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Speaker 1: Paul, we have Welcome to the Bloomberg Markets Podcast. I'm

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Speaker 1: Paul Sweene. It alongside my co host Matt Miller.

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

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Speaker 2: and Bloomberg experts, along with the essential market news right

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Speaker 2: to talk about Bloomberg Markets.

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Speaker 1: Podcast podcasts or wherever you listen to podcasts and at

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Speaker 1: Bloomberg dot com slash podcast.

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Speaker 3: And then Dan ives over at web Bush, who, as

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Speaker 3: we know, is definitely someone who's a well known tech bull.

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Speaker 3: And you actually released Wait.

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Speaker 4: You have to do a fashion update before you do anything.

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Speaker 1: We have to take a look at this disturb and

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Speaker 1: Dan streaming on YouTube, so you can't hide the shirt.

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Speaker 1: It's out there.

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Speaker 5: Okay, I love it.

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Speaker 3: It's great. So you were talking to your clients about

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Speaker 3: how you still see tex stocks againing about twelve to

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Speaker 3: fifteen percent in the second half of the year even

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Speaker 3: after this gang buster riley that we've seen in gross stocks,

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Speaker 3: obviously led by Ai. Tell us about your case when

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Speaker 3: it comes to continued strength there in the second half.

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Speaker 6: But look, we believe it's a new tech bowl market

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Speaker 6: that's forming and ultimately something that's covered tech. Going back

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Speaker 6: to nineties. This I view as a nineteen ninety five

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Speaker 6: Internet movement, in other words, not nineteen ninety nine dot

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Speaker 6: com bubble burst. You know, I believe based on everything

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Speaker 6: we've done, this is the biggest transformational tech trend that

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Speaker 6: we've seen. And we were talking about a trillion dollars

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Speaker 6: of incremental spend that's now coming into the tech ecosystem,

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Speaker 6: which is why, in my opinion, this is a green

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Speaker 6: light to own tech. I think it's gonna get broad

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Speaker 6: and broader in terms of the rally. And many of

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Speaker 6: the bears that continue to say this is hype, they're

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Speaker 6: not talking to the CIOs, the CTOs, the it spent

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Speaker 6: where well, I think it's something unlike I've ever seen.

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Speaker 1: And that's kind of where I wanted to go down

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Speaker 1: because you've got this perspective, long time perspective in the

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Speaker 1: tech space. In your words, when you're talking to your

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Speaker 1: institutional investor clients, and I know you've been on the

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Speaker 1: road a lot, what how do you define AI Artificial intelligence?

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Speaker 1: Because I hear it thrown about by every company that

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Speaker 1: walks through that door, whether they're selling pet food or

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Speaker 1: whether they're a tech company. It's everywhere.

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Speaker 6: It seems like, well, ultimately, it's really like when you

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Speaker 6: think about the shift to cloud and what we've seen

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Speaker 6: with data processing power, it was all about you have data,

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Speaker 6: whether it's coming in consumer tech, but how do you

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Speaker 6: ultimately monetize how do you ultimately mind that data? When

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Speaker 6: you look at the models coming out, of course it's Nadella,

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Speaker 6: Microsoft Chat, GPT really lead in Nvidia in terms of

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Speaker 6: the foundation, this is the start of what I'll cause

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Speaker 6: an AI revolution. The use cases now that we're seeing,

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Speaker 6: Let's give example, developers, they potentially now could do things

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Speaker 6: in an hour that.

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Speaker 5: Took them three weeks.

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Speaker 6: Advertising could Ultimately there's there's use cases things that could

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Speaker 6: take a month now could take basically two hours. And

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Speaker 6: the and who benefits it's the software, it's the chips,

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Speaker 6: it's the echo sit That's why I believe this this

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Speaker 6: is a gold rush that really is going to define,

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Speaker 6: in my opinion, a new bowl market for tech that's

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Speaker 6: just starting.

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Speaker 3: When you were talking about how you're having these conversations

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Speaker 3: with these industry players, what specifically are they telling you

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Speaker 3: and where are those pockets of strength when it comes

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Speaker 3: to say enterprise spending.

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Speaker 5: Yeah, and I think part is we go back.

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Speaker 6: We sat here in January right hard Landing, dark clouds,

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Speaker 6: Armaguet and the whole. So obviously none of that's happened.

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Speaker 6: But now the differences at enterprise from a CIO perspective,

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Speaker 6: they've gone from yellow light to now potentially green light.

