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

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

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

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

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Speaker 1: or watch us live on YouTube.

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Speaker 2: Billy Lipteltz joining us. Obviously, the memestocks. This was all

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Speaker 2: the rage but GameStop today, so they came out with

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Speaker 2: earnings kind of unexpectedly. They also were in the market

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Speaker 2: issuing shares. I feel like it took them a minute

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Speaker 2: to get it together.

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Speaker 3: No did, and that's really been the thing we've been

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Speaker 3: writing about it this week. We wrote I Believe on

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Speaker 3: Wednesday basically saying the window to capitalize was narrowing, if

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Speaker 3: not closing, because GameStop their quarter ended a few weeks ago,

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Speaker 3: so they needed preliminary results before they could file to

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Speaker 3: potentially sell shares. They did all of that this morning,

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Speaker 3: but Alex, as you point out, the stock was trading

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Speaker 3: north of fifty bucks on Tuesday. Before the even got

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Speaker 3: a chance to sell, it crashed back below twenty eight.

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Speaker 3: So now we're seeing selling pressure down twenty seven percent.

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Speaker 3: Fifty five million shares have traded so far. Again, an

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Speaker 3: ATM allows them and allows their bank to sell up

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Speaker 3: to forty five million chairs, not necessarily a sign that

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Speaker 3: they have, but reading between the lines would mean that

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Speaker 3: they probably are.

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Speaker 4: Do we have any better idea here? Who any of

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Speaker 4: these fifty five million share people are?

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Speaker 3: Like?

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Speaker 4: Who's trading this stuff? Again? Is it going to go

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Speaker 4: back to your thoughts about that the quant hedge funds

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Speaker 4: that are kind of really generating the activity.

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Speaker 3: Yeah, and we're still not seeing really the volume that

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Speaker 3: we saw on Fidelity, on interactive brokers, on some of

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Speaker 3: the retail platforms that we did see back in twenty

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Speaker 3: twenty one.

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Speaker 4: And that's that's data. You guys can see the publican see.

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Speaker 3: You can see it if you have an account, so

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Speaker 3: like I had to create a Fidelity it used to

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Speaker 3: not be behind the paywall.

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Speaker 5: They put it behind the paywall.

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Speaker 3: But like, if you have an account, you can see

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Speaker 3: what on a given day people are buying and selling.

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Speaker 5: It's just the orders, but it's mixed at SPENSA. I

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Speaker 5: do not want a personal account.

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Speaker 4: Oh, I thought you were just did it for reporting?

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Speaker 3: No, I do now expense a premium exitcount, though that

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Speaker 3: was a recent edition nice, good small ones.

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Speaker 2: CRETA won't mind the big the small ones matter. Do

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Speaker 2: we know why it didn't last longer? Is this just

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Speaker 2: basically there's weren't as many shorts out there? Is that

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Speaker 2: why the Memestock rally was a hot minute.

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Speaker 5: It's a really different backdrop.

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Speaker 3: So not only do you not have people stuck at

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Speaker 3: home looking for entertainment, flush with stimulus checks, short interest

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Speaker 3: is about twenty five percent. It used to be north

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Speaker 3: of one hundred back in the peak mania. A lot

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Speaker 3: of Again, the buying and selling that we saw Monday

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Speaker 3: and Tuesday seemed to have Wall Street's fingerprints, so it

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Speaker 3: never really got retail traders back in like back in

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Speaker 3: twenty twenty one. And also you had a quiet Monday

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Speaker 3: and Tuesday session ahead of the inflation data on Wednesday.

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Speaker 3: And then you add in the fact that investors, retail

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Speaker 3: traders probably bought in and were sitting on big paper losses,

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Speaker 3: so you provide an exit rant for them.

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Speaker 5: Why would they not take advantage?

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Speaker 6: Did Bill Gross make money on this with his straddle?

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Speaker 5: He still is those expired today.

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Speaker 3: I haven't talked to him since he told you he

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Speaker 3: laid out the initial trade, so he might have closed

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Speaker 3: it or tapered it down, but that straddle still is

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Speaker 3: very much in played in the in the money, assuming

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Speaker 3: shares don't.

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Speaker 4: So he bought calls and puts at forty dollars, right.

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Speaker 5: He sold you logged in, So now I'm off the terminal.

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Speaker 3: Oh but you were using my log I sat down

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Speaker 3: where you.

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Speaker 5: Were marriage off.

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Speaker 6: Though.

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Speaker 4: There's a certain person, a morning anchor that leaves his

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Speaker 4: Twitter account open.

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Speaker 6: Does I find that helpful?

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Speaker 7: Yes?

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Speaker 1: I know.

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Speaker 6: Let me.

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Speaker 4: I want to go to another area here. But we

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Speaker 4: were just talking about Reddit and how that was such

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Speaker 4: a great ipo and they had some good news today,

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Speaker 4: pop on the stock up again. Here we have a

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Speaker 4: number of really good IPOs performing over the last couple

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Speaker 4: of months. I got David Solomon up on stages saying,

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Speaker 4: the backlogs building. We're all my IPOs.

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Speaker 5: I need to see five or six a week. The

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Speaker 5: backlog has been building for three years.

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Speaker 3: Of every banker you talked to is like, the backlog's

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Speaker 3: been better than it's ever been.

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Speaker 4: I want to see days when we have four printing,

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Speaker 4: like on a Thursday.

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Speaker 5: Yeah, well, don't hold your breath.

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Speaker 6: So what are we waiting for here? At this point?

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Speaker 3: Well, the real thought is is drawing back investors.

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Speaker 5: So you have read it performing well.

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Speaker 3: You have a stera of labs, Viking, Bougie cruises for

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Speaker 3: lack of a better frame, all performing well.

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Speaker 5: But you have to get deals that are priced well.

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Speaker 3: To Paul's point, make the investors money, make the sponsors happy,

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Speaker 3: and perform well. And that's really still kind of weaning

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Speaker 3: back into the system. There is still uncertainty around how

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Speaker 3: much of a slowdown what we have in the summer,

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Speaker 3: just because bankers and investors are on vacation. We do

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Speaker 3: have an election in November. It seems like we will

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Speaker 3: have a more active than usual June July than historically speaking.

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Speaker 3: But the big focus still is on that post Labor

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Speaker 3: Day window, which will be kind of compressed because you

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Speaker 3: want to get out before the election, but also when

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Speaker 3: people are actually at their desks and you can go

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Speaker 3: visit t Row and Fidelity and have a real pitche.

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Speaker 4: There are days back in the day when Morgan Stanley

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Speaker 4: Goldman's as a credit Swisser song, we do have to

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Speaker 4: get on the phone and say, all right, guys, we're

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Speaker 4: all going to be in New York on Thursday. We

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Speaker 4: got to coordinate this are you do your lunch at

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Speaker 4: the Four Seasons eleven, I'll do mine at the Intercontinental twelve?

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Speaker 4: You do years. That's how busy it was, you know.

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Speaker 4: And and because you get those those windows, I.

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Speaker 6: Feel like I haven't this window.

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Speaker 3: Well that's and when I talk to investors in bankers,

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Speaker 3: the big argument that they make is the window has

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Speaker 3: always been open for good companies. It's just that good

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Speaker 3: companies who have access to the private markets maybe don't

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Speaker 3: want to go out just yet and be.

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Speaker 5: The first here.

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Speaker 4: I mean, these Pea guys whining. They've been sitting in

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Speaker 4: these things for five, six, seven, eight, ten years.

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Speaker 5: They're looking for an exit.

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Speaker 4: Well, like, okay, if you don't want to like you know,

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Speaker 4: if you don't want to take the streets mark, then

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Speaker 4: let's hit it. Let's go.

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Speaker 5: Well, that's the big issue on a down round. You

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Speaker 5: have to call it a down round.

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Speaker 3: You have to cave a little bit on valuation, especially

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Speaker 3: because a lot of to your point, a lot of

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Speaker 3: these pe assets are heavily straddled with debt, and investors

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Speaker 3: don't want debt right now. They want clean uh stories

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Speaker 3: they can be sold to them. Viking was one that's

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Speaker 3: interesting that has a large detlog, but it's viewed as

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Speaker 3: kind of being recession proof because it's wealthy people, older

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Speaker 3: people taking cruises.

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Speaker 2: Interesting, you just did a flux there. He called it

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Speaker 2: a flex before wealthy people taking cruises. All right, Bailey,

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Speaker 2: thanks a lot, super appreciated.

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Speaker 6: Happy weekend.

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Speaker 2: Billy Lipschaltz joining us Ipo market as well as the

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Speaker 2: Meme stocks.

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

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

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Speaker 1: Android Auto with the Bloomberg Business. You can also listen

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

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Speaker 1: just say Alexa playing Bloomberg eleven thirty.

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Speaker 2: It has been definitely a week. We had some nice records.

