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Speaker 1: This is the Bloomberg Surveillance Podcast. I'm Tom Keene along

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Speaker 1: with Paul Sweeney. Join us each day for insight from

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Speaker 1: the best in economics, finance, investment, and international relations. You

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Speaker 1: can also watch the show live on YouTube. Visit the

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Speaker 1: Bloomberg Podcast channel on YouTube to see the show weekday

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Speaker 1: mornings from seven to ten am Eastern from our global

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Speaker 1: headquarters in New York City. Subscribe to the podcast on Apple, Spotify,

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Speaker 1: or anywhere else you listen, and always on Bloomberg Radio,

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Speaker 1: the Bloomberg Terminal, and the Bloomberg Business App. Cardinal rule

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Speaker 1: number one is when you're in an equity business, listen

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Speaker 1: to the bond guys. The bond guys never listen to

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Speaker 1: the equity guys. But you know, if you're like a

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Speaker 1: gene monster, you really pay attention to what the debt

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Speaker 1: side looks like as you look at your area. Gene

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Speaker 1: Monsters area for decades Biper Jeffrey and now on his

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Speaker 1: own gen Monster really looking at technology gene Robert Schiffman

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Speaker 1: of Bloomberg Intelligence said, look, Microsoft's a tripaa. Apple's just

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Speaker 1: below that. Microsoft could do twenty billion debt offering right

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Speaker 1: now and not you know, affect anything at all. Do

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Speaker 1: we have any understanding of their free cash flow? And

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Speaker 1: Gene one idea, you're on their free cash flow. Apple's

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Speaker 1: retired thirty nine percent of their shares in the last decade.

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Speaker 1: Do we understand their profit?

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Speaker 2: I don't think so, Tom.

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Speaker 3: When you think of the free cash hold with Apple,

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Speaker 3: we're talking fifty to sixty billion dollars per year.

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Speaker 2: That's free cash flow.

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Speaker 3: When they talk about their current cash position, call it

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Speaker 3: roughly seventy five billion. I mean they've continued to try

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Speaker 3: to whittle that down return that to investors. Welcome meta

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Speaker 3: to that conversation regarding returning capital. But Apple, is you

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Speaker 3: in that it is? I mean, it's it's mind boggling.

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Speaker 3: If you look at the past year, revenue has basically

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Speaker 3: been down two and a half percent, but they've generated

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Speaker 3: called it sixty billion in free cash flow, and I

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Speaker 3: think it just speaks to the strength of their margins.

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Speaker 1: Paul I put this out last night in preparation for

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Speaker 1: Gene Monster that I'm just had it with the sophomoric

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Speaker 1: growthiness study of the media, and it's about the profit

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Speaker 1: and as Gene mentions, Paul Zuckerberg gets the twenty twenty

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Speaker 1: three trophy. The way he shifted Facebook to a profit

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Speaker 1: juggernaf is.

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Speaker 4: Jaw dropping, it really is, and the stock got rewarded

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Speaker 4: for it too, more than doubling here, Hey Gene, So

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Speaker 4: last night busy night for you and busy night for

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Speaker 4: tech investors. Apple, Amazon, Meta.

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Speaker 5: I want to start with.

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Speaker 4: Apple here because for me and I think for a

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Speaker 4: lot of investors, it's very much about China here, and

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Speaker 4: I'm not sure I heard anything from Coopertino last night

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Speaker 4: that makes me feel more comfortable about what's happening in China.

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Speaker 4: My concern is maybe the consumer's moving away from Western

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Speaker 4: products in general and maybe Apple in particular.

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Speaker 5: How do you think about China?

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Speaker 3: China hit the wall. It was down thirteen percent year

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Speaker 3: of year, is done two and a half percent year

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Speaker 3: year in this September quarter, so we saw that deceleration.

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Speaker 3: The macro has definitely impacted that. If you look at

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Speaker 3: broader Apple business, it was up ten percent in Europe,

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Speaker 3: fifteen percent in Japan two percent. So if you think

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Speaker 3: about what's going on at Apple, it really can be

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Speaker 3: zeroed into the weakness in China. China's twenty percent of

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Speaker 3: their business. And then the question comes to what does

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Speaker 3: China look like going forward, and I think the answer

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Speaker 3: is to continue to expect it to be down kind

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Speaker 3: of this ten to fifteen percent for the next few quarters.

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Speaker 3: There is the question is this just a macro issue

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Speaker 3: related to China or tier point, is there just a

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Speaker 3: structural shift away from Western products and Apple in particular.

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Speaker 3: And what we saw was some conflicting data. We saw

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Speaker 3: the China business being down like we talked about, but

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Speaker 3: we also saw that Apple in terms of its market share.

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Speaker 3: IDC reputable source said that Apple that the iPhone is

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Speaker 3: now the best selling smartphone in China for the first

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Speaker 3: time in the December quarter. And so I think the

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Speaker 3: answer is this is more about the macro. I do

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Speaker 3: want to put one other other one just slightly finer

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Speaker 3: point on this. There is a dynamic of what's going

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Speaker 3: on with the government and their commentary about buying Apple products.

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Speaker 2: Now, if you look at the number of government.

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Speaker 3: Employees, this basically can create about a three percent headwind

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Speaker 3: to China's business. It's not a formal policy to avoid

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Speaker 3: Apple products. It's been this informal kind of wink and nod,

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Speaker 3: But I think that that probably plays in all this too.

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Speaker 2: When you roll it all together.

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Speaker 3: I still think China's a big opportunity for Apple. India

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Speaker 3: is becoming a bigger opportunity too, and I think that

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Speaker 3: I think this storm will pass.

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Speaker 4: Yeah, and that's kind of I think the message we

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Speaker 4: heard from Tim Cook, I think he's probably taken a little.

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Speaker 5: Bit more of a longer view.

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Speaker 4: All right, let's switch to Meta. You mentioned that, and

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Speaker 4: just what a story here, I mean, and you know,

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Speaker 4: Zuckerberg gets a lot of criticism, and he obviously was

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Speaker 4: on the hill again recently, but boy did they turn

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Speaker 4: this company around in the last eighteen months. It was,

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Speaker 4: you know, since its inception was a top line growth story.

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Speaker 4: And then he wisely wisely focused on costs and profitability,

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Speaker 4: and boy, that's paid off for the company.

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Speaker 5: What's your view there'll met.

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Speaker 3: Him The cost is' that's nice.

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Speaker 2: Absolutely has paid off.

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Speaker 3: I think there's a bigger dynamic plane out here which

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Speaker 3: is just related to their demand. And they had their

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Speaker 3: revenue was up twenty five percent year over year in

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Speaker 3: the December quarter. They guided you got to go to

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Speaker 3: the high end of their guidance range because they're probably

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Speaker 3: going to exceed. That would imply an acceleration to thirty percent.

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Speaker 3: Their price per AD was up two percent year over year.

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Speaker 3: A year ago, their price per AD was two percent.

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Speaker 3: So that's outside of the whole expense control. The AD

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Speaker 3: market is better. But there's something The bigger thing going

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Speaker 3: on is this is that their products people want. I mean,

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Speaker 3: that's the maybe optimistic way to say it. The more

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Speaker 3: skeptical way to say it is their addictive deep Water

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Speaker 3: does own meta. This is something that I struggle with,

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Speaker 3: but we saw it in the numbers in the engagement.

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Speaker 3: It's playing through to the revenue. One just other quick

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Speaker 3: thought on the engagement number here, they're da used.

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Speaker 2: This is a metric.

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Speaker 3: They're no longer going to give daily active users. But

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Speaker 3: that was two point one billion for Facebook. That was

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Speaker 3: up six percent year over year. It's an acceleration over

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Speaker 3: the past few quarters. Remarkable testimony to how people say

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Speaker 3: they don't want social but they spend all their time.

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Speaker 6: Yeah.

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Speaker 1: Well, the daily average use of Instagram and the Keen

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Speaker 1: household is in calcule. You can't do that number. We

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Speaker 1: have this gene monster for this entire half hour commercial

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Speaker 1: freees with deep Water Asset Management gene. Here's the real world.

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Speaker 1: It's owned by portfolios x up one percent right now,

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Speaker 1: and then we're gonna pick on our good friend Will

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Speaker 1: dan Off up in Boston. He's been doing this for

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Speaker 1: a few years at a shop called Fidelity. It's as

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Speaker 1: large as holding. It's grown out where Meta Facebook is

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Speaker 1: twelve point x percent of Will Danof's contra fund, but

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Speaker 1: he's probably under owned it as well. Just the share

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Speaker 1: price today moves Contrafund two percent off the leap in Meta.

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Speaker 1: Are these stocks under owned by institutional Wall Street?

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Speaker 2: I think Meta is.

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Speaker 3: I think there's still some controversy around just what is

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Speaker 3: ultimately you know, what does their advertising business look like.

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Speaker 3: I think there's concern about regulation related to it. If

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Speaker 3: you look at the multiple relative to the other big

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Speaker 3: tech companies, it's still trades at a discount, so it's

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Speaker 3: going to be I bet the earnings go up. I'm

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Speaker 3: more than twenty percent today, the stock up eighteen percent

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Speaker 3: pre market.

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Speaker 2: I bet earnings go up.

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Speaker 3: So I think the multiple probably is actually surprising, and

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Speaker 3: to stay in that low twenty range where you look

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Speaker 3: at the rest of big tech kind of loosely in

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Speaker 3: the low thirty range, and so based on that, I

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Speaker 3: don't think this is over owned, and I think that

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Speaker 3: there's again there's a room to go. I think this

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Speaker 3: should trade in line with the rest of larger tech.

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Speaker 1: Okay, so they're three trillion a guy an uber bowl

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Speaker 1: like Dana, iives is what Paul Ford four trillion added up?

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Speaker 1: Geene monster in someday these four stocks, the Magnificent four whatever, Nvidia,

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Speaker 1: I'm not there yet. I know Gene is. I'm not

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Speaker 1: there yet, but Geane they're going to have an all

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Speaker 1: in value of say fifteen trillion dollars? Are they like

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Speaker 1: the Rockefellers standard oil eyed to tarble of one hundred

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Speaker 1: and twenty years ago, where there's going to be a

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Speaker 1: primal cry there too big? Are we getting there quickly?

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Speaker 3: I think we were there three years ago. And I

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Speaker 3: think that you know, this is lessons learned from the

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Speaker 3: past here just about can things keep going about bubbles?

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Speaker 3: There was everything bubble a couple years ago. See what

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Speaker 3: happened two thousand. So I'm skeptical. I want to be

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Speaker 3: just cautious, like can this keep going? But just the

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Speaker 3: case that I think these will continue to get going.

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Speaker 3: It is as simple as artificial intelligence. I think that

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Speaker 3: this is something that now Apple has stepped into the game.

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Speaker 3: That is is much of the rancor that goes on

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Speaker 3: Capitol Hill related to large cap tech. I think lawmakers

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Speaker 3: understand that these companies having strong domestic companies that are

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Speaker 3: building AI. I think they understand the importance of that

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Speaker 3: from a national security standpoint, and so I think that

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Speaker 3: they'll be always talk of regulation, but I think these

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Speaker 3: companies will continue to get bigger.

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Speaker 1: Are you watching unc Duke this weekend with Vision Pro?

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Speaker 5: Now I'm gonna go old school.

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Speaker 1: You're gonna go old school? Yeah?

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Speaker 5: I can't, but watch?

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Speaker 1: What would you want? I at least help here? What

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Speaker 1: would you when I use vision Pro for I?

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Speaker 5: I don't know. Well, that's kind of where I want

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Speaker 5: to go with the gior Gene. Where are we?

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Speaker 4: Where is Mark Zuckerberg these days when you speak to him,

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Speaker 4: when you hear his commentary with the metaverse that was

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Speaker 4: at best at distraction for this company and at worst

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Speaker 4: maybe a real suckhole in terms of a capital investment.

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Speaker 5: Where are we with that?

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Speaker 3: I hope we're wrapping it up, but I think the

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Speaker 3: reality is that they're doubling down. And again mentioned we're

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Speaker 3: investors of the meta We wish they would essentially pair

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Speaker 3: back what they're doing there.

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Speaker 2: The simple reason is.

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Speaker 3: They had a billion in revenue first quarter of a

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Speaker 3: billion in revenue, and Reality Labs they lost four billion.

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Speaker 3: They said that the amount of losses annually are going

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Speaker 3: to increase. This is going to be fifteen billion dollar plus.

