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Speaker 1: Welcome to the Bloomberg Markets Podcast. I'm Paul Sweeney alongside

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Speaker 1: my co host Matt Miller.

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Speaker 2: Every business day we bring you interviews from CEOs, market pros,

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Speaker 2: and Bloomberg experts, along with essential market moving news.

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Speaker 1: Find the Bloomberg Markets podcast called Apple Podcasts or wherever

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Speaker 1: you listen to podcasts, and at Bloomberg dot com slash podcast.

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Speaker 1: What a take a look at some eco data coming

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Speaker 1: at here. I'm looking at the leading Economic indicators.

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Speaker 3: Let me see what I pop it up on my

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Speaker 3: ECO screen, which.

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Speaker 1: Gives you all the economic data out there. The Leading

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Speaker 1: Economic Index came in. It negative zero point six percent. I

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Speaker 1: guess the bad news is it's it's a negative number,

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Speaker 1: but it's in line with expectations and it's better than

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Speaker 1: the prior month. But let's get some perspective on what

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Speaker 1: this leading economic indicator means. Dana Peterson, she's a chief

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Speaker 1: economist at the Conference Board. She joins us here. So

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Speaker 1: Dana put this number in perspective for us.

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Speaker 4: Sure, I mean this number is just in a string

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Speaker 4: of really bad numbers of negative LAI prints the last year,

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Speaker 4: and it continues to suggest when you look at the

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Speaker 4: year over year measure of this that there's a recession.

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Speaker 4: It's probably starting right about now. And indeed, when we

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Speaker 4: look at the components, most of the components have been

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Speaker 4: very weak, not only in the last month but over

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Speaker 4: the last six months. And certainly credit conditions have been tightening,

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Speaker 4: consumer confidence has been weaker, housing and activities been on

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Speaker 4: the downturn. The only areas that have been somewhat better

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Speaker 4: were certainly the labor market indicators even leading it. But

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Speaker 4: even JABOS claims, which are leading indicators, have been picking

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Speaker 4: up a little bit as the labor market is showing

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Speaker 4: some signs of cracks.

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Speaker 1: So, I mean, give us a sense, though, Dana, how

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Speaker 1: is the trend here? You know, it's tough to read here,

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Speaker 1: but what are you seeing in some of the data?

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Speaker 1: Is you kind of do a three month or six

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Speaker 1: months kind of trailing kind of look at this thing.

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Speaker 4: Sure, so definitely a year on years down eight percent,

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Speaker 4: so that's not great. And when we look at the

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Speaker 4: six months that's down eight point seven percent, and so

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Speaker 4: both these measures are pretty weak. Certainly three months, looking

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Speaker 4: at three months, it's still down. So no matter what

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Speaker 4: gauge or transformation you do on these data, it's telling

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Speaker 4: us the same story that the economy is weakening, it's flowing.

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Speaker 4: We've already seen housing activity cool. Businesses are investing less.

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Speaker 4: But the key thing is the labor market and consumer

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Speaker 4: spending on services. We saw in the retail sales data

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Speaker 4: that consumers are still pleased to go out and go

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Speaker 4: to restaurants and they are interested in services. And also

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Speaker 4: the jobs market is holding up. But like I said,

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Speaker 4: there are some signs of weakness, certainly amongst those former

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Speaker 4: pandemic darlings that are restructuring at this time. But you

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Speaker 4: still have a lot of those services industries where you

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Speaker 4: have to physically show up to work still hiring. So

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Speaker 4: that's the challenge certainly for our CEI, which is the

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Speaker 4: current economic indicator, and that has not moved really, it's

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Speaker 4: continuing to sow strength because two out of the four

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Speaker 4: measures are from the labor market.

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Speaker 1: So at the conference board, what is your kind of

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Speaker 1: economic outlook in terms of recession?

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Speaker 3: Are we having one? Are we in one?

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Speaker 1: How deep will it be, how prolonged will it be?

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Speaker 1: Where are you guys as you take a look at

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Speaker 1: your data.

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Speaker 4: Sure, we do believe a recession is going to happen.

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Speaker 4: It will be short and shallow. We're thinking that we're

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Speaker 4: probably going to see negative GDP growth in the second

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Speaker 4: quarter where we are right now, and then it'll deepen

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Speaker 4: in the third quarter, be a little less bad in

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Speaker 4: the fourth quarter, and then by the beginning of next

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Speaker 4: year we'll be coming out of it. So not too bad,

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Speaker 4: but certainly a recession nonetheless.

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Speaker 1: So in terms of inflation, that's clearly what our Federal

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Speaker 1: Reserve is looking at here. And we heard comments from

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Speaker 1: some FED members just over the last couple of days

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Speaker 1: they were down in Amelia Island, Florida at the Atlanta

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Speaker 1: FED conference Amelia Island, Florida. I mean, have a not Cleveland, John,

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Speaker 1: I mean, who's kidding who here? But so, what do

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Speaker 1: you think the FED is going to do with some

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Speaker 1: of this data we're seeing here?

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Speaker 3: Dana?

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Speaker 1: Do you think this Fed's going to pause here? Do

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Speaker 1: you think they even maybe even think about raising rates?

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Speaker 1: Or where do you think we are here with a FED?

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Speaker 4: Sure they're looking at everything. If they're looking at the

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Speaker 4: leading indicators, yes, that does signal recession. But they have

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Speaker 4: indicated that they are prepared for some quote unquote pain,

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Speaker 4: which would be a mild recession. It's necessary to bring

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Speaker 4: out inflation. And when we look at inflation, certainly last

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Speaker 4: week we received the CPI, we saw some positive momentum downward,

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Speaker 4: but still in all, both the headline and the core

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Speaker 4: are pretty elevated. And the key drivers of underlying inflation

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Speaker 4: right now are food prices on the good side, but

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Speaker 4: on the services side, it's still housing in the form

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Speaker 4: of rent. We should start seeing that come off in

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Speaker 4: a couple of months, reflecting what's happened in home price valuations,

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Speaker 4: but it's still those services, especially for travel and hotels

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Speaker 4: and restaurants and healthcare, that are still very sticky, and

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Speaker 4: that's what that's being challenged by. So against that backdrop,

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Speaker 4: I would imagine the Fed would still look to raise

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Speaker 4: interest rates at least one more time, and then once

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Speaker 4: they're finished raising rates to keep them there elevated for

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Speaker 4: the balands of this year.

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Speaker 5: Dana Creadiegupta in New York here kind of hopping into

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Speaker 5: this conversation. You're an economist by trade, of course, but

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Speaker 5: from a markets perspective that doesn't seem to be priced

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Speaker 5: in at all. It feels like the markets are looking

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Speaker 5: ahead to cuts, are essentially just saying that the federal

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Speaker 5: reserve is done hiking.

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Speaker 6: And the core of that thesis is.

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Speaker 5: Not only the recession call that you all were talking

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Speaker 5: about at the beginning of the segment, but also the

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Speaker 5: idea that the lags still haven't fully taken effect. Are

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Speaker 5: you in that camp that we haven't seen the full

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Speaker 5: effect of the tightening thus far, Well.

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Speaker 4: No, we haven't seen the full effect because the lags

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Speaker 4: are variable. It depends on what you're talking about. So

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Speaker 4: certainly the housing market that's the first area of the

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Speaker 4: economy to experience weakness when interest rates rise, and we've

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Speaker 4: seen that. Also, businesses have pulled back on investments and

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Speaker 4: capex and equipment. They're still spending on intellectual property that's

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Speaker 4: kind of the digital transformation angle, but certainly businesses have

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Speaker 4: pulled back. Consumers have also pulled back on durables. First

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Speaker 4: of all, they're more focused on services, but also durables

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Speaker 4: that anything that needs to be financed is more expensive.

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Speaker 4: So the last piece of the puzzle really is services.

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Speaker 4: So we're still waiting for that shoe to fall. But

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Speaker 4: regarding what markets are anticipating, yes, you're right, markets are

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Speaker 4: pricing in either I think most likely, you know, some

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Speaker 4: kind of recession and thinking that if GDP numbers go

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Speaker 4: negative or jobless or i'm sorry, jobs really weakened, that

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Speaker 4: the Fed's going to blink. But I think the FED

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Speaker 4: would only blink if it's really bad. And also, inflation

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Speaker 4: gauges we're moving in the right direction right now, They're

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Speaker 4: still very sticky, and I think the FED will would

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Speaker 4: tolerate a mild recession in favor of addressing inflation. Inflation

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Speaker 4: is the biggest problem in their view.

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Speaker 1: All right, So I'm thinking about the labor market here

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Speaker 1: and again we had a little bit better and expected

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Speaker 1: jobs claims today. Where do you think the unemployment rate goes?

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Speaker 1: I mean, I guess maybe just more broadly, are you

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Speaker 1: kind of surprised that the labor market is as strong

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Speaker 1: as it is and do you expect it to weaken

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Speaker 1: materially going forward.

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Speaker 4: I'm not surprised the labor market is as strong as

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Speaker 4: it is, because the big difference between this potential recession

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Speaker 4: and others is labor shortages. We have millions of baby

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Speaker 4: boomers leaving the market the labor market, and there are

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Speaker 4: enough younger people to work and replace them, and so

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Speaker 4: businesses are caught in the mind. So they are many

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Speaker 4: of them, according to our own survey, are hoarding workers.

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Speaker 4: So they're not letting people go because they think if

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Speaker 4: there is a recession, we'll be short, it will be shallow.

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Speaker 4: And by the way, we don't want to everyone go

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Speaker 4: and then have to bring them back at a higher

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Speaker 4: pricing point. So that's why the labor market is showing

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Speaker 4: signs of resiliency. It's not that it's out of step

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Speaker 4: with weakness in the economy. It's because you have these

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Speaker 4: severe labor shortages.

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Speaker 5: When java severe labor shortages, Dana in just three thirty seconds,

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Speaker 5: excuse me, are you worried at all about some sort

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Speaker 5: of bifurcation when we're talking about kind of higher income

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Speaker 5: lower income jobs or bisectors thirty seconds?

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Speaker 6: Is that a concern for you at all?

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Speaker 4: Well, I mean you're seeing it really is bisector So

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Speaker 4: again the pandemic darlings, they are letting people go. The

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Speaker 4: industries where you have to physically show up to work,

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Speaker 4: they are hiring people, and everyone else in the middle,

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Speaker 4: which is a lot, is not doing anything. So you

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Speaker 4: are seeing this segmentation in the labor market.

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Speaker 3: All right, Dana, thank you so much. We really appreciate it.

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Speaker 1: Dan Peterson, chief Economists for the Conference Board, joining us today. Again,

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Speaker 1: the leading economic indicator released by the Conference Board came

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Speaker 1: in negative zero point six percent, in line with expectations.

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Speaker 3: I don't know.

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Speaker 1: I guess the good news is it's it's better than

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Speaker 1: last month. But the bad news is it's still negative

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Speaker 1: and consistently negative. So looking for turn there. Certain parts

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Speaker 1: of the consumer remained very strong, as Dana was mentioning,

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Speaker 1: but again the Conference Board calling for a shallow recession.

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Speaker 7: You're listening to the team Can's are live program Bloomberg

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Speaker 7: Markets weekdays at ten am Eastern on Bloomberg dot com,

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Speaker 7: the iHeartRadio app and the Bloomberg Business app, or listen

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Speaker 7: on demand wherever you get your podcasts.

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Speaker 1: You know what we've been mentioning just more and more

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Speaker 1: and more and more over the last several months, is

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Speaker 1: artificial intelligence. The kids call it AI. It's on the

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Speaker 1: tip of every CEO's tongue. Doesn't matter what business they're in,

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Speaker 1: where they are they a tech company, a non ten company.

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Speaker 1: Everybody's talking about it AI. I'm convinced it's just a

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Speaker 1: gooster stock multiple but we'll see what how real this is?

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Speaker 3: It is real. It's see how this scene plays out.

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Speaker 1: Now, when you think about AI, you think about there's

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Speaker 1: got to be an ETF for AI, right.

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Speaker 6: I mean, there's an ETF for everything.

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Speaker 3: There's an ETF for everything.

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Speaker 1: We've got the folks who have one of these ETFs.

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Speaker 1: Chris Natividad, he's a co founder in CIO and Art Amador,

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Speaker 1: co founder and COO of Equbot. Equbot has it? Am

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Speaker 1: I going right there? That's right equbot boom hit that?

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Speaker 3: All right?

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Speaker 1: So do you guys have an AI powered e t

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Speaker 1: F A I e Q is the ticker art tell

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Speaker 1: us about what's going on there?

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Speaker 3: Yeah?

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Speaker 6: Absolutely?

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Speaker 8: So what we what we do with aiq's It leverages

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Speaker 8: IBM Watson's natural language processing and we analyze millions of

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Speaker 8: news articles, social media posts, all of this unstructured data,

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Speaker 8: and then we marry it with traditional data things like

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Speaker 8: financials macro oh sorry, financials macro data. In order to

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Speaker 8: make predictions on different prices, AIQ analyzes about five thousand

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Speaker 8: US companies and then invest in about one hundred and

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Speaker 8: twenty five and fifty names that have the highest opportunity

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Speaker 8: for appreciation based on those those market signals.

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Speaker 1: So all right, so talk to us about a little

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Speaker 1: bit about kind of what are some of the names

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Speaker 1: you guys have in there, and kind of how do

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Speaker 1: you screen to put names in there, because again, there

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Speaker 1: seems to be so many people that are really looking

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Speaker 1: at this.

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Speaker 3: Chris.

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Speaker 9: Yeah, So when we think about AI, it's pattern recognition

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Speaker 9: and that's what our system's doing. And some of the

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Speaker 9: top names that we actually see are are AI related.

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Speaker 10: So names like.

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Speaker 9: Pollunteer and Google. I mean, these are are heavily referenced

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Speaker 9: in a lot of the unstructured data that aren't mentioned.

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Speaker 9: And when we think about it, you know, do we

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Speaker 9: think there are going to be more instances of folks

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Speaker 9: coming on social media and talking about companies and moving

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Speaker 9: stock prices more in the future or less? And I

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Speaker 9: definitely think it's pointing to that more.

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Speaker 3: I think you're right.

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Speaker 1: I just want to read a red headline just coming

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Speaker 1: across the tape. Equity Residential founder and chairman Samuel Zell

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Speaker 1: he dies at age eighty one.

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Speaker 3: Will at more reporting on that coming up.

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Speaker 5: Yeah, it's a pretty significant story. Of course, when we're

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Speaker 5: talking about his group, the equity Office I believe purchased

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Speaker 5: by Blackstone right about Yeah, I'm not.

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Speaker 3: Sure if I know Samzell has been mister real estate,

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Speaker 3: mister Reed.

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Speaker 6: Yeah, absolutely decades.

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Speaker 5: We're going to get Shanlie bassek in here asapt to

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Speaker 5: cover that. But let's stick with the AI story for now.

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Speaker 5: Let's start with the idea of whether or not AI

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Speaker 5: is actually overpriced. It feels like the majority of the

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Speaker 5: games in the last few months or so, especially in

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Speaker 5: the tech market, have come from this AI mentioned as

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Speaker 5: Paul said, is there any concern that this is kind

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Speaker 5: of the end of it, this is peak AI?