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Speaker 2: We haven't seen this kind of move in months for

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Speaker 2: the S and P.

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Speaker 6: So what do you do?

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Speaker 2: On a headline level, we may continue to be a

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Speaker 2: little bit boring, but underneath there's some really individual stock

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Speaker 2: moves that are gaining a lot of traction here. So

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Speaker 2: let's get more with Shelby McFadden, investment analyst at Motley

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Speaker 2: Full Asset Management. She is joining us. So, Shelby, are

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Speaker 2: we in a world where like the headline level is

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Speaker 2: going to continue to grind higher, but it's going to

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Speaker 2: be those individual names where you get the action, you know.

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Speaker 6: I think that's absolutely possible.

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Speaker 8: And part of it is we've seen the evidence of

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Speaker 8: that happening for not just one year, but two years, right.

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Speaker 8: We've seen it go from the S and P five

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Speaker 8: hundred to sort of the S and P seven to

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Speaker 8: the SMP four, And so it's not completely unreasonable to

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Speaker 8: have to pop the hood and really see, breaking into

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Speaker 8: sectors and industries, what's really going on in the market.

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Speaker 8: And so if you're sort of just interesting in owning

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Speaker 8: the overall market and being okay with it being driven

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Speaker 8: by fewer than ten stocks, sure, but the inverse is

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Speaker 8: not necessarily true that how those top ten are doing

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Speaker 8: is necessarily representative of the overall market.

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Speaker 4: Shelby, we had a I guess a weaker than expected

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Speaker 4: retail sales number yesterday, yet combined with some pretty strong

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Speaker 4: sales numbers out of Walmart. How do you square those

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Speaker 4: two things? How do you view the consumer out there?

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Speaker 8: I think when we sort of look at those two things,

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Speaker 8: we see the fact that consumers are looking for value.

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Speaker 8: And so when we do look at Walmart. We consider

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Speaker 8: the fact that general merchandise for that retailer is still

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Speaker 8: weaker than they would like to see, and a lot

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Speaker 8: of the performance comes from the necessities like food and

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Speaker 8: different staples.

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Speaker 5: So when we see those.

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Speaker 8: Weaker retail sales, we're realizing that discretionary income is still

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Speaker 8: being pressured quite a bit, and that when people are

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Speaker 8: looking to spend what discretionary income they have, they're looking

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Speaker 8: to get value. So in the past they may have

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Speaker 8: been a little bit indifferent about saying, let's just go

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Speaker 8: ahead and order the TV from this online retailer or

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Speaker 8: pick it up from this standard big box retailer. Now

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Speaker 8: they're considering, you know what, let's check out Walmart. I

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Speaker 8: think they actually have a lot of value that they

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Speaker 8: can offer us.

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Speaker 2: For those of you who didn't hear yesterday's show, Paul

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Speaker 2: is going to a Walmart for the first time in

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Speaker 2: a long time.

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Speaker 6: Is going to some channel checks.

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Speaker 2: Tomorrow, right, Yep, it's going to be huge. So Walmart

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Speaker 2: stock at a fifty two week high today. Is this

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Speaker 2: priced in? Or is there more upside here? You think

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Speaker 2: to Walmart?

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Speaker 8: I think part of what we're also seeing in the

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Speaker 8: sort of little Walmart run up is the fact that

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Speaker 8: they've made that decision i think within the last couple

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Speaker 8: of weeks to close a lot of their health clinics.

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Speaker 8: And they're also having some staff reduction and they're cracking

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Speaker 8: down on folks working remotely, so they're doing some of

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Speaker 8: that op X as well. So what we're seeing now

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Speaker 8: is the effect of the bottom line boost. So at

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Speaker 8: first Walmart was able to sort of hold onto their

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Speaker 8: top line for a bit longer than others.

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Speaker 5: I think what's being priced.

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Speaker 8: In now is the fact that they are leaning out

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Speaker 8: in ways that they don't usually take on. They traditionally

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Speaker 8: just kind of cut their hourly workforce, but now they're

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Speaker 8: really looking at the big picture of investment, and I

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Speaker 8: think that's the sort of pop in the stock we

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Speaker 8: saw yesterday.

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Speaker 4: So Shelby, I guess one of the questions here is,

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Speaker 4: I guess the lower end of consumer, how are they

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Speaker 4: dealing with? Where are they going with their dollars? Is

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Speaker 4: it to are they staying with Walmart target, They're going

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Speaker 4: down to the dollar stores? How is the lower end

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Speaker 4: consumer how are they spending their discretionary income.

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Speaker 8: I think what we've seen in a combination of between

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Speaker 8: Walmart and they're sort of not retailers but manufacturers that

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Speaker 8: have branded material. Is that consumers are choosing brands even

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Speaker 8: at the lower end where there is the most premiumization

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Speaker 8: that they require, But then they are choosing more of

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Speaker 8: a Walmart over a Target at the lower end because

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Speaker 8: at this point you can't really stop how much food

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Speaker 8: you're going to need. You can try and make your

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Speaker 8: clothing and other sort of discretionary items last a little

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Speaker 8: bit longer, stretch them a bit longer, but food is

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Speaker 8: just such a requirement. And so when we then combine

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Speaker 8: that with what we saw from the financials companies, we're

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Speaker 8: seeing that at the lower end they are just using

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Speaker 8: less credit, trying to get paid off what they can,

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Speaker 8: and so if they're just working with cash, it's going

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Speaker 8: to be all right, let's get all the discretionary stuff

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Speaker 8: that we have to get on top of the staples

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Speaker 8: we need at a place like Walmart. And I'm not

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Speaker 8: quite sure that we're seeing that absolute trade down into say,

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Speaker 8: food shopping at dollar stores quite yet. I don't think

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Speaker 8: we're there just yet.

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Speaker 6: I don't.

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Speaker 2: No, man, food shopping a dollar stores feels like you

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Speaker 2: are asking for expired stuff.

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Speaker 5: Uh, it's a tough.

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Speaker 4: Read and the person at the checkout because you don't

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Speaker 4: want that, sir, like.

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Speaker 2: It's green, Why do you fund something that's green. To

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Speaker 2: that point, Shelby, there's tons of retail action next week, right,

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Speaker 2: get Macy's, Target, you do, get the dollar stores, Ross stores, like,

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Speaker 2: get the specialty guys.

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Speaker 6: What's going to be a macro signal.

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Speaker 8: For you here? To me, I think it's actually going

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Speaker 8: to be these sort of the sort of middle to

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Speaker 8: lower end, the Ross, the Burlington, the Dollar stores, letting

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Speaker 8: us know how much of a higher income customer share

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Speaker 8: they're picking up, because it's been very obvious that they

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Speaker 8: were going to get more of the middle to lower end.

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Speaker 8: That's the whole point of their of their retail premise.

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Speaker 1: Right.

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Speaker 8: So the question now is how much of the upper

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Speaker 8: end consumer are you raining in for Ross? Are you

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Speaker 8: capturing some of the consumer that was going to Marshals

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Speaker 8: or even Nordstrom Rack? Similar for Burlington? And then when

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Speaker 8: we look at Target, are they able to capture or

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Speaker 8: hold on to any middle income to lower income share

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Speaker 8: because we do know that they have a little bit

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Speaker 8: more of a premium product.

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Speaker 2: Just so we're clear here in the journal, today, there

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Speaker 2: was an article about Tjmax and why it's awesome because

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Speaker 2: they were like anyone who's anyone can now go and

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Speaker 2: buy like super high designers for like a third of

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Speaker 2: the price. I love TJ Max. It's like in my blood.

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Speaker 2: But I mean it was in the journal. I'm just saying,

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Speaker 2: so now it's already happening. But now, like you know,

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Speaker 2: real people with like serious money, like real housewives people

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Speaker 2: are doing it, but them out of the year.

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Speaker 6: Know that according from the.

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Speaker 4: Journal, very good what the journal has it? Yeah, I'm

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Speaker 4: going with it a Shelby And in some of your

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Speaker 4: stock picks here, one of them is ken View. Just

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Speaker 4: remind her that was to spin out from Johnson and

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Speaker 4: Johnson I guess talk to us about Kenview.

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Speaker 3: Yeah.

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Speaker 8: What's interesting about ken View is they sort of followed

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Speaker 8: in a trend that a lot of pharmaceutical companies were

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Speaker 8: doing where they went ahead and separated their consumer brands.

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Speaker 8: So we're talking about, you know, the tail and alls

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Speaker 8: of the world into a completely separate company. And what

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Speaker 8: interested me about that going into earning season was whether

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Speaker 8: or not they would be able to sort of maintain

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Speaker 8: that demand on premiumization when we think about the fact

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Speaker 8: that if I need tile at all, I could just

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Speaker 8: buy a seat of minifit and it's cheaper. And what

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Speaker 8: they showed us in earnings is that they've been able

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Speaker 8: to do it. So the brands that they've cultivated have

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Speaker 8: held onto people to spend that extra dollar or dollar

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Speaker 8: fifty on baby wipes, on feminine products, on pain and

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Speaker 8: other healthcare. So that was a great signal to us

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Speaker 8: that to some degree, with the right mix of pricing,

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Speaker 8: you're able to hold onto people for even brand named staples,

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Speaker 8: And I think that is really important.