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Speaker 3: This is real numbers, even for these big companies. They're

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Speaker 3: spending a lot. What Apple is showing today with Vision

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Speaker 3: pro from a technology from a price standpoint, it's seven

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Speaker 3: times higher. From a technology point, it's probably thirty x better.

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Speaker 3: And I think ultimately is that what Meta wants to

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Speaker 3: be spatial computing, the metaverse for the masses is that

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Speaker 3: I do believe that that will roll out eventually, so

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Speaker 3: but I wish that they just wouldn't spend as much.

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Speaker 3: Spend five billion a year, not fifteen billion a year.

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Speaker 3: But to answer your question, Paul, where are they? They're doubling,

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Speaker 3: tripling down on this, And I kind of see this

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Speaker 3: as BlackBerry versus the iPhone to you.

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Speaker 1: To Global Wall Street, we start strolling on the seven

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Speaker 1: o'clock or a gene monster with his deep water asset

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Speaker 1: management here on this huge societal juggernaut. We're all writing

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Speaker 1: the technology company. Speaking of technology, Apple car play, We

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Speaker 1: are humble. I got goosebumps over like you know the

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Speaker 1: January numbers, Apple car play on Apple on Android, Bloomberg

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Speaker 1: Business app downloaded free and it works, it's safer, it's better,

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Speaker 1: as Apple says. And on YouTube, Ian Bremer, technologist Yep,

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Speaker 1: picks up Godfreed Jeff Gottfried's work at Pure Research in

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Speaker 1: New Jersey over what do we actually consume? And Facebook's

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Speaker 1: right up there, in Instagram's right up there. But paul

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Speaker 1: Ian Bremer points out eight and ten Americans are engaged

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Speaker 1: with YouTube. We're on YouTube, Bloomberg podcasts, look for it.

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Speaker 5: Absolutely, we're there. Hey.

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Speaker 4: The third company reporting last night, Amazon Boy another really good.

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Speaker 1: I thought they would have liked they were terrible.

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Speaker 4: Stocks up about six and a half percent pre market,

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Speaker 4: trading up about forty percent over the trailing twelve months.

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Speaker 2: Here.

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Speaker 5: What did you hear from Amazon last night, Geen, Well.

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Speaker 3: This one, just in terms of the stock reaction, is

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Speaker 3: a surprise to me. I would if you'd have given

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Speaker 3: me the press release beforehand.

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Speaker 2: Out has said it's down a few percent.

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Speaker 3: So what we heard last night was that AWS, which

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Speaker 3: is typically the pressure point that people focus on, was

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Speaker 3: up thirteen percent that was compares to twelve and a

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Speaker 3: half percent growth in the September quarter and twelve point

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Speaker 3: two percent in the June quarter, So we saw I

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Speaker 3: would say, a fifty basis points acceleration. Compare that to

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Speaker 3: what Google Cloud did. They showed a three hundred and

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Speaker 3: twenty basis point acceleration of twenty six percent, an Azure

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Speaker 3: one hundred bases point acceleration to thirty percent. Put all

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Speaker 3: this together, AWS should and continue to be the focus

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Speaker 3: for Amazon investors. And in fact, yes it showed acceleration,

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Speaker 3: but it still is losing share notably. And I think

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Speaker 3: when you fast forward this six twelve, twenty four months

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Speaker 3: from now and think about how these platforms are going

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Speaker 3: to be adding more AI capabilities for the people that

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Speaker 3: use them, I think that advantage Microsoft and Google. So

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Speaker 3: I think it's going to be increasingly competitive. The reason

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Speaker 3: why the stock is up is because they beat earnings

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Speaker 3: by operating income by fifty percent.

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Speaker 2: They crushed it. And this is like the roller coaster

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Speaker 2: game of Amazon and earnings.

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Speaker 3: You get a boom bust cycle for two or three quarters,

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Speaker 3: and then a boom cycle and then a bus cycle.

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Speaker 2: And so Paul to put it all together. I think

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Speaker 2: Amazon quarter was good, it wasn't great.

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Speaker 3: They got people excited about this rufus, this assistant shopper.

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Speaker 2: I think it's pretty cool.

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Speaker 3: I think it's gonna help engagement, help people find products

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Speaker 3: better on Amazon. Good use of AI, but to me

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Speaker 3: doesn't justify what's happening with the stock this morning.

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Speaker 4: All right, let's step back a little bit, Geene, we

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Speaker 4: kind of ran through the big three tech names are

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Speaker 4: reported last night. I love to get your view here

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Speaker 4: now that we've had a little bit of perspective here

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Speaker 4: four five, six quarters where we've had a lot of

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Speaker 4: these companies talking about generative AI. I love to get

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Speaker 4: your view as kind of how you think it kind

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Speaker 4: of really sits as a theme in technology.

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Speaker 5: Is it real?

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Speaker 1: Is it?

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Speaker 5: And how material is it? And how do you play it?

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Speaker 1: So?

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Speaker 2: Is it real? Yes? How material is it?

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Speaker 3: The example My framework is that if you do the

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Speaker 3: scale of zero to one hundred, we've talked about this

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Speaker 3: zero to one hundred, one hundred being electricity, I would

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Speaker 3: put the smartphone at in terms of importance. Electricity is

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Speaker 3: one hundred, smartphones twenty five, The Internet's fifty.

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Speaker 2: And I think jenering of AI and AI.

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Speaker 3: More broadly, ninety, I think it is just a step function,

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Speaker 3: and I think we're entering it's going to be a

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Speaker 3: three to five year bowl market that's going to culminate

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Speaker 3: in a bubble. By definition, almost has to happen. If

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Speaker 3: this is in fact, ninety out of one hundred, we

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Speaker 3: will see a bubble. So I'm I'm on board that

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Speaker 3: these can be transformative. I mean, we saw Tim Cook

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Speaker 3: finally utter those two letters yesterday in the prepared remarks,

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Speaker 3: and I think they're it's setting Apple up to enter

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Speaker 3: as with a foundation model this year. So big picture,

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Speaker 3: it's I think it's hard to under under states how

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Speaker 3: meaningful of a change this is going to be.

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Speaker 1: Gene, I wanted to shift here too long ago and

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Speaker 1: far away when we hung on every word you said

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Speaker 1: at Piper Jeffrey about should we be in these stocks.

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Speaker 1: There's a lot of people listening and watching on YouTube

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Speaker 1: and car play who are saying I'm in this or

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Speaker 1: I think I'm in it, I four oh one K

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Speaker 1: or I'm not in it, and damn I got to

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Speaker 1: get into it. But I'm scared, stiff. I want you

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Speaker 1: to talk the five year vision of these companies that

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Speaker 1: have come so far in the last decade. Do you

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Speaker 1: extrapolate out the present trends? Is there a lesser slope

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Speaker 1: or dare I say, can they even get convexity and

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Speaker 1: accelerate free cash flow revenue growth their position within American society.

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Speaker 3: I think when you look at the kind of scope

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Speaker 3: of where wealth will be created around AI, I generally

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Speaker 3: think of it as in three buckets. I think that

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Speaker 3: there's the we'll call it the safe bet, and I

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Speaker 3: think that is this megacap tech. And I think it's

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Speaker 3: not just own the magnificent seven.

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Speaker 2: I think they are.

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Speaker 3: I think they're I think the best one surprisingly is Google.

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Speaker 3: I think the best opportunity that's underappreciated relative to what's happened.

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Speaker 2: With the other ones, I think Google.

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Speaker 3: So this is not investment advice, but from our perspective,

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Speaker 3: we onwn Google a meta and so continue to do that.

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Speaker 3: Continue to think that's a great place to be, even

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Speaker 3: with the upside that they've had. But we also believe

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Speaker 3: that AI is going to have a profound impact on

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Speaker 3: companies that are less well known.

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Speaker 2: We have an ETF the ticker's loup.

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Speaker 3: It's a frontier tech, which means that it effectively identifies

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Speaker 3: what are the transformative themes over the next three to

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Speaker 3: five years. AI is a big part of that, but

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Speaker 3: I think some of these smaller companies that haven't had

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Speaker 3: this tremendous run, I think that's a meaningful opportunity as well.

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Speaker 3: And then the last piece is what we call the

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Speaker 3: ordained late stage private companies. These are companies like Hugging

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Speaker 3: Face and Data Bricks and open AI and Thropic. These

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Speaker 3: are companies that are peer play AI companies. And so

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Speaker 3: I want to go back and just answer your question specifically, Tom,

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Speaker 3: is that yes, I think you should continue to own

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Speaker 3: a select companies within the magnificent seven because I think

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Speaker 3: they'll continue to benefit. But as far as trying to

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Speaker 3: get outperformance relative to the market, I think you need

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Speaker 3: to try to search for some of those less well

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Speaker 3: known sub hundred billion dollars we call that small now

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Speaker 3: sub one hundred billion dollars small tech companies.

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Speaker 7: I'm long weighing lambs the weight labs exactly, all right,

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Speaker 7: So gene in your fund here, what are some of

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Speaker 7: the names that you guys own that maybe maybe people

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Speaker 7: don't think about.

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Speaker 4: I mean, everybody's you know, kind of feels like they

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Speaker 4: have their own call on in the apples of the world,

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Speaker 4: of the Amazon's of the world, other parts of the

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Speaker 4: tech space that you think are underappreciated out there.

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Speaker 3: Yeah, we actually go and look for companies off that

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Speaker 3: beaten path. I mean, there's one company, New Bank, Tipker

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Speaker 3: and you as in November uniform and New Bank is

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Speaker 3: the fastest growing bank in Latin America. They've basically gone

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Speaker 3: from nothing to about eight percent share. Like one in

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Speaker 3: two people in Brazil use a form of New Bank.

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Speaker 3: But they're using artificial intelligence to help better identify get

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Speaker 3: the underbank population to be banked. And it's been a

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Speaker 3: great use case of that and it works. It works

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Speaker 3: for risk management. So New Bank is a company that

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Speaker 3: we own Coupong maybe you may not, maybe a new one.

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Speaker 3: Think of this as the Amazon of South Korea. Uh,

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Speaker 3: and they basically have a pole position there. South Korea

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Speaker 3: is doing relatively well. And then the last one Marcato

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Speaker 3: Libre yep, and I hope both of you are sitting

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Speaker 3: down for this one. Just recently we added we spent

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Speaker 3: a lot of time seven years working in China covering

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Speaker 3: these being bringing a lot of these companies public. The

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Speaker 3: tech companies in China. We've just in the last few

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Speaker 3: weeks actually made some investments in some Chinese equities tech equities.

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Speaker 1: WHOA, I think we'll leave it there. I gotta go

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Speaker 1: call my broker, Gene Munster. Generous of you tow b

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Speaker 1: with us through this half hour, We're going to do

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Speaker 1: a roundtable here. Mona Mahagen Joints from Edward Jones, senior

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Speaker 1: investment strategist in Sarah House from Wells Fargo. We're going

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Speaker 1: to try to keep him for two hours. I believe

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Speaker 1: Sarah's got to go at some point. Jay Brison at

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Speaker 1: a tantrum and says she can only do so much

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Speaker 1: time with us, and Mona's with us. We're thrilled of both.

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Speaker 1: You really with two different remits, Sarah, I got to

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Speaker 1: go to you first because I got with revision a

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Speaker 1: four hundred and seventy nine thousand statistic. Do we blame

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Speaker 1: this in seasonality? Is this because Amazon and Bloomingdale's hired

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Speaker 1: more people? What's the asterisk Wells Fargo sees in this

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Speaker 1: buoyant report?

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Speaker 8: Sure so, I mean, I think overall, the message is

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Speaker 8: this job market that is still incredibly strong despite making

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Speaker 8: all the efforts for the bed told the economy. But

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Speaker 8: I think you're right in that there probably is some

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Speaker 8: flattery from the seasonals of January is the month where

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Speaker 8: we tend to get the biggest move and non seasonally

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Speaker 8: adjusted number, So if you see any sort of deviation

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Speaker 8: from that, that can flatter the January numbers. We saw

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Speaker 8: that last January as well. But I think the message

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Speaker 8: here is that we're just not seeing the same degree

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Speaker 8: of layoffs that we took typically would in January, and

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Speaker 8: that's why we're getting such a robust headline number again

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Speaker 8: here in January.