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Speaker 9: Absolutely not. And what just came out a few minutes

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Speaker 9: ago is Goldman talking about the potential of AI rallying

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Speaker 9: equity markets thirty percent the coming year. This morning we

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Speaker 9: hear about news coming from the US government about putting

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Speaker 9: in AI regulatory bodies, right, and so what we see

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Speaker 9: from the data is that there's an AI arms race.

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Speaker 9: You know, people the big players, Apples, the Googles, IBM,

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Speaker 9: all of these large tech companies are investing in AIS.

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Speaker 9: So there's going to be more data. It's coming and

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Speaker 9: come quicker, and it's going to help us have the

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Speaker 9: opportunity to really grow our businesses and what we do

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Speaker 9: in our daily lives.

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Speaker 1: So I found out. I was wondering about these two guys.

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Speaker 1: Now I figured it out.

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Speaker 3: They both got NBA from Berkeley.

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Speaker 1: I mean, some smart dudes at Berkeley Outstanding School. So

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Speaker 1: talk to us about like kind of what you think

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Speaker 1: the future of AI is, and maybe let let's start

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Speaker 1: with generative AI. Can you explain what generative AI is

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Speaker 1: because I think that's when I think of A, that's

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Speaker 1: what I think is really really AI.

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Speaker 8: Yeah, so generative AI is is all the all the

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Speaker 8: hot rage right now on our partner. I've being with

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Speaker 8: Watson actually just came out with just just came out

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Speaker 8: with Watson X, which leverages generative AI, which is leveraging transformers.

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Speaker 8: But the way that we think about generative AI is

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Speaker 8: it helps put things in context. Right, So, if you're

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Speaker 8: using chat GBT, you ask it a question, it gives

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Speaker 8: you an answer, Then you ask it another question and

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Speaker 8: it gets the context right of the of the previous

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Speaker 8: the previous question, uh for the next answer right.

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Speaker 11: So it really helps put context around things.

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Speaker 8: And so when we think about how we're using how

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Speaker 8: we're using it, we have something called a convolutional knowledge

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Speaker 8: graph that helps combine structure and unstructured data and so

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Speaker 8: when we analyze news article, social media posts, it's not

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Speaker 8: just about the sentiment, it's about the intent, right, how

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Speaker 8: things are connected. And so what generate AI is going

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Speaker 8: to do is going to provide more context which could

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Speaker 8: lead to better predictions, whether it's markets or whether you're

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Speaker 8: applying it to whatever particular problem you're you're trying to attack.

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Speaker 5: When you talk about so that's generative AI. It feels

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Speaker 5: like AI falls into a lot of different types of

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Speaker 5: of usable things. Paul, like I said, Paul says this

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Speaker 5: every day he goes pets dot Com could be could

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Speaker 5: be using AI.

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Speaker 6: There's dog food companies that are using AI, but there

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Speaker 6: are different types. Can you walk us through just kind

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Speaker 6: of the other use cases for it?

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Speaker 9: Yeah, so you think about image recognition, right, how you know,

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Speaker 9: thinking about medical discovery and how it's impacting and positively

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Speaker 9: helping folks live better, healthier lives. Right, helped us discover

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Speaker 9: the vaccines and treatments related to COVID more recently. Right,

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Speaker 9: it helped me out this past Mother's Day, what do

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Speaker 9: I need to be buying my mother?

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Speaker 12: Right?

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Speaker 3: And you know there are.

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Speaker 9: Other folks at all stages of life that AI can

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Speaker 9: really help you put things in front of you that

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Speaker 9: you're not thinking about pattern recognition. Again, when we see

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Speaker 9: the evolution of the hardware right with quantum computing, we

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Speaker 9: truly see some great opportunities. And especially as our e

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Speaker 9: t fai e Q improves on some of the different

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Speaker 9: forms of trade timing. I think the best days are

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Speaker 9: still ahead for a lot of these different players in

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Speaker 9: the AI investment space.

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Speaker 1: Right talk to us about the actual ETF here, How

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Speaker 1: much is in that fund, how did the launch go?

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Speaker 3: How big you think it can get?

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Speaker 8: Yeah, so the opportunities tremendous. So AIQ was the first

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Speaker 8: AI powered exchange trade or fund. It was the first

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Speaker 8: time anyone was willing to kind of publicly put in

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Speaker 8: you know, AI algram out there in the in the marketplace.

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Speaker 8: And as of today, it's about one hundred million dollars

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Speaker 8: in assets. But we have different indices out there that

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Speaker 8: are being leveraged in banking products and insurance products, and

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Speaker 8: in total, Equbot's got about five billion dollars in tracking

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Speaker 8: AI indices and strategies, and so we think ai EQ

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Speaker 8: could easily be over over a billion dollars and be

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Speaker 8: one of the marquye AI funds and I think right now,

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Speaker 8: I think it is the largest AI fund.

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Speaker 1: Hey, Christy, I see like you know Palenteer, I'm sure

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Speaker 1: looking at your top ten holdings, Palenteer, cloud Fair, dat Data, Dog,

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Speaker 1: I get all that stuff, McKesson and Albemarle, you know

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Speaker 1: kind of there's a healthcare company, a chemicals company. Why

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Speaker 1: are they in that in your ETA?

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Speaker 9: Yeah, you think about AI. It's pattern recognition, right. You

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Speaker 9: think about some of the different things that can impact

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Speaker 9: a stock price, the unstructured data about that our released

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Speaker 9: from from news reports, syndicate research. You think about some

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Speaker 9: of the technicals we may not be looking at, you know,

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Speaker 9: the stochastics and mac and how are these impacting different

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Speaker 9: stock prices? That The thing I'm trying to get to

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Speaker 9: is different data points drive different stocks, right, And so

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Speaker 9: having a neural network helping you supercharge your investment portfolio

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Speaker 9: and understand and look at the data unbiased because quite honestly,

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Speaker 9: us humans have a lot of different bias when we

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Speaker 9: invest and select these stocks. Saying, hey, if I'm looking

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Speaker 9: at all this data, which are the highest companies that

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Speaker 9: are going to have that highest chance of market appreciation?

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Speaker 9: And that's what it excites us. Again, we see the

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Speaker 9: investment companies improving, the algorithms themselves improving, and so we

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Speaker 9: still feel the fund's best days are still ahead.

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Speaker 5: But album moral, for example, we don't have about a

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Speaker 5: minute left. It's a lithium company, isn't it, or like

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Speaker 5: a lithium mining company. From a fundamental perspective, how does

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Speaker 5: that flow into the AI story or is this purely

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Speaker 5: stock performance?

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Speaker 9: Now again, fundamentals are just a single component, and those

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Speaker 9: are just data points. You need to think about all

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Speaker 9: of these structured data that you're talking about, all the

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Speaker 9: different namonics that we're using on Bloomberg, but the unstructured

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Speaker 9: data as well, and so you know that's really the

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Speaker 9: opportunity how to look at it in aggregate.

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Speaker 1: All right, that's extraordinarily interesting because I've been talking everybody's

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Speaker 1: been talking about it AI pretty I mean, it's not

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Speaker 1: just us, but everybody's talking about AI. Everybody's talking about ETFs.

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Speaker 1: So why not have an AI E t F and these.

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Speaker 6: Marriage of them both exactly what Katie Grafeld.

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Speaker 3: Exactly exactly right? All right.

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Speaker 1: Artm Amador COO and co founder of Qubot and Chris Natividad,

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Speaker 1: cio of e Equalbot they joining us both here in

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Speaker 1: a Bloomberg Interactive Broker studio.

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Speaker 3: We appreciate that talking about AI e Q.

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Speaker 7: You're listening to the tape Cat's are live program Bloomberg

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Speaker 7: Markets weekdays at ten am Eastern on Bloomberg Radio, the

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Speaker 7: tune in app, Bloomberg dot Com, and the Bloomberg Business App.

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Speaker 7: You can also listen live on Amazon Alexa from our

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Speaker 7: flagship New York station, Just Say Alexa play Bloomberg eleven thirty.

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Speaker 1: The news that just broke was really interesting, particularly for

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Speaker 1: folks that you know, play a lot in the real

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Speaker 1: estate business. Think about the real estate investment trust business.

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Speaker 3: Sam Zel passed away at the age of eighty one.

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Speaker 1: Shelley Basic joints us here she covers all things Wall Street.

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Speaker 3: Chanelie, what's your sense here?

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Speaker 1: Sam Zel again, just a giant in the real estate business,

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Speaker 1: in the investment business, in global finance business, A big.

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Speaker 13: Name, certainly a shock to anybody who knew him. I'm

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Speaker 13: messaging with some folks in the investment community now, and

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Speaker 13: it's just very sad news. He was only really eighty

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Speaker 13: one years old and very much was you know, very

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Speaker 13: active very recently. I mean, we've had him pretty recently

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Speaker 13: on Bloomberg television as well. Remember, yes, why is he

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Speaker 13: so well known? He book a residential you know, real

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Speaker 13: estate company public and that was really kind of a

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Speaker 13: novel thing when it was happening. I remember he had

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Speaker 13: been the founder and chairman of Equity Residential. That's a

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Speaker 13: ticker EQR. He was founder and chairman. And so really

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Speaker 13: he had been in the real estate business guy since college.

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Speaker 13: I mean, this was his life's work. And in addition

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Speaker 13: to his work in the real estate business, you have

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Speaker 13: to kind of think about just the wide reach he

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Speaker 13: had across many different institutions, from Northwestern University, Warton Reichman University,

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Speaker 13: he has exposure to private education institution in Israel. So

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Speaker 13: he really, you know, Chicago born, makes sense to have

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Speaker 13: such a close tie to Northwestern as well. So a

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Speaker 13: very large figure in finance who has passed away.

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Speaker 1: And just according to Rich go On the Bloomberg terminal

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Speaker 1: had a net worth of five point nine billion dollars.

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Speaker 1: That made him the ranked four and at twenty third

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Speaker 1: in terms of the Rich go List. So again, he's

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Speaker 1: had obviously a long and very lucrative career.

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Speaker 5: Yeah, and we got to go back to kind of

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Speaker 5: his equity residential shaw I mentioned kind of the real

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Speaker 5: estate presence that he had a thirty one billion dollar

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Speaker 5: apartment owner, developer and operator, and of course that was

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Speaker 5: an S and P five hundred member before I believe

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Speaker 5: being acquired by Blackstone.

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Speaker 13: If I'm well, no, equity residential currently is still a

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Speaker 13: large company, but you're you're thinking about equity office. Yeah,

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Speaker 13: I think that was a historic deal. So I'm glad

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Speaker 13: you brought that up. Actually, I was just messaging with

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Speaker 13: a banker that helped sell that company to Blackstone back

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Speaker 13: before the financial crisis. So to the point that you

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Speaker 13: were making here is that he was not only a

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Speaker 13: big office sorry, a residential real estate owner, he was

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Speaker 13: a massive deal maker, which was really what also kind

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Speaker 13: of solidified his ties across Wall Street. I remember an

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Speaker 13: interview I had done with him in one of the

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Speaker 13: top bankers vice chairman of Institutional group over at the

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Speaker 13: City Group a while back, and it was on Bloomberg Television,

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Speaker 13: and they were just reminiscing about the good old days.

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Speaker 13: Does every crisis deal era, But yes, you don't, really.

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Speaker 11: You don't make them like that anymore.

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Speaker 13: There are very few tycoons in this industry as large

438
00:22:09,800 --> 00:22:10,400
Speaker 13: as Sam Zal.

439
00:22:10,640 --> 00:22:13,280
Speaker 1: You know, he's obviously a real estate person, and I

440
00:22:13,320 --> 00:22:15,480
Speaker 1: spent my career in the media industry, and our paths

441
00:22:15,560 --> 00:22:17,840
Speaker 1: crossed a little bit in the mid two thousands because

442
00:22:18,160 --> 00:22:20,560
Speaker 1: he backed the eight point three billion dollar buyout of

443
00:22:20,560 --> 00:22:24,800
Speaker 1: media company Tribune in two thousand and seven, And you know,

444
00:22:24,800 --> 00:22:28,160
Speaker 1: that was a time when people were unsure really where

445
00:22:28,200 --> 00:22:30,440
Speaker 1: the newspaper media and the Tribune at the time owned

446
00:22:30,480 --> 00:22:32,320
Speaker 1: a bunch of television stations as well.

447
00:22:32,920 --> 00:22:35,040
Speaker 6: In the Chicago parent couple of Chicago.

448
00:22:35,480 --> 00:22:38,399
Speaker 1: Chicago Tribune and back in the day, the Cubs as well,

449
00:22:38,480 --> 00:22:41,560
Speaker 1: so kind of do it tying in that Chicago connection.

450
00:22:41,600 --> 00:22:44,320
Speaker 1: So it wasn't just real estate. He was obviously an

451
00:22:44,359 --> 00:22:45,600
Speaker 1: investor in other areas as well.

452
00:22:45,680 --> 00:22:45,880
Speaker 12: Yeah.

453
00:22:45,880 --> 00:22:48,040
Speaker 13: Absolutely, And to the point that you're making too, a

454
00:22:49,040 --> 00:22:52,719
Speaker 13: deal maker in other areas outside of real estate as well.

455
00:22:52,760 --> 00:22:56,600
Speaker 13: I think his dynamic personality when it came to doing deals,

456
00:22:57,040 --> 00:23:00,440
Speaker 13: being bold and buying assets was a defined feature of

457
00:23:00,440 --> 00:23:02,080
Speaker 13: samsou Shani Basik.

458
00:23:02,240 --> 00:23:05,680
Speaker 5: Thank you as always, our Wall Street correspondent all over

459
00:23:06,000 --> 00:23:07,199
Speaker 5: the Sam Cell story.

460
00:23:08,119 --> 00:23:11,520
Speaker 7: You're listening to the team. Ken's a live program Bloomberg

461
00:23:11,560 --> 00:23:14,919
Speaker 7: Markets weekdays at ten am. Eastern on Bloomberg dot com,

462
00:23:15,040 --> 00:23:18,200
Speaker 7: the iHeartRadio app and the Bloomberg Business app or listening

463
00:23:18,240 --> 00:23:20,360
Speaker 7: on demand wherever you get your podcasts.

464
00:23:22,680 --> 00:23:22,760
Speaker 14: Ye.

465
00:23:22,760 --> 00:23:24,600
Speaker 1: All right, let's go to Matt shut Now he's covers

466
00:23:24,680 --> 00:23:28,760
Speaker 1: all the Washington DC stuff for Bloomberg Intelligence and Supreme

467
00:23:28,840 --> 00:23:30,680
Speaker 1: Court upheld section.

468
00:23:30,600 --> 00:23:31,879
Speaker 3: Two thirty here today.

469
00:23:31,920 --> 00:23:35,359
Speaker 1: Now I know for it's social media liability shield and

470
00:23:35,359 --> 00:23:38,639
Speaker 1: this is a big deal for the social media companies

471
00:23:38,640 --> 00:23:41,160
Speaker 1: in general. So, Matt, how important? What tell us what

472
00:23:41,359 --> 00:23:44,440
Speaker 1: the Supreme Court ruled today? Number one and number two?