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Speaker 6: And you're starting to see ultimately they're looking, they're looking

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Speaker 6: over the road and they're not seeing cars company and

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Speaker 6: it's ai that today is less than one percent it spend.

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Speaker 6: We think next year that's potentially eight to ten percent.

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Speaker 4: Wow.

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Speaker 5: And that's that's.

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Speaker 6: Ultimately look in my view, and it's totally in PA.

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Speaker 6: I've talked about it for decades. Is that the transformational

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Speaker 6: tech themes if you put them in and you talk

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Speaker 6: about you know, evaluation, downgrade today and meta, you put

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Speaker 6: them in a little pe box on some historical doing

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Speaker 6: your spreadsheet valuations from your office building. I get it

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Speaker 6: to me, you can't put them in that box. That's

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Speaker 6: why ultimately you've missed the Apples, the Tesla's, the Netflix,

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Speaker 6: the metas and everything else if you don't understand the

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Speaker 6: transformational growth theme. And that's why right now I think

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Speaker 6: it's the fourth Industrial Revolution that's planning out.

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Speaker 1: Now what we've seen, and I guess it feels to me,

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Speaker 1: Dan that we are in the very top of the

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Speaker 1: first inning here, and what we're saying, what we're hearing

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Speaker 1: from people like you and from others in the tech space,

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Speaker 1: is just buy some of the really good tech names

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Speaker 1: that receive the tech spend anyway. They're going to be

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Speaker 1: on the forefront when and that's a Microsoft for example,

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Speaker 1: and some others, and in the chip companies, when do

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Speaker 1: you think there will be that Facebook moment, that Google

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Speaker 1: moment where we're a company born of AI. Like Google

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Speaker 1: was for search, Facebook was for social, When do you

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Speaker 1: think we'll have that first big AI born company and

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Speaker 1: where it come from?

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Speaker 6: Well, I think also a lot of those AI born

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Speaker 6: companies potentially might get acquired before they're actually born.

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Speaker 5: And I think that's why.

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Speaker 6: You can see massive consolidation across tech right. A lot

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Speaker 6: of these tech companies have more cash in some countries.

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Speaker 6: But then I think if you look at it by

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Speaker 6: the d I think over the next few quarters, we're

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Speaker 6: gonna have what I'll call that Apple two thousand and

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Speaker 6: seven iPhone moment, that Internet nineteen ninety five moment. We

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Speaker 6: saw it with the guidance heard around the world from

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Speaker 6: Jensen and Video the four billion dollar guidance raise. But

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Speaker 6: I think that's what we're starting to see right now.

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Speaker 6: Even when we come to earnings over the next month.

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Speaker 6: Of course, investors are going to focus on what numbers

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Speaker 6: look like guidance next quarter or two, But there's a

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Speaker 6: much bigger story playing out here, and that's why I

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Speaker 6: think a lot of these technames continue to be Rock

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Speaker 6: or Gibraltar Lake.

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Speaker 3: I'm glad you brought up Apple because there's a lot

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Speaker 3: of companies like that where during the Internet boom you

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Speaker 3: might not have realized certain companies to that would have

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Speaker 3: benefited years later. Where do you think are the winners

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Speaker 3: that might be getting overlooked and where do you think potent?

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Speaker 3: Are the losers right now that are just behind the game?

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Speaker 6: Sure, and I think when you look at Apple, cook

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Speaker 6: continues to play chess where others play checkers, and I

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Speaker 6: think it's an installed based play, best install based in

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Speaker 6: the world. From a consumer perspective, iPhone fifteen, I believe

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Speaker 6: it's going to be a mini super cycle AI. This

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Speaker 6: is just the start of what's going to be that

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Speaker 6: drum roll in terms of what I've us as an

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Speaker 6: AI App Store over the next twelve to eighteen months.

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Speaker 6: I think that's being overlooked as an AI play. I

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Speaker 6: think salesforce being overlooked from an AI perspective. I do

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Speaker 6: think you have some pure play AI names, even like

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Speaker 6: you look at names like palan Tier and others that

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Speaker 6: are really from a use case perspective, and I think

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Speaker 6: it's really the software echoesis. I think it's the software

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Speaker 6: names right now that I really put an asterisk around,

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Speaker 6: because that's going to get the predominant amount of Aspen.

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Speaker 3: What about Adobe, because I feel like that was a

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Speaker 3: player maybe people weren't focusing enough on and then all

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Speaker 3: of a sudden we had that strong report when it

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Speaker 3: comes to that AI potential there well.