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Speaker 1: I mean the jobs numbers, I mean people. I understand

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Speaker 1: there's a standard here and everybody gets it wrong. I

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Speaker 1: get that, but it's months after months after months, we're

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Speaker 1: getting nowhere near one fifty or one twenty. David Kelly

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Speaker 1: over at JP Morgan Sarah House is looking for some

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Speaker 1: form of migration to zero or even a negative statistic.

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Speaker 1: Why are we eating so many jobs? Is it lower

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Speaker 1: decile staff jobs. Is it a complete underestimation of the

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Speaker 1: technology overlay? What's the why for two hundred whatever thousand

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Speaker 1: per month?

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Speaker 8: Yes, I think one aspect is we're still getting I

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Speaker 8: think pretty strong growth in labor supply. We can't compare

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Speaker 8: the month and month changes here with the January numbers

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Speaker 8: because you you did get some population control adjustments. But

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Speaker 8: you know, overall, you you saw the labor force participation

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Speaker 8: rate hanging there and it was up about four tenths

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Speaker 8: of the percentage point last year. And so that's helping

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Speaker 8: helping businesses bill positions meet with still very strong consumer

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Speaker 8: and even and even business business demand. So I think

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Speaker 8: that that's a big that's a big part of it

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Speaker 8: as well. And I think just in some industries like leisure,

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Speaker 8: in hospitality, government for example, there's still catchup hiring going

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Speaker 8: on from the pandemic as well, which I think, Hey.

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Speaker 4: Mona, I'm mona, I'm looking at the tier treasure were

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Speaker 4: now up sixteen basis points four point three six percent.

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Speaker 4: So from your perspective moment, does that kind of take

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Speaker 4: any ambiguity around a March rate cut?

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Speaker 5: Does that take that off the table? Now?

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Speaker 9: Yeah?

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Speaker 6: I think it does. And you know, look, I think

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Speaker 6: most market participants we were fifty to fifty ahead of

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Speaker 6: the FED meeting in terms of a March rate cut. Anyway,

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Speaker 6: I think now people are looking towards May and June

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Speaker 6: for the FED to start its rate cutting cycle. We

438
00:23:21,080 --> 00:23:24,000
Speaker 6: do still think that they will pivot from this pause

439
00:23:24,080 --> 00:23:27,040
Speaker 6: to rate cuts later this year, but Jerome Powell told

440
00:23:27,080 --> 00:23:29,520
Speaker 6: us on Wednesday he's willing to take his time. He

441
00:23:29,560 --> 00:23:32,320
Speaker 6: doesn't want to see inflation pick up again. And in fact,

442
00:23:32,320 --> 00:23:34,399
Speaker 6: this job to report this morning may give him a

443
00:23:34,400 --> 00:23:35,879
Speaker 6: bit of pause when he sees that four and a

444
00:23:35,920 --> 00:23:39,720
Speaker 6: half percent wage gain figure, average hourly earnings figure. I'd

445
00:23:39,760 --> 00:23:41,800
Speaker 6: be curious to see what the mix of you know,

446
00:23:41,880 --> 00:23:44,800
Speaker 6: new jobs at it is if we are seeing higher

447
00:23:45,240 --> 00:23:48,119
Speaker 6: paid workers take up more of that mix versus the

448
00:23:48,160 --> 00:23:51,919
Speaker 6: lower skilled, lower paid parts of the market. But certainly

449
00:23:51,920 --> 00:23:54,600
Speaker 6: what we're watching is the strength in this US economy

450
00:23:55,119 --> 00:23:58,800
Speaker 6: and inflation that you know, immaculately has been continuing to

451
00:23:58,840 --> 00:24:01,360
Speaker 6: move lower despite the strength the US economy. We want

452
00:24:01,359 --> 00:24:03,680
Speaker 6: to see that continue in the next few months before

453
00:24:03,680 --> 00:24:05,159
Speaker 6: we start seeing that rate cutting cycle.

454
00:24:05,560 --> 00:24:07,320
Speaker 4: Yeah, just the you know, the highest estimate in the

455
00:24:07,359 --> 00:24:11,880
Speaker 4: Bloomberg survey of seventy seven economists was three hundred thousand,

456
00:24:12,080 --> 00:24:15,240
Speaker 4: so it just kind of blew away all expectations. So Mona,

457
00:24:15,400 --> 00:24:17,560
Speaker 4: given that, how do you position it like you're gonna

458
00:24:17,560 --> 00:24:20,119
Speaker 4: be talking to clients all day today. How are you

459
00:24:20,119 --> 00:24:23,400
Speaker 4: going to position kind of how this data point impacts

460
00:24:23,680 --> 00:24:27,520
Speaker 4: the fed Mona's going to be made by meta by meta'

461
00:24:28,920 --> 00:24:32,000
Speaker 4: So attis is impact your market outlook here? Yeah?

462
00:24:32,040 --> 00:24:34,520
Speaker 6: You know, look, I think it kind of reaffirmed some

463
00:24:34,520 --> 00:24:36,880
Speaker 6: of the things messages we've been giving to our clients

464
00:24:36,920 --> 00:24:39,160
Speaker 6: as well, which is one we do think if twenty

465
00:24:39,200 --> 00:24:41,919
Speaker 6: twenty three was the year of narrow leadership, you know,

466
00:24:42,160 --> 00:24:45,680
Speaker 6: folks piled into megacap tech, the AI trade. They also

467
00:24:45,720 --> 00:24:48,840
Speaker 6: piled into cash and CDs, cash like instruments, we didn't

468
00:24:48,880 --> 00:24:52,040
Speaker 6: think twenty twenty four is a year of broadening in participation.

469
00:24:52,119 --> 00:24:54,879
Speaker 6: So yes, we still like the AI tech trade. We

470
00:24:54,880 --> 00:24:57,400
Speaker 6: think it's in early innings of a long term secular

471
00:24:57,520 --> 00:25:00,240
Speaker 6: bull market, but we want to make sure that we

472
00:25:00,280 --> 00:25:02,840
Speaker 6: have exposure to other parts of the market that may

473
00:25:02,880 --> 00:25:05,120
Speaker 6: play some catch up, especially in an environment where we're

474
00:25:05,160 --> 00:25:08,159
Speaker 6: not seeing any cracks in the economy thus far, we

475
00:25:08,200 --> 00:25:10,840
Speaker 6: could see cyclical parts of the market, even parts of

476
00:25:10,960 --> 00:25:14,399
Speaker 6: value and across the market cast space as well. We

477
00:25:14,440 --> 00:25:18,440
Speaker 6: would say also with yields remaining relatively higher not returning

478
00:25:18,440 --> 00:25:20,919
Speaker 6: anywhere near the zero bound, we think longer term that

479
00:25:20,960 --> 00:25:23,800
Speaker 6: means better balance in your portfolios between growth and value

480
00:25:23,960 --> 00:25:26,680
Speaker 6: and bonds. They continue to play a critical element here

481
00:25:26,720 --> 00:25:27,119
Speaker 6: as well.

482
00:25:27,400 --> 00:25:30,320
Speaker 1: If you're just joining us. Mona Mahadj and Edward Jones,

483
00:25:30,320 --> 00:25:33,120
Speaker 1: senior investment strategists with us for this entire half hour.

484
00:25:33,440 --> 00:25:37,120
Speaker 1: Sarah House with Wells Fargo, their senior economist, as well,

485
00:25:37,160 --> 00:25:40,480
Speaker 1: as we look at a stunning American jobs report. There's

486
00:25:40,520 --> 00:25:42,760
Speaker 1: no other way to put it. I don't know the calculator,

487
00:25:42,760 --> 00:25:45,359
Speaker 1: I don't the HP twelve seas off right now, but

488
00:25:45,440 --> 00:25:47,480
Speaker 1: all you got to know is look at four hundred

489
00:25:47,480 --> 00:25:51,320
Speaker 1: thousand in the last sixty days, over six hundred thousand

490
00:25:51,880 --> 00:25:55,439
Speaker 1: jobs created in America. Robert up in Connecticut, you know

491
00:25:56,320 --> 00:25:58,800
Speaker 1: he's got the bloody Mary going on a Friday. Artraguore

492
00:25:59,000 --> 00:26:02,200
Speaker 1: and Robert, thank you for sending in the nice information

493
00:26:02,280 --> 00:26:06,760
Speaker 1: there in seasonality on retail, on healthcare as well. Sarah,

494
00:26:06,760 --> 00:26:08,840
Speaker 1: I'm gonna ask you a question you don't want to answer,

495
00:26:08,880 --> 00:26:12,520
Speaker 1: but it's rude Friday. You got to be kidding me

496
00:26:13,160 --> 00:26:17,000
Speaker 1: that Jay Powell wasn't brief on this for that FED

497
00:26:17,119 --> 00:26:20,719
Speaker 1: press conference. Now, he may have not have known the

498
00:26:20,840 --> 00:26:23,720
Speaker 1: numbers because you know, we play by rules in America,

499
00:26:24,240 --> 00:26:28,000
Speaker 1: but you don't think someone like Sarah House. The Sarah

500
00:26:28,080 --> 00:26:31,119
Speaker 1: House at the eCos building tapped him on the shoulder

501
00:26:31,119 --> 00:26:33,679
Speaker 1: and said, excuse me, sir, it's going to be a

502
00:26:33,800 --> 00:26:37,160
Speaker 1: ginormous number on Friday. Sarah House. Did he know that?

503
00:26:38,840 --> 00:26:39,600
Speaker 3: They don't think so.

504
00:26:39,680 --> 00:26:42,240
Speaker 8: I can't say for certain, but I think in a

505
00:26:42,400 --> 00:26:45,480
Speaker 8: recent interview with David Rubinstein he indicated that he probably

506
00:26:45,520 --> 00:26:48,560
Speaker 8: doesn't get those numbers until Thursday. And you know, the

507
00:26:49,200 --> 00:26:51,640
Speaker 8: BLS does cut it close in terms of how many

508
00:26:51,720 --> 00:26:54,200
Speaker 8: days they have to prepare this, and so this was

509
00:26:54,240 --> 00:26:58,560
Speaker 8: a Hay report as well. So my implination is I

510
00:26:58,600 --> 00:27:00,760
Speaker 8: don't believe he had these actual numbers.

511
00:27:01,040 --> 00:27:03,760
Speaker 1: Sarah House. Seasonality. I got a lot of emails coming

512
00:27:03,800 --> 00:27:08,119
Speaker 1: in from a season pros retired pros in that come on,

513
00:27:08,240 --> 00:27:13,320
Speaker 1: it's January. Was this retail, healthcare and oddities? When you

514
00:27:13,400 --> 00:27:15,960
Speaker 1: dive into this a say afternoon with Team Bryson, are

515
00:27:15,960 --> 00:27:18,359
Speaker 1: you going to look at it being seasonality? Sarah House.

516
00:27:20,000 --> 00:27:22,800
Speaker 8: Well, again, it comes down to the fact that we

517
00:27:23,000 --> 00:27:25,720
Speaker 8: just did not see as many layoffs as we usually

518
00:27:25,840 --> 00:27:28,359
Speaker 8: would this time of year. Now, there were some soft

519
00:27:28,359 --> 00:27:30,399
Speaker 8: spots in the report, so you had a pretty meager

520
00:27:30,440 --> 00:27:33,600
Speaker 8: gain in leisure and hospitality, But you all saw other

521
00:27:33,680 --> 00:27:38,000
Speaker 8: sectors which actually can see even noticeable declines in January,

522
00:27:38,040 --> 00:27:42,080
Speaker 8: like professional business services that posted a nice increase as well.

523
00:27:42,200 --> 00:27:45,040
Speaker 8: Retail was very strong. So I think that suggests that

524
00:27:45,560 --> 00:27:49,240
Speaker 8: you saw retail companies they didn't hire as much holiday workers,

525
00:27:49,320 --> 00:27:50,920
Speaker 8: but that also means that they don't have to let

526
00:27:50,960 --> 00:27:54,360
Speaker 8: go as many holiday weekers. So that's probably flattering that

527
00:27:54,480 --> 00:27:56,640
Speaker 8: the rough of forty five thousand and eight we saw

528
00:27:56,640 --> 00:27:59,280
Speaker 8: on detail on a seasonally just basis, Hey, mol.