473
00:23:44,480 --> 00:23:45,400
Speaker 1: Why is that important?

474
00:23:46,040 --> 00:23:50,040
Speaker 15: Yeah, Paul. So, so today the Supreme Court announced its

475
00:23:50,080 --> 00:23:53,360
Speaker 15: decision on this case about the liability shield, and basically

476
00:23:53,400 --> 00:23:55,720
Speaker 15: what the Court did is is punt on it. And

477
00:23:55,800 --> 00:23:59,560
Speaker 15: so this is great news for the companies because what

478
00:23:59,680 --> 00:24:02,560
Speaker 15: was really surprising was that the Court took this case

479
00:24:02,600 --> 00:24:06,159
Speaker 15: at all and it heard argument in February on it

480
00:24:06,240 --> 00:24:09,240
Speaker 15: because there was really no division among the courts of

481
00:24:09,280 --> 00:24:14,040
Speaker 15: appeals that basically all the courts agreed that this liability

482
00:24:14,040 --> 00:24:17,200
Speaker 15: shield protected all the social media companies that just because

483
00:24:17,200 --> 00:24:21,840
Speaker 15: somebody posts something problematic, you can't go sue Google because

484
00:24:21,840 --> 00:24:25,080
Speaker 15: someone posted that, And all the courts had agreed on

485
00:24:25,160 --> 00:24:27,359
Speaker 15: that and the fact that the Supreme Court took this

486
00:24:27,600 --> 00:24:31,480
Speaker 15: case suggested, WHOA, maybe they're going to make changes to

487
00:24:31,560 --> 00:24:35,720
Speaker 15: this long standing liability shield. Well, today we found out

488
00:24:35,760 --> 00:24:38,040
Speaker 15: that isn't the case. The court punted on the case

489
00:24:38,080 --> 00:24:41,119
Speaker 15: and said, look, we don't even have to address the

490
00:24:41,160 --> 00:24:44,760
Speaker 15: liability shield. This claim wasn't very strong in the first place,

491
00:24:44,800 --> 00:24:46,359
Speaker 15: so we're not even going to go there. So they

492
00:24:46,480 --> 00:24:50,119
Speaker 15: basically punted on it entirely. But that really eases what

493
00:24:50,200 --> 00:24:52,600
Speaker 15: could have been a substantial risk if all of a

494
00:24:52,640 --> 00:24:54,879
Speaker 15: sudden this liability shield had a big hole in it.

495
00:24:55,280 --> 00:24:57,520
Speaker 1: All Right, we're talking to Matt chetn Holm Bloomberg Intelligence

496
00:24:57,800 --> 00:25:01,919
Speaker 1: covering the Supreme Court case talking about social media liability.

497
00:25:01,920 --> 00:25:04,160
Speaker 1: I want to thank Shanali Bassk. She kind of gave

498
00:25:04,200 --> 00:25:07,120
Speaker 1: us the latest on the passing of sam Zel. Shanali

499
00:25:07,119 --> 00:25:10,320
Speaker 1: Basque covers all things Wall Street for us. So, Matt,

500
00:25:10,480 --> 00:25:13,800
Speaker 1: who brought this case and are they going to come

501
00:25:13,840 --> 00:25:14,240
Speaker 1: back again?

502
00:25:14,320 --> 00:25:14,760
Speaker 3: Do you think?

503
00:25:15,520 --> 00:25:15,720
Speaker 12: Yeah?

504
00:25:15,800 --> 00:25:19,800
Speaker 15: So, So, this case was brought against Google by by

505
00:25:20,000 --> 00:25:23,159
Speaker 15: someone a family of a person who was killed in

506
00:25:23,200 --> 00:25:26,800
Speaker 15: a terrorist attack, and and the allegation was that that

507
00:25:27,000 --> 00:25:32,920
Speaker 15: Google recommended that content, that that led to the harm

508
00:25:33,359 --> 00:25:35,760
Speaker 15: and and so this is an on you know, there's

509
00:25:36,240 --> 00:25:39,320
Speaker 15: endless amounts of harm in the real world that can

510
00:25:39,359 --> 00:25:43,719
Speaker 15: be caused in linked back to social media postings, and

511
00:25:43,760 --> 00:25:48,520
Speaker 15: so in theory, if if the companies like Google or

512
00:25:48,560 --> 00:25:53,840
Speaker 15: Facebook could could be sued every time they promote harmful content,

513
00:25:54,640 --> 00:25:58,320
Speaker 15: it opens up, you know, endless litigation, hundreds of millions

514
00:25:58,359 --> 00:26:01,359
Speaker 15: of dollars a year of cases they would have to fight.

515
00:26:01,920 --> 00:26:04,320
Speaker 15: And that's why it was a big deal that the Court,

516
00:26:04,400 --> 00:26:08,320
Speaker 15: you know, took this because it suggested maybe they want

517
00:26:08,359 --> 00:26:11,439
Speaker 15: to make changes. But but you know, we kind of

518
00:26:11,480 --> 00:26:13,760
Speaker 15: dialed that back a little bit in February when the

519
00:26:13,800 --> 00:26:16,800
Speaker 15: Court really dug in on this and asked questions of

520
00:26:16,840 --> 00:26:20,639
Speaker 15: the Council on both sides. It really we had a

521
00:26:20,680 --> 00:26:23,920
Speaker 15: pretty clear signal at that point the justices weren't ready

522
00:26:23,960 --> 00:26:25,879
Speaker 15: to make major changes. They didn't want to break the

523
00:26:25,920 --> 00:26:30,439
Speaker 15: Internet basically, and so and that's what we're seeing today

524
00:26:30,480 --> 00:26:34,280
Speaker 15: with this decision is Oops, maybe we shouldn't have gone here,

525
00:26:34,320 --> 00:26:36,760
Speaker 15: Maybe we shouldn't have taken this case. We're not going

526
00:26:36,800 --> 00:26:39,280
Speaker 15: to touch Section two thirty at this point. But you're right,

527
00:26:39,560 --> 00:26:42,000
Speaker 15: this is not the end of the story. Congress is

528
00:26:42,040 --> 00:26:44,720
Speaker 15: going to keep looking at whether it should change the

529
00:26:44,760 --> 00:26:47,679
Speaker 15: liability shield. And you're going to see more cases brought

530
00:26:47,760 --> 00:26:50,480
Speaker 15: and more tests coming down the road, but this eases

531
00:26:50,560 --> 00:26:51,680
Speaker 15: the immediate concern.

532
00:26:52,160 --> 00:26:53,960
Speaker 3: All right, Matt, thanks so much for joining us on

533
00:26:53,960 --> 00:26:55,000
Speaker 3: short note. Really appreciated.

534
00:26:55,040 --> 00:26:58,320
Speaker 1: Matt schetting home bloombergntellents down in Washington, DC giving us

535
00:26:58,520 --> 00:26:59,880
Speaker 1: the latest from the Supreme.

536
00:27:00,640 --> 00:27:03,800
Speaker 7: You're listening to the tape Cat's are live program Bloomberg

537
00:27:03,840 --> 00:27:07,439
Speaker 7: Markets weekdays at ten am Eastern on Bloomberg Radio, the

538
00:27:07,480 --> 00:27:10,720
Speaker 7: tune in app, Bloomberg dot Com, and the Bloomberg Business App.

539
00:27:10,760 --> 00:27:13,560
Speaker 7: You can also listen live on Amazon Alexa from our

540
00:27:13,600 --> 00:27:18,000
Speaker 7: flagship New York station, Just say Alexa play Bloomberg eleven thirty.

541
00:27:20,600 --> 00:27:22,200
Speaker 3: I think some of the big news coming out this morning.

542
00:27:22,200 --> 00:27:23,840
Speaker 1: There's a lot of news, as are typically is, but

543
00:27:23,880 --> 00:27:27,359
Speaker 1: Speaker Kevin McCarthy said he expects the House to consider

544
00:27:27,400 --> 00:27:30,399
Speaker 1: a deal on the federal debt limit next week, offering

545
00:27:30,440 --> 00:27:34,680
Speaker 1: his most positive takeout on the negotiations to avoid a default. Negotiators,

546
00:27:34,720 --> 00:27:37,800
Speaker 1: he told reporters Thursday, are in a quote much better

547
00:27:37,880 --> 00:27:40,880
Speaker 1: place now. I can see now where a deal can

548
00:27:40,920 --> 00:27:42,040
Speaker 1: come together, he said.

549
00:27:42,080 --> 00:27:45,080
Speaker 3: So that's good news. Let's check in with Liz McCormick.

550
00:27:45,119 --> 00:27:49,679
Speaker 1: She covers the treasury markets like no other for Bloomberg News,

551
00:27:50,320 --> 00:27:52,760
Speaker 1: and she and Alexander Harris have a great article out

552
00:27:52,800 --> 00:27:53,760
Speaker 1: here that I love the headline.

553
00:27:53,800 --> 00:27:54,760
Speaker 3: It really grabs your attention.

554
00:27:54,920 --> 00:27:58,920
Speaker 1: A one trillion dollar T bill deluge is painful risk

555
00:27:59,000 --> 00:28:01,560
Speaker 1: of a debt limit deal. So, Liz, what do you

556
00:28:01,640 --> 00:28:04,560
Speaker 1: mean by that is? If there were a debt default,

557
00:28:04,840 --> 00:28:07,639
Speaker 1: they would try to, I don't know, cover themselves with

558
00:28:07,680 --> 00:28:08,480
Speaker 1: a bunch of T bills.

559
00:28:09,600 --> 00:28:13,920
Speaker 14: Well, no, not exactly that ball. And hey, so if

560
00:28:13,960 --> 00:28:16,879
Speaker 14: there's not a debt default and we get which, like

561
00:28:16,920 --> 00:28:20,199
Speaker 14: you were saying, McCarthy's comments today seem very positive that

562
00:28:20,240 --> 00:28:22,600
Speaker 14: they may get a deal next week. So once we

563
00:28:22,680 --> 00:28:25,440
Speaker 14: get kind of back to status quo that Janet Yellen

564
00:28:25,600 --> 00:28:28,680
Speaker 14: is not working under extraordinary measures, they have to kind

565
00:28:28,720 --> 00:28:31,400
Speaker 14: of resume normal policy, which part of that is they

566
00:28:31,400 --> 00:28:33,959
Speaker 14: have to rebuild there. They carry a cash buffer they

567
00:28:34,000 --> 00:28:36,480
Speaker 14: call the you know, Treasury General Account in case of

568
00:28:36,520 --> 00:28:39,520
Speaker 14: emergency tech failure that they can fund themselves for a

569
00:28:39,600 --> 00:28:41,719
Speaker 14: few days without having an issue new debt, and that

570
00:28:41,880 --> 00:28:44,640
Speaker 14: is really dwindled down. So they have to sell this

571
00:28:44,760 --> 00:28:47,480
Speaker 14: deluge of bills to help build that up again, and

572
00:28:47,520 --> 00:28:49,600
Speaker 14: that's what we're getting at, which some of the mechanics,

573
00:28:49,680 --> 00:28:52,040
Speaker 14: the way it works, is going to ultimately, you know,

574
00:28:52,280 --> 00:28:55,880
Speaker 14: pull reserves out of the system and drain liquidity.

575
00:28:56,320 --> 00:28:58,720
Speaker 5: Well, Liz, even if we get these headlines, it doesn't

576
00:28:58,720 --> 00:28:59,880
Speaker 5: feel like the markets.

577
00:28:59,520 --> 00:29:00,720
Speaker 6: Are acting much to it.

578
00:29:00,760 --> 00:29:03,080
Speaker 5: I'm the equity markets popped briefly. We're still hired by

579
00:29:03,120 --> 00:29:04,920
Speaker 5: four tens and one percent on the Sep. Five hundred

580
00:29:05,040 --> 00:29:08,200
Speaker 5: from like two tenths higher, so not a massive move there.

581
00:29:08,240 --> 00:29:10,560
Speaker 5: But even the bond market note necessarily the spike and

582
00:29:10,640 --> 00:29:13,760
Speaker 5: yields I would have expected, Why what's going on here?

583
00:29:13,840 --> 00:29:14,080
Speaker 12: Is this?

584
00:29:14,240 --> 00:29:16,680
Speaker 6: Is this reservation or is this priced in? How do

585
00:29:16,720 --> 00:29:17,360
Speaker 6: you interpret that?

586
00:29:18,280 --> 00:29:20,320
Speaker 14: Well, I think, I mean that's a great point because

587
00:29:20,320 --> 00:29:22,600
Speaker 14: we were looking like, even though you know how they

588
00:29:22,920 --> 00:29:25,040
Speaker 14: avoid the bills that are going to mature right at

589
00:29:25,080 --> 00:29:28,160
Speaker 14: the X date that's around June one, those bills remain

590
00:29:28,280 --> 00:29:31,120
Speaker 14: way above that, so there's that kink in the bill curve.

591
00:29:31,440 --> 00:29:34,280
Speaker 14: So I think investors are saying, yeah, this all sounds good,

592
00:29:35,000 --> 00:29:37,200
Speaker 14: but we won't believe it till it's all done, because

593
00:29:37,200 --> 00:29:40,320
Speaker 14: we've been through this before that. I mean, most people say, yeah,

594
00:29:40,360 --> 00:29:41,960
Speaker 14: a deal's going to get done, because I've been through

595
00:29:42,000 --> 00:29:45,080
Speaker 14: this movie. It happens, but you know, twenty eleven was

596
00:29:45,080 --> 00:29:48,000
Speaker 14: a mess. And I think until it's all signed on

597
00:29:48,080 --> 00:29:50,760
Speaker 14: the dot line, some of these traders, especially in the

598
00:29:50,800 --> 00:29:54,840
Speaker 14: rates market in the Bill area most especially, they're just

599
00:29:54,880 --> 00:29:56,560
Speaker 14: not going to kind of say, oh, let me jump

600
00:29:56,560 --> 00:29:58,800
Speaker 14: in until we know for sure the deal is done. Plus,

601
00:29:58,840 --> 00:30:01,080
Speaker 14: like I think you guys were talking early Lori Logan's

602
00:30:01,120 --> 00:30:04,320
Speaker 14: comments about you know, June may be in play. That's

603
00:30:04,360 --> 00:30:06,240
Speaker 14: got the short end rates higher.

604
00:30:06,640 --> 00:30:08,040
Speaker 11: So there's some cross currents.

605
00:30:08,040 --> 00:30:10,080
Speaker 14: But I do think the market has to kind of

606
00:30:10,120 --> 00:30:11,600
Speaker 14: really see it to believe. And like you said, the

607
00:30:11,640 --> 00:30:13,880
Speaker 14: stock market didn't go nuts, right, I mean went up

608
00:30:13,880 --> 00:30:17,680
Speaker 14: a little, but I just think we know how it goes.

609
00:30:17,720 --> 00:30:20,200
Speaker 14: Then you know, President Biden could say X, and you know,

610
00:30:20,240 --> 00:30:21,840
Speaker 14: all of a sudden, we're back to square zero.

611
00:30:21,960 --> 00:30:23,200
Speaker 12: So but it does look good.