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Speaker 2: Your last pick here that you have on the notes

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Speaker 2: here is master Card. Why do you like this dog

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Speaker 2: versus some of the other guys.

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Speaker 5: Yeah.

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Speaker 8: One of the things we love about MasterCard really in

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Speaker 8: general is that they're really just a toll taker, right,

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Speaker 8: so even in a sort of downturn, the rails still

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Speaker 8: have to be used to process payment. The other thing

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Speaker 8: is really their weight to credit compared to debit. So

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Speaker 8: we do know that a while ago we had that

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Speaker 8: legislation that really compressed on debit fees. So when we

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Speaker 8: look at their competitor, we see that they've got that

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Speaker 8: better mix to that sort of higher fee for using

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Speaker 8: the credit rails, and also knowing that consumers leaning a

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Speaker 8: little bit more on credit in these types of times,

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Speaker 8: at least in the middle to upper end, generates more

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Speaker 8: fees for MasterCards. So when we think about the credit

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Speaker 8: to debit mix, plus their international opportunities, we feel that

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Speaker 8: they're really well positioned when it comes to the payment

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Speaker 8: network space.

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Speaker 4: All right, Shelby, thank you so much for joining us.

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Speaker 4: Shelby McFadden Investment analys that Motley Fool Asset Management joining

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Speaker 4: us from Alexandria, Virginia via zoom. You may show you

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Speaker 4: how cutting edge I am on fintech. When I go

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Speaker 4: buy my salad today for lunch, you can use Apple pay.

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Speaker 6: Hey, look at you. What happens to the water cash

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Speaker 6: in your pocket?

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Speaker 4: I still have the water cash. It just it lasts

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Speaker 4: a lot longer. Like when I was growing up, even

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Speaker 4: as a young adult, I would only use credit cards

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Speaker 4: for big purchase and special one off type of things,

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Speaker 4: not for day to day I use cash cut now.

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Speaker 4: It's it's the exact I think after in society. It's

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Speaker 4: just used it for everything.

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Speaker 9: I'm sorry.

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Speaker 6: Michael Bar Michael Bar, Oh my god.

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Speaker 9: First of all, he pulls out a lot of cash,

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Speaker 9: A couple of one hundred dollars bills. Absolutely, you know,

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Speaker 9: not the deed the boardwalk like I have in mind,

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Speaker 9: but wow, what happened to cash?

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Speaker 5: You know?

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Speaker 4: The only reason I have cash? And John Tucker can

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Speaker 4: back me up on this. You go down to the

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Speaker 4: Jersey Shore. That's a cash economy down. Do you walk

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Speaker 4: up to a Jersey Shore bar, you don't apple pay,

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Speaker 4: You throw a fifty or hondy on the bar and

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Speaker 4: that's how you announce your uh.

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Speaker 6: I also love the use a hundy because I say

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Speaker 6: that at home. My husband like refuses to talk to me.

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Speaker 2: But to that point, but actually, tipping is the problem.

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Speaker 2: Like if you don't have cash, I never carry cash anymore,

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Speaker 2: and then tipping is huge. Like if you go get

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Speaker 2: a Manny petty, it's like, oh god, you guys have

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Speaker 2: venmo and then you're like scrambling and it's stressful.

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Speaker 4: It's a changing world out there in terms of world.

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Speaker 4: You have to be on top of this fintech And

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Speaker 4: a lot of people say it's just by Master Carter

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Speaker 4: resent that gets your exposure to fintech because it's all

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Speaker 4: got a over their systems. And remember take care of

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Speaker 4: your brant tenders, ladies and gentlemen. Exactly right.

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

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

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Speaker 1: Android Auto with the Bloomberg Business App. Listen on demand

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

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Speaker 2: Just get to tech. There's been some fun news. I

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Speaker 2: was taking a look at this Reddit open ai thing.

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Speaker 2: So apparently the deal between the two helps display Reddit

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Speaker 2: its content and also train open ai systems on its

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Speaker 2: partners data. So this is kind of what they promised

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Speaker 2: when the iPod and then maybe a little extra. Man

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Speaker 2: Deep Singh joins us now he is a Bloomberg Senior

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Speaker 2: technology analyst, mandeep give me some of the particulars here.

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Speaker 7: Well, they haven't told us who is paying whom. It's

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Speaker 7: just that I mean, look, man, we know Google pays

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Speaker 7: Reddit almost sixty million a year for its content, and

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Speaker 7: in return, I think Reddit benefits more in the sense that, yes,

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Speaker 7: they get paid, and they also get traffic from Google.

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Speaker 7: You know Google displaying reddits links on its search. But

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Speaker 7: in this case, I think open ai probably needs Reddit

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Speaker 7: data more simply because you have to constantly train these llms.

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Speaker 7: I mean, Reddit has almost one billion posts a year.

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Speaker 7: Look at the magnitude of the user generated content. So yes,

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Speaker 7: OpenAI has the most powerful LLM out there. But llms

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Speaker 7: needs these large data sets, and that's where Reddit is

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Speaker 7: one of the most coveted data sets on the open Internet.

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Speaker 7: If you think about it, Twitter being another one. There

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Speaker 7: are other forums that generate a lot of user generated content,

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Speaker 7: but that's what you need to constantly train these Larzagrig models.

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Speaker 7: And I thought Google's LM had an advantage because one

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Speaker 7: they were training on these Reddit data sets, which open

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Speaker 7: Ai didn't have license for. And also it establishes a

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Speaker 7: trend that you have to pay for content. Now it's

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Speaker 7: not free anymore, so every content provider will start charging

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Speaker 7: a licensing fee.

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Speaker 6: Jump on.

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Speaker 4: You know what's impressive about mandeep so many things on

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Speaker 4: a Friday in studio.

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Speaker 6: I totally knew you were going to bring this up now.

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Speaker 4: So he basically leaves our bi effort in New York

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Speaker 4: because the senior management is safely asconced leading from the

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Speaker 4: RUAR in Princeton. So he's our guy here in the

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Speaker 4: world's financial capital. But what I love about the Reddit

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Speaker 4: story is I love this Reddit manager team. They get

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Speaker 4: the gold star.

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Speaker 1: Now.

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Speaker 4: They went public saying they were going to do something,

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Speaker 4: and they did it, and the IPO shareholders are benefiting.

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Speaker 4: That is such a class move man. They want to

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Speaker 4: come back and sell more stock, I'll buy it.

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Speaker 7: The rays And look, I mean, compare them to a

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Speaker 7: company like Pinterests. Same number of daily active users, same

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Speaker 7: number of monthly active users. Pinterest rates that are twenty

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Speaker 7: five billion market gap. Reddit is still a ten billion

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Speaker 7: dollar market gap. Yes, there is a revenue gap. I mean,

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Speaker 7: obviously Pinterests generates three point five billion in revenue. Reddits

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Speaker 7: is much smaller. But in terms of engagement user trends,

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Speaker 7: everything looks similar.

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Speaker 2: So we'll get to Snowflake in a second. But I

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Speaker 2: have one more silly question. So when I'm going on

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Speaker 2: Google and I'm searching something now and then it gets

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Speaker 2: me eventually to all the here's what Reddit's saying and

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Speaker 2: all those links, is that because of these kind of partnerships.

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Speaker 7: I mean, look, Reddit again has the most authentic user

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Speaker 7: content communities that people want to know. Like, they don't

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Speaker 7: want canned LLLM generated answers. People want real answers where

444
00:19:48,480 --> 00:19:52,040
Speaker 7: users are having an engagement and discussing a topic. And

445
00:19:52,080 --> 00:19:57,480
Speaker 7: that's why Google is prioritizing reddits results in their ten

446
00:19:57,520 --> 00:20:00,320
Speaker 7: blue links. I mean, yes, they are adding genera like

447
00:20:00,520 --> 00:20:05,159
Speaker 7: AI overviews and all that, but they are prioritizing Reddit

448
00:20:05,520 --> 00:20:08,879
Speaker 7: in their first ten links because they find these links

449
00:20:08,920 --> 00:20:11,000
Speaker 7: to be more engaging for their users.

450
00:20:11,119 --> 00:20:12,920
Speaker 4: And can let me just give these guys a pat

451
00:20:12,960 --> 00:20:16,600
Speaker 4: pat on the back here Reddit when public September seventeenth

452
00:20:16,880 --> 00:20:18,920
Speaker 4: to twenty twenty four to thirty four dollars to share

453
00:20:19,160 --> 00:20:21,119
Speaker 4: the stocks at sixty three and change here today up

454
00:20:21,200 --> 00:20:24,520
Speaker 4: eighty five percent. That's a nice trade. It's a nice investment.