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Speaker 6: No, I think you saw from Adobe, you song from

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Speaker 6: Mango dB, and I think those are the barometers that

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Speaker 6: were now starting to see Adobe that's been just a

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Speaker 6: phenomenal job that they've been able to mine into the

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Speaker 6: install base. And I think that's just the tip of

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Speaker 6: the iceberg of what I believe we're going to see

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Speaker 6: over the next month. Look, we can maybe start to

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Speaker 6: do some sort of you know, clock to see who

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Speaker 6: could actually say AI the most in their conference rights,

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Speaker 6: and that's why Krueger and to that maybe because some

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Speaker 6: get above one hundred.

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Speaker 5: But that's why the big question is going to.

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Speaker 6: Be who are the fakes versus the real ones?

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Speaker 3: So what do you think are the risks to your

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Speaker 3: bowl case?

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Speaker 6: So the risks clearly macro things take longer. You've had

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Speaker 6: multiple expansion for six months. Investors are patient, but then

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Speaker 6: all of a sudden if it doesn't come to fruition,

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Speaker 6: you know, and then I think the geopolitical situation US

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Speaker 6: China continues to sort of be that you know, when

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Speaker 6: dark cloud. But this is going to be patience. And

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Speaker 6: that's why I view it as these are moments that

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Speaker 6: happen every twenty thirty years, and right now we just

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Speaker 6: happen to be living through what I view as a

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Speaker 6: nineteen ninety five Internet movement.

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Speaker 1: All right. We mentioned Google earlier and to Just's point Losers.

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Speaker 1: Initially when this kind of hit my consciousness six months ago,

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Speaker 1: AI I said, oh, that's gonna be a real competitor,

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Speaker 1: maybe the first real competitor to Google. And you think

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Speaker 1: about just search in general, how do you think about

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Speaker 1: Google and its position visa.

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Speaker 6: Clearly at first black Eye in terms of coming out.

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Speaker 6: They missed in Microsoft, you know, beat them to the game.

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Speaker 6: I think for Google the golden goose, it's actually not Search.

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Speaker 6: I think it's really the cloud. I mean that is

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Speaker 6: what from an enterprise perspective, they could potentially redefine themselves

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Speaker 6: on AI. And this is also it's not a zero

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Speaker 6: some game. It's not just one player. We're talking about

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Speaker 6: We view conservatively eight hundred billion potentially in the trillions

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Speaker 6: of now incremental it spent.

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Speaker 5: We're six months was not here yep.

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Speaker 1: Just it's extraordinary and you've got to think, you know,

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Speaker 1: a winner like Google with the engineering chops, not to

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Speaker 1: mention the balance sheet, we'll be a player there. Hey, Dan,

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Speaker 1: thanks so much for joining us. I appreciate you coming

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Speaker 1: in studio. Despite the shirt, despite the shirt, it's good stuff.

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Speaker 1: Dan ives. He's a senior analyst at wood Bush Securities,

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Speaker 1: And what we love about Dan is a he's very

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Speaker 1: clear and his his points and his convictions. Definitely, we've

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Speaker 1: got the perspective to say, hey, let's put this into

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Speaker 1: context of what we've seen in past cycles.

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Speaker 3: Hey, he's been doing this a long time.

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Speaker 1: He's been doing it a long time, and it's certainly

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Speaker 1: helpful to us newbies trying to get our heads around

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Speaker 1: what is a I looking at this market here, you know,

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Speaker 1: kind of hanging in here. We're about one thirty one

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Speaker 1: percent on the S and P five hundred getting in

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Speaker 1: a nastac up about a half a percent. So tech is,

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Speaker 1: as Dan has been saying for a long time, continues

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Speaker 1: to perform.

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Speaker 4: You're listening to the team Ken's are Live program Bloomberg

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Speaker 4: Markets weekdays at ten am Eastern on Bloomberg dot com,

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Speaker 4: the iHeartRadio app and the Lower Business at or listen

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Speaker 4: on demand wherever you get your podcasts.

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Speaker 1: You know, one of the numbers that always sticks out

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Speaker 1: of me is when we look at the Jolts number,

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Speaker 1: you know, the job opening Yes, that's the number that

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Speaker 1: historically ran five six million job openings, but it's been.

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Speaker 3: Close to ten right consistently.