612
00:30:23,280 --> 00:30:26,080
Speaker 14: Our DC reporting seems to indicate that I.

613
00:30:26,120 --> 00:30:29,440
Speaker 1: Were also joined by Michael McKee, Bloomberg Economics corresponding. He

614
00:30:29,520 --> 00:30:31,680
Speaker 1: joins us here in a Bloomberg studio, Michael, what do

615
00:30:31,760 --> 00:30:33,600
Speaker 1: you make of this? Is this kind of what we

616
00:30:33,680 --> 00:30:36,480
Speaker 1: expected from a timing perspective here, and what does it mean?

617
00:30:36,560 --> 00:30:37,560
Speaker 3: Overall, do you think.

618
00:30:37,600 --> 00:30:40,360
Speaker 16: Well, it might even be sooner than we expected kind

619
00:30:40,400 --> 00:30:44,040
Speaker 16: of timing perspective, since everybody's betting on a last minute

620
00:30:44,080 --> 00:30:47,440
Speaker 16: deal and we have until approximately June first. I think

621
00:30:47,440 --> 00:30:50,880
Speaker 16: this is, though, the way it normally plays out. Now.

622
00:30:50,920 --> 00:30:54,360
Speaker 16: The complicating factor here is what we're talking about is

623
00:30:55,160 --> 00:31:00,320
Speaker 16: the White House and the Republican leader on the ill

624
00:31:00,640 --> 00:31:04,200
Speaker 16: maybe making progress together. The question is, then, what do

625
00:31:04,280 --> 00:31:07,680
Speaker 16: the rank and file in both parties think, because we've

626
00:31:07,680 --> 00:31:10,400
Speaker 16: talked a lot about Republicans and whether or not they

627
00:31:10,440 --> 00:31:15,479
Speaker 16: will go along with anything Kevin McCarthy negotiates. And then

628
00:31:15,520 --> 00:31:17,560
Speaker 16: the Democrats have been very upset in the Senate too

629
00:31:17,560 --> 00:31:20,600
Speaker 16: about the possibility that Biden would give away too much

630
00:31:21,040 --> 00:31:26,040
Speaker 16: and for one thing, agree to work requirements for federal aid.

631
00:31:26,360 --> 00:31:30,800
Speaker 16: So we have to see how the main bodies of

632
00:31:31,320 --> 00:31:34,920
Speaker 16: the House and Senate react in each party before we'll

633
00:31:34,920 --> 00:31:37,440
Speaker 16: have a better idea of whether this can actually pass.

634
00:31:37,480 --> 00:31:40,600
Speaker 16: So you've got those two cliffs. The negotiators have to

635
00:31:40,600 --> 00:31:42,760
Speaker 16: reach agreement, but then they have to sell it to

636
00:31:42,840 --> 00:31:43,680
Speaker 16: their people.

637
00:31:44,280 --> 00:31:45,800
Speaker 6: And this of course follows.

638
00:31:45,840 --> 00:31:47,680
Speaker 5: I think that news maybe a day or two ago

639
00:31:47,760 --> 00:31:49,680
Speaker 5: that President Biden had already kind of slimmed down his

640
00:31:49,760 --> 00:31:52,560
Speaker 5: negotiating team as well, which is a positive sign.

641
00:31:53,320 --> 00:31:55,160
Speaker 6: Mike, talk to us about the read through for the

642
00:31:55,160 --> 00:31:56,680
Speaker 6: Federal Reserve. There is there.

643
00:31:56,520 --> 00:31:57,040
Speaker 3: Any we know?

644
00:31:57,080 --> 00:32:01,640
Speaker 5: Truman Powell was, as so Jenny Yellen very confident about, Well,

645
00:32:01,640 --> 00:32:02,440
Speaker 5: they'll get to a deal.

646
00:32:02,480 --> 00:32:02,880
Speaker 12: They have to.

647
00:32:02,960 --> 00:32:04,520
Speaker 6: They have to raise the debt ceiling in some way.

648
00:32:04,960 --> 00:32:06,320
Speaker 6: Any read through here for the.

649
00:32:06,240 --> 00:32:07,400
Speaker 3: Fed not yet.

650
00:32:07,840 --> 00:32:10,320
Speaker 16: The Fed, like everybody else, is watching from the outside

651
00:32:10,320 --> 00:32:13,640
Speaker 16: what's going on. They have come up with their plans

652
00:32:13,640 --> 00:32:17,040
Speaker 16: for what they would do if we did reach the

653
00:32:17,080 --> 00:32:20,200
Speaker 16: debt ceiling, if we went over that cliff, which they

654
00:32:20,200 --> 00:32:23,280
Speaker 16: are disclosing, but we know from history kind of the

655
00:32:23,320 --> 00:32:26,959
Speaker 16: things that they're looking at, it would obviously have a

656
00:32:26,960 --> 00:32:29,880
Speaker 16: major impact on the June FED decision if we were

657
00:32:29,960 --> 00:32:34,200
Speaker 16: to go over the cliff. But I don't think they

658
00:32:34,200 --> 00:32:37,680
Speaker 16: are gonna I was asked about this morning. They're not

659
00:32:37,800 --> 00:32:40,800
Speaker 16: really talking about it or putting it out there as

660
00:32:40,880 --> 00:32:45,840
Speaker 16: an issue because it's so fluid and so uncertain that

661
00:32:45,920 --> 00:32:48,560
Speaker 16: there isn't really anything they can say. Obviously, if we

662
00:32:48,720 --> 00:32:51,080
Speaker 16: go over the cliff, they have to probably react, but

663
00:32:51,160 --> 00:32:53,760
Speaker 16: if we don't, it doesn't make any difference.

664
00:32:54,400 --> 00:32:57,520
Speaker 1: Hey, Liz again in your reporting, you talk about this

665
00:32:57,560 --> 00:33:00,360
Speaker 1: one trillion dollar number in a te bill more. Is

666
00:33:00,400 --> 00:33:04,080
Speaker 1: that something the market can handle an orderly fashion, do

667
00:33:04,120 --> 00:33:06,400
Speaker 1: you think or is that going to be really disruptive?

668
00:33:07,480 --> 00:33:10,000
Speaker 14: Well, you know, I will say in the Treasury Department,

669
00:33:10,040 --> 00:33:11,960
Speaker 14: who I've talked to off and on for years.

670
00:33:11,720 --> 00:33:13,120
Speaker 6: They have a plan.

671
00:33:13,200 --> 00:33:14,840
Speaker 14: They know this is going to be a lot. They

672
00:33:14,960 --> 00:33:17,600
Speaker 14: reached out to the primary dealers ahead of their last

673
00:33:17,640 --> 00:33:20,040
Speaker 14: kind of gathering and said, how do we do this,

674
00:33:20,200 --> 00:33:22,520
Speaker 14: you know, in the best way to not cause disruption?

675
00:33:22,720 --> 00:33:25,680
Speaker 14: So I think Paul, they're going to be careful. I mean,

676
00:33:25,720 --> 00:33:29,000
Speaker 14: it will filter through to rates and such, but they

677
00:33:29,040 --> 00:33:31,480
Speaker 14: don't want it to be so disruptive that there's all

678
00:33:31,480 --> 00:33:34,520
Speaker 14: these dislocations. So whether they're going to try to spread

679
00:33:34,520 --> 00:33:38,040
Speaker 14: it out enough. Some of these over a trillion issuance

680
00:33:38,120 --> 00:33:40,360
Speaker 14: is out through the third quarter, so I think the

681
00:33:40,440 --> 00:33:42,760
Speaker 14: US Treasury Department is going to be very careful. They've

682
00:33:42,800 --> 00:33:45,440
Speaker 14: also seen this movie before where they've had to unfortunately

683
00:33:45,480 --> 00:33:48,400
Speaker 14: get their cash balance painfully low and then look to

684
00:33:48,400 --> 00:33:51,880
Speaker 14: build it up very fast. But either way, the net effects,

685
00:33:52,080 --> 00:33:54,040
Speaker 14: you know, from the way the mechanics work, is we

686
00:33:54,040 --> 00:33:57,080
Speaker 14: should see reserves come down somewhat in the banking system.

687
00:33:57,120 --> 00:33:59,160
Speaker 14: But you know, let's hope and I have trust in

688
00:33:59,160 --> 00:34:01,920
Speaker 14: them that the Treasury Department can do it, you know,

689
00:34:02,400 --> 00:34:04,640
Speaker 14: with as least pain as possible for the market.

690
00:34:05,240 --> 00:34:07,560
Speaker 5: Mike, in our last minute here, when you're talking about

691
00:34:07,680 --> 00:34:09,719
Speaker 5: government spending at the end of the day, when you're

692
00:34:09,719 --> 00:34:11,839
Speaker 5: talking about the nuts and bolts of this deal, how

693
00:34:11,920 --> 00:34:15,640
Speaker 5: much of it could be inflationary.

694
00:34:15,719 --> 00:34:19,960
Speaker 16: It's an interesting question. If a cut back on spending,

695
00:34:20,080 --> 00:34:23,240
Speaker 16: then you would have some deflationary effects, but it's generally

696
00:34:23,280 --> 00:34:25,680
Speaker 16: spread out over such a long period of time that

697
00:34:25,920 --> 00:34:28,920
Speaker 16: it wouldn't have much of an impact on what's going on.

698
00:34:29,280 --> 00:34:31,319
Speaker 16: What might have more of an impact is what Liz

699
00:34:31,400 --> 00:34:33,600
Speaker 16: was talking about. If they have to rebuild the TGA

700
00:34:33,800 --> 00:34:38,160
Speaker 16: very quickly and liquidity drains out, that could have an impact.

701
00:34:38,360 --> 00:34:40,400
Speaker 16: But we'll have to see how that plays out.

702
00:34:40,719 --> 00:34:42,759
Speaker 1: Hi, Michael McKay, thanks so much for jumping in here.

703
00:34:42,800 --> 00:34:46,359
Speaker 1: Michael McKay, Bloomberg's economics correspondent, joining us here in our

704
00:34:46,360 --> 00:34:49,720
Speaker 1: Bloomberg Interactive Broker studio and List McCormick, chief corresponding Global

705
00:34:49,840 --> 00:34:54,080
Speaker 1: macro markets, breaking down this story today on Bloomberg News.

706
00:34:54,200 --> 00:34:56,840
Speaker 1: Check out her reporting there. She joined us some Bloomberg

707
00:34:56,920 --> 00:34:57,960
Speaker 1: News on the phone.

708
00:34:58,239 --> 00:35:01,880
Speaker 7: You're listening to the tape our live program, Bloomberg Markets

709
00:35:01,920 --> 00:35:05,319
Speaker 7: weekdays at ten am Eastern on Bloomberg Radio, the tune

710
00:35:05,360 --> 00:35:08,319
Speaker 7: in app, Bloomberg dot Com, and the Bloomberg Business App.

711
00:35:08,360 --> 00:35:11,160
Speaker 7: You can also listen live on Amazon Alexa from our

712
00:35:11,200 --> 00:35:20,480
Speaker 7: flagship New York station, Just say Alexa Play Bloomberg eleven thirty's.

713
00:35:17,920 --> 00:35:20,920
Speaker 1: Thrilled to have our next guest here in studio, Lauramartin,

714
00:35:20,960 --> 00:35:25,080
Speaker 1: Managing Director, Senior Media and Internet Analyst at Needham Folks.

715
00:35:25,360 --> 00:35:27,279
Speaker 1: There are good animals out there, and then there are

716
00:35:27,440 --> 00:35:29,920
Speaker 1: rock stars, and Laura Martin is a rock star, covering

717
00:35:30,000 --> 00:35:33,080
Speaker 1: the media, the internet space, wherever this stuff evolves. Now

718
00:35:33,120 --> 00:35:35,319
Speaker 1: she's into ad tech and who knows what else, but

719
00:35:35,360 --> 00:35:38,600
Speaker 1: Laura Martin joins us here in our Bloomberg Interactive Brookers studio. Hey, Lore,

720
00:35:38,600 --> 00:35:42,800
Speaker 1: you guys had your eighteenth annual Needum Company Investor Conference

721
00:35:42,840 --> 00:35:46,319
Speaker 1: this week in New York City. A bunch of CEOs coming,

722
00:35:46,320 --> 00:35:47,680
Speaker 1: a bunch of institutional investors.

723
00:35:47,719 --> 00:35:49,600
Speaker 3: What were some of your takeaways from that look?

724
00:35:49,640 --> 00:35:51,840
Speaker 17: I would say the most disturbing takeaway was a panel

725
00:35:51,880 --> 00:35:54,360
Speaker 17: I ran where two of the very smart people in

726
00:35:54,400 --> 00:35:57,560
Speaker 17: this panel said the future of advertising has no humans

727
00:35:57,560 --> 00:36:00,120
Speaker 17: in it. It is one hundred percent generated AI. It

728
00:36:00,160 --> 00:36:03,720
Speaker 17: is all a B testing mad no mad men, no,

729
00:36:03,719 --> 00:36:07,479
Speaker 17: no mad men like Madison Avenue closed like it's going

730
00:36:07,520 --> 00:36:09,719
Speaker 17: to the dogs. And it was going to be one

731
00:36:09,760 --> 00:36:13,160
Speaker 17: hundred percent like generative AI making the ad, putting the

732
00:36:13,200 --> 00:36:15,400
Speaker 17: title on it, seeing if you clicked, doing a B

733
00:36:15,560 --> 00:36:18,359
Speaker 17: testing doing the next one. That no human beings would

734
00:36:18,360 --> 00:36:20,360
Speaker 17: be involved in advertising. So I don't know where that

735
00:36:20,400 --> 00:36:23,480
Speaker 17: leaves Coke and Pepsi and perfume companies. But according to

736
00:36:23,560 --> 00:36:26,359
Speaker 17: these guys, it's all about the tech and generative AI.

737
00:36:26,560 --> 00:36:28,320
Speaker 3: We've heard that everywhere.

738
00:36:28,360 --> 00:36:31,120
Speaker 6: Don Draper will be very upset about to hear that.

739
00:36:31,480 --> 00:36:33,560
Speaker 1: We were just had some guests in here that have

740
00:36:33,920 --> 00:36:38,360
Speaker 1: an ETF. It's called AI eq EQ so an ETF

741
00:36:38,840 --> 00:36:39,880
Speaker 1: on all things AI.

742
00:36:40,160 --> 00:36:40,600
Speaker 12: Yeah.

743
00:36:40,800 --> 00:36:41,200
Speaker 3: Wild.

744
00:36:41,719 --> 00:36:43,880
Speaker 6: What's the timeframe on that though? Is that happening like

745
00:36:44,080 --> 00:36:44,560
Speaker 6: next year?

746
00:36:44,760 --> 00:36:47,080
Speaker 17: So no, they were saying that to him this was

747
00:36:47,200 --> 00:36:50,000
Speaker 17: the end state of where advertising. The pail was called

748
00:36:50,040 --> 00:36:52,640
Speaker 17: future of advertising. So he's saying the end state is

749
00:36:52,680 --> 00:36:55,239
Speaker 17: that we all know quote, we all know this is

750
00:36:55,280 --> 00:36:57,919
Speaker 17: where the end state is in ten years, humans aren't

751
00:36:57,960 --> 00:37:00,480
Speaker 17: involved in advertising it's one hundred percent yet out of AI.