529
00:27:59,200 --> 00:28:01,359
Speaker 4: One, when I see it GDP print for the first

530
00:28:01,520 --> 00:28:04,760
Speaker 4: quarter of three point three percent significantly better than expected,

531
00:28:04,800 --> 00:28:08,639
Speaker 4: I see the labor market significantly stronger than expected. It

532
00:28:08,720 --> 00:28:10,040
Speaker 4: kind of makes me feel like I want to run

533
00:28:10,040 --> 00:28:12,400
Speaker 4: out and buy cyclical stocks if the economy is doing

534
00:28:12,440 --> 00:28:15,639
Speaker 4: better than expected. How do you feel about those types

535
00:28:15,720 --> 00:28:17,560
Speaker 4: of names coming out of the Great Midwest.

536
00:28:18,840 --> 00:28:21,040
Speaker 6: Yeah, you know, Actually it's a great point and something

537
00:28:21,040 --> 00:28:22,800
Speaker 6: that we have been considering. And if you know, I'll

538
00:28:22,840 --> 00:28:26,080
Speaker 6: give you an interesting stat when we look historically in

539
00:28:26,160 --> 00:28:29,480
Speaker 6: an environment where the FED starts cutting rates and we

540
00:28:29,560 --> 00:28:32,800
Speaker 6: are not in a recession, probably perhaps the environment we're

541
00:28:32,840 --> 00:28:35,560
Speaker 6: in now versus the FED starts cutting rates and we

542
00:28:35,680 --> 00:28:39,080
Speaker 6: are in a downturn, a recession. The market outcomes are

543
00:28:39,160 --> 00:28:41,960
Speaker 6: much better, of course in the non recessionary scenario, and

544
00:28:42,000 --> 00:28:43,720
Speaker 6: in fact, the S and P in the twelve months

545
00:28:43,760 --> 00:28:46,000
Speaker 6: after the first rate cut is up about eight to

546
00:28:46,040 --> 00:28:49,840
Speaker 6: ten percent versus flat to slightly down in the recessionary scenario.

547
00:28:49,920 --> 00:28:53,200
Speaker 6: So we do think there is still historical precedent. The

548
00:28:53,320 --> 00:28:55,640
Speaker 6: history may not repeat itself, but there is some precedent

549
00:28:56,280 --> 00:28:58,240
Speaker 6: for better market outcomes, and we think the parts of

550
00:28:58,240 --> 00:29:00,440
Speaker 6: the market that will show those better outcomes are exactly

551
00:29:00,520 --> 00:29:03,240
Speaker 6: the ones that you highlighted. We like the cyclical parts

552
00:29:03,280 --> 00:29:06,800
Speaker 6: of the market, you know, areas like industrials for example, especially

553
00:29:06,840 --> 00:29:09,200
Speaker 6: if global growth starts to place and ketchup in the

554
00:29:09,200 --> 00:29:11,360
Speaker 6: back half of the year, could do well. And we're

555
00:29:11,520 --> 00:29:13,960
Speaker 6: you know, taking any opportunity we can really on with

556
00:29:14,080 --> 00:29:16,920
Speaker 6: volatility to position ourselves for those those rebounds.

557
00:29:17,160 --> 00:29:19,160
Speaker 1: Sarah House, We're gonna let you go now to duties

558
00:29:19,160 --> 00:29:21,760
Speaker 1: at Wells Fargo. Lost to write up on this stunning

559
00:29:21,840 --> 00:29:24,480
Speaker 1: jobs report. Thank you so much to you and team

560
00:29:24,520 --> 00:29:28,240
Speaker 1: Brison for perspective this morning. Mona ma Hodgen continues with

561
00:29:28,360 --> 00:29:30,920
Speaker 1: us Edward Jones here commercial free in this half hour

562
00:29:31,440 --> 00:29:34,160
Speaker 1: of a bang up jobs report, just no other way

563
00:29:35,040 --> 00:29:37,280
Speaker 1: to put it. We welcome all of you on Apple,

564
00:29:37,320 --> 00:29:40,720
Speaker 1: car Play and YouTube again your search Google? Right, you

565
00:29:40,800 --> 00:29:41,240
Speaker 1: use bing?

566
00:29:41,320 --> 00:29:43,720
Speaker 4: Paul's pretty much a Google person.

567
00:29:43,720 --> 00:29:47,960
Speaker 5: Matt Miller is a Bing. Matt Miller's a chat. Yes

568
00:29:48,000 --> 00:29:49,160
Speaker 5: he has made I have to.

569
00:29:49,120 --> 00:29:52,920
Speaker 1: Look at Miller's the text study. Yes, I guess I

570
00:29:52,960 --> 00:29:55,080
Speaker 1: gotta look at Bing. I'm not using Bang, I'm looking

571
00:29:55,120 --> 00:29:57,920
Speaker 1: Safari or whatever. But you go YouTube.

572
00:29:58,080 --> 00:30:00,520
Speaker 5: It's got Bloomberg to help me.

573
00:30:00,560 --> 00:30:01,240
Speaker 1: Are you bings?

574
00:30:01,840 --> 00:30:02,520
Speaker 2: I'm Google?

575
00:30:02,960 --> 00:30:04,760
Speaker 1: Mona, Are you Bing or Google or what?

576
00:30:05,160 --> 00:30:05,320
Speaker 9: Oh?

577
00:30:05,560 --> 00:30:06,600
Speaker 6: Oh Google for sure?

578
00:30:06,760 --> 00:30:09,640
Speaker 1: Okay, Mona, Mahadgen weighing in there. That's the Edward Jones

579
00:30:09,880 --> 00:30:13,160
Speaker 1: perspective here personally. So let me say, good morning YouTube,

580
00:30:13,240 --> 00:30:16,480
Speaker 1: Bloomberg Podcasts, Good morning Matt Miller. Look it up on

581
00:30:16,600 --> 00:30:20,120
Speaker 1: bing if you can, Mona, I need to reset here.

582
00:30:20,360 --> 00:30:23,480
Speaker 1: The tech cash flows are stunning.

583
00:30:23,600 --> 00:30:23,800
Speaker 10: Yeah.

584
00:30:23,880 --> 00:30:26,680
Speaker 1: Now they make up twenty five thirty percent of the

585
00:30:26,840 --> 00:30:29,600
Speaker 1: S and P five hundred. Let's say they go to

586
00:30:29,680 --> 00:30:35,200
Speaker 1: thirty five percent or forty percent where the math doesn't

587
00:30:35,240 --> 00:30:38,720
Speaker 1: work in retirement planning, how do you at Edward Jones

588
00:30:38,960 --> 00:30:39,440
Speaker 1: deal with that?

589
00:30:40,520 --> 00:30:42,400
Speaker 6: Yeah, it's a great call out. Look when you look

590
00:30:42,400 --> 00:30:44,560
Speaker 6: at any US index, but particularly the S and P

591
00:30:44,680 --> 00:30:47,600
Speaker 6: five hundred, which is pretty much a global benchmark. Forty

592
00:30:47,640 --> 00:30:50,640
Speaker 6: five percent of that index does come from growth tech

593
00:30:51,400 --> 00:30:54,920
Speaker 6: type of stocks and sectors, and so even if you

594
00:30:55,040 --> 00:30:58,479
Speaker 6: are just an index investor, you are forty five percent

595
00:30:58,600 --> 00:31:01,479
Speaker 6: invested in technology. And of course, when we think about

596
00:31:01,520 --> 00:31:03,760
Speaker 6: a global stock market, it doesn't look the same in

597
00:31:03,800 --> 00:31:07,240
Speaker 6: Europe or even in Asia. In Europe in particular, much

598
00:31:07,280 --> 00:31:13,200
Speaker 6: more heavily weighted in value parts of the market industrials, manufacturing, financials.

599
00:31:13,600 --> 00:31:15,680
Speaker 6: So when we think about, you know, putting together a

600
00:31:15,720 --> 00:31:18,800
Speaker 6: diversified portfolio, US is still the largest part of that.

601
00:31:18,840 --> 00:31:21,640
Speaker 6: And within that we still see value in and we

602
00:31:21,680 --> 00:31:25,280
Speaker 6: talked about this briefly earlier in the technology space, because

603
00:31:25,800 --> 00:31:27,880
Speaker 6: not only have we seen cash flows increase, we've seen

604
00:31:27,960 --> 00:31:31,120
Speaker 6: earnings increase quite a bit as well, and we do

605
00:31:31,160 --> 00:31:35,000
Speaker 6: think the AI boom and trade continues over the next

606
00:31:35,000 --> 00:31:37,440
Speaker 6: five to ten years. Some of that has been priced in.

607
00:31:37,520 --> 00:31:40,800
Speaker 6: You know, the mag seven was up seventy percent plus

608
00:31:40,800 --> 00:31:43,720
Speaker 6: on average last year, so we are starting to see

609
00:31:44,640 --> 00:31:46,280
Speaker 6: we are starting to see a bit of that priced in.

610
00:31:47,080 --> 00:31:49,680
Speaker 6: We think we do get better opportunities in the weeks

611
00:31:49,680 --> 00:31:52,440
Speaker 6: and months ahead but we don't want to give up

612
00:31:52,480 --> 00:31:55,000
Speaker 6: on that trade. Early on. We do think that not

613
00:31:55,040 --> 00:31:57,920
Speaker 6: only will we see those index players benefit, but over

614
00:31:57,960 --> 00:32:00,600
Speaker 6: time we'll see other parts of the more could benefit

615
00:32:00,600 --> 00:32:02,000
Speaker 6: from the products that come out of it. So the

616
00:32:02,000 --> 00:32:06,160
Speaker 6: manufacturing even financial sector will ultimately benefit from AI as well.

617
00:32:06,160 --> 00:32:08,040
Speaker 6: But you have to think about where all those docks

618
00:32:08,040 --> 00:32:08,840
Speaker 6: are housed.

619
00:32:08,600 --> 00:32:13,000
Speaker 1: Paul, can we have a surveillance moment of silence for

620
00:32:13,120 --> 00:32:17,720
Speaker 1: Neil Dutta at Renmac These job numbers are like Neil

621
00:32:17,760 --> 00:32:22,080
Speaker 1: Dutta numbers where he's saying, look, we're creating jobs, and

622
00:32:22,400 --> 00:32:26,800
Speaker 1: the heart of his thesis is we're creating inflation adjusted

623
00:32:26,920 --> 00:32:32,760
Speaker 1: constructive wages, which goes into consumption. Most of us spend

624
00:32:32,800 --> 00:32:37,280
Speaker 1: our paychecks we do, which goes into GDP nominal revenue,

625
00:32:37,480 --> 00:32:39,360
Speaker 1: and that goes over to Mona Madgen's world.

626
00:32:39,480 --> 00:32:43,040
Speaker 5: It does, and you know, all the Sweeny offspring are employed.

627
00:32:43,160 --> 00:32:43,680
Speaker 5: So that's good.

628
00:32:43,720 --> 00:32:46,840
Speaker 4: That's kind of my personal gauge of employment out there.

629
00:32:46,880 --> 00:32:48,560
Speaker 5: In the world. Mona.

630
00:32:49,200 --> 00:32:51,240
Speaker 4: Let's talk about valuation here a right smack in the

631
00:32:51,240 --> 00:32:53,320
Speaker 4: middle of earnings. We had some really good tech numbers

632
00:32:53,440 --> 00:32:56,040
Speaker 4: last night from some of the big tech names. Broadly speaking,

633
00:32:56,560 --> 00:32:59,400
Speaker 4: you know, if the barecase out there that we speak

634
00:32:59,400 --> 00:33:02,000
Speaker 4: to some people that I'm cautious, they're going to call invaluation.

635
00:33:02,040 --> 00:33:03,520
Speaker 4: They're going to say the stocks have kind of gotten

636
00:33:03,520 --> 00:33:07,440
Speaker 4: ahead of earnings out there. Do you have concern or

637
00:33:07,440 --> 00:33:09,360
Speaker 4: what level of concern do you have about valuation in

638
00:33:09,360 --> 00:33:09,920
Speaker 4: this market?

639
00:33:10,560 --> 00:33:12,280
Speaker 6: Yeah, you know, it's a good call out. And we

640
00:33:12,440 --> 00:33:14,880
Speaker 6: had seen some valuation expansion. If you look at the

641
00:33:14,920 --> 00:33:17,160
Speaker 6: S and P five hundred last year, it went from

642
00:33:17,200 --> 00:33:19,440
Speaker 6: about seventeen times about twenty times by the end of

643
00:33:19,480 --> 00:33:22,320
Speaker 6: the year. Of course, if you look one layer underneath

644
00:33:22,360 --> 00:33:23,920
Speaker 6: the surface, if you look at the S and P

645
00:33:24,160 --> 00:33:28,520
Speaker 6: equal weight valuation, that's still actually pretty reasonable about sixteen times.