455
00:20:24,560 --> 00:20:26,280
Speaker 4: They should be doing some more stock and more companies

456
00:20:26,320 --> 00:20:28,120
Speaker 4: should become in public. I don't know why the full

457
00:20:28,280 --> 00:20:32,359
Speaker 4: Gates aren't just gushing with IPOs. What is Snowflake and

458
00:20:32,400 --> 00:20:34,040
Speaker 4: what's their AI strategy?

459
00:20:34,320 --> 00:20:37,960
Speaker 7: Well, so, Snowflake is a layer that's on top of

460
00:20:38,000 --> 00:20:42,719
Speaker 7: your hyperscalers. You'd remember hyperscalers gives you the compute the database.

461
00:20:43,040 --> 00:20:46,600
Speaker 7: So Snowflake is like, I will aggregate all your data

462
00:20:46,680 --> 00:20:50,119
Speaker 7: into a nice dashboard. That was their value proposition. They

463
00:20:50,160 --> 00:20:54,959
Speaker 7: were immensely successful until this whole generative AI and LLLM

464
00:20:55,040 --> 00:20:59,399
Speaker 7: wave popped up, which was very disruptive because one Snowflake

465
00:20:59,440 --> 00:21:02,439
Speaker 7: didn't have any of their large anguid models that all

466
00:21:02,480 --> 00:21:06,600
Speaker 7: these companies, the hyperscalers have, And then obviously, abruptly their

467
00:21:06,680 --> 00:21:10,280
Speaker 7: CEO resigned and they appointed a new CEO, and his

468
00:21:10,440 --> 00:21:14,240
Speaker 7: playbook seems to be to develop an LLM because otherwise

469
00:21:14,280 --> 00:21:17,120
Speaker 7: they're at the risk of getting disintermediated. Look, it's still

470
00:21:17,160 --> 00:21:21,440
Speaker 7: a high growth company, still mid twenty percent growth, hugely impressive.

471
00:21:21,920 --> 00:21:25,520
Speaker 7: But for a while they couldn't put a step wrong.

472
00:21:25,600 --> 00:21:28,480
Speaker 7: They were growing forty percent plus and so suddenly that

473
00:21:28,640 --> 00:21:31,600
Speaker 7: growth decelerated, and I think it was because of the

474
00:21:32,119 --> 00:21:34,359
Speaker 7: not having an LLLM. So that's what they're trying to

475
00:21:34,400 --> 00:21:35,120
Speaker 7: solve for here.

476
00:21:35,280 --> 00:21:37,760
Speaker 2: So they're in talks to acquire RecA.

477
00:21:37,840 --> 00:21:38,439
Speaker 6: I'm saying that right.

478
00:21:38,560 --> 00:21:40,879
Speaker 2: Yeah, Areca AI for more than a billion, so that

479
00:21:40,920 --> 00:21:42,560
Speaker 2: would solve their LLM problem.

480
00:21:42,680 --> 00:21:45,560
Speaker 7: I don't know if it's gonna solve because with llms,

481
00:21:45,600 --> 00:21:51,120
Speaker 7: the clear leaders have been established, it's Open AI, Google, Mistral,

482
00:21:51,280 --> 00:21:54,040
Speaker 7: and Meta, the four or five players. All the smaller

483
00:21:54,080 --> 00:21:58,720
Speaker 7: players are finding it's too expensive to constantly train their llms.

484
00:21:58,960 --> 00:22:03,240
Speaker 7: So the con solidation is happening. And now obviously you

485
00:22:03,280 --> 00:22:06,879
Speaker 7: know they are getting sold, but in this case, Snowflake

486
00:22:07,440 --> 00:22:11,119
Speaker 7: bought them first. I'm sure Amazon is looking to buy somebody,

487
00:22:11,200 --> 00:22:14,040
Speaker 7: and so are other company Apple probably it is looking

488
00:22:14,040 --> 00:22:16,960
Speaker 7: to buy someone as well, because that's what you need.

489
00:22:17,000 --> 00:22:20,520
Speaker 7: You need your own LLM. You can't rely on someone

490
00:22:20,560 --> 00:22:23,240
Speaker 7: else to provide the LLM if you're a large tech.

491
00:22:23,119 --> 00:22:27,440
Speaker 4: How many large language model companies are there out there?

492
00:22:28,359 --> 00:22:30,320
Speaker 4: Is my son starting up one, then he's gonna get

493
00:22:30,320 --> 00:22:31,679
Speaker 4: bought after like a gajillion dollars.

494
00:22:31,960 --> 00:22:35,560
Speaker 7: It's very hard to start an LLLM company one because

495
00:22:35,560 --> 00:22:38,520
Speaker 7: you need a lot of compute the Nvidia GPUs, you

496
00:22:38,720 --> 00:22:42,040
Speaker 7: need a lot of data like Reddit is one data set.

497
00:22:42,119 --> 00:22:45,679
Speaker 7: You need thousands of data sets to create your LLLM

498
00:22:45,720 --> 00:22:49,200
Speaker 7: foundational model, and then you have to constantly train these models.

499
00:22:49,600 --> 00:22:52,680
Speaker 7: So this is an expensive proposition and I feel that's

500
00:22:52,720 --> 00:22:55,560
Speaker 7: why the hyperscalers are the ones who have the capex

501
00:22:55,600 --> 00:22:57,399
Speaker 7: and the d pockets to do this.

502
00:22:57,760 --> 00:23:00,000
Speaker 6: And okay, bear with me on this dumb question.

503
00:23:00,640 --> 00:23:03,000
Speaker 2: And we care about this because why because then I

504
00:23:03,040 --> 00:23:05,040
Speaker 2: can go on Google and then they tell me stuff.

505
00:23:06,280 --> 00:23:09,600
Speaker 7: Think of you know, co pilots and AI agents, all

506
00:23:09,600 --> 00:23:13,240
Speaker 7: these new concepts that are coming to the scene are

507
00:23:13,440 --> 00:23:17,520
Speaker 7: enabled by llms because llms have the IQ of an

508
00:23:17,640 --> 00:23:20,679
Speaker 7: average human being, and then it can be you know,

509
00:23:20,800 --> 00:23:25,240
Speaker 7: customized to do specific tasks according to you know, customer

510
00:23:25,320 --> 00:23:29,880
Speaker 7: service or booking travel. But basically, behind the scenes, you've

511
00:23:29,920 --> 00:23:33,439
Speaker 7: got a very intelligent algorithm that has a knowledge of

512
00:23:33,640 --> 00:23:35,600
Speaker 7: you know, the volte and you can talk to it,

513
00:23:35,640 --> 00:23:38,720
Speaker 7: you can converse in it. It's not canned answers. It's

514
00:23:38,800 --> 00:23:42,480
Speaker 7: not a database that you're searching for. It's an intelligent person.

515
00:23:42,560 --> 00:23:45,000
Speaker 6: Okay, this is not like me asking Alexa what's the

516
00:23:45,000 --> 00:23:45,520
Speaker 6: weather like?

517
00:23:45,600 --> 00:23:47,680
Speaker 2: And she's not like, I don't understand your question.

518
00:23:48,040 --> 00:23:48,159
Speaker 4: Like.

519
00:23:48,359 --> 00:23:51,119
Speaker 5: It's not that No, God, it's much more sophisticated.

520
00:23:51,920 --> 00:23:52,360
Speaker 10: All right.

521
00:23:52,680 --> 00:23:56,840
Speaker 4: I mean, whatever video next week and Vidia next week

522
00:23:57,200 --> 00:23:59,640
Speaker 4: starts up ninety percentage year, they better Is this another

523
00:23:59,760 --> 00:24:01,560
Speaker 4: quarter where they have to knock it out of the ballpark?

524
00:24:01,880 --> 00:24:05,480
Speaker 7: I mean, the basic thing is are they under shipping demands? Still?

525
00:24:05,560 --> 00:24:07,760
Speaker 4: Are they under shipping demand?

526
00:24:08,400 --> 00:24:14,120
Speaker 7: Because everyone corroborates that vir supply constraint when it comes

527
00:24:14,119 --> 00:24:18,520
Speaker 7: to Nvidia GPUs, So they will keep writing this until

528
00:24:18,600 --> 00:24:21,480
Speaker 7: the you know, the demand environment is such a demand

529
00:24:21,520 --> 00:24:25,800
Speaker 7: exceed supply, and I think they have a good shot

530
00:24:25,840 --> 00:24:27,120
Speaker 7: of beating and raising a game.

531
00:24:27,280 --> 00:24:28,800
Speaker 6: The options market is interesting.