752
00:37:01,600 --> 00:37:03,560
Speaker 1: So let me frame this after people Lara has been

753
00:37:03,600 --> 00:37:05,480
Speaker 1: covering media for a long time.

754
00:37:05,560 --> 00:37:07,160
Speaker 3: Started you know, I'm talking.

755
00:37:06,880 --> 00:37:11,680
Speaker 1: Newspapers, radio, TV, the big entertainment guys like viacommon Disney

756
00:37:11,680 --> 00:37:13,600
Speaker 1: and now all the Internet, the metas in the world

757
00:37:13,640 --> 00:37:14,520
Speaker 1: and all that kind of stuff.

758
00:37:14,920 --> 00:37:17,440
Speaker 3: The biggest, one of the biggest disruptions.

759
00:37:17,000 --> 00:37:19,799
Speaker 1: I think we've seen in that timeframe has been a

760
00:37:19,960 --> 00:37:25,520
Speaker 1: the Internet, but be streaming. Streaming has just disrupted your industry,

761
00:37:25,640 --> 00:37:27,040
Speaker 1: the global big media industry.

762
00:37:27,440 --> 00:37:28,520
Speaker 3: How do you think this shakes out?

763
00:37:28,600 --> 00:37:31,920
Speaker 1: What does it mean for Disney, for Paramount, for Warner Brothers, Discovery,

764
00:37:32,280 --> 00:37:33,040
Speaker 1: and for Netflix.

765
00:37:33,680 --> 00:37:35,480
Speaker 17: So I think one of the things that some people

766
00:37:35,640 --> 00:37:39,400
Speaker 17: miss on the investment side is that media, when you

767
00:37:39,440 --> 00:37:43,560
Speaker 17: and I followed it together, Paul was really either local,

768
00:37:43,600 --> 00:37:46,279
Speaker 17: which is what newspapers are, or then it was over

769
00:37:46,320 --> 00:37:49,120
Speaker 17: the air broadcast, which was like a city, you know,

770
00:37:49,120 --> 00:37:52,080
Speaker 17: a d M A, or then cable made it regional.

771
00:37:52,600 --> 00:37:54,440
Speaker 17: And now what's happened is streaming has made it global.

772
00:37:54,719 --> 00:37:57,160
Speaker 17: So you can actually price it about half in the

773
00:37:57,280 --> 00:37:59,759
Speaker 17: US because you're going to get ten cents for South

774
00:37:59,760 --> 00:38:02,440
Speaker 17: of and you're going to get eight cents from Germany,

775
00:38:02,480 --> 00:38:04,719
Speaker 17: and you're going to go to Africa and get three cents,

776
00:38:04,719 --> 00:38:06,480
Speaker 17: And when you add all that up, it will end

777
00:38:06,560 --> 00:38:08,760
Speaker 17: up at one hundred and ten percent of a US

778
00:38:08,920 --> 00:38:11,960
Speaker 17: only or two hundred percent of a local only market.

779
00:38:12,480 --> 00:38:15,399
Speaker 17: So media becoming global, but the returns on capital when

780
00:38:15,400 --> 00:38:17,680
Speaker 17: you go offshore are really low because you have to

781
00:38:17,719 --> 00:38:19,959
Speaker 17: lose money in a bunch of those countries in the beginning,

782
00:38:20,000 --> 00:38:22,520
Speaker 17: because we have the biggest AD market. So I think

783
00:38:22,560 --> 00:38:26,440
Speaker 17: these economics of companies who are only US are overstated

784
00:38:26,480 --> 00:38:28,400
Speaker 17: because they are going to have to spend money to

785
00:38:28,440 --> 00:38:31,759
Speaker 17: be global over time, because they have to compete with

786
00:38:31,800 --> 00:38:36,200
Speaker 17: the big global empires like Netflix and HBO, Max and Disney.

787
00:38:36,239 --> 00:38:38,080
Speaker 17: They're all going to be global in the end because

788
00:38:38,120 --> 00:38:40,240
Speaker 17: they need some of their economics to come from outside

789
00:38:40,239 --> 00:38:40,560
Speaker 17: the US.

790
00:38:40,600 --> 00:38:42,400
Speaker 5: I'm glad you mentioned the Netflix story because isn't that

791
00:38:42,400 --> 00:38:44,759
Speaker 5: their entire strategy that their most of their growth is

792
00:38:44,760 --> 00:38:48,200
Speaker 5: coming from abroad, not stateside. The last time you were

793
00:38:48,200 --> 00:38:50,560
Speaker 5: on our show, you called for an ad of recession,

794
00:38:50,719 --> 00:38:51,680
Speaker 5: which you're timing on that.

795
00:38:52,239 --> 00:38:54,560
Speaker 17: Yeah, So I would say in every case I interviewed

796
00:38:54,560 --> 00:38:57,920
Speaker 17: twenty CEOs in street, either streaming or they all have advertising.

797
00:38:58,160 --> 00:39:00,919
Speaker 17: I've sort of an advertising analyst, So what I would

798
00:39:00,960 --> 00:39:04,319
Speaker 17: say is every single guy is seeing strength somewhere so

799
00:39:04,760 --> 00:39:07,960
Speaker 17: in their CEOs, so they're sort of hopelessly optimistic always.

800
00:39:08,320 --> 00:39:10,239
Speaker 17: So some guys are saying, we're seeing more strength in

801
00:39:10,280 --> 00:39:12,200
Speaker 17: the EU than in the US. Other guys are saying,

802
00:39:12,239 --> 00:39:14,760
Speaker 17: I'm seeing strengthen autos, which would be great because autos

803
00:39:14,840 --> 00:39:16,960
Speaker 17: is like half of where it was pre COVID. I'm

804
00:39:16,960 --> 00:39:20,600
Speaker 17: seeing strength in retail CpG, consumer product, packaged goods coming

805
00:39:20,640 --> 00:39:23,960
Speaker 17: back through these retail media networks. Every CEO said he's

806
00:39:23,960 --> 00:39:26,600
Speaker 17: seeing strengthen something, but if you push him, he says, oh,

807
00:39:26,719 --> 00:39:30,120
Speaker 17: but by the way travels down or financial services into

808
00:39:30,160 --> 00:39:32,960
Speaker 17: our tech spending em andy. Everybody says that the film

809
00:39:33,000 --> 00:39:36,840
Speaker 17: studios not advertising. That's called media and entertainment advertising, and

810
00:39:36,880 --> 00:39:40,280
Speaker 17: they are high premium payers. They pay for interactive adue

811
00:39:40,280 --> 00:39:43,640
Speaker 17: and it's full color takeovers, and so that everybody says

812
00:39:43,640 --> 00:39:46,440
Speaker 17: that's week. So the question is when all of these

813
00:39:46,760 --> 00:39:49,719
Speaker 17: categories of advertise come back, because they have eighty percent margins,

814
00:39:49,880 --> 00:39:50,879
Speaker 17: but it is not yet.

815
00:39:51,200 --> 00:39:53,920
Speaker 1: So this week is also in addition to the week

816
00:39:53,960 --> 00:39:56,799
Speaker 1: of your commerce, the week of upfront. That's when a broadcasting,

817
00:39:56,800 --> 00:39:58,960
Speaker 1: cable and networks say come to New York. They get

818
00:39:59,000 --> 00:40:01,600
Speaker 1: all the advertisers on Madison Avenue who still have jobs

819
00:40:01,880 --> 00:40:04,680
Speaker 1: and say, hey, here are shows for next season. Pony

820
00:40:04,760 --> 00:40:07,120
Speaker 1: up some money and advertising help us pre prefund our

821
00:40:07,360 --> 00:40:10,279
Speaker 1: slate for next year. A why do we still have

822
00:40:10,280 --> 00:40:12,439
Speaker 1: an upfront? And B what was the tone this year?

823
00:40:13,040 --> 00:40:14,920
Speaker 17: So I was saying the most important thing this year

824
00:40:14,920 --> 00:40:16,759
Speaker 17: and it's been a transition, but this year they went

825
00:40:16,800 --> 00:40:19,080
Speaker 17: all in is they'd say, here's ten pieces of the

826
00:40:19,120 --> 00:40:22,799
Speaker 17: new content. These eight are for our streaming service, and

827
00:40:22,840 --> 00:40:24,800
Speaker 17: they don't mention the linear TV network.

828
00:40:24,880 --> 00:40:25,080
Speaker 7: Wow.

829
00:40:25,080 --> 00:40:27,399
Speaker 17: In the olden days, you'd window, you'd say it's gonna

830
00:40:27,440 --> 00:40:29,279
Speaker 17: be on linear TV first, or it's going to be

831
00:40:29,320 --> 00:40:30,920
Speaker 17: on streaming first, and then we're gonna put it on

832
00:40:30,960 --> 00:40:33,879
Speaker 17: linear TV three weeks later because then you can get

833
00:40:33,920 --> 00:40:37,319
Speaker 17: different windows. But no, nobody's doing that. Nope, everybody's saying

834
00:40:37,360 --> 00:40:39,480
Speaker 17: we're releasing this and some of the some of the

835
00:40:39,560 --> 00:40:44,880
Speaker 17: content feel absolutely feels prime time broadcast worthy three million

836
00:40:44,920 --> 00:40:47,160
Speaker 17: dollars an hour, but they're putting it on their streaming

837
00:40:47,320 --> 00:40:49,680
Speaker 17: platform and they will release it weekly like a normal

838
00:40:49,719 --> 00:40:53,440
Speaker 17: broadcaster does. It won't be binge viewed, but the money

839
00:40:53,440 --> 00:40:56,279
Speaker 17: going into this streaming is worthy of over the air

840
00:40:56,880 --> 00:40:58,680
Speaker 17: trawelvision from pre covid.

841
00:40:59,239 --> 00:41:02,200
Speaker 5: Well that you're talking about the streaming story because it

842
00:41:02,280 --> 00:41:05,200
Speaker 5: brings me to kind of tech and advertising and the

843
00:41:05,200 --> 00:41:05,839
Speaker 5: mixture there.

844
00:41:05,840 --> 00:41:08,360
Speaker 6: But traditional tech like Apple or Tesla, for example.

845
00:41:08,360 --> 00:41:10,839
Speaker 5: I think Tesla made headlines this week and saying that

846
00:41:10,840 --> 00:41:12,440
Speaker 5: Elon Mussing, you know, we're going to dip our toe

847
00:41:12,480 --> 00:41:15,719
Speaker 5: into advertising, and it turned their stock into into.

848
00:41:15,480 --> 00:41:17,799
Speaker 6: A lot of green. What's your take on that?

849
00:41:17,880 --> 00:41:21,279
Speaker 5: Like traditional kind of hardware auto companies dipping their toe

850
00:41:21,280 --> 00:41:22,640
Speaker 5: into advertising, you know.

851
00:41:22,680 --> 00:41:25,920
Speaker 17: It is my point of view, advertising is like heroin.

852
00:41:26,160 --> 00:41:29,040
Speaker 17: It has eighty percent margins, and the minute you have reached,

853
00:41:29,120 --> 00:41:32,680
Speaker 17: especially like a Tesla, think how desirable that target audience

854
00:41:32,800 --> 00:41:36,000
Speaker 17: is and they're captive in your cars Like airport advertising,

855
00:41:36,400 --> 00:41:39,080
Speaker 17: you know, you suddenly have a very narrow tarrogate market

856
00:41:39,080 --> 00:41:41,960
Speaker 17: that's really valuable and hard to reach because those people

857
00:41:42,000 --> 00:41:44,960
Speaker 17: pay extra for their streaming services not to have ads.

858
00:41:45,560 --> 00:41:48,960
Speaker 17: So it is I think advertising is an easy revenue stream.

859
00:41:49,320 --> 00:41:51,839
Speaker 17: Wall Street loves multiple revenue streams and it has eighty

860
00:41:51,880 --> 00:41:54,440
Speaker 17: percent margins. So once you build a direct salesforce or

861
00:41:54,480 --> 00:41:56,680
Speaker 17: you go programmatic where you don't have to do anything

862
00:41:56,760 --> 00:41:59,680
Speaker 17: except hire one of these big tech stacks. Then you

863
00:41:59,719 --> 00:42:02,279
Speaker 17: get all this new money and Wall Street loves it,

864
00:42:02,320 --> 00:42:05,080
Speaker 17: and it's actually a really good in my opinion. Maybe

865
00:42:05,120 --> 00:42:07,040
Speaker 17: it's a little invasive to your customer, but if he's

866
00:42:07,040 --> 00:42:10,120
Speaker 17: a wealthy customer and you're showing in private islands off

867
00:42:10,160 --> 00:42:13,040
Speaker 17: Hawaii like that, actually you could argue is value added

868
00:42:13,040 --> 00:42:14,759
Speaker 17: to him because he didn't know it existed and now

869
00:42:14,800 --> 00:42:17,040
Speaker 17: he knows where to go on vacation in Christmas Lard.

870
00:42:17,080 --> 00:42:19,399
Speaker 1: You've covered, you know, your coverage is the big media

871
00:42:19,400 --> 00:42:21,560
Speaker 1: companies to tech companies and all that enabling stuff.

872
00:42:21,560 --> 00:42:23,120
Speaker 3: What's your top pick right these days?

873
00:42:23,840 --> 00:42:26,920
Speaker 17: So we're still of a view that we're in we

874
00:42:27,040 --> 00:42:29,040
Speaker 17: might still go into recession. So I think it's a

875
00:42:29,040 --> 00:42:31,000
Speaker 17: little too early to do advertising. So we would be

876
00:42:31,080 --> 00:42:35,239
Speaker 17: an Apple okay, defensive, no advertising yet to speak of advertising.

877
00:42:35,280 --> 00:42:38,920
Speaker 17: Revenue stream on the horizon. So I like that is

878
00:42:39,120 --> 00:42:41,680
Speaker 17: ninety billion dollars of free cash frow, nice and liquid.

879
00:42:41,800 --> 00:42:43,760
Speaker 17: If I'm wrong, you can get out today. After tomorrow

880
00:42:43,800 --> 00:42:46,080
Speaker 17: you won't move the shares. So I would say Apple

881
00:42:46,080 --> 00:42:49,160
Speaker 17: will be my top pick until it's clear that advertising

882
00:42:49,320 --> 00:42:51,400
Speaker 17: is not going to go through some kind of hard landing,

883
00:42:51,440 --> 00:42:53,279
Speaker 17: soft landing, downdraft.

884
00:42:52,800 --> 00:42:54,360
Speaker 1: And then it might be some of the more the

885
00:42:54,440 --> 00:42:56,200
Speaker 1: viacoms of Disney's, those types of things that do have

886
00:42:56,200 --> 00:42:57,040
Speaker 1: a more advertising.