646
00:33:28,640 --> 00:33:30,960
Speaker 6: And of course what drove the valuation expansion for the

647
00:33:31,000 --> 00:33:34,960
Speaker 6: most part was that magnificent seven technology trades. So that's

648
00:33:35,000 --> 00:33:36,760
Speaker 6: why we think, you know, when we think about the

649
00:33:36,840 --> 00:33:40,040
Speaker 6: year ahead, yes we'll get earnings growth, but the other

650
00:33:40,080 --> 00:33:42,400
Speaker 6: part of the story is where can we get valuation expansion,

651
00:33:42,440 --> 00:33:45,640
Speaker 6: And we do think more scope for valuation expansion is

652
00:33:45,680 --> 00:33:48,600
Speaker 6: in that equal weight the four ninety three stocks that

653
00:33:48,760 --> 00:33:50,160
Speaker 6: may have some catch up to play.

654
00:33:50,280 --> 00:33:53,280
Speaker 1: So you're talking about PEP expansion, you're talking about you're

655
00:33:53,320 --> 00:33:56,800
Speaker 1: modeling out within Somebody's blended four oh one K and

656
00:33:57,040 --> 00:34:00,720
Speaker 1: equity pe multiple expand this year.

657
00:34:02,240 --> 00:34:04,200
Speaker 6: Yeah, we think certain parts of the market we talked

658
00:34:04,240 --> 00:34:07,800
Speaker 6: about value cyclicals, all of those still are pretty reasonably valued.

659
00:34:07,800 --> 00:34:09,680
Speaker 6: Some of them are even priced to you know, this

660
00:34:09,719 --> 00:34:13,160
Speaker 6: recessionary environment that hasn't showed up yet. And once we

661
00:34:13,239 --> 00:34:15,600
Speaker 6: are confirmed that we continue to get at least decent

662
00:34:15,600 --> 00:34:19,799
Speaker 6: growth over the next three quarters, we could see evaluation expansion. Also,

663
00:34:19,920 --> 00:34:22,520
Speaker 6: keep in mind, when the FED is cutting rates and

664
00:34:22,600 --> 00:34:25,600
Speaker 6: interest rates are overtime moving lower, that's better for the

665
00:34:25,640 --> 00:34:26,799
Speaker 6: valuation story as well.

666
00:34:27,000 --> 00:34:30,239
Speaker 1: Mona, thank you, Mona Mahajen, Edward James, you guys just

667
00:34:30,440 --> 00:34:34,680
Speaker 1: really brilliant here on this allocation between bonds in equities.

668
00:34:39,360 --> 00:34:43,200
Speaker 1: I have anticipated this. And Paul Sweeney, how did we

669
00:34:43,239 --> 00:34:45,880
Speaker 1: get these two guys? Guys? This is like it's spring

670
00:34:46,000 --> 00:34:49,880
Speaker 1: training where pitchers catch your show up and everybody's there

671
00:34:50,440 --> 00:34:53,880
Speaker 1: and then the two studs wander out about eight days later.

672
00:34:55,040 --> 00:34:58,759
Speaker 1: Just take ten seconds here, how did you find Ani

673
00:34:58,880 --> 00:34:59,480
Speaker 1: rog Rana?

674
00:35:01,000 --> 00:35:03,719
Speaker 4: We got very fortunate because when you're building a research department,

675
00:35:04,000 --> 00:35:07,719
Speaker 4: arguably you know you have to lead with technology, just

676
00:35:07,760 --> 00:35:09,759
Speaker 4: like the market leads with technology. You have to have

677
00:35:09,840 --> 00:35:12,760
Speaker 4: this technology. Yeah, you have to have a world class

678
00:35:12,760 --> 00:35:15,520
Speaker 4: technology research team. And we're able to do that in

679
00:35:15,560 --> 00:35:17,600
Speaker 4: the way we did that as we hired man Deep

680
00:35:17,640 --> 00:35:21,280
Speaker 4: Singh and onnarag Rana. They both are senior technology channels

681
00:35:21,280 --> 00:35:24,160
Speaker 4: and they built out a global technology research.

682
00:35:24,080 --> 00:35:26,799
Speaker 5: Team for Bloomberg Intelligence. So we appreciate all their hard work.

683
00:35:26,960 --> 00:35:29,719
Speaker 4: And now these two folks are front and center here

684
00:35:29,719 --> 00:35:32,239
Speaker 4: on a day when you've got some big, big tech

685
00:35:32,360 --> 00:35:35,280
Speaker 4: earnings and I'm gonna start, I'm gonna start with Apple,

686
00:35:35,320 --> 00:35:39,160
Speaker 4: So that means I go to anurag Rana, Honoraga. Did

687
00:35:39,200 --> 00:35:44,200
Speaker 4: we learn anything from Apple? And it's positioned in China?

688
00:35:44,200 --> 00:35:46,480
Speaker 4: Because China is twenty percent of their revenue. It looks

689
00:35:46,520 --> 00:35:48,960
Speaker 4: like it's more competitive, It looks like the economy is

690
00:35:49,000 --> 00:35:50,799
Speaker 4: not as strong as they would like to see it.

691
00:35:51,440 --> 00:35:53,879
Speaker 4: What do we learn, if anything, about Apple and their

692
00:35:53,880 --> 00:35:57,200
Speaker 4: growth opportunities in China?

693
00:35:57,320 --> 00:35:59,000
Speaker 11: You know, what we learned is yes, long term, it

694
00:35:59,080 --> 00:36:00,799
Speaker 11: is a big story, but at least for the next

695
00:36:00,800 --> 00:36:03,040
Speaker 11: tweld through eighteen months, you know, you could say that

696
00:36:03,080 --> 00:36:05,120
Speaker 11: things are going to not be as smooth over there

697
00:36:05,239 --> 00:36:07,719
Speaker 11: as they you know, try to figure out how to

698
00:36:07,719 --> 00:36:10,680
Speaker 11: be more competitive give more rebates, try to work with

699
00:36:10,760 --> 00:36:13,719
Speaker 11: more carriers. So Apple has their work cut out for

700
00:36:13,760 --> 00:36:16,400
Speaker 11: them in China, and we are not expecting a rebound

701
00:36:16,440 --> 00:36:18,120
Speaker 11: anytime soon, all right.

702
00:36:18,160 --> 00:36:20,279
Speaker 4: So that's we'll get back to that because there's a

703
00:36:20,280 --> 00:36:23,600
Speaker 4: lot to peel there on a rug on Apple Man Deep.

704
00:36:23,960 --> 00:36:28,000
Speaker 4: We also had Amazon last night, and we also had

705
00:36:28,200 --> 00:36:30,640
Speaker 4: Google last night. Let's start with Google because it kind

706
00:36:30,680 --> 00:36:33,200
Speaker 4: of goes to that. Oh I'm sorry, I met at Yeah,

707
00:36:33,200 --> 00:36:33,759
Speaker 4: thank you very much.

708
00:36:33,800 --> 00:36:34,839
Speaker 5: I met at Facebook.

709
00:36:34,960 --> 00:36:35,160
Speaker 1: Yeah.

710
00:36:35,440 --> 00:36:35,840
Speaker 5: Whatever.

711
00:36:36,200 --> 00:36:38,680
Speaker 4: So what do we learned from Meta last night? Just

712
00:36:38,719 --> 00:36:41,359
Speaker 4: about the digital advertising environment out there?

713
00:36:42,520 --> 00:36:45,680
Speaker 9: Yeah, I mean it was a beat and race quarter

714
00:36:45,960 --> 00:36:50,320
Speaker 9: and that too, you know, with expectations being high. Clearly

715
00:36:50,600 --> 00:36:54,279
Speaker 9: they delivered, and I think the tailwind was coming from

716
00:36:54,360 --> 00:36:59,040
Speaker 9: multiple fronts. So one, the digital ad pricing environment is

717
00:36:59,040 --> 00:37:03,000
Speaker 9: getting better, so that means, you know, online advertisers are

718
00:37:03,040 --> 00:37:07,040
Speaker 9: spending more on ads and that's helping the pricing. And

719
00:37:07,080 --> 00:37:11,279
Speaker 9: the other thing is the relevance of AI for all

720
00:37:11,320 --> 00:37:14,320
Speaker 9: these ad companies. I mean, Meta is definitely the scale

721
00:37:14,360 --> 00:37:19,080
Speaker 9: player and they have invested heavily in AI and that

722
00:37:19,320 --> 00:37:24,040
Speaker 9: has actually helped their ad infrastructure far more than anyone

723
00:37:24,080 --> 00:37:26,720
Speaker 9: else out there. So that showed up in the numbers,

724
00:37:26,760 --> 00:37:28,960
Speaker 9: and you know that that was the reason for the beat.

725
00:37:29,280 --> 00:37:32,160
Speaker 1: I want to dovetail here, guys and folks. This goes

726
00:37:32,200 --> 00:37:36,360
Speaker 1: to just the sheer margin profitability of all these companies

727
00:37:36,360 --> 00:37:38,840
Speaker 1: that mandate and in and run. All you got to

728
00:37:38,920 --> 00:37:42,719
Speaker 1: know is Apple because they're actually making something. Maybe it's

729
00:37:42,760 --> 00:37:45,600
Speaker 1: a little lower ebit don margin, it's like thirty forty

730
00:37:45,640 --> 00:37:48,239
Speaker 1: percent whatever the number is. And the rest of them

731
00:37:48,239 --> 00:37:51,000
Speaker 1: are minting money with a fifty cents every dollar they

732
00:37:51,000 --> 00:37:54,040
Speaker 1: bring in, they're basically bringing fifty cents down to be

733
00:37:54,080 --> 00:37:58,400
Speaker 1: distributed for cash flow. Michael Momison over at Morgan Stanley

734
00:37:58,440 --> 00:38:02,400
Speaker 1: Guys writes up a twenty five essay here on increasing

735
00:38:02,560 --> 00:38:07,680
Speaker 1: returns about what technology is doing is it gets bigger

736
00:38:07,800 --> 00:38:11,040
Speaker 1: and bigger. Mandy, let me start with you. Do you

737
00:38:11,239 --> 00:38:15,680
Speaker 1: just extrapolate out these growth lines? I mean, is Peter

738
00:38:15,920 --> 00:38:19,200
Speaker 1: or Zaga Lazard would say the glide pass here? Are

739
00:38:19,200 --> 00:38:23,440
Speaker 1: they just smooth extrapolations out three years and five years? Man?

740
00:38:23,480 --> 00:38:23,760
Speaker 9: Deep?

741
00:38:25,000 --> 00:38:25,080
Speaker 1: No?

742
00:38:25,239 --> 00:38:29,080
Speaker 9: I mean look at Meta right last year we're talking about,

743
00:38:29,280 --> 00:38:32,080
Speaker 9: you know, whether this company can grow and you know,

744
00:38:32,239 --> 00:38:37,160
Speaker 9: engagement was a concern and they had negative sales growth,

745
00:38:37,200 --> 00:38:42,000
Speaker 9: you know for three quarters, So clearly it's not smooth.

746
00:38:42,280 --> 00:38:45,480
Speaker 9: And every time we have you know a period of

747
00:38:45,520 --> 00:38:49,080
Speaker 9: thirty to forty percent growth, it follows with you know,

748
00:38:49,239 --> 00:38:52,800
Speaker 9: low growth or no growth, especially on the ad side.

749
00:38:53,120 --> 00:38:56,120
Speaker 9: But what they have shown time and again is you know,

750
00:38:56,360 --> 00:39:00,319
Speaker 9: at the online ad business is secular. There is that

751
00:39:00,360 --> 00:39:03,239
Speaker 9: shit right and I would say we're closer to saturation

752
00:39:03,440 --> 00:39:06,600
Speaker 9: than we were probably three years back. So at the

753
00:39:06,640 --> 00:39:09,239
Speaker 9: scale these companies are, it will be hard for them

754
00:39:09,239 --> 00:39:11,640
Speaker 9: to maintain that twenty percent plus grow interog.