532
00:24:29,160 --> 00:24:32,320
Speaker 2: Isn't pricing the same kind of exuberance that we've seen

533
00:24:32,320 --> 00:24:34,800
Speaker 2: in the past, which doesn't necessarily mean anything. It just

534
00:24:34,880 --> 00:24:38,440
Speaker 2: means that maybe positioning is in a particular place going

535
00:24:38,440 --> 00:24:40,720
Speaker 2: in and we're not expecting that exuberant call buying.

536
00:24:40,960 --> 00:24:43,399
Speaker 6: Man Deep, thank you so much. We appreciate you always.

537
00:24:43,400 --> 00:24:47,240
Speaker 2: Man keep saying. Bloomberg Intelligence Senior Technology and Lest.

538
00:24:48,520 --> 00:24:52,400
Speaker 1: You're listening to the Bloomberg Intelligence Podcast, catch us live

539
00:24:52,480 --> 00:24:55,520
Speaker 1: weekdays at ten am Eastern on Apple car Play and

540
00:24:55,520 --> 00:24:58,440
Speaker 1: Android Auto with the Bloomberg Business app. You can also

541
00:24:58,520 --> 00:25:01,720
Speaker 1: listen live on Amazon Alexa from our flagship New York

542
00:25:01,760 --> 00:25:05,200
Speaker 1: station just say Alexa playing Bloomberg eleven thirty.

543
00:25:06,920 --> 00:25:09,000
Speaker 2: This is Bloomberg Intelligence Radio. We bring you all the

544
00:25:09,000 --> 00:25:11,880
Speaker 2: top news and business, economics and finance through our lens

545
00:25:11,880 --> 00:25:13,440
Speaker 2: of our Bloomberg Intelligence analysts.

546
00:25:13,440 --> 00:25:14,960
Speaker 6: They're four hundred of them worldwide.

547
00:25:15,000 --> 00:25:18,399
Speaker 2: They covered two thousand companies and one hundred and thirty industries.

548
00:25:18,440 --> 00:25:20,920
Speaker 6: We got you covered. We just talked about retail.

549
00:25:21,200 --> 00:25:24,240
Speaker 2: We saw Walmart, we got Macy's targets and dollar stores

550
00:25:24,640 --> 00:25:27,360
Speaker 2: also coming up. So we go nerdy because we can

551
00:25:27,400 --> 00:25:29,680
Speaker 2: do that on Bloomberg Intelligence Radio and we can talk

552
00:25:29,720 --> 00:25:33,200
Speaker 2: to a forest and paper products analyst. It's a box analyst.

553
00:25:33,560 --> 00:25:36,120
Speaker 2: Our box is shipping. Where's the demand? How's it going?

554
00:25:36,200 --> 00:25:40,000
Speaker 2: Ryan Fox is a Bloomberg Intelligence market analyst for forest

555
00:25:40,040 --> 00:25:42,760
Speaker 2: and paper products and he joins us. Now, Ryan, we

556
00:25:42,800 --> 00:25:45,560
Speaker 2: are super excited to talk to you about this. How's

557
00:25:45,560 --> 00:25:46,720
Speaker 2: the box business?

558
00:25:47,359 --> 00:25:50,760
Speaker 10: Well, the boxing business is not seen as best days.

559
00:25:50,800 --> 00:25:53,960
Speaker 10: We had the worst first quarter in the last eight years.

560
00:25:54,720 --> 00:25:57,080
Speaker 10: Box shipments are down over the last two years almost

561
00:25:57,080 --> 00:26:00,359
Speaker 10: ten percent. And maybe there's some silver lining because the

562
00:26:00,400 --> 00:26:04,160
Speaker 10: second quarter, statistically speaking of the last thirty years has

563
00:26:04,600 --> 00:26:07,560
Speaker 10: more often than not been a better quarter than the first.

564
00:26:07,600 --> 00:26:10,480
Speaker 10: So we do see maybe there's going to be an increase.

565
00:26:11,520 --> 00:26:14,840
Speaker 10: On average increase was three point two percent, like I said,

566
00:26:14,880 --> 00:26:17,760
Speaker 10: over the last thirty years. However, given the backdrop that

567
00:26:17,760 --> 00:26:20,560
Speaker 10: we're seeing in the broader economy, we think we're going

568
00:26:20,600 --> 00:26:24,560
Speaker 10: to see a more muted increase, maybe more like one

569
00:26:24,600 --> 00:26:27,800
Speaker 10: percent sequentially, which would still be a year over year

570
00:26:27,840 --> 00:26:29,080
Speaker 10: to client of about one percent.

571
00:26:29,680 --> 00:26:32,800
Speaker 4: So Ryan, I know you and other analysts investors look

572
00:26:32,800 --> 00:26:34,560
Speaker 4: at the year on year kind of growth rates and

573
00:26:34,600 --> 00:26:36,280
Speaker 4: that's kind of big. Just give us a sense of

574
00:26:36,320 --> 00:26:40,400
Speaker 4: the absolute numbers, like how many boxes whatever units you use,

575
00:26:41,080 --> 00:26:44,199
Speaker 4: are being used, like today versus twenty nineteen.

576
00:26:45,160 --> 00:26:49,359
Speaker 10: Yeah, so in one of the best terms is in

577
00:26:49,560 --> 00:26:52,280
Speaker 10: billions of square feet. Right, so when you were to

578
00:26:52,359 --> 00:26:53,960
Speaker 10: if you were to take a box and unfold it

579
00:26:54,000 --> 00:26:55,560
Speaker 10: and lay it out flat, you know, you just do

580
00:26:55,800 --> 00:26:56,639
Speaker 10: link them with and all that.

581
00:26:57,280 --> 00:27:00,360
Speaker 4: So comes my job to break them down of course.

582
00:27:00,440 --> 00:27:02,600
Speaker 10: Understood, Well, yeah, you got a flatinum and then put

583
00:27:02,600 --> 00:27:03,360
Speaker 10: them in your recycling.

584
00:27:04,320 --> 00:27:08,359
Speaker 4: Is that just throws them in their recycling? Well, trying

585
00:27:08,400 --> 00:27:09,879
Speaker 4: to think out of the side of the box.

586
00:27:10,040 --> 00:27:13,040
Speaker 2: Oh no, no, no.

587
00:27:12,800 --> 00:27:14,720
Speaker 4: No, right, all right, right, let's get back to it

588
00:27:14,840 --> 00:27:17,440
Speaker 4: just again today's demand versus you know, pre pandemic.

589
00:27:18,160 --> 00:27:21,160
Speaker 10: Yeah. So in twenty nineteen that volume was like three

590
00:27:21,240 --> 00:27:25,000
Speaker 10: hundred and ninety three billion square feet, right, a big,

591
00:27:25,040 --> 00:27:27,760
Speaker 10: big number. During the pandemic, it grew to four hundred

592
00:27:27,800 --> 00:27:30,840
Speaker 10: and sixteen billion square feet. Last year it fell to

593
00:27:31,040 --> 00:27:34,760
Speaker 10: three eighty and this year our projections, our mid year

594
00:27:34,760 --> 00:27:36,840
Speaker 10: outlook says it's going to fall again to about three

595
00:27:37,000 --> 00:27:40,720
Speaker 10: seventy six. Yes, again a slight decline over last year.

596
00:27:41,960 --> 00:27:45,720
Speaker 10: We're just not seeing super strong economic signals and our

597
00:27:45,840 --> 00:27:49,600
Speaker 10: box so our box producers. You know, Green Markets is

598
00:27:49,600 --> 00:27:52,920
Speaker 10: a Bloomberg company. They go out and they survey box

599
00:27:52,960 --> 00:27:56,480
Speaker 10: producers all across the US, and they have a good signal.

600
00:27:56,760 --> 00:27:59,440
Speaker 10: Right The signal right now is that second quarter could

601
00:27:59,480 --> 00:28:01,160
Speaker 10: be up as much, which is three and a half percent,

602
00:28:01,240 --> 00:28:05,080
Speaker 10: which is in line with the statistic statistics we've seen

603
00:28:05,119 --> 00:28:09,800
Speaker 10: over time. However, again, first quarter was down substantially, so

604
00:28:10,280 --> 00:28:12,800
Speaker 10: we think there's some optimism rror and we would we

605
00:28:12,840 --> 00:28:15,760
Speaker 10: would guide down based on that known optimism error.

606
00:28:16,040 --> 00:28:18,800
Speaker 2: Ryan, this is a really dumb question, but I feel

607
00:28:18,840 --> 00:28:21,159
Speaker 2: like the trend is don't ship stuff in a box

608
00:28:21,680 --> 00:28:24,600
Speaker 2: like I can order from different places. That shoes, I

609
00:28:24,640 --> 00:28:26,640
Speaker 2: agree that they're in a shoe box, but you don't

610
00:28:26,640 --> 00:28:29,520
Speaker 2: put them like another box. You just ship that shoe box.

611
00:28:29,920 --> 00:28:31,680
Speaker 2: Is that eating into this market at all?