887
00:42:57,480 --> 00:42:59,719
Speaker 17: So good question, Paul. I mean what I would say

888
00:42:59,760 --> 00:43:01,359
Speaker 17: is I cover a lot of companies that are one

889
00:43:01,400 --> 00:43:03,400
Speaker 17: hundred percent advertising. So if we're going to have an

890
00:43:03,440 --> 00:43:07,480
Speaker 17: advertising bounce, an advertising company will double, triple, quadruple. I

891
00:43:07,520 --> 00:43:09,640
Speaker 17: want to be at one hundred percent ad driven, which

892
00:43:09,640 --> 00:43:12,960
Speaker 17: sort of begs the question, we're what buyer buys Disney

893
00:43:12,960 --> 00:43:17,400
Speaker 17: and Paramount and Fox now they're neither a fender, you know, Fisher.

894
00:43:17,120 --> 00:43:19,000
Speaker 3: Foul exactly right, all R. Laura Martin, thank you so

895
00:43:19,080 --> 00:43:19,919
Speaker 3: much for joining us.

896
00:43:20,040 --> 00:43:22,960
Speaker 1: Laura Martin is a managing director, senior media and Internet

897
00:43:23,040 --> 00:43:25,440
Speaker 1: analyst at Need. I'm a real joy to get here

898
00:43:25,560 --> 00:43:27,680
Speaker 1: in our studio, shoes in town for her conference, a

899
00:43:27,680 --> 00:43:30,319
Speaker 1: lot of investors, a lot of media company CEO's time

900
00:43:30,400 --> 00:43:30,879
Speaker 1: well spent.

901
00:43:31,280 --> 00:43:34,400
Speaker 7: You're listening to the tape Cat's are live program Bloomberg

902
00:43:34,440 --> 00:43:38,040
Speaker 7: Markets weekdays at ten am Eastern on Bloomberg Radio, the

903
00:43:38,080 --> 00:43:41,319
Speaker 7: tune in app, Bloomberg dot com, and the Bloomberg Business app.

904
00:43:41,360 --> 00:43:44,200
Speaker 7: You can also listen live on Amazon Alexa from our

905
00:43:44,200 --> 00:43:48,560
Speaker 7: flagship New York station, Just say Alexa play Bloomberg eleven thirty.

906
00:43:49,800 --> 00:43:53,480
Speaker 1: If we're talking about homebuilders, let's continue that discussion. Homebuilders

907
00:43:53,520 --> 00:43:57,040
Speaker 1: are capitalizing on a seemingly unquenchable thirst for new housing

908
00:43:57,320 --> 00:44:00,960
Speaker 1: as buyers struggle with limited inventories and mortgage rates.

909
00:44:01,040 --> 00:44:02,600
Speaker 3: I wondered who wrote that wonderful line.

910
00:44:02,760 --> 00:44:05,719
Speaker 1: Norah Melinda, Equity's reporter for Bloomberg News, joins us live

911
00:44:05,760 --> 00:44:09,440
Speaker 1: in our Bloomberg Interactive Brokers studio. So noorah, there's not

912
00:44:09,480 --> 00:44:11,840
Speaker 1: a lot of inventory out there, right, So we really

913
00:44:12,320 --> 00:44:13,880
Speaker 1: if people want to buy houses, they get up buy,

914
00:44:13,920 --> 00:44:15,280
Speaker 1: they're probably gonna be buying new houses.

915
00:44:15,320 --> 00:44:16,080
Speaker 3: I guess right.

916
00:44:16,200 --> 00:44:17,480
Speaker 11: I mean, thanks for having me on.

917
00:44:18,640 --> 00:44:21,360
Speaker 10: Existing home sales actually used to make up about ninety

918
00:44:21,400 --> 00:44:24,000
Speaker 10: percent of the housing market, and so that didn't really

919
00:44:24,040 --> 00:44:27,640
Speaker 10: leave as much space for new homebuilders that I continue

920
00:44:27,680 --> 00:44:31,440
Speaker 10: to watch. But you know, we're seeing with rising mortgage

921
00:44:31,520 --> 00:44:33,759
Speaker 10: rates and really a lot of people not wanting to

922
00:44:33,800 --> 00:44:36,359
Speaker 10: move out of the houses that they already own, there's

923
00:44:36,400 --> 00:44:39,399
Speaker 10: not much inventory left and so this has really made

924
00:44:39,440 --> 00:44:42,920
Speaker 10: a complete perfect runway for home builders. They're soaring it's

925
00:44:42,960 --> 00:44:45,240
Speaker 10: really a rebound store. You know, homebuilders were down almost

926
00:44:45,239 --> 00:44:47,960
Speaker 10: as much as forty percent last year, so we saw

927
00:44:48,000 --> 00:44:51,560
Speaker 10: that last June, and now they're up about trading above

928
00:44:51,719 --> 00:44:56,440
Speaker 10: about twenty seven percent above their two hundred day moving average.

929
00:44:56,480 --> 00:44:58,879
Speaker 10: So it's a really stellar thing to look at right now.

930
00:44:59,280 --> 00:45:02,640
Speaker 18: Is that perform or mens linked to more homes for

931
00:45:02,800 --> 00:45:05,520
Speaker 18: people like us to all go out and buy, hopefully.

932
00:45:05,160 --> 00:45:07,120
Speaker 11: Someday, Definitely, I hope so.

933
00:45:07,400 --> 00:45:09,880
Speaker 10: I mean, that's what I'm hearing from a lot of

934
00:45:09,880 --> 00:45:11,839
Speaker 10: the sources that I'm speaking to. It seems as though

935
00:45:11,880 --> 00:45:15,239
Speaker 10: homebuilders are just building rapidly. They're really trying to meet

936
00:45:15,239 --> 00:45:18,320
Speaker 10: this demand. Of course, we already see a really really

937
00:45:18,360 --> 00:45:21,520
Speaker 10: strong demand with the fact of the housing crisis. You know,

938
00:45:21,640 --> 00:45:24,799
Speaker 10: there aren't enough homes for people to move in, and

939
00:45:24,840 --> 00:45:28,240
Speaker 10: there's really really high demand, and so homebuilders may actually

940
00:45:28,239 --> 00:45:31,000
Speaker 10: be solving that problem that we are seeing in the

941
00:45:31,040 --> 00:45:32,120
Speaker 10: housing market right now.

942
00:45:32,840 --> 00:45:34,920
Speaker 3: What are homebuilders building?

943
00:45:35,000 --> 00:45:37,080
Speaker 1: I Mean, the concern I've heard from a lot of

944
00:45:37,080 --> 00:45:38,840
Speaker 1: folks is one of the big problems we have in

945
00:45:38,880 --> 00:45:41,760
Speaker 1: the housing stock in the US is there's not enough

946
00:45:42,239 --> 00:45:44,879
Speaker 1: entry level housing. You know, the builders are out there

947
00:45:44,920 --> 00:45:47,520
Speaker 1: building the McMansions, why because that's where the margin is.

948
00:45:47,560 --> 00:45:50,640
Speaker 1: I understand that, but that's not really meeting market demand.

949
00:45:51,440 --> 00:45:53,520
Speaker 1: What are they buying these what are they building these days?

950
00:45:53,600 --> 00:45:54,520
Speaker 11: Right for the average ones?

951
00:45:54,560 --> 00:45:58,000
Speaker 10: You know, maybe we're not looking for those million dollar mansions,

952
00:45:58,480 --> 00:46:02,080
Speaker 10: but not not yet at this point at least. But

953
00:46:02,239 --> 00:46:04,040
Speaker 10: you know, we are seeing a lot of companies like

954
00:46:04,080 --> 00:46:06,920
Speaker 10: maybe Dr Horton offering incentives to get people to move

955
00:46:06,920 --> 00:46:09,680
Speaker 10: into their homes that they're building. So I know, der

956
00:46:09,840 --> 00:46:12,800
Speaker 10: Horton had just said at a conference earlier this week

957
00:46:12,880 --> 00:46:16,200
Speaker 10: that it's buying down rates on about sixty five percent

958
00:46:16,239 --> 00:46:19,279
Speaker 10: of its sales, and so things like this. If you're

959
00:46:19,280 --> 00:46:21,560
Speaker 10: seeing a mortgage rate of maybe six point five percent

960
00:46:21,640 --> 00:46:24,080
Speaker 10: and Dr Horton or another company like it is willing

961
00:46:24,120 --> 00:46:26,439
Speaker 10: to bring your mortgage rate down to five point five

962
00:46:26,560 --> 00:46:28,840
Speaker 10: or maybe five percent even, it's going to make you

963
00:46:28,840 --> 00:46:32,600
Speaker 10: feel a little bit more inclined to maybe make that purchase.

964
00:46:33,360 --> 00:46:37,399
Speaker 18: Do we know, though, which kind of income bracket they're

965
00:46:37,440 --> 00:46:40,400
Speaker 18: targeting with the home builds in particular, or is it

966
00:46:40,480 --> 00:46:42,680
Speaker 18: just kind of across the board and then they're offering

967
00:46:42,760 --> 00:46:45,160
Speaker 18: these incentives to get whoever they can.

968
00:46:45,160 --> 00:46:45,640
Speaker 11: To get in.

969
00:46:45,800 --> 00:46:47,279
Speaker 10: So what I've been hearing is it tends to be

970
00:46:47,360 --> 00:46:49,200
Speaker 10: more so across the board. But as you said to

971
00:46:49,239 --> 00:46:51,000
Speaker 10: your earlier point, there are a lot of you know,

972
00:46:51,320 --> 00:46:54,719
Speaker 10: maybe Middle America that are looking for homes and may

973
00:46:54,760 --> 00:46:56,480
Speaker 10: not be able to afford it. So I think that's

974
00:46:56,520 --> 00:46:58,040
Speaker 10: something that we'll have to look forward to as we

975
00:46:58,080 --> 00:47:00,520
Speaker 10: continue to iron and look through the earnings.

976
00:47:00,840 --> 00:47:04,239
Speaker 1: So it looks like for these you know, homebuilders, I'm

977
00:47:04,239 --> 00:47:07,040
Speaker 1: looking at like Toll Brothers and DH Horton and think

978
00:47:07,080 --> 00:47:10,279
Speaker 1: things like that, they're still building because even though the

979
00:47:10,360 --> 00:47:13,600
Speaker 1: rates are mortgage rates are six seven percent versus you know,

980
00:47:13,640 --> 00:47:17,919
Speaker 1: like three percent up until just recently, they're still seeing

981
00:47:17,960 --> 00:47:18,320
Speaker 1: the demand.

982
00:47:18,960 --> 00:47:20,000
Speaker 11: They are seeing demand.

983
00:47:20,600 --> 00:47:21,200
Speaker 12: Is it just like.

984
00:47:21,160 --> 00:47:23,959
Speaker 1: Doing Florida and Texas? I mean that's where everybody's moving,

985
00:47:24,239 --> 00:47:27,160
Speaker 1: you know, not me. But is that kind of where

986
00:47:27,160 --> 00:47:28,480
Speaker 1: the demand is or is it more broader?

987
00:47:28,680 --> 00:47:30,840
Speaker 11: I mean, I think it's pretty brought across markets.

988
00:47:30,840 --> 00:47:32,600
Speaker 10: I mean, obviously in New York City we even have

989
00:47:32,719 --> 00:47:36,200
Speaker 10: issues with you know, intense housing demand. So we are

990
00:47:36,239 --> 00:47:39,440
Speaker 10: really seeing it across multiple markets. But as you mentioned,

991
00:47:39,480 --> 00:47:43,000
Speaker 10: you know, the Florida's and maybe the Texas states are

992
00:47:43,120 --> 00:47:45,040
Speaker 10: maybe seeing a little bit more there.

993
00:47:45,320 --> 00:47:48,280
Speaker 18: Yeah, And I know, Nora that you look more macro

994
00:47:48,719 --> 00:47:50,840
Speaker 18: entire sectors when it comes to real estate, Can you

995
00:47:50,840 --> 00:47:54,040
Speaker 18: talk a little bit about why we're seeing the rally

996
00:47:54,360 --> 00:47:57,720
Speaker 18: in you know, the home builder sector in particular now

997
00:47:58,080 --> 00:48:01,359
Speaker 18: versus you know, among prior to that as a month

998
00:48:01,400 --> 00:48:02,040
Speaker 18: forward from them.

999
00:48:02,800 --> 00:48:05,040
Speaker 10: I mean, I think all of this conversation in regards

1000
00:48:05,080 --> 00:48:07,400
Speaker 10: you know, to the FED and just the macro environment

1001
00:48:07,400 --> 00:48:09,840
Speaker 10: that we're currently in, my sources are saying that this

1002
00:48:09,920 --> 00:48:12,800
Speaker 10: is just the prime time for home builders. They're seeing,

1003
00:48:12,840 --> 00:48:16,000
Speaker 10: you know, they're taking market share from where maybe a

1004
00:48:16,040 --> 00:48:18,840
Speaker 10: lot of existing home sale companies you know, would have

1005
00:48:18,920 --> 00:48:21,200
Speaker 10: been making up in the market, but now they have

1006
00:48:21,320 --> 00:48:24,920
Speaker 10: this runway with less competition, we're seeing low inventory and

1007
00:48:24,960 --> 00:48:26,400
Speaker 10: they're able to solve that problem.

1008
00:48:26,440 --> 00:48:28,640
Speaker 11: So this is the perfect timing for them to really soar.

1009
00:48:29,680 --> 00:48:34,439
Speaker 1: LB one commodity is the generic one that I use

1010
00:48:34,640 --> 00:48:40,400
Speaker 1: for lumber, and it's just been so incredibly volatile, and

1011
00:48:40,840 --> 00:48:44,160
Speaker 1: I can't imagine being a person whose job is to

1012
00:48:44,160 --> 00:48:47,200
Speaker 1: build houses, and that's I got to think that's one

1013
00:48:47,239 --> 00:48:49,960
Speaker 1: of my big raw materials. What do the builders say

1014
00:48:50,000 --> 00:48:53,160
Speaker 1: about just the cost to build these houses, whether it's

1015
00:48:53,239 --> 00:48:56,040
Speaker 1: lumber or other products, in terms of actually building them,

1016
00:48:56,040 --> 00:48:57,160
Speaker 1: what's that doing to their margins.

1017
00:48:57,480 --> 00:48:59,640
Speaker 10: Yeah, I mean I think that that is a place

1018
00:48:59,680 --> 00:49:01,560
Speaker 10: that a lot lot of people have been turning their attention.

1019
00:49:01,760 --> 00:49:04,000
Speaker 10: I mean, obviously in the past we had all these

1020
00:49:04,040 --> 00:49:07,640
Speaker 10: issues with being able to actually, you know, get these materials,

1021
00:49:07,719 --> 00:49:09,480
Speaker 10: and so now as you're pointing out, you know, looking

1022
00:49:09,480 --> 00:49:11,640
Speaker 10: at lumber and all of these different prices, how is

1023
00:49:11,640 --> 00:49:14,680
Speaker 10: that all pricing in and how much of a cost

1024
00:49:15,120 --> 00:49:16,520
Speaker 10: is that on these companies.

1025
00:49:16,920 --> 00:49:18,719
Speaker 11: That's something that we'll have to just wait and see.