755
00:39:11,719 --> 00:39:13,640
Speaker 1: I think I saw Tim Cook, I think over at

756
00:39:13,680 --> 00:39:18,359
Speaker 1: Fox doing the reality the virtual Vision pro reality in

757
00:39:18,360 --> 00:39:20,960
Speaker 1: front of the Apple store in New York. I stood

758
00:39:20,960 --> 00:39:24,400
Speaker 1: in front of that store a lifetime ago with my

759
00:39:24,520 --> 00:39:27,799
Speaker 1: favorite Lawrence Haverty, who come in tech before you got

760
00:39:27,840 --> 00:39:30,000
Speaker 1: you guys were you know, these guys man deep in

761
00:39:30,040 --> 00:39:33,640
Speaker 1: the anagets slide rules going in undergraduate school when Larry

762
00:39:33,640 --> 00:39:36,800
Speaker 1: Haverty was working it up. And guess what Apple was

763
00:39:36,840 --> 00:39:39,160
Speaker 1: going out of business. Then it's going out of business

764
00:39:39,200 --> 00:39:42,480
Speaker 1: now Anti rak Rna. Do you see the innovation trend

765
00:39:42,520 --> 00:39:46,880
Speaker 1: at Apple that will sustain these glide pass sustain this

766
00:39:47,040 --> 00:39:47,640
Speaker 1: cash flow?

767
00:39:49,160 --> 00:39:51,400
Speaker 11: Yeah, I know, I'm not that concerned about Apple in

768
00:39:51,400 --> 00:39:53,680
Speaker 11: the long run, I think they'll be fine. The problem

769
00:39:53,760 --> 00:39:55,680
Speaker 11: is what happens in the next two to three years,

770
00:39:55,960 --> 00:39:58,760
Speaker 11: and that's where some of the problem is just because

771
00:39:59,040 --> 00:40:01,640
Speaker 11: China is not growing. China is the big growth driver

772
00:40:01,880 --> 00:40:04,200
Speaker 11: in the near term. The vision pro is not going

773
00:40:04,280 --> 00:40:07,440
Speaker 11: to do anything to you know, accelerate their growth rates.

774
00:40:08,000 --> 00:40:10,680
Speaker 11: It's really you know, all the phones that they're selling

775
00:40:10,719 --> 00:40:13,120
Speaker 11: in emerging market and China is the big market. So

776
00:40:13,560 --> 00:40:16,200
Speaker 11: for US I, we're not concerned about Apple in the

777
00:40:16,200 --> 00:40:18,120
Speaker 11: long run, but in the short run, I think it's

778
00:40:18,160 --> 00:40:20,600
Speaker 11: going to be very hard for them to grow, you know,

779
00:40:20,640 --> 00:40:23,800
Speaker 11: anything north of five percent unless they throw that Jenai

780
00:40:23,960 --> 00:40:26,359
Speaker 11: stuff that they you know, talked about on the call.

781
00:40:26,680 --> 00:40:28,759
Speaker 11: If anything cool comes out of that in the next

782
00:40:28,760 --> 00:40:31,000
Speaker 11: six to eight months, then I'm going to be wrong.

783
00:40:31,160 --> 00:40:34,799
Speaker 1: Paul iPhone GPT. It just means you can order take

784
00:40:34,840 --> 00:40:37,759
Speaker 1: out faster and Rod Royn i' man Deep Singh. There's

785
00:40:37,760 --> 00:40:41,120
Speaker 1: Bloomberg Intelligence. We continue with us here commercial free this

786
00:40:41,160 --> 00:40:41,680
Speaker 1: half hour.

787
00:40:42,040 --> 00:40:45,400
Speaker 4: All right, Ana, rag my good friend Laura Martin overt

788
00:40:45,800 --> 00:40:48,239
Speaker 4: need Aman Company. She's hat the piece of research which

789
00:40:48,239 --> 00:40:52,719
Speaker 4: I think is really interesting. Google Meta Amazon. On their

790
00:40:52,719 --> 00:40:57,440
Speaker 4: conference calls, they mentioned either AI or generative AI like

791
00:40:58,239 --> 00:41:02,040
Speaker 4: fifty sixty times each Apple like none at all?

792
00:41:02,200 --> 00:41:03,400
Speaker 5: Here is Apple?

793
00:41:04,120 --> 00:41:07,239
Speaker 4: What are they doing or not doing with AI? Are

794
00:41:07,239 --> 00:41:09,120
Speaker 4: they not kind of getting that buzz?

795
00:41:10,719 --> 00:41:13,040
Speaker 11: I think they were caught, you could say, off the

796
00:41:13,040 --> 00:41:16,319
Speaker 11: guard a couple of years ago when chat GPT came out.

797
00:41:16,480 --> 00:41:19,000
Speaker 11: We've been very stronger. I think they are. They sat

798
00:41:19,040 --> 00:41:20,440
Speaker 11: on the call that they're going to have an event

799
00:41:20,600 --> 00:41:23,360
Speaker 11: sometime later this year. And if they are able to

800
00:41:23,400 --> 00:41:25,920
Speaker 11: release some tools or you know, some cool features in

801
00:41:25,960 --> 00:41:29,920
Speaker 11: the phone that allows them to actually capitalize on some

802
00:41:29,960 --> 00:41:32,520
Speaker 11: of this buzz, then what's going to happen is if

803
00:41:32,560 --> 00:41:36,280
Speaker 11: and if, if the iPhone sixteen has those features embedded

804
00:41:36,280 --> 00:41:38,920
Speaker 11: in them, then you can actually create a new refresh

805
00:41:38,960 --> 00:41:41,640
Speaker 11: cycle that we have been waiting for. Remember we've been

806
00:41:41,640 --> 00:41:43,759
Speaker 11: talking about the extension of the restress cycle for the

807
00:41:43,840 --> 00:41:46,720
Speaker 11: last three to five years. And if if that happens,

808
00:41:46,840 --> 00:41:49,480
Speaker 11: and it's a very very big if, then iPhone sixteen

809
00:41:49,560 --> 00:41:52,640
Speaker 11: can truly you know, put a dentse to my thesis

810
00:41:52,640 --> 00:41:55,080
Speaker 11: of slow growth and we see, you know, a big

811
00:41:55,160 --> 00:41:55,879
Speaker 11: number next year.

812
00:41:55,960 --> 00:41:56,360
Speaker 5: Interesting.

813
00:41:56,560 --> 00:41:59,520
Speaker 4: All right, So Mandy, just going on the AI theme here.

814
00:41:59,560 --> 00:42:02,600
Speaker 4: I know you and the research team at Bloomberg Intelligence

815
00:42:02,600 --> 00:42:04,879
Speaker 4: have done a lot of research on this. You guys

816
00:42:04,920 --> 00:42:07,640
Speaker 4: have a big definitive report out on AI. I guess

817
00:42:07,719 --> 00:42:10,239
Speaker 4: my question is do we have a sensor how much

818
00:42:10,280 --> 00:42:13,480
Speaker 4: of this AI I'm using air quotes for those on

819
00:42:13,719 --> 00:42:18,200
Speaker 4: the radio. Tech spending is incremental or is it just

820
00:42:18,640 --> 00:42:22,160
Speaker 4: putting another name on usual tech spending on hardware and software.

821
00:42:23,360 --> 00:42:25,839
Speaker 9: I mean, based on what we are seeing right now,

822
00:42:25,880 --> 00:42:28,279
Speaker 9: you know, with the semi company, especially the likes of

823
00:42:28,440 --> 00:42:32,719
Speaker 9: Nvidia and amb it sounds like, you know, this spending

824
00:42:33,000 --> 00:42:37,320
Speaker 9: is incremental. And yes, companies are slowing down, they're spending

825
00:42:37,480 --> 00:42:41,520
Speaker 9: on traditional servers, so there is some market share shift

826
00:42:41,560 --> 00:42:45,040
Speaker 9: going on as well. But clearly, you know, I would

827
00:42:45,120 --> 00:42:48,560
Speaker 9: talk about above trend growth for the next two three

828
00:42:48,600 --> 00:42:51,120
Speaker 9: years and that's where our you know, one point three

829
00:42:51,239 --> 00:42:55,400
Speaker 9: trillion number for twenty thirty two comes from. Half of

830
00:42:55,440 --> 00:43:01,840
Speaker 9: that is spending on data centers, hardware, real changing the infrastructure,

831
00:43:02,120 --> 00:43:04,920
Speaker 9: and you know the software part will follow as we

832
00:43:04,960 --> 00:43:10,400
Speaker 9: are seeing with the hyperscalers, Microsoft, Amazon and Google also benefiting,

833
00:43:10,440 --> 00:43:14,080
Speaker 9: so their growth rates should go up as well. But clearly,

834
00:43:14,160 --> 00:43:16,959
Speaker 9: you know there is incremental spending around are.

835
00:43:17,360 --> 00:43:19,880
Speaker 1: When you talk to the people, is it within the

836
00:43:19,920 --> 00:43:22,239
Speaker 1: culture and I'm reading I haven't read the whole book yet,

837
00:43:22,280 --> 00:43:25,200
Speaker 1: I'm actually reading every word of it. Sacha Ndella's one

838
00:43:25,239 --> 00:43:29,799
Speaker 1: volume biography is spectacular about the courage he had coming

839
00:43:29,840 --> 00:43:32,680
Speaker 1: out of southern India, and there's too much cricket talk

840
00:43:32,719 --> 00:43:35,279
Speaker 1: in it. That's the only problem I have. When you

841
00:43:35,320 --> 00:43:40,440
Speaker 1: guys talk to the cultural the Double's operational research people,

842
00:43:41,040 --> 00:43:44,120
Speaker 1: do they believe in the financial profitability of what they're

843
00:43:44,160 --> 00:43:47,880
Speaker 1: living every day? Mandy, the guys like you and anaerog

844
00:43:48,040 --> 00:43:52,680
Speaker 1: Mandeep saying, do they believe in the link over to

845
00:43:53,000 --> 00:43:55,160
Speaker 1: profitability that we're observing right now?

846
00:43:57,000 --> 00:44:01,040
Speaker 9: Yeah, I mean, I think especially with a company like Microsoft,

847
00:44:01,120 --> 00:44:03,839
Speaker 9: you can see that you know, they have priced their

848
00:44:03,920 --> 00:44:06,680
Speaker 9: services in a way where a co pilot they are

849
00:44:06,800 --> 00:44:11,480
Speaker 9: charging an additional twenty dollars subscription. So look, they are

850
00:44:11,560 --> 00:44:16,040
Speaker 9: making a big investment and it is you know, to

851
00:44:16,120 --> 00:44:19,520
Speaker 9: acquire customers to drive that stickiness. But I think it

852
00:44:19,560 --> 00:44:20,240
Speaker 9: will be profitable.

853
00:44:20,280 --> 00:44:22,920
Speaker 1: Okay, at first principles here, let's get right back to basics.

854
00:44:22,960 --> 00:44:25,240
Speaker 1: Lisa Matail is going to be with her daughter this weekend,

855
00:44:25,600 --> 00:44:28,360
Speaker 1: and Lisa wants her daughter to be brilliant. So Lisa's

856
00:44:28,360 --> 00:44:30,759
Speaker 1: going to say to her daughter, Dear, let's sign up

857
00:44:30,760 --> 00:44:33,600
Speaker 1: for co pilot, Mandy. When I sign up for co

858
00:44:33,760 --> 00:44:37,440
Speaker 1: pilot GPT, what a God's name do I get?

859
00:44:38,680 --> 00:44:38,960
Speaker 12: Well?

860
00:44:39,080 --> 00:44:42,480
Speaker 9: So it brings productivity, right, and at the end of

861
00:44:42,600 --> 00:44:46,040
Speaker 9: the day, if we are more productive in our jobs,

862
00:44:46,080 --> 00:44:48,799
Speaker 9: if we can get rid of the mundane things that

863
00:44:48,880 --> 00:44:51,560
Speaker 9: we do in our day to day jobs that the redundant. Think,

864
00:44:52,440 --> 00:44:56,560
Speaker 9: I mean, I think that's what drives productivity and efficiency.

865
00:44:56,600 --> 00:44:58,560
Speaker 9: And we know, I mean, look at how far we

866
00:44:58,600 --> 00:45:02,839
Speaker 9: have come with PCs and Word and Excel. What kind

867
00:45:02,880 --> 00:45:06,000
Speaker 9: of productivity it gives you. So this is like taking

868
00:45:06,040 --> 00:45:09,080
Speaker 9: one step further in terms of what are the redundant

869
00:45:09,080 --> 00:45:11,240
Speaker 9: things that you do in Word or Excel.

870
00:45:11,440 --> 00:45:13,200
Speaker 1: So, Paul, you're going to ramp up You're going to

871
00:45:13,239 --> 00:45:15,560
Speaker 1: ramp up copilot, sure, and it's going to make you

872
00:45:15,600 --> 00:45:19,560
Speaker 1: more efficient in ordering the marginal park role.