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Speaker 1: You know, I'm like, where did all these people go

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Speaker 1: during the pandemic. And one issue that comes up is

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Speaker 1: there's some ages, maybe some people are kind of aging

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Speaker 1: out of the workforce. But I want to get a

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Speaker 1: sense of kind of you know, what ageism means in

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Speaker 1: the labor market and for the economy. And our next

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Speaker 1: guest did a lot of work on that. Mikhaela Grimm,

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Speaker 1: senior economists for Alliance, joins us via Zoom. So, MICHAELA,

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Speaker 1: talk to us about kind of what your work showed

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Speaker 1: as it relates to agism in the workforce. Are people

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Speaker 1: just simply aging out of the workforce.

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Speaker 7: What we saw, especially when we looked at the pension systems,

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Speaker 7: where we see the need that people should work longer

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Speaker 7: or should stay longer in the work in the working

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Speaker 7: age population. To cussion the demographic effect on the pension

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Speaker 7: systems but on the labor markets as well, is that

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Speaker 7: when politicians propose the increase of retirement age, it's often

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Speaker 7: answered with protests like we saw in France, because people

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Speaker 7: are afraid that they don't find the job in higher

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Speaker 7: ages and that any increase in retirement age just means

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Speaker 7: longer unemployment before retirement and in the end a kind

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Speaker 7: of a hidden pension cut.

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Speaker 8: And these fears.

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Speaker 7: Are not out of the blue, because we see that

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Speaker 7: in higher ages the chances to be unemployed for longer

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Speaker 7: are higher.

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Speaker 8: The long term unemployment.

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Speaker 7: Rates of the age group fifty five plus are much

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Speaker 7: higher than in the average workforce population between twenty five

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Speaker 7: and fifty four. For example, and if older workers apply

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Speaker 7: for a new job, they are often facing stereotypes about

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Speaker 7: older workers. And given these environment, many people who get

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Speaker 7: unemployed after the age of fifty five either drop out

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Speaker 7: of the labor force or decide to go into a retirement.

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Speaker 3: How does this differentiate when you're looking on particular regions

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Speaker 3: in countries, say the US versus Europe.

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Speaker 7: In the US, we see a lower rate of longer

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Speaker 7: unemployment in higher ages, and it's already a little bit

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Speaker 7: higher the activity.

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Speaker 8: Ratio than in the U, for example.

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Speaker 7: And we see also that in countries where the retirement

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Speaker 7: ages are already higher, like in speed for example, on Singapore,

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Speaker 7: you see much higher activity ratio is among the older

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Speaker 7: workforce population.

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Speaker 1: So, MICHAELA, you know, I guess I was surprised when

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Speaker 1: I saw the you know, the incredible uprising public uprising

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Speaker 1: in France when they were talking about raising the retirement age.

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Speaker 1: I think it was from sixty two to sixty four.

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Speaker 1: It didn't seem like that big a deal to me.

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Speaker 1: Maybe that's just the US bias. Did that surprise you

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Speaker 1: or is that consistent with some of the some of

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Speaker 1: the data you've seen.

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Speaker 7: Well, from the open point of view, we were surprised

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Speaker 7: as well because our government agreed to increase the retirement

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Speaker 7: age to sixty seven already. But we see also that

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Speaker 7: in France the unemployment ratio of all the workers is

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Speaker 7: higher than in Germany, for example, and therefore that was

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Speaker 7: the concern, for example, that if the government pushes the

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Speaker 7: retirement age sixty four, it just would mean a higher

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Speaker 7: rate of our longer term of unemployment before retiring. In

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Speaker 7: the end, we saw that in surveys that this was

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Speaker 7: one big concern of the working edge population.

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Speaker 3: When we're looking at the US specifically, what's been the

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Speaker 3: main catalyst that's keeping older workers out of the workforce

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Speaker 3: longer and how much did the pandemic end up exacerbating this.

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Speaker 7: We we saw in service as well that many older

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Speaker 7: workers decided to retire early. But we also seen in

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Speaker 7: in in service of from r P that they face

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Speaker 7: age discrimination. That there's example, for example, a wish to

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Speaker 7: participate more in training measures, that there is a wish

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Speaker 7: to have more flexible working hours, to be able to

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Speaker 7: choose word to work.

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Speaker 8: To have more chance to work from home, to work remotely.

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Speaker 7: And still I think companies or many companies that are

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Speaker 7: only not only in the US as we did not

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Speaker 7: face the impact of demographic change until recently and uh

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Speaker 7: it paused for example in many countries, uh at now baby,

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Speaker 7: we must start to retire. Now companies feel more the

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Speaker 7: pressure to adapt to the needs of an aging workforce population.