1026
00:49:18,920 --> 00:49:20,440
Speaker 1: But they've had some pretty good earnings, right, I mean,

1027
00:49:20,840 --> 00:49:24,200
Speaker 1: Time has reported some pretty good earnings, So as you said,

1028
00:49:24,200 --> 00:49:26,480
Speaker 1: this is kind of the time to be in that

1029
00:49:27,120 --> 00:49:29,319
Speaker 1: to be in that sector, I guess. And knowing you're

1030
00:49:29,760 --> 00:49:31,640
Speaker 1: a reporter, you quote a couple of analysts on the

1031
00:49:31,640 --> 00:49:32,800
Speaker 1: street or pretty.

1032
00:49:32,600 --> 00:49:35,360
Speaker 10: Bullish, definitely, I mean, most analysts that I've spoken to

1033
00:49:35,440 --> 00:49:37,920
Speaker 10: are very bullish. And as you mentioned there a lot

1034
00:49:38,000 --> 00:49:41,799
Speaker 10: of these past earnings have been positive, but not only that,

1035
00:49:41,880 --> 00:49:44,320
Speaker 10: but also positive forecast to come, which has been you know,

1036
00:49:44,440 --> 00:49:46,440
Speaker 10: kind of having people raise an eyebrow. Of course, we're

1037
00:49:46,480 --> 00:49:49,759
Speaker 10: in this really tough economy right now and in this

1038
00:49:49,760 --> 00:49:53,160
Speaker 10: tough macro environment, but they're still forecasting maybe some more

1039
00:49:53,160 --> 00:49:56,120
Speaker 10: positivity to come and saying that demand will continue.

1040
00:49:56,360 --> 00:49:57,840
Speaker 11: So to your point, I think.

1041
00:49:57,680 --> 00:49:59,440
Speaker 10: Maybe a lot of people are looking at this as

1042
00:49:59,440 --> 00:50:01,200
Speaker 10: a space to be Maybe it could be a space

1043
00:50:01,239 --> 00:50:01,960
Speaker 10: that will continue to.

1044
00:50:01,960 --> 00:50:05,960
Speaker 18: Flourish within this earning season. What did people like most

1045
00:50:06,040 --> 00:50:09,000
Speaker 18: when it came to, you know, earning's calls for home builders?

1046
00:50:09,360 --> 00:50:10,520
Speaker 6: What the street like most?

1047
00:50:10,680 --> 00:50:13,759
Speaker 10: Yes, the street was really paying attention to my earlier point,

1048
00:50:13,760 --> 00:50:18,520
Speaker 10: the forecast that in addition to orders beats, that's a

1049
00:50:18,560 --> 00:50:20,960
Speaker 10: really really big metric that we look at in regards

1050
00:50:20,960 --> 00:50:24,560
Speaker 10: to the homebuilders sector, and a lot of these companies

1051
00:50:24,600 --> 00:50:27,960
Speaker 10: were just having orders beats across the board. So that

1052
00:50:28,080 --> 00:50:29,440
Speaker 10: just you know, kind of gives you more of a

1053
00:50:29,480 --> 00:50:31,720
Speaker 10: positive sentiment for the market.

1054
00:50:31,480 --> 00:50:32,279
Speaker 3: When I think about it.

1055
00:50:32,520 --> 00:50:35,719
Speaker 1: You know, home being built unders a bunch in my

1056
00:50:35,800 --> 00:50:38,400
Speaker 1: town being built right now. There's just a swarm of

1057
00:50:38,440 --> 00:50:40,839
Speaker 1: people there. You know, they've all got their nail guns

1058
00:50:40,880 --> 00:50:43,879
Speaker 1: and whatnot. But what are the builders saying about getting labor?

1059
00:50:43,880 --> 00:50:46,080
Speaker 1: Because I know, for a while, like everybody else, that

1060
00:50:46,120 --> 00:50:46,680
Speaker 1: was a problem.

1061
00:50:46,880 --> 00:50:50,920
Speaker 10: Yes, and you know, we've had like multiple conversations and

1062
00:50:51,040 --> 00:50:54,799
Speaker 10: multiple articles and conversations coming out about labor. But I

1063
00:50:54,800 --> 00:50:57,520
Speaker 10: think that is a place that people are really tuned

1064
00:50:57,520 --> 00:51:00,000
Speaker 10: into right now, do we have enough people to act

1065
00:51:00,000 --> 00:51:03,160
Speaker 10: actually get the job done? And it seems that it's varied,

1066
00:51:03,239 --> 00:51:05,400
Speaker 10: but it does seem as though like it still tends

1067
00:51:05,440 --> 00:51:08,640
Speaker 10: to be like more of a positive amount of individuals

1068
00:51:08,640 --> 00:51:11,560
Speaker 10: who are actually able to get, you know, to work

1069
00:51:11,560 --> 00:51:12,319
Speaker 10: and get the job done.

1070
00:51:12,400 --> 00:51:13,800
Speaker 11: That's what I've been hearing from sources.

1071
00:51:13,920 --> 00:51:15,520
Speaker 3: I'm looking at Toll Brothers right here.

1072
00:51:15,600 --> 00:51:17,640
Speaker 1: The A n R function just gives you a sense

1073
00:51:17,640 --> 00:51:20,719
Speaker 1: of analyst rating, analyst recommendation. So for Toll Brothers, which

1074
00:51:20,719 --> 00:51:22,719
Speaker 1: I think is you know, kind of me kind of

1075
00:51:22,719 --> 00:51:25,279
Speaker 1: a one of the belt bell Weathers in the group

1076
00:51:26,280 --> 00:51:30,799
Speaker 1: ten buy ratings, seven holds and two cells, so kind

1077
00:51:30,800 --> 00:51:33,319
Speaker 1: of split, you know people. I guess the analysts are

1078
00:51:33,320 --> 00:51:35,359
Speaker 1: trying to figure out, hey, is this trade played out?

1079
00:51:35,760 --> 00:51:37,440
Speaker 3: Is more to go? So we'll see how that goes.

1080
00:51:37,800 --> 00:51:40,320
Speaker 1: They had some good earnings this quarter coming from the

1081
00:51:40,360 --> 00:51:42,399
Speaker 1: builders normal Linda, thanks so much for joining us here.

1082
00:51:42,440 --> 00:51:45,280
Speaker 1: Nor Is, the equities reporter for Bloomberg News. Her report

1083
00:51:45,280 --> 00:51:48,600
Speaker 1: here on home builders sore to new highs on insatiable

1084
00:51:48,680 --> 00:51:49,920
Speaker 1: housing demand.

1085
00:51:49,920 --> 00:51:50,400
Speaker 3: How about that?

1086
00:51:50,560 --> 00:51:54,160
Speaker 1: So but you got to think, Maddie, they got you know,

1087
00:51:54,200 --> 00:51:56,400
Speaker 1: I'd like to see I guess the marketplace would like

1088
00:51:56,440 --> 00:51:58,600
Speaker 1: to see some more affordable housing go out there for

1089
00:51:58,800 --> 00:52:01,280
Speaker 1: folks as opposed to just another mcmanson.

1090
00:52:01,560 --> 00:52:04,680
Speaker 7: You're listening to the tape Cat's are live program Bloomberg

1091
00:52:04,760 --> 00:52:08,360
Speaker 7: Markets weekdays at ten am Eastern on Bloomberg Radio, the

1092
00:52:08,400 --> 00:52:11,640
Speaker 7: tune in app, Bloomberg dot Com, and the Bloomberg Business App.

1093
00:52:11,680 --> 00:52:14,480
Speaker 7: You can also listen live on Amazon Alexa from our

1094
00:52:14,520 --> 00:52:18,920
Speaker 7: flagship New York station Just Say Alexa playing Bloomberg eleven thirty.

1095
00:52:20,719 --> 00:52:24,680
Speaker 1: All right, Fandeep Singer's senior Analysty covers technology from Bloomberg Intelligence.

1096
00:52:24,800 --> 00:52:26,279
Speaker 1: I have no idea what he does, but he's in

1097
00:52:26,360 --> 00:52:29,400
Speaker 1: our studio, so I'm going to ask him to explain

1098
00:52:29,560 --> 00:52:33,080
Speaker 1: chat GPT. Not to someone who's like a five year old,

1099
00:52:33,120 --> 00:52:35,280
Speaker 1: to someone who's like a fifty nine year old. Okay,

1100
00:52:35,719 --> 00:52:38,759
Speaker 1: explain chat GPT, pretend on fifty nine years old.

1101
00:52:39,200 --> 00:52:43,800
Speaker 12: Well, and I'll preface it by saying, we did a

1102
00:52:43,880 --> 00:52:47,560
Speaker 12: survey where we wanted to see where is chat GPT

1103
00:52:48,000 --> 00:52:50,440
Speaker 12: kind of resonating the most? And we found out in

1104
00:52:50,520 --> 00:52:53,840
Speaker 12: that survey the early adopters are the gen zs and

1105
00:52:54,040 --> 00:52:56,719
Speaker 12: you know, the sixteen to thirty four year olds. The

1106
00:52:56,840 --> 00:52:59,640
Speaker 12: thirty five plus are still behind when it comes to

1107
00:53:00,360 --> 00:53:04,000
Speaker 12: trying it out and just in terms of their usage.

1108
00:53:04,080 --> 00:53:07,440
Speaker 12: So clearly it's a gen Z phenomenon.

1109
00:53:06,960 --> 00:53:08,080
Speaker 3: Which you're not even gen Z.

1110
00:53:08,440 --> 00:53:15,239
Speaker 12: Well I'm not, but I covered the space. Sorry, there

1111
00:53:15,320 --> 00:53:18,120
Speaker 12: you go, so sure, line a line, But I.

1112
00:53:18,200 --> 00:53:20,239
Speaker 18: Am in the group that's using it every single day.

1113
00:53:20,840 --> 00:53:23,840
Speaker 12: And I think in our survey was obvious that people

1114
00:53:24,400 --> 00:53:27,520
Speaker 12: prefer it over the traditional search. They find it to

1115
00:53:27,600 --> 00:53:31,080
Speaker 12: be more useful time saver, and they are willing to

1116
00:53:31,200 --> 00:53:33,920
Speaker 12: pay for it, but only pay up to ten dollars,

1117
00:53:34,000 --> 00:53:36,239
Speaker 12: So that was the difference, Like even though the current

1118
00:53:36,280 --> 00:53:39,960
Speaker 12: subscription is twenty dollars, the propensity to pay for it

1119
00:53:40,200 --> 00:53:43,640
Speaker 12: is still low. They want it free, as is expected,

1120
00:53:43,719 --> 00:53:47,600
Speaker 12: and so I think the conclusion from that was ads

1121
00:53:47,640 --> 00:53:50,759
Speaker 12: will still be the way this thing is gonna get monetized.

1122
00:53:50,800 --> 00:53:54,480
Speaker 12: And Google showed in their io event last week like

1123
00:53:54,640 --> 00:53:58,719
Speaker 12: you can have a follow up reply tab and then

1124
00:53:58,840 --> 00:54:01,440
Speaker 12: they can save the content of the prior search. So

1125
00:54:01,920 --> 00:54:05,560
Speaker 12: we'rely the search page as we know it will evolve,

1126
00:54:05,960 --> 00:54:07,960
Speaker 12: and that is getting more and more obvious with this

1127
00:54:08,239 --> 00:54:10,240
Speaker 12: you know, chat GPT revolution.

1128
00:54:10,480 --> 00:54:13,120
Speaker 1: Why is Google at a fifty two week high at

1129
00:54:13,160 --> 00:54:15,480
Speaker 1: one hundred and you know, twenty two dollars.

1130
00:54:15,680 --> 00:54:16,920
Speaker 3: Isn't this bad for Google?

1131
00:54:17,080 --> 00:54:20,759
Speaker 1: Or is Google saying We're gonna use chat GPT better

1132
00:54:20,800 --> 00:54:21,440
Speaker 1: than everybody else?

1133
00:54:21,560 --> 00:54:24,480
Speaker 12: So just to make that distinction, chat gipt is a

1134
00:54:24,640 --> 00:54:28,360
Speaker 12: foundational model, a large language model that is trained on

1135
00:54:28,680 --> 00:54:32,319
Speaker 12: rich open source Internet data set as well as any

1136
00:54:32,360 --> 00:54:36,640
Speaker 12: proprietary data set that OpenAI has. Google has its own

1137
00:54:37,040 --> 00:54:39,560
Speaker 12: large language model. It's called Palm two. They had a

1138
00:54:39,600 --> 00:54:42,400
Speaker 12: first iteration and now this is the second version. And

1139
00:54:42,520 --> 00:54:45,440
Speaker 12: think of you know, any Internet company in this space

1140
00:54:46,560 --> 00:54:50,400
Speaker 12: has the potential to develop their own large language model

1141
00:54:50,719 --> 00:54:53,919
Speaker 12: simply because of how rich their business is. Like all

1142
00:54:54,000 --> 00:54:56,600
Speaker 12: the social media platforms. I mean, I put meta in

1143
00:54:56,680 --> 00:55:00,320
Speaker 12: that same bucket, they have their own foundational large anguage

1144
00:55:00,360 --> 00:55:03,040
Speaker 12: model because of the rich data that they have from

1145
00:55:03,280 --> 00:55:07,000
Speaker 12: their platform. So I think Internet companies in general are

1146
00:55:07,080 --> 00:55:11,000
Speaker 12: at an advantage in this generative AI race simply because

1147
00:55:11,280 --> 00:55:13,760
Speaker 12: they have a lot of their own data. Software companies,

1148
00:55:13,800 --> 00:55:16,520
Speaker 12: on the other hand, Microsoft didn't have a choice but

1149
00:55:16,719 --> 00:55:20,080
Speaker 12: to partner with chat gipt because Microsoft doesn't have its

1150
00:55:20,120 --> 00:55:23,680
Speaker 12: own data. So how do you develop that foundational large

1151
00:55:23,719 --> 00:55:24,400
Speaker 12: language model.

1152
00:55:24,440 --> 00:55:26,759
Speaker 7: You need data and the way you get data is.

1153
00:55:26,760 --> 00:55:30,080
Speaker 12: One through open Internet, but also your own first party data,

1154
00:55:30,120 --> 00:55:33,440
Speaker 12: which is what Google has, Meta has and chatchipt has

1155
00:55:33,480 --> 00:55:34,800
Speaker 12: shown they also have that.

1156
00:55:36,640 --> 00:55:37,560
Speaker 3: Well, I'm tossing.

1157
00:55:38,440 --> 00:55:40,560
Speaker 18: I have a million questions I could ask you about this,

1158
00:55:40,680 --> 00:55:42,719
Speaker 18: but I guess, like not to sound kind of like

1159
00:55:42,760 --> 00:55:45,600
Speaker 18: a crypto enthusiast type, but if it the whole point

1160
00:55:45,680 --> 00:55:48,680
Speaker 18: is that it's open source, why would I ever consider

1161
00:55:48,880 --> 00:55:49,440
Speaker 18: paying for it?

1162
00:55:50,360 --> 00:55:54,080
Speaker 12: You know what I mean? Because the quality of the

1163
00:55:54,200 --> 00:55:58,640
Speaker 12: results matter. So you're going to use chat cipt or

1164
00:55:58,840 --> 00:56:03,040
Speaker 12: anything equivalent only if it's better than traditional search. So

1165
00:56:03,200 --> 00:56:06,440
Speaker 12: the quality of the search really matters here, and based

1166
00:56:06,480 --> 00:56:09,239
Speaker 12: on the quality and the use case, you may be

1167
00:56:09,400 --> 00:56:10,279
Speaker 12: willing to pay for it.