873
00:45:19,800 --> 00:45:20,279
Speaker 5: That's right.

874
00:45:20,640 --> 00:45:21,600
Speaker 1: The Jersey Shore this.

875
00:45:21,520 --> 00:45:25,839
Speaker 4: Summery on the Grill and Jersey Shore, an Rod talk

876
00:45:25,840 --> 00:45:28,360
Speaker 4: to us about just kind of your sense of just

877
00:45:28,480 --> 00:45:31,439
Speaker 4: overall tech spending here. I know it's a good question.

878
00:45:31,520 --> 00:45:33,239
Speaker 4: You guys have talked about that question. The rate of

879
00:45:33,320 --> 00:45:36,120
Speaker 4: growth slowing, do you expect a reacceleration here.

880
00:45:37,440 --> 00:45:40,160
Speaker 11: Oh yes, Actually, I've talked about this quite a bit,

881
00:45:40,239 --> 00:45:43,920
Speaker 11: so you know, we did a survey back in November

882
00:45:43,920 --> 00:45:46,319
Speaker 11: December and we really got some really good results out

883
00:45:46,320 --> 00:45:48,520
Speaker 11: of it. We think this year is going to be

884
00:45:48,560 --> 00:45:51,320
Speaker 11: the first year of rebound after two years of hunted spending.

885
00:45:51,600 --> 00:45:53,799
Speaker 11: You know, we've talked about cloud, for example, quite a bit.

886
00:45:54,440 --> 00:45:58,000
Speaker 11: Amazon's cloud commitments were just released this morning. They're up

887
00:45:58,080 --> 00:46:00,920
Speaker 11: forty one percent, so that's a mass of acceleration from

888
00:46:01,000 --> 00:46:03,680
Speaker 11: last quarter over the next twelve months. We do see,

889
00:46:04,000 --> 00:46:06,760
Speaker 11: We do expect, you know, sales growth rates for almost

890
00:46:07,000 --> 00:46:10,839
Speaker 11: all major categories in cloud to pick up, perhaps more

891
00:46:11,120 --> 00:46:13,080
Speaker 11: at a pasta rates more in the second half than

892
00:46:13,120 --> 00:46:15,600
Speaker 11: the perst half. And I think that puts it into

893
00:46:15,719 --> 00:46:19,040
Speaker 11: us into a very good twenty twenty five because if

894
00:46:19,040 --> 00:46:22,280
Speaker 11: you have strong bookings sometime this year, they can translate

895
00:46:22,320 --> 00:46:24,279
Speaker 11: into much faster revenue growth next year.

896
00:46:24,400 --> 00:46:26,160
Speaker 1: I mean, man, if you don't give me any couch time,

897
00:46:26,200 --> 00:46:28,080
Speaker 1: what I do, Folks when I decompress her as I

898
00:46:28,120 --> 00:46:30,480
Speaker 1: go up to our acclaimed food court and I sit

899
00:46:30,560 --> 00:46:32,960
Speaker 1: on the couch. You know, the food court in London

900
00:46:33,120 --> 00:46:35,480
Speaker 1: is better than here. That's saying, you know, and the

901
00:46:35,680 --> 00:46:38,680
Speaker 1: Washington is the best Washington's the best food court. We haven't,

902
00:46:39,120 --> 00:46:40,680
Speaker 1: but I'm up with the food court. I'm putting down

903
00:46:40,719 --> 00:46:43,520
Speaker 1: the cheese. It's and you know, you know, I'm having fun.

904
00:46:43,520 --> 00:46:45,839
Speaker 1: An rog gives me couch time. I never get any

905
00:46:45,880 --> 00:46:50,759
Speaker 1: couch time now. So Mandy, my question to you that

906
00:46:50,800 --> 00:46:54,200
Speaker 1: i'd ask you sitting on a couch is this stuff's

907
00:46:54,280 --> 00:47:01,319
Speaker 1: a large part of our diversified portfolios, index funds or

908
00:47:01,480 --> 00:47:05,120
Speaker 1: active managers that we have. Do we own too much

909
00:47:05,400 --> 00:47:09,000
Speaker 1: meta the moon shot? That is today from where you

910
00:47:09,080 --> 00:47:13,960
Speaker 1: sit in our conservative money, do we own too much tech?

911
00:47:15,560 --> 00:47:19,520
Speaker 9: I mean, look, these companies, the six tech companies in

912
00:47:19,560 --> 00:47:23,279
Speaker 9: the Magnificent seven. They are wonderful businesses. There is no

913
00:47:23,440 --> 00:47:27,000
Speaker 9: doubt about you know, how good they are at scale

914
00:47:27,560 --> 00:47:30,560
Speaker 9: and how they keep growing, you know, year after year,

915
00:47:30,760 --> 00:47:34,160
Speaker 9: and they are very profitable. So clearly you know they

916
00:47:34,200 --> 00:47:37,360
Speaker 9: have an outsized influence in our lives and there is

917
00:47:37,400 --> 00:47:40,040
Speaker 9: a good reason, you know, to be proud of them,

918
00:47:40,200 --> 00:47:43,600
Speaker 9: as you know American companies that have a global presence,

919
00:47:43,680 --> 00:47:47,440
Speaker 9: and that's why you know they will keep delivering. The

920
00:47:47,520 --> 00:47:51,000
Speaker 9: question remains what is the right valuation? And that's where

921
00:47:51,080 --> 00:47:54,120
Speaker 9: you know you look at a company trading at thirty

922
00:47:54,640 --> 00:47:57,360
Speaker 9: thirty five times earnings, and you have to ask yourself

923
00:47:58,040 --> 00:48:00,600
Speaker 9: how much good news is priced in? And yes, it's

924
00:48:00,640 --> 00:48:04,600
Speaker 9: a phenomenal business. It's growing at a great pace. But

925
00:48:04,719 --> 00:48:07,920
Speaker 9: then there is a price that you pay as a shareholder.

926
00:48:08,080 --> 00:48:12,400
Speaker 9: And I think after a while, a lot of the

927
00:48:12,400 --> 00:48:15,359
Speaker 9: growth is pricing and it becomes very crowded. And that's

928
00:48:15,960 --> 00:48:16,719
Speaker 9: what you have to ask.

929
00:48:17,000 --> 00:48:18,960
Speaker 1: I mean, Paul, I know your shopping this weekend. The

930
00:48:19,000 --> 00:48:21,600
Speaker 1: price you pay, You're gonna be over at the Apple

931
00:48:21,600 --> 00:48:24,360
Speaker 1: Store like Tim Cook today And the answer is you

932
00:48:24,400 --> 00:48:26,320
Speaker 1: got to go for the Vision Pro ye. This is

933
00:48:26,360 --> 00:48:27,600
Speaker 1: just a question about.

934
00:48:27,400 --> 00:48:31,239
Speaker 4: It, absolutely, Apple CEO Tim Cook, he opened the flagship

935
00:48:31,320 --> 00:48:34,480
Speaker 4: Fifth Avenue Apple Store today's greeting customers.

936
00:48:35,000 --> 00:48:38,640
Speaker 5: Why it's Vision Pro release day? How about that?

937
00:48:39,000 --> 00:48:41,759
Speaker 4: I'm not sure Vision Pro is ana rog Are you

938
00:48:41,760 --> 00:48:43,880
Speaker 4: going to rush out to the Apple Store in the

939
00:48:43,920 --> 00:48:48,080
Speaker 4: Greater Chicago area and buy your Vision Pro today.

940
00:48:48,280 --> 00:48:49,799
Speaker 11: I'm going to go test it out, but I'm not

941
00:48:49,800 --> 00:48:52,400
Speaker 11: going to buy it. It is too much, too expensive

942
00:48:52,400 --> 00:48:53,839
Speaker 11: for me. But I'm going to go play it ound

943
00:48:53,880 --> 00:48:55,600
Speaker 11: with it. I'm going to ask people what they think

944
00:48:55,600 --> 00:48:56,080
Speaker 11: about it.

945
00:48:56,520 --> 00:48:59,279
Speaker 5: But all right, so what does it cost? Do we

946
00:48:59,320 --> 00:49:00,440
Speaker 5: know the price of the thing.

947
00:49:01,400 --> 00:49:02,480
Speaker 11: Three five hundred.

948
00:49:02,560 --> 00:49:04,239
Speaker 1: Oh, I mean who's.

949
00:49:03,920 --> 00:49:06,719
Speaker 5: Buying this thing? Is it? Is it just the gamers?

950
00:49:06,960 --> 00:49:11,680
Speaker 1: Or Okay, what did you learn about the six hundred apps? Seriously?

951
00:49:11,760 --> 00:49:14,160
Speaker 1: Ani Rog? I mean, Mandy, you know is gonna buy it?

952
00:49:14,200 --> 00:49:17,000
Speaker 1: You're not, Ana Rog? But of the six hundred apps, like,

953
00:49:17,040 --> 00:49:20,440
Speaker 1: what's the app that will make people spend that money?

954
00:49:20,480 --> 00:49:22,400
Speaker 1: Make businesses spend that money?

955
00:49:23,680 --> 00:49:23,879
Speaker 9: See?

956
00:49:23,920 --> 00:49:26,359
Speaker 11: For consumer it's really gaming. I mean that's really where

957
00:49:26,360 --> 00:49:28,279
Speaker 11: it boils out to Disney. You know, you can watch

958
00:49:28,280 --> 00:49:31,400
Speaker 11: a Disney movie. They have really talked about it. But

959
00:49:31,480 --> 00:49:34,640
Speaker 11: I personally think the commercial use is probably far better

960
00:49:34,960 --> 00:49:37,400
Speaker 11: because if you have an architect, if you look at engineering,

961
00:49:37,800 --> 00:49:40,400
Speaker 11: you know, those kinds of things. I think, you know,

962
00:49:40,719 --> 00:49:44,560
Speaker 11: makes a lot of tense, but but but it's not.

963
00:49:44,719 --> 00:49:46,560
Speaker 11: This is not a game changer. You know, you sell

964
00:49:46,600 --> 00:49:49,319
Speaker 11: one million units, you make three million dollars, that's three

965
00:49:49,360 --> 00:49:52,080
Speaker 11: point three point five that's nothing, right, appen, Let's wrap.

966
00:49:51,880 --> 00:49:54,920
Speaker 1: It up, Mandy. If you've got the Vision Pro thing on,

967
00:49:55,680 --> 00:49:58,080
Speaker 1: are you going to synergize here? And are you gonna

968
00:49:58,080 --> 00:50:00,800
Speaker 1: have Microsoft Activision am I I'm going to be playing

969
00:50:00,840 --> 00:50:03,360
Speaker 1: Diablo for on my Apple Vision Pro.

970
00:50:05,080 --> 00:50:08,440
Speaker 9: Well, so I think again, it's it's a cool device.

971
00:50:08,640 --> 00:50:12,120
Speaker 9: My problem with a VR headsets and with Vision Pro

972
00:50:12,719 --> 00:50:15,120
Speaker 9: most likely is going to be how much time can

973
00:50:15,160 --> 00:50:19,200
Speaker 9: I wear it for it? Because these are still bulky like.

974
00:50:19,320 --> 00:50:23,040
Speaker 9: You can use them for entertainment, but I just can't imagine,

975
00:50:23,040 --> 00:50:25,400
Speaker 9: you know, putting the headset for two three hours. So

976
00:50:26,040 --> 00:50:29,520
Speaker 9: it gives you that immersive experience, but I still think,

977
00:50:29,960 --> 00:50:31,839
Speaker 9: you know, it's too heavy for me to use.

978
00:50:31,960 --> 00:50:35,400
Speaker 1: You're on a Bloomberg terminal. Go to Bloomberg Intelligence required

979
00:50:35,440 --> 00:50:39,480
Speaker 1: read and Rana man Deep Singh after this tech juggernaut

980
00:50:40,280 --> 00:50:56,160
Speaker 1: a weakened with our newspapers, Lisa Mattello, Lisa, what do

981
00:50:56,160 --> 00:50:56,600
Speaker 1: you start with?

982
00:50:56,760 --> 00:50:57,120
Speaker 9: All right?