612
00:28:32,800 --> 00:28:36,440
Speaker 10: It certainly is. And I could talk for an hour

613
00:28:36,440 --> 00:28:39,160
Speaker 10: about this but I won't. But yes, you're exactly right.

614
00:28:40,440 --> 00:28:44,520
Speaker 10: There is a there's a definite move by brands to

615
00:28:44,760 --> 00:28:48,040
Speaker 10: reduce their waste and become more sustainable. So you are

616
00:28:48,080 --> 00:28:51,400
Speaker 10: seeing companies like Amazon ulize paper bags and things like that.

617
00:28:52,400 --> 00:28:54,920
Speaker 4: So talk to us about the green options out there.

618
00:28:54,920 --> 00:28:55,920
Speaker 4: What are the green options?

619
00:28:57,080 --> 00:28:59,200
Speaker 10: Well, it depends on what you mean by green, right,

620
00:28:59,400 --> 00:29:03,080
Speaker 10: So they are well, so there are two main initiatives, right,

621
00:29:03,120 --> 00:29:05,320
Speaker 10: So the first green initiative is to find something that's

622
00:29:05,360 --> 00:29:08,720
Speaker 10: more circular in nature, like a paper bag, because most

623
00:29:08,720 --> 00:29:13,240
Speaker 10: people identify paper as a more sustainable, more circular option. Now,

624
00:29:13,680 --> 00:29:16,400
Speaker 10: if you're looking at the data, that paper may have

625
00:29:16,440 --> 00:29:20,280
Speaker 10: a higher greenhouse gas emission index. So while it's more circular,

626
00:29:20,360 --> 00:29:22,720
Speaker 10: it's going to have it's it's trade offs. If you're

627
00:29:22,720 --> 00:29:25,960
Speaker 10: looking for chasing a greenhouse gas initiative, maybe you're going

628
00:29:25,960 --> 00:29:29,280
Speaker 10: to a plastic something that's lighter in weight. Very broadly speaking,

629
00:29:29,800 --> 00:29:33,080
Speaker 10: greenhouse gas emissions follow weight, So if you're using a

630
00:29:33,200 --> 00:29:35,960
Speaker 10: very light weight paper or plastic bag, it will likely

631
00:29:35,960 --> 00:29:40,080
Speaker 10: have a lower GHG emission, but it's less circular. So

632
00:29:40,160 --> 00:29:41,800
Speaker 10: it really depends on the goal of the company.

633
00:29:42,840 --> 00:29:45,840
Speaker 2: Are people willing to pay more for greener packaging?

634
00:29:45,960 --> 00:29:46,560
Speaker 6: Do we know that?

635
00:29:46,720 --> 00:29:46,800
Speaker 2: Is?

636
00:29:46,800 --> 00:29:48,920
Speaker 10: They say they are, They say they are, but no one,

637
00:29:49,000 --> 00:29:51,000
Speaker 10: no one's really, no one really is. I mean, at

638
00:29:51,000 --> 00:29:54,680
Speaker 10: the end of the day, it's I think it's all

639
00:29:55,440 --> 00:29:57,680
Speaker 10: it's just opinion and it doesn't really mean anything.

640
00:29:58,120 --> 00:30:00,240
Speaker 4: So what did some of the big what are the

641
00:30:00,280 --> 00:30:02,320
Speaker 4: companies that you follow that if if thats just want

642
00:30:02,320 --> 00:30:04,120
Speaker 4: to get exposure to this, where should they look?

643
00:30:05,320 --> 00:30:07,200
Speaker 10: Well, I mean on the on the Bloomberg terminal are

644
00:30:07,320 --> 00:30:12,640
Speaker 10: our packaging dashboard? PACKG is the call numbers there we

645
00:30:12,680 --> 00:30:16,000
Speaker 10: follow International Paper, West Rock Packaging Corporation of America are

646
00:30:16,080 --> 00:30:18,400
Speaker 10: the big three in the US. They represent about a

647
00:30:18,400 --> 00:30:19,920
Speaker 10: forty five billion dollars market cap.

648
00:30:21,880 --> 00:30:24,040
Speaker 2: What represents the best kind of value? Like, how are

649
00:30:24,040 --> 00:30:26,480
Speaker 2: these guys sort of trading right now? What's priced?

650
00:30:26,520 --> 00:30:26,600
Speaker 3: In?

651
00:30:27,640 --> 00:30:31,680
Speaker 10: A great question? That's way above my scope. So I'm

652
00:30:31,720 --> 00:30:34,920
Speaker 10: more of a market analyst and follow the bigger industry trends, supply,

653
00:30:35,080 --> 00:30:38,560
Speaker 10: demand and pricing. I don't really get into the stocks

654
00:30:38,640 --> 00:30:40,080
Speaker 10: themselves or the companies themselves.

655
00:30:40,120 --> 00:30:40,680
Speaker 6: That's fair enough.

656
00:30:40,920 --> 00:30:44,520
Speaker 4: So can I make an inference about inflation out there

657
00:30:44,560 --> 00:30:47,479
Speaker 4: by looking at volumes of packages being shipped and all

658
00:30:47,480 --> 00:30:48,320
Speaker 4: that kind of stuff?

659
00:30:50,400 --> 00:30:51,000
Speaker 10: Absolutely?

660
00:30:51,440 --> 00:30:51,920
Speaker 6: What is it?

661
00:30:51,960 --> 00:30:52,560
Speaker 4: So? How does that work?

662
00:30:52,640 --> 00:30:52,960
Speaker 1: Yeah?

663
00:30:53,000 --> 00:30:57,960
Speaker 10: Well, well so there is there's some discrepancy. So right now,

664
00:30:58,000 --> 00:30:59,320
Speaker 10: if you were to look at like some of the

665
00:30:59,320 --> 00:31:03,360
Speaker 10: federal data, a real PCE for goods, for instance, that

666
00:31:03,440 --> 00:31:06,280
Speaker 10: number is still very very high relative to the fact

667
00:31:06,320 --> 00:31:09,960
Speaker 10: that consumer goods are not trending very well. I mean,

668
00:31:10,240 --> 00:31:14,400
Speaker 10: if a very high number ninety percent plus of consumer

669
00:31:14,400 --> 00:31:16,920
Speaker 10: goods ride in a corgated box and that volume is

670
00:31:16,920 --> 00:31:19,920
Speaker 10: down ten percent essentially over the last two years, how

671
00:31:19,960 --> 00:31:23,240
Speaker 10: in the world is our goods spending still up? If

672
00:31:24,120 --> 00:31:26,720
Speaker 10: if it's a real number and it's an adjusted for inflation,

673
00:31:27,200 --> 00:31:30,080
Speaker 10: there's there's a disconnect there, and I have some speculation

674
00:31:30,160 --> 00:31:32,520
Speaker 10: on why that is. I think, you know, historically speaking,

675
00:31:32,560 --> 00:31:35,600
Speaker 10: when you think of things like software. Thirty years ago,

676
00:31:35,720 --> 00:31:38,000
Speaker 10: software came, it was an actual thing, it was a

677
00:31:38,040 --> 00:31:41,680
Speaker 10: disc that may still be one of the items. It's

678
00:31:41,720 --> 00:31:45,040
Speaker 10: accounted as a good, but it's no longer a good.

679
00:31:45,080 --> 00:31:47,600
Speaker 10: It's it's a downloadable thing, it's a software as a service,

680
00:31:47,640 --> 00:31:51,240
Speaker 10: it's a something else. And so there's probably some noise

681
00:31:51,280 --> 00:31:53,120
Speaker 10: in there that's not allowing us to see some of

682
00:31:53,120 --> 00:31:53,680
Speaker 10: this clearly.

683
00:31:54,040 --> 00:31:54,920
Speaker 6: That's really interesting.

684
00:31:55,360 --> 00:31:57,240
Speaker 2: Thanks a lot, Ryan, this was great. We both really

685
00:31:57,280 --> 00:32:00,320
Speaker 2: loved this segment. A Ryan Fox, Boomberg Intelligence Analyst, covers

686
00:32:00,360 --> 00:32:04,240
Speaker 2: paper packaging trends, sort of diving in to all of that.

687
00:32:06,400 --> 00:32:10,280
Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live

688
00:32:10,360 --> 00:32:13,880
Speaker 1: weekdays at ten am Eastern on applecar Play and Android

689
00:32:13,920 --> 00:32:16,680
Speaker 1: Auto with the Bloomberg Business App. You can also listen

690
00:32:16,800 --> 00:32:19,880
Speaker 1: live on Amazon Alexa from our flagship New York station.

691
00:32:20,280 --> 00:32:23,040
Speaker 1: Just say Alexa play Bloomberg eleven thirty.

692
00:32:24,880 --> 00:32:28,080
Speaker 2: I'm Alex the alongside Paul Sweeney. This is Bloomberg Intelligence Radio.