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Speaker 8: That was not the case in recent years.

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Speaker 7: Therefore, I think, since many workers since still face age discrimination,

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Speaker 7: the step to say okay, I drop out or take

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Speaker 7: a lower page job, or even have to they take

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Speaker 7: a lower page job that does not fit to my

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Speaker 7: qualifications than earlier time.

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Speaker 1: It is an opten What subsidies are, what support? I

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Speaker 1: guess can governments provide companies to maybe you know, hire

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Speaker 1: more older workers. Is there anything that is there a

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Speaker 1: role that governments can play in this.

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Speaker 7: Well, we've seen in some countries they are already kind

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Speaker 7: of advertise think campaigns in place like in Singapore.

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Speaker 8: Where the there is a company, the government.

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Speaker 7: Tries to to push for a cultural change, for change

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Speaker 7: of the perception of all the workers. But what governments

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Speaker 7: can also do as we see a need for reskilling,

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Speaker 7: for upskilling, for lifelong learning.

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Speaker 8: It's especially.

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Speaker 7: Smaller companies who often cannot afford, for example, these kind

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Speaker 7: of measures. So it would be one measure to UH

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Speaker 7: support companies when offering training trainings for their workers. It

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Speaker 7: could be also possible to support subsidy or grant subsidies

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Speaker 7: of when employing all the workers coming from long term unemployment.

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Speaker 7: It would also be a measure to support part time employment.

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Speaker 8: UH. It could be a measure to say, okay.

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Speaker 7: We reduce contribution rates or taxes for workers after after

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Speaker 7: specific age. Then other measures could be when we look

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Speaker 7: at the pension systems to make it possible to combine

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Speaker 7: work and retirement, part time retirement, or as I said,

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Speaker 7: to come get away from the mental retirement age, make

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Speaker 7: retirement ages more flexible and for example grand and right

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Speaker 7: to work.

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Speaker 1: Yep, all right, all interesting stuff there. Companies, countries, politicians

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Speaker 1: have to think about this as the global population continues

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Speaker 1: to age.

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Speaker 4: You're listening to the tape cats are live program Bloomberg

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Speaker 4: flagship New York station. Just say Alexa, play Bloomberg eleven thirty.

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Speaker 3: Yeah, we'll get straight to our next guest who's a

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Speaker 3: veteran investor, George Bass, chairman at Sanders Bors Harris joints

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Speaker 3: us on Zoom to discuss all things markets and his

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Speaker 3: stock pick.

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Speaker 1: You know, just when I came on Wall Street in

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Speaker 1: the mid sixties, George was, I believe, the chairman of

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Speaker 1: Prudential Base, which Bach company proceeded, and then Prudential bought Base,

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Speaker 1: one of the top from One Street, and I'll tell

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Speaker 1: you what, one of the best research departments on the.

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Speaker 3: Street at the time, and we're excited for him to

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Speaker 3: be here. George, I wanted to pick your brain because

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Speaker 3: in the last couple of months you had been making

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Speaker 3: some comments about how you thought the big tech riley

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Speaker 3: had run its course, but you still were thinking the

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Speaker 3: broader equity market would approach new record highs this year.

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Speaker 3: Are you still thinking that'll be the case for the

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Speaker 3: second half of the year.

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Speaker 9: The majority of the experts, the pundits seemed to be

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Speaker 9: on the side of the market weekening. As we get

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Speaker 9: further into the year, there's a bit of a war

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Speaker 9: between what I'd call Morgan Stanley on the one hand,

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Speaker 9: which is predicating a one hundred and eighty some dollar

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Speaker 9: earnings on the S and P five hundred and then

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Speaker 9: the more optimistic to twenty plus s and P earnings

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Speaker 9: estimates of Golden and Sacks, with again the majority of

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Speaker 9: the experts, and they are smart people, falling on the

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Speaker 9: more negative camp. I do think that they're wrong. And

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Speaker 9: by the way, I'll say that they interest to Wall

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Speaker 9: Street was more in the nineteen seventies and the sixties.

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Speaker 9: But back then, way back then, five percent interest rates

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Speaker 9: were common. They were accepted by business, and businesses operated

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Speaker 9: very profitably with comfortable profit margins with interest rates at

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Speaker 9: five percent. That's happening again today. Over the last decade,

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Speaker 9: we sort of forgot that managements and consumers and the

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Speaker 9: economy can live all right with interest rates at a

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Speaker 9: five percent or so level, And that I think is

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Speaker 9: an optimistic note for the market and earnings and the

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Speaker 9: economy going forward.