983
00:50:57,280 --> 00:50:59,960
Speaker 12: I remember Royal Cribbing came out with figures yesterday, but

984
00:51:00,239 --> 00:51:02,960
Speaker 12: now they're big news. Is this new ship Icon of

985
00:51:03,000 --> 00:51:06,479
Speaker 12: Disease monstrous? It's a monstrous I know you're not into cruising, Paul,

986
00:51:06,600 --> 00:51:09,360
Speaker 12: I will be soon, okay, sir. So maybe here we

987
00:51:09,400 --> 00:51:13,240
Speaker 12: go three hundred and forty five per person per day, okay,

988
00:51:13,239 --> 00:51:16,600
Speaker 12: But here's the thing. It's this multi level ultimate family townhouse.

989
00:51:16,920 --> 00:51:18,479
Speaker 12: It's going to run you an average of one hundred

990
00:51:18,520 --> 00:51:19,479
Speaker 12: thousand dollars a week.

991
00:51:20,320 --> 00:51:21,440
Speaker 5: Well, I think that you.

992
00:51:21,440 --> 00:51:24,239
Speaker 12: Had of your league two bedrooms more than twenty five

993
00:51:24,280 --> 00:51:27,160
Speaker 12: thousand square feet. That's like bigger than the average apartment

994
00:51:27,360 --> 00:51:31,360
Speaker 12: in most places. It has a lot of balconies, multi slides, lounges,

995
00:51:31,400 --> 00:51:34,520
Speaker 12: hot tubs, game rooms. But here's the point, it's reserved

996
00:51:34,560 --> 00:51:36,319
Speaker 12: for most of twenty twenty four.

997
00:51:36,760 --> 00:51:38,440
Speaker 5: Weird are spending.

998
00:51:38,280 --> 00:51:40,279
Speaker 4: Yep, we're down in Aruba, just a couple weeks to

999
00:51:40,320 --> 00:51:42,640
Speaker 4: go in every day. The cruise ships are just flying

1000
00:51:42,680 --> 00:51:44,960
Speaker 4: in there. So the cruising biz is back baby.

1001
00:51:45,080 --> 00:51:46,160
Speaker 5: Oh yeah, that's one.

1002
00:51:46,040 --> 00:51:49,000
Speaker 1: Of the most important things. Ellen Zenner said today. She said, look,

1003
00:51:49,040 --> 00:51:50,880
Speaker 1: it is a bifurcated America.

1004
00:51:51,040 --> 00:51:53,719
Speaker 5: Yep. And that's not good. I mean that's it's not good.

1005
00:51:53,800 --> 00:51:56,840
Speaker 5: That's not good. We are we read them the middle class.

1006
00:51:57,239 --> 00:51:59,360
Speaker 1: Next, Lisa Potato, Yes.

1007
00:51:59,200 --> 00:52:01,680
Speaker 12: This one's for you. It's a little dog theme here

1008
00:52:01,680 --> 00:52:03,880
Speaker 12: we have going. Okay, so this is from the Times.

1009
00:52:03,880 --> 00:52:06,840
Speaker 12: There's a study of about six hundred thousand British dogs, okay,

1010
00:52:06,840 --> 00:52:08,960
Speaker 12: from more than one hundred and fifty breeds. It turns

1011
00:52:08,960 --> 00:52:12,959
Speaker 12: out small dogs with prominent noses they live longer, oh yeah,

1012
00:52:13,000 --> 00:52:14,960
Speaker 12: than the bigger flat faced dog.

1013
00:52:15,239 --> 00:52:15,439
Speaker 9: Going.

1014
00:52:15,600 --> 00:52:18,320
Speaker 12: So like the French bulldog for example, it has the flat.

1015
00:52:18,120 --> 00:52:19,640
Speaker 5: Which is the most popular breath these days.

1016
00:52:19,719 --> 00:52:21,920
Speaker 12: Yes, it is, it is, it is. But you know

1017
00:52:22,040 --> 00:52:25,279
Speaker 12: the short snouts, they have respiratory problems. They can get

1018
00:52:25,320 --> 00:52:28,200
Speaker 12: heat stroke things like that. So that's the problem with

1019
00:52:28,280 --> 00:52:28,840
Speaker 12: those dogs.

1020
00:52:28,880 --> 00:52:31,200
Speaker 1: Do you know my little one, the vet that I

1021
00:52:31,239 --> 00:52:34,359
Speaker 1: go to in New York, she's absolutely lovely. And what's

1022
00:52:34,400 --> 00:52:37,480
Speaker 1: great about her it's the horse Man Vetinary school. You

1023
00:52:37,560 --> 00:52:40,080
Speaker 1: make the check right out to her kids prep schools. Yes,

1024
00:52:40,680 --> 00:52:42,359
Speaker 1: you know. You go in and it's like you think

1025
00:52:42,400 --> 00:52:44,240
Speaker 1: you're going to spend two hundred and eighty two dollars

1026
00:52:44,280 --> 00:52:46,880
Speaker 1: and it's like nine hundred and fourteen dollars. You just

1027
00:52:46,920 --> 00:52:49,239
Speaker 1: write it out to horse Man. I mean, she's just

1028
00:52:49,280 --> 00:52:51,959
Speaker 1: the time safe. It just write it out Director horse Man.

1029
00:52:52,120 --> 00:52:55,879
Speaker 1: My vetwent apoplectic on this at one point, and she said, yeah,

1030
00:52:55,880 --> 00:52:59,240
Speaker 1: there is a bread issue here. And I'm very pleased

1031
00:52:59,280 --> 00:53:03,040
Speaker 1: to say that. Bill and kennel fere Cotons, which are

1032
00:53:03,080 --> 00:53:06,799
Speaker 1: in their own way a little twisted, but they have

1033
00:53:07,040 --> 00:53:10,560
Speaker 1: like the normal nose and that's better. It's like it's like,

1034
00:53:10,640 --> 00:53:11,560
Speaker 1: actually a big.

1035
00:53:11,400 --> 00:53:15,360
Speaker 12: Deal in dog I did not see today who knew?

1036
00:53:14,880 --> 00:53:18,520
Speaker 12: Who knew? And female dogs live longer than male dogs.

1037
00:53:18,520 --> 00:53:19,480
Speaker 5: If really know that as well?

1038
00:53:19,560 --> 00:53:20,280
Speaker 9: Yeah, make sense.

1039
00:53:20,600 --> 00:53:25,439
Speaker 1: I think that's true of other humans. Well, missus Keane

1040
00:53:25,440 --> 00:53:28,960
Speaker 1: asked me with one foot in the casket.

1041
00:53:28,640 --> 00:53:31,520
Speaker 10: Do you call Ken? Prutt wants to ask why do

1042
00:53:31,640 --> 00:53:34,960
Speaker 10: men die before their wives? His answer, because they want to?

1043
00:53:36,640 --> 00:53:37,480
Speaker 10: That was just Ken.

1044
00:53:37,600 --> 00:53:38,320
Speaker 9: What do you go?

1045
00:53:39,360 --> 00:53:43,080
Speaker 1: John Tucker helping his ye it providing congestion in the studio.

1046
00:53:44,440 --> 00:53:47,479
Speaker 12: The last from the journal. This is about twenty something

1047
00:53:47,520 --> 00:53:51,000
Speaker 12: year olds. They're no longer hanging out like how Matteo

1048
00:53:51,120 --> 00:53:52,200
Speaker 12: used to do back in the day.

1049
00:53:52,320 --> 00:53:52,879
Speaker 9: You go to the.

1050
00:53:52,800 --> 00:53:55,400
Speaker 12: Clubs delayed at night. No, that's not they're in bed

1051
00:53:55,680 --> 00:53:56,840
Speaker 12: by nine pm.

1052
00:53:57,640 --> 00:53:58,359
Speaker 1: Huge.

1053
00:53:58,520 --> 00:54:02,160
Speaker 12: Yeah, it's the the link between sleep and better health.

1054
00:54:02,239 --> 00:54:05,000
Speaker 12: They say they believe they're not going out.

1055
00:54:05,280 --> 00:54:07,720
Speaker 1: Well, I saw this week the first time Jim Carrey

1056
00:54:07,800 --> 00:54:11,040
Speaker 1: was ever on TV. It was a Johnny Carson. Everyone

1057
00:54:11,040 --> 00:54:13,360
Speaker 1: should see it on YouTube. Jim Carrey as a child

1058
00:54:13,800 --> 00:54:16,800
Speaker 1: and it's all there. Everything he became is there, absolutely

1059
00:54:16,880 --> 00:54:21,720
Speaker 1: mesmerizing twenty minutes of YouTube video. Paul in the media business,

1060
00:54:21,760 --> 00:54:24,440
Speaker 1: we stayed up to eleven thirty to watch Johnny Carson

1061
00:54:24,520 --> 00:54:26,240
Speaker 1: shore every Now. Why did this stop?

1062
00:54:26,680 --> 00:54:29,440
Speaker 4: Well, no, that late night viewing. Late night viewing is

1063
00:54:29,440 --> 00:54:31,640
Speaker 4: still pretty good. Late night TV is still important. They

1064
00:54:31,680 --> 00:54:33,319
Speaker 4: still invest a lot, They still make a lot of

1065
00:54:33,320 --> 00:54:36,040
Speaker 4: money in that late late night block there, so it's

1066
00:54:36,040 --> 00:54:40,080
Speaker 4: still important for television. But I guess it's not. According

1067
00:54:40,080 --> 00:54:41,759
Speaker 4: to the Wall Street Journal, the days of hanging out

1068
00:54:41,800 --> 00:54:44,080
Speaker 4: late for people in their twenties is over.

1069
00:54:44,160 --> 00:54:45,520
Speaker 5: I'm trying to think about my kids, But.

1070
00:54:45,480 --> 00:54:48,040
Speaker 12: Do you think about businesses are changing? Like businesses are

1071
00:54:48,040 --> 00:54:51,600
Speaker 12: doing these matinee parties that started like five pm. That's

1072
00:54:51,640 --> 00:54:52,399
Speaker 12: what they're starting to say.

1073
00:54:52,440 --> 00:54:56,759
Speaker 5: For me, that's my day's happy think do you guys think?

1074
00:54:56,920 --> 00:54:59,359
Speaker 1: Seriously? John Tucker jump in here as well. Do you

1075
00:54:59,360 --> 00:55:02,279
Speaker 1: guys think because his kids are ill mannered? I mean,

1076
00:55:02,320 --> 00:55:04,080
Speaker 1: it's a it's a better picture of what's going on.

1077
00:55:04,680 --> 00:55:07,719
Speaker 1: Do you think it's the pandemic? My basic take is

1078
00:55:07,760 --> 00:55:11,919
Speaker 1: the ramifications of COVID are with us far more now

1079
00:55:11,960 --> 00:55:14,040
Speaker 1: than we're living, you know, day to day with our kids,

1080
00:55:14,360 --> 00:55:15,280
Speaker 1: the dogs, everyone.

1081
00:55:15,320 --> 00:55:17,239
Speaker 12: What do you think, Lisa, that's a good point. I

1082
00:55:17,239 --> 00:55:18,879
Speaker 12: didn't even think of it about it that way.

1083
00:55:18,960 --> 00:55:21,120
Speaker 1: We went to bed at nine pm with COVID because

1084
00:55:21,680 --> 00:55:24,279
Speaker 1: there's nothing to do. John, what do you think now?

1085
00:55:24,440 --> 00:55:26,839
Speaker 10: I was looking at my easy pass receipts because they

1086
00:55:26,880 --> 00:55:29,560
Speaker 10: had to pay it this morning. And I'm like, what's

1087
00:55:29,600 --> 00:55:33,480
Speaker 10: this one three am on the Garden State Parkway. I'm like,

1088
00:55:34,080 --> 00:55:37,440
Speaker 10: that's my daughter and I'm like freaking out, and then

1089
00:55:37,440 --> 00:55:42,399
Speaker 10: I realized, oh no, it's me. Now they're no, they're

1090
00:55:42,400 --> 00:55:45,040
Speaker 10: not in better early. No, this does not apply to

1091
00:55:45,080 --> 00:55:46,959
Speaker 10: my children, not years.

1092
00:55:47,560 --> 00:55:49,239
Speaker 5: But they're like going to bet at nine p not

1093
00:55:49,280 --> 00:55:49,640
Speaker 5: my son.

1094
00:55:50,080 --> 00:55:53,279
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1095
00:55:53,320 --> 00:55:58,080
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1096
00:55:58,160 --> 00:56:02,200
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1097
00:56:02,320 --> 00:56:06,359
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1098
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1099
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1100
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1101
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