --> 0:31:06.520
<v Speaker 4>Mister full disclosure, folks, mister Bloomberg, with his wonderful philanthropy,

0:31:06.960 --> 0:31:11.680
<v Speaker 4>is expanding out on Madison Avenue to the Gagosian Gallery building,

0:31:11.680 --> 0:31:13.760
<v Speaker 4>which is I gets right across from the Carlisle. It's

0:31:13.760 --> 0:31:17.280
<v Speaker 4>a spectacular building and Mike and the architects are doing

0:31:17.280 --> 0:31:20.400
<v Speaker 4>it justice. And I think it's some of those galleries

0:31:21.040 --> 0:31:22.400
<v Speaker 4>in Gagosian's building.

0:31:22.440 --> 0:31:25.480
<v Speaker 3>I mean, I'm sorry the orange tape to the wall.

0:31:26.280 --> 0:31:30.480
<v Speaker 6>You know, folks on YouTube, we have an orange tape

0:31:30.520 --> 0:31:33.320
<v Speaker 6>to the wall in homage to the banana. What do

0:31:33.400 --> 0:31:35.160
<v Speaker 6>we get for the orange? Do you think it's.

0:31:35.000 --> 0:31:37.920
<v Speaker 3>Called still tank at the end of it. It's a

0:31:38.040 --> 0:31:38.720
<v Speaker 3>still life.

0:31:39.240 --> 0:31:40.920
<v Speaker 1>I just want to know if it's the same banana

0:31:40.920 --> 0:31:42.000
<v Speaker 1>from twenty nineteen.

0:31:43.160 --> 0:31:43.640
<v Speaker 3>Exactly.

0:31:43.720 --> 0:31:44.200
<v Speaker 5>You don't know.

0:31:44.280 --> 0:31:45.160
<v Speaker 8>It doesn't say.

0:31:45.440 --> 0:31:47.040
<v Speaker 3>It's what we call installation.

0:31:47.240 --> 0:31:50.520
<v Speaker 5>If you read art news, Okay, the latest.

0:31:50.520 --> 0:31:53.200
<v Speaker 3>It came from Art Basel, Basil. I don't know my name,

0:31:54.680 --> 0:31:57.760
<v Speaker 3>Damien Goes, Alex Steele. I think Goes. Probably I've never

0:31:57.800 --> 0:32:01.360
<v Speaker 3>gone to Art Basel. No, no, mclone. Yes, he is

0:32:01.400 --> 0:32:05.040
<v Speaker 3>so tasteful when he goes. He wears socks exactly. That's it.

0:32:05.040 --> 0:32:05.600
<v Speaker 5>It's formal.

0:32:06.120 --> 0:32:07.880
<v Speaker 8>Are you done?

0:32:07.280 --> 0:32:10.360
<v Speaker 1>I love your edition with the orange tape to.

0:32:10.320 --> 0:32:13.200
<v Speaker 4>The Okay, I got a live chat here YouTube that

0:32:14.200 --> 0:32:17.200
<v Speaker 4>Wicked is going to flop. I mean, what do you think, Lisa, Oh,

0:32:17.320 --> 0:32:20.000
<v Speaker 4>I don't telegraph butchered it in London.

0:32:20.000 --> 0:32:24.160
<v Speaker 1>Don't Ariana. All all the kids are talking about going.

0:32:24.240 --> 0:32:27.400
<v Speaker 1>They're dressing up, some of them, so yeah, it's an event.

0:32:28.040 --> 0:32:31.800
<v Speaker 3>Like how Lisa Matteo, are you going dressed.

0:32:31.560 --> 0:32:33.280
<v Speaker 8>Like I'm going to Gladiator?

0:32:34.240 --> 0:32:36.200
<v Speaker 6>You're going Gladiator?

0:32:37.160 --> 0:32:37.480
<v Speaker 7>I don't.

0:32:37.760 --> 0:32:40.000
<v Speaker 1>I just love that. Denzel Washington's big.

0:32:41.040 --> 0:32:44.200
<v Speaker 3>Lisa Mateo, thank you so much. Our newspaper segment.

0:32:44.320 --> 0:32:48.760
<v Speaker 2>This is the Bloomberg Surveillance Podcast, available on Apple, Spotify,

0:32:48.920 --> 0:32:53.040
<v Speaker 2>and anywhere else you get your podcasts. Listen live each weekday,

0:32:53.120 --> 0:32:56.040
<v Speaker 2>seven to ten a m. Eastern on Bloomberg dot Com,

0:32:56.160 --> 0:32:59.760
<v Speaker 2>the iHeartRadio app, tune In, and the Bloomberg Business app.

0:33:00.080 --> 0:33:03.080
<v Speaker 2>You can also watch us live every weekday on YouTube

0:33:03.360 --> 0:33:05.160
<v Speaker 2>and always on the Bloomberg terminal