1168
00:56:10,480 --> 00:56:13,160
Speaker 18: You know, right now it's free and it's doing what

1169
00:56:13,280 --> 00:56:15,160
Speaker 18: I need. You know what I mean, Like, why would

1170
00:56:15,160 --> 00:56:17,840
Speaker 18: we offer it? Why would you start to pay for something?

1171
00:56:18,160 --> 00:56:21,840
Speaker 12: So chat GPT plus has no restriction on the number

1172
00:56:21,920 --> 00:56:24,920
Speaker 12: of queries you can have in a day. It has

1173
00:56:25,040 --> 00:56:28,840
Speaker 12: some more features around the experience you have as a

1174
00:56:28,920 --> 00:56:32,840
Speaker 12: paid user versus a freemium user. So a casual user

1175
00:56:33,040 --> 00:56:35,480
Speaker 12: is happy to be a freemium user, but if you are,

1176
00:56:36,200 --> 00:56:39,560
Speaker 12: you know, using this for hundreds of queries in a day,

1177
00:56:39,760 --> 00:56:42,239
Speaker 12: and you are a prolific I mean, you really like

1178
00:56:42,360 --> 00:56:45,200
Speaker 12: this tool, then I think you may be willing to

1179
00:56:45,239 --> 00:56:45,880
Speaker 12: pay for it, So.

1180
00:56:45,920 --> 00:56:47,920
Speaker 18: Maybe it's more of a company play. Then do you

1181
00:56:48,040 --> 00:56:50,280
Speaker 18: see that being the main customer?

1182
00:56:51,440 --> 00:56:54,520
Speaker 12: I would say it has work related use cases. I

1183
00:56:54,560 --> 00:56:57,640
Speaker 12: mean we are talking about white collar jobs being affected

1184
00:56:57,760 --> 00:57:01,000
Speaker 12: by chat GIPT in certain cases get automated. How do

1185
00:57:01,040 --> 00:57:04,360
Speaker 12: you drive that productivity? I mean, ultimately, it has to

1186
00:57:04,480 --> 00:57:07,360
Speaker 12: save time, it has to make you productive and that

1187
00:57:07,560 --> 00:57:11,160
Speaker 12: in those cases, again, I am of the belief, you know,

1188
00:57:11,440 --> 00:57:14,719
Speaker 12: ads will still be the predominant way this thing gets monetized,

1189
00:57:15,040 --> 00:57:17,760
Speaker 12: but in a corporate setting, you're not willing to look

1190
00:57:17,800 --> 00:57:20,800
Speaker 12: at ADS while you're using this tool, so you'll probably

1191
00:57:20,840 --> 00:57:21,560
Speaker 12: subscribe to it.

1192
00:57:21,840 --> 00:57:24,960
Speaker 3: Does Bloomberg Intelligence have a chat GPT primer?

1193
00:57:27,000 --> 00:57:31,360
Speaker 12: We have our own generative AI forecast, and we actually

1194
00:57:31,480 --> 00:57:35,040
Speaker 12: will have a detailed segment level analysis. It's still not published,

1195
00:57:35,120 --> 00:57:38,720
Speaker 12: so I'm just giving you a teaser there, okay, but

1196
00:57:38,920 --> 00:57:43,320
Speaker 12: we will have our own segmentation for this entire market,

1197
00:57:43,400 --> 00:57:46,120
Speaker 12: which is I think overused at this point of time.

1198
00:57:46,640 --> 00:57:49,400
Speaker 18: Well, that's interesting because we were talking yesterday about Steve

1199
00:57:49,480 --> 00:57:52,360
Speaker 18: Cohen saying you know AI, it's the big thing, which, yeah,

1200
00:57:52,640 --> 00:57:56,080
Speaker 18: we've been discussing this, but if tech as a sector

1201
00:57:56,160 --> 00:57:59,040
Speaker 18: has sort of peaked, is AI the place to find

1202
00:57:59,120 --> 00:58:00,640
Speaker 18: growth right now?

1203
00:58:00,800 --> 00:58:03,880
Speaker 12: I mean there is no doubt that this will drive

1204
00:58:03,960 --> 00:58:06,720
Speaker 12: the next leg of growth. Now the question is how

1205
00:58:06,800 --> 00:58:10,280
Speaker 12: do you monetize it? Who gets displaced in terms of

1206
00:58:10,480 --> 00:58:13,560
Speaker 12: you know, the incumbents that are getting affected. But when

1207
00:58:13,600 --> 00:58:15,600
Speaker 12: it comes to the next leg of growth, I mean

1208
00:58:15,680 --> 00:58:19,840
Speaker 12: this is huge simply because everyone realizes.

1209
00:58:19,800 --> 00:58:22,360
Speaker 3: Now we have this, is this AI or is this

1210
00:58:22,560 --> 00:58:24,960
Speaker 3: chat GPT? Explain the difference or what.

1211
00:58:25,520 --> 00:58:28,360
Speaker 12: I would say large language models. So chat GPT is

1212
00:58:28,600 --> 00:58:32,040
Speaker 12: one of the large language models that is out there.

1213
00:58:32,440 --> 00:58:35,000
Speaker 12: As I said, Google has their own large language model,

1214
00:58:35,120 --> 00:58:37,960
Speaker 12: Meta has their own large alguage models. And you're going

1215
00:58:38,040 --> 00:58:38,880
Speaker 12: to see more.

1216
00:58:38,760 --> 00:58:41,080
Speaker 3: And more language models. Okay, yes, because and.

1217
00:58:41,440 --> 00:58:43,880
Speaker 12: The reason why they're called large language is because of

1218
00:58:44,000 --> 00:58:48,160
Speaker 12: the number of parameters. We're talking about one hundred billion parameters.

1219
00:58:48,200 --> 00:58:51,960
Speaker 12: Think of a regression model, it's got five ten different variables.

1220
00:58:52,040 --> 00:58:55,400
Speaker 12: We're talking about billions of variables. In terms of training

1221
00:58:56,160 --> 00:58:56,840
Speaker 12: an algorithm.

1222
00:58:57,600 --> 00:58:59,080
Speaker 3: Do we need it right? Do we need to regulate

1223
00:58:59,120 --> 00:58:59,440
Speaker 3: this thing?

1224
00:59:00,520 --> 00:59:05,720
Speaker 12: And that's where Samuel Altman's testimony comes into limelight. I mean,

1225
00:59:05,760 --> 00:59:10,200
Speaker 12: obviously he was grilled this week around the safety issues.

1226
00:59:10,280 --> 00:59:14,880
Speaker 12: And look, from a large company's standpoint like Google, they

1227
00:59:14,920 --> 00:59:18,840
Speaker 12: would want regulation because this will prevent everyone else from

1228
00:59:19,000 --> 00:59:22,120
Speaker 12: really competing in this space. And the bar is even higher,

1229
00:59:22,920 --> 00:59:25,640
Speaker 12: like to even train a large anguage model, you need

1230
00:59:25,760 --> 00:59:29,680
Speaker 12: ten thousand GPUs. So we're talking about a big upfront

1231
00:59:29,760 --> 00:59:32,600
Speaker 12: investment here in terms of anybody who wants to compete

1232
00:59:32,600 --> 00:59:36,240
Speaker 12: in this space. Otherwise, you just take what chatchipt offers you,

1233
00:59:36,600 --> 00:59:39,320
Speaker 12: which is a large anguage model they've already trained. You're

1234
00:59:39,480 --> 00:59:42,840
Speaker 12: just a user of that language model and building an

1235
00:59:42,840 --> 00:59:45,760
Speaker 12: application on top of it. Think of it as the

1236
00:59:45,880 --> 00:59:49,840
Speaker 12: app store. You're building apps on an iOS, but iOS

1237
00:59:50,000 --> 00:59:53,200
Speaker 12: is the operating system. Chat Gipt is giving you a platform.

1238
00:59:53,240 --> 00:59:55,560
Speaker 12: You're building an app on top of it. And so

1239
00:59:55,840 --> 00:59:59,880
Speaker 12: that's the kind of war that I foresee is Google

1240
01:00:00,120 --> 01:00:03,240
Speaker 12: having its own large Anglin model Meta and this is

1241
01:00:03,320 --> 01:00:06,680
Speaker 12: like the operating system of equivalent Android iOS and you know.

1242
01:00:06,880 --> 01:00:09,360
Speaker 1: So on all right, I just my own survey right now,

1243
01:00:09,760 --> 01:00:12,440
Speaker 1: like the group chat for my four children, I asking

1244
01:00:12,480 --> 01:00:14,640
Speaker 1: them how they use chat GPT, so I like know

1245
01:00:14,720 --> 01:00:17,680
Speaker 1: what to what I hear Okay, No.

1246
01:00:17,880 --> 01:00:20,920
Speaker 18: Well it's it's a very important question, Paul. And I

1247
01:00:21,240 --> 01:00:23,920
Speaker 18: guess I wonder too what they're using it for, because

1248
01:00:24,000 --> 01:00:26,560
Speaker 18: you're comparing it to search, but I don't use it.

1249
01:00:26,720 --> 01:00:28,800
Speaker 18: I still google things that I just want to search.

1250
01:00:28,920 --> 01:00:32,000
Speaker 18: And then there are other things like writing emails that

1251
01:00:32,040 --> 01:00:33,280
Speaker 18: I use chat GPT for.

1252
01:00:33,520 --> 01:00:35,560
Speaker 3: So what instruction do you give write an email?

1253
01:00:35,640 --> 01:00:35,800
Speaker 7: Teach?

1254
01:00:35,800 --> 01:00:37,800
Speaker 18: It gives me so specific, which is what I really

1255
01:00:37,960 --> 01:00:40,200
Speaker 18: like about it. I can say, write an email to

1256
01:00:40,360 --> 01:00:43,920
Speaker 18: my boss that is professional but also casual that says

1257
01:00:44,480 --> 01:00:46,320
Speaker 18: I'm just making this up. I'd like to request a

1258
01:00:46,400 --> 01:00:49,000
Speaker 18: week off to whatever for this thing, and it'll write

1259
01:00:49,040 --> 01:00:51,600
Speaker 18: it for you. And then you can say back, that

1260
01:00:51,920 --> 01:00:53,680
Speaker 18: was too long, cut it in half, and it'll do

1261
01:00:53,760 --> 01:00:55,680
Speaker 18: it in a second. And I just don't want to

1262
01:00:55,720 --> 01:00:57,600
Speaker 18: have to think about writing the email, so it does.

1263
01:00:57,520 --> 01:00:57,880
Speaker 14: It for you.

1264
01:00:58,160 --> 01:01:00,400
Speaker 18: Or you can say, make an itinerary for for a

1265
01:01:00,440 --> 01:01:02,600
Speaker 18: week in Switzerland and it sends you a whole thing.

1266
01:01:02,800 --> 01:01:06,480
Speaker 18: It can write a video script with suggestions, so that strings.

1267
01:01:06,240 --> 01:01:09,440
Speaker 1: Up, Like I thought, schools write my turn paper on X, X,

1268
01:01:09,520 --> 01:01:11,200
Speaker 1: Y and Z, so the New York City schools and

1269
01:01:11,200 --> 01:01:12,880
Speaker 1: there's news out and I know you guys send it

1270
01:01:12,920 --> 01:01:14,400
Speaker 1: to me. But there's news out today that the New

1271
01:01:14,440 --> 01:01:18,800
Speaker 1: York City had kind of forbidden chat GPT in New

1272
01:01:18,840 --> 01:01:19,480
Speaker 1: York City schools.

1273
01:01:19,520 --> 01:01:20,360
Speaker 3: They've just rescinded that.

1274
01:01:21,440 --> 01:01:24,280
Speaker 12: Yeah, because I think the co pilot use case is

1275
01:01:24,400 --> 01:01:28,040
Speaker 12: very interesting. Can it help you learn a concept faster

1276
01:01:28,240 --> 01:01:32,440
Speaker 12: because you have this you know, assistant that is available

1277
01:01:32,480 --> 01:01:35,680
Speaker 12: where you can understand the concept because this thing is

1278
01:01:35,880 --> 01:01:38,720
Speaker 12: an assisting you in the R and D?

1279
01:01:39,120 --> 01:01:42,080
Speaker 1: Is there a feeling that all right, we're I was

1280
01:01:42,120 --> 01:01:44,000
Speaker 1: gonna use a baseball analogy, but that may not work

1281
01:01:44,040 --> 01:01:45,840
Speaker 1: for you, but I'm gonna go there. We're not even

1282
01:01:45,960 --> 01:01:48,400
Speaker 1: this is using cricket very top yet, the very top

1283
01:01:48,440 --> 01:01:50,640
Speaker 1: of the first inning, right, we're on the on deckshert.

1284
01:01:50,680 --> 01:01:52,600
Speaker 3: We haven't even started the game yet. That's how really

1285
01:01:52,680 --> 01:01:53,040
Speaker 3: we feel.

1286
01:01:53,200 --> 01:01:56,720
Speaker 12: We're talking about foundational models here. The apps will be

1287
01:01:56,840 --> 01:01:59,960
Speaker 12: built on top of these foundational models. So, as I said,

1288
01:02:00,000 --> 01:02:02,800
Speaker 12: I think gofeed as an operating system equivalent. Right now,

1289
01:02:02,880 --> 01:02:05,880
Speaker 12: you have that base operating system. The apps will be

1290
01:02:06,000 --> 01:02:07,080
Speaker 12: developed as we go on.

1291
01:02:07,920 --> 01:02:09,480
Speaker 3: All right, I'll go there.

1292
01:02:09,600 --> 01:02:11,960
Speaker 1: I'll wait for your primer to come out from Bloomberg Intelligence,

1293
01:02:11,960 --> 01:02:13,680
Speaker 1: because that's how I learned most stuff I don't know,

1294
01:02:13,840 --> 01:02:17,880
Speaker 1: Man Deep Seeing, senior analyst technology for Bloomberg Intelligence, joining

1295
01:02:17,960 --> 01:02:20,360
Speaker 1: us here in a Bloomberg Interactive Brokers studio, making us

1296
01:02:20,360 --> 01:02:22,040
Speaker 1: even more confused, if that's possible.

1297
01:02:25,560 --> 01:02:28,600
Speaker 2: Thanks for listening to the Bloomberg Markets podcast. You can

1298
01:02:28,680 --> 01:02:32,400
Speaker 2: subscribe and listen to interviews at Apple Podcasts or whatever

1299
01:02:32,560 --> 01:02:36,160
Speaker 2: podcast platform you prefer. I'm Matt Miller. I'm on Twitter

1300
01:02:36,480 --> 01:02:38,560
Speaker 2: at Matt Miller nineteen seventy three.

1301
01:02:38,840 --> 01:02:41,200
Speaker 3: And on ball Sweeney I'm on Twitter at pt Sweeney.

1302
01:02:41,320 --> 01:02:44,000
Speaker 1: Before the podcast, you can always catch us worldwide at

1303
01:02:44,000 --> 01:02:44,760
Speaker 1: Bloomberg Radio