693
00:32:28,280 --> 00:32:30,440
Speaker 2: We cover all the top news and economics and finance

694
00:32:30,480 --> 00:32:33,120
Speaker 2: through a lens of our Bloomberg Intelligence analysts. They cover

695
00:32:33,640 --> 00:32:37,400
Speaker 2: two thousand companies one hundred and thirty industries worldwide, and

696
00:32:37,400 --> 00:32:40,080
Speaker 2: there's about four hundred of them and a large chunk

697
00:32:40,120 --> 00:32:43,480
Speaker 2: of them actually cover ESG. So let's rewind to about

698
00:32:43,480 --> 00:32:47,120
Speaker 2: four years ago, middle of COVID. Everyone was talking about ESG.

699
00:32:47,440 --> 00:32:50,000
Speaker 2: Blackrock was talking about allocating a bunch to ESG.

700
00:32:50,440 --> 00:32:53,520
Speaker 6: This was where all the money was supposed to be flowing.

701
00:32:53,920 --> 00:32:57,280
Speaker 2: A new report from Sin Contractor She's Bloomberg Intelligence senior

702
00:32:57,480 --> 00:33:02,680
Speaker 2: ESG strategists says that liquidation of US ESG ETFs reached

703
00:33:02,720 --> 00:33:05,880
Speaker 2: almost thirty percent of all closings so far this year

704
00:33:05,920 --> 00:33:10,280
Speaker 2: and could continue amid some slowing our flows, particularly if

705
00:33:10,320 --> 00:33:14,560
Speaker 2: the backlash we've now seen in the US on ESG continues. Geen,

706
00:33:14,680 --> 00:33:17,040
Speaker 2: great to see you, thank you for joining in studio.

707
00:33:17,680 --> 00:33:19,800
Speaker 2: So I guess let's take a step back for a second,

708
00:33:19,840 --> 00:33:23,800
Speaker 2: and ESG it's environmental, social and governance. Right, where did

709
00:33:23,840 --> 00:33:26,560
Speaker 2: that go so wrong? Like how do we get from

710
00:33:26,640 --> 00:33:28,240
Speaker 2: twenty twenty to here?

711
00:33:29,200 --> 00:33:31,560
Speaker 11: So Alex, just to you know, paint a few numbers.

712
00:33:32,440 --> 00:33:35,520
Speaker 11: In the first quarter, we saw about four billion in outflows.

713
00:33:35,560 --> 00:33:38,280
Speaker 11: I mean, compare this to twenty twenty one, which is

714
00:33:38,320 --> 00:33:42,400
Speaker 11: about I want to say, close to fifty billion in influats. Now,

715
00:33:43,600 --> 00:33:45,880
Speaker 11: I wouldn't call it where we went wrong. I think

716
00:33:45,920 --> 00:33:49,400
Speaker 11: what we're seeing today is slowing flows because you know,

717
00:33:49,440 --> 00:33:52,040
Speaker 11: the market is maturing. But at the same time, these

718
00:33:52,120 --> 00:33:56,280
Speaker 11: outflows that we're seeing, they're very concentrated to a few funds,

719
00:33:56,280 --> 00:33:59,360
Speaker 11: so actually no il repurpose. Where we went wrong is

720
00:33:59,400 --> 00:34:02,880
Speaker 11: that that rise in assets was very concentrated to a

721
00:34:02,920 --> 00:34:06,200
Speaker 11: few funds and a few investors. So it's very noticeable

722
00:34:06,200 --> 00:34:08,320
Speaker 11: when that comes out, and that's what we're seeing.

723
00:34:09,320 --> 00:34:12,279
Speaker 4: So who are those investors or those investor types and

724
00:34:12,840 --> 00:34:14,000
Speaker 4: why are they pulling the money out?

725
00:34:14,320 --> 00:34:19,200
Speaker 11: So one example is ESGU. It's the icehes ESG aware

726
00:34:19,200 --> 00:34:22,640
Speaker 11: it's probably the largest ESG ETF in the world. Last

727
00:34:22,719 --> 00:34:26,400
Speaker 11: year it's saw about nine billion in outflows, and we

728
00:34:26,480 --> 00:34:29,359
Speaker 11: attribute this to model portfolio changes, So you know, that

729
00:34:29,440 --> 00:34:32,160
Speaker 11: could be allocative or it could be due to ESG.

730
00:34:32,280 --> 00:34:36,440
Speaker 11: For example, you know, an investor doesn't want equity anyone,

731
00:34:36,520 --> 00:34:38,759
Speaker 11: they just want cash. They just you know, sell out

732
00:34:38,760 --> 00:34:42,000
Speaker 11: of equity. So whether it's ESG, whether it's not, to

733
00:34:42,040 --> 00:34:44,640
Speaker 11: be honest, we don't know. A lot of these outflows

734
00:34:44,640 --> 00:34:48,000
Speaker 11: are also from European investors. I wan't attribute at that

735
00:34:48,120 --> 00:34:49,440
Speaker 11: to the backlash.

736
00:34:49,080 --> 00:34:51,600
Speaker 2: Meaning that the backlash has been more in the US

737
00:34:51,680 --> 00:34:54,680
Speaker 2: versus say, in Europe. Correct, you do we need to

738
00:34:54,800 --> 00:34:59,240
Speaker 2: separate the the S and the G, separate the social

739
00:34:59,239 --> 00:35:01,600
Speaker 2: from the government's from the environmental or at least separate

740
00:35:01,640 --> 00:35:02,480
Speaker 2: the environmental part.

741
00:35:02,960 --> 00:35:05,760
Speaker 11: I would say no, I mean, I know that's an argument,

742
00:35:05,840 --> 00:35:09,200
Speaker 11: but I still think that ESG is about material rising

743
00:35:09,280 --> 00:35:12,080
Speaker 11: opportunities to a particular company. For some it's E, for

744
00:35:12,160 --> 00:35:14,520
Speaker 11: some it's S. So it's going to be very hard

745
00:35:14,520 --> 00:35:15,880
Speaker 11: to talk about this separately.

746
00:35:16,520 --> 00:35:20,439
Speaker 4: To what extent is the environment for flows in ESG

747
00:35:20,640 --> 00:35:23,600
Speaker 4: in the US versus Europe or US versus the rest world?

748
00:35:24,000 --> 00:35:24,960
Speaker 4: How are they different?

749
00:35:25,520 --> 00:35:28,840
Speaker 11: Very different? Okay, so again the US four billion and

750
00:35:28,880 --> 00:35:32,440
Speaker 11: outflows the first quarter, you're a total flows for about

751
00:35:32,440 --> 00:35:34,239
Speaker 11: six billion in in flows so the rest of it

752
00:35:34,360 --> 00:35:39,800
Speaker 11: was almost entirely Europe. So Europe continues to sort of it.

753
00:35:40,120 --> 00:35:42,800
Speaker 4: Like everything in this country, it's been politicized.

754
00:35:43,040 --> 00:35:47,640
Speaker 11: Yes, okay, well the slowing influence is the politics. But

755
00:35:47,680 --> 00:35:51,280
Speaker 11: the outflows are this concentration. Sorry, Alex, but I was gonna.

756
00:35:51,040 --> 00:35:52,960
Speaker 2: Say, but but but also isn't it just that for

757
00:35:53,000 --> 00:35:56,960
Speaker 2: a while, ESG tracked tech because just on a pure

758
00:35:57,080 --> 00:35:59,319
Speaker 2: e level, and in S and G.

759
00:35:59,400 --> 00:36:00,960
Speaker 6: They just have employees.

760
00:36:01,320 --> 00:36:04,799
Speaker 2: They until the data center situation came up, they were

761
00:36:04,840 --> 00:36:08,279
Speaker 2: just emitting less carbon because they're not industrial companies. They're

762
00:36:08,280 --> 00:36:12,560
Speaker 2: not cyclical companies in that way. Well, now what so.

763
00:36:13,200 --> 00:36:15,279
Speaker 11: Alex, if you're asking, you know, is this shriven by

764
00:36:15,320 --> 00:36:18,680
Speaker 11: performance because es she tends to a certain way, I

765
00:36:18,719 --> 00:36:21,440
Speaker 11: would say I don't think so. I mean, twenty twenty

766
00:36:21,480 --> 00:36:24,640
Speaker 11: two was a bad year. Yes, but last year ESG

767
00:36:24,760 --> 00:36:28,120
Speaker 11: ETFs were about performing with the market about fifty percent

768
00:36:28,200 --> 00:36:31,759
Speaker 11: beat the market, which is average. But we still saw

769
00:36:31,840 --> 00:36:35,600
Speaker 11: so many outflows. It's that concentration from a few funds,

770
00:36:35,600 --> 00:36:39,240
Speaker 11: not this you know, mass exodus because of a certain factor.

771
00:36:39,880 --> 00:36:40,279
Speaker 5: Are there?

772
00:36:40,360 --> 00:36:42,279
Speaker 4: Do we know where we are in that fun and

773
00:36:42,320 --> 00:36:44,440
Speaker 4: flow situation? The folks that have pulled it out. Are

774
00:36:44,440 --> 00:36:47,680
Speaker 4: we near or at or nowhere near that that the

775
00:36:47,719 --> 00:36:48,840
Speaker 4: point of that being over? Do we know?

776
00:36:49,320 --> 00:36:51,480
Speaker 11: We don't know. I call it a bit of a pause.

777
00:36:51,600 --> 00:36:55,279
Speaker 11: I mean, unless all this concentration risk unravels, I don't

778
00:36:55,280 --> 00:36:57,040
Speaker 11: know when we're going to stop seeing outflows at the

779
00:36:57,040 --> 00:36:59,040
Speaker 11: same time we're not really seeing in flows.

780
00:36:59,600 --> 00:37:02,080
Speaker 2: So I guess how many products are out there now?

781
00:37:02,880 --> 00:37:04,239
Speaker 6: And how many products should there be?

782
00:37:05,800 --> 00:37:09,280
Speaker 11: I right say roughly. I would say globally it's definitely

783
00:37:09,360 --> 00:37:12,560
Speaker 11: north of a thousand. In the US, it's probably not

784
00:37:12,760 --> 00:37:15,200
Speaker 11: of five hundred, again, very roughly.

785
00:37:14,960 --> 00:37:16,720
Speaker 4: And mostly these are ETFs.

786
00:37:17,000 --> 00:37:19,920
Speaker 11: These are these are ETFs. Su yes, only ETFs, and

787
00:37:19,960 --> 00:37:24,560
Speaker 11: they spun across multiple things right esg. Gender, climate, clean energy,

788
00:37:24,800 --> 00:37:28,279
Speaker 11: all these different themes. How many should there be? I

789
00:37:28,600 --> 00:37:30,439
Speaker 11: don't know. And I would say, Alex, you know that's

790
00:37:31,000 --> 00:37:33,239
Speaker 11: a little bit of a reason for these liquidations. There

791
00:37:33,320 --> 00:37:36,840
Speaker 11: was such an oversupply in twenty twenty and twenty twenty

792
00:37:36,880 --> 00:37:41,600
Speaker 11: one that these uncompetitive strategies are shutting down. And that's normal.

793
00:37:41,760 --> 00:37:46,239
Speaker 4: So if I will dan Off, portfolio manager of Fidelity Contrafund,

794
00:37:46,920 --> 00:37:49,280
Speaker 4: who's done a great job for me, thank you, will

795
00:37:50,880 --> 00:37:54,680
Speaker 4: do those funds where to what extent are they factoring

796
00:37:54,719 --> 00:37:58,440
Speaker 4: in ESG analysis into their investment criteria. Is it different

797
00:37:58,440 --> 00:38:00,000
Speaker 4: today than it was two or three years ago?

798
00:38:01,040 --> 00:38:03,719
Speaker 11: It depends. I mean, I think a lot of the

799
00:38:03,760 --> 00:38:06,200
Speaker 11: conversations are shifting a little bit. You have you know,

800
00:38:06,360 --> 00:38:09,239
Speaker 11: dedicated ESG funds. Climate is in the name, es she

801
00:38:09,360 --> 00:38:11,360
Speaker 11: is in the name, But then you have these broad

802
00:38:11,800 --> 00:38:15,520
Speaker 11: esc integrated funds, and those are always you know, considering

803
00:38:15,640 --> 00:38:19,200
Speaker 11: risks and opportunities. But I think it's changed as data availability.

804
00:38:19,280 --> 00:38:21,760
Speaker 11: You have just a lot more data around climate change,

805
00:38:21,760 --> 00:38:25,040
Speaker 11: around physical risks, So people are asking these more of

806
00:38:25,120 --> 00:38:27,799
Speaker 11: all questions around, you know, things that I wouldn't have

807
00:38:27,880 --> 00:38:30,000
Speaker 11: asked probably either a few years back.

808
00:38:30,920 --> 00:38:34,080
Speaker 2: I guess my question always is until you can financially

809
00:38:34,120 --> 00:38:38,239
Speaker 2: measure the benefit of a strategy that is focused on ESG,

810
00:38:39,280 --> 00:38:41,040
Speaker 2: it's going to be very difficult and then make a

811
00:38:41,080 --> 00:38:44,000
Speaker 2: case for an investor or CEO of why these strategies matter.

812
00:38:44,440 --> 00:38:46,960
Speaker 6: Have we been able to do that yet?

813
00:38:47,320 --> 00:38:52,120
Speaker 11: So my research is starting to answer that. So I'm

814
00:38:52,120 --> 00:38:55,680
Speaker 11: starting to do industrial level analysis into what impacts risk

815
00:38:55,760 --> 00:39:00,680
Speaker 11: and returns. For example, in materials, in governance, we find

816
00:39:00,840 --> 00:39:03,680
Speaker 11: lower drawdowns to lower risk. We still do you know,

817
00:39:03,760 --> 00:39:07,560
Speaker 11: explore that and expand that, but I will stay within

818
00:39:07,680 --> 00:39:10,440
Speaker 11: the E and S. It's particularly hard because of the

819
00:39:10,440 --> 00:39:13,839
Speaker 11: limited data historically, right, yeah, all.

820
00:39:13,840 --> 00:39:16,160
Speaker 2: Right, Sheen, thanks a lot. She hain't contractor Bloomberg Intelligence

821
00:39:16,239 --> 00:39:19,160
Speaker 2: and your ESG a strategist. I think, at least for

822
00:39:19,320 --> 00:39:22,200
Speaker 2: my work in energy transition space, there's no carbon price.

823
00:39:22,400 --> 00:39:24,399
Speaker 2: So you can't do that on the environmental side because

824
00:39:24,440 --> 00:39:26,160
Speaker 2: you can't say, look, you admit.

825
00:39:26,040 --> 00:39:27,160
Speaker 6: This, it costs this.

826
00:39:27,400 --> 00:39:30,319
Speaker 2: There's nothing concrete that. Then a CEO can go to

827
00:39:30,360 --> 00:39:31,919
Speaker 2: the board or go to shareholders and be.

828
00:39:31,800 --> 00:39:33,319
Speaker 6: Like, look, guys, this is why I'm doing this.

829
00:39:33,880 --> 00:39:35,439
Speaker 4: Ah, it is a big problem.

830
00:39:35,480 --> 00:39:36,080
Speaker 6: It's a big problem.

831
00:39:36,160 --> 00:39:37,920
Speaker 2: Yes, and then how to do that and you have

832
00:39:37,960 --> 00:39:39,879
Speaker 2: to have everyone agree on it or else you're doing

833
00:39:39,880 --> 00:39:42,680
Speaker 2: a cap and trade situation, which is very confusing. You're

834
00:39:42,719 --> 00:39:44,799
Speaker 2: just gonna wind up importing something from say China or

835
00:39:44,840 --> 00:39:46,879
Speaker 2: India where it has higher emissions. And then why would

836
00:39:46,880 --> 00:39:49,840
Speaker 2: you pay more for something that's internal in eternal, I

837
00:39:49,840 --> 00:39:52,640
Speaker 2: mean in country. I think that that's definitely a big problem.

838
00:39:53,000 --> 00:39:55,360
Speaker 2: And also, how do you measure success of say diversity

839
00:39:55,360 --> 00:39:58,240
Speaker 2: and inclusion. Is it a straight up numbers on your board?

840
00:39:58,239 --> 00:40:00,640
Speaker 2: But does that actually materially make a difference unless you're

841
00:40:00,640 --> 00:40:01,640
Speaker 2: open to their ideas.

842
00:40:01,760 --> 00:40:03,560
Speaker 4: Yeah, I can talk well. In data. You can go

843
00:40:03,600 --> 00:40:07,000
Speaker 4: to the FA screen for any security and there's an

844
00:40:07,200 --> 00:40:09,959
Speaker 4: ESG tab In addition to income statement, balance sheet, cash

845
00:40:09,960 --> 00:40:12,600
Speaker 4: flow statement, there's a separate tap for ESG and that's

846
00:40:12,600 --> 00:40:14,840
Speaker 4: for Shaheen and a lot of other folks at Bloomberg's

847
00:40:14,840 --> 00:40:17,520
Speaker 4: Global Data try to aggregate a lot of this data

848
00:40:17,560 --> 00:40:20,240
Speaker 4: as we lets help out our clients for ESG analysis.

849
00:40:20,320 --> 00:40:24,320
Speaker 1: I love that This is the Bloomberg Intelligence podcast, available

850
00:40:24,320 --> 00:40:27,759
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851
00:40:27,920 --> 00:40:30,960
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852
00:40:31,040 --> 00:40:34,560
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853
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Speaker 1: Bloomberg Business app. You can also watch us live every

854
00:40:37,840 --> 00:40:41,000
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