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Speaker 1: George, you know, technology has been such a leader of

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Speaker 1: this market really since the Great Financial Crisis. I mean

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Speaker 1: it just felt like, you know, really leadership position in

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Speaker 1: the marketplace. Do you still think that's the case here

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Speaker 1: in a world where again you've got rising interest rates

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Speaker 1: into your treasuries at four point seventy five percent, Can

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Speaker 1: tech still be the leader.

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Speaker 9: Technology stocks have been balancing with interest rates on the

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Speaker 9: assumption that the futurejourneys of technology companies will be a

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Speaker 9: function of dividends paid well well out many years from now.

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Speaker 9: But get back to the base case. Technology and the

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Speaker 9: changes in technology are what's driving growth, productivity, constructive change

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Speaker 9: in economies worldwide. That's not going to fold. My supposition

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Speaker 9: is that the big, big technology companies, the fang pluses,

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Speaker 9: the seven stocks that have driven almost all the games

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Speaker 9: in the SMP this year, are so darn big that

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Speaker 9: they can't grow very rapidly anymore. They're great companies, they're

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Speaker 9: doing extremely well, they're financial fortresses, but simply as a

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Speaker 9: matter of scale, they've reached the point of diminishing returns.

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Speaker 9: And so I'm looking at what I term mid tech

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Speaker 9: companies as the next wave of fundamentally justified, high growth,

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Speaker 9: high contributor companies, where the stocks are way down from

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Speaker 9: the perhaps unjustified levels of last year, but have an

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Speaker 9: enormous amount of upside potential, and that's going to start

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Speaker 9: to be recognized as we get into the second half

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Speaker 9: of this year. So I'm saying by mid sell the

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Speaker 9: big fangy type stocks as we get into the second

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Speaker 9: half of twenty twenty three.

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Speaker 1: George, you've held leadership positions on Wall Street for decades,

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Speaker 1: again going all the way back to E. F. Hutton

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Speaker 1: and Base, some of the all time iconic brands on

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Speaker 1: Wall Street. What's your view of the business of Wall

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Speaker 1: Street these days?

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Speaker 9: The business of Wall Street is much more commoditized these

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Speaker 9: days than it was back then, but it is still

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Speaker 9: the found from which money for companies to grow and

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Speaker 9: to expand and to diversify comes. And it's also probably

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Speaker 9: the best place for individuals to put their savings, their

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Speaker 9: excess cash to work. So Wall Street continues to be

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Speaker 9: a marvelous place, much less humanistic than it used to be,

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Speaker 9: but vital to the economy and by the way, one

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Speaker 9: of our big national advantages. We have the ability to

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Speaker 9: create money for growth much more efficiently than other economies,

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Speaker 9: and it's apt to give us a competitive edge that

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Speaker 9: extends into the next twenty or thirty years.

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Speaker 3: With that said, George, we got to know what are

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Speaker 3: your top stock picks.

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Speaker 9: Well, again, consistent with my mid tech theory, I would

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Speaker 9: use as example specifics but also as examples Teledoc. Teledoc

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Speaker 9: reaches over fifty million Americans. It's a company that is

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Speaker 9: not yet profitable all those cash flow positive. If they

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Speaker 9: can take just a fair slice of their client or

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Speaker 9: patient base and convert it to a subscription model at

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Speaker 9: fair economic prices, they have enormous ability to earn a

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Speaker 9: great deal of money. Trade desk is what the ad

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Speaker 9: agencies were back in the nineteen sixties and seventies when

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Speaker 9: I started. Trade desk is a way of placing advertising

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Speaker 9: or seeking advertisers on an efficient basis, and its ability

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Speaker 9: to grow exponentially over the next ten years is I

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Speaker 9: think almost unrivaled. Mercarlil Libra, the Amazon of South America

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Speaker 9: and Central America is very profitable, but it's just scratching

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Speaker 9: the surface. So that's a trio of mid tech companies.

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Speaker 9: They're sizable, but they're converting from a revenue growth model

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Speaker 9: to a profit growth model.

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Speaker 1: George, one of the themes that's really come into this

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Speaker 1: market over the last year or so is artificial intelligence AI.

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Speaker 1: And we had a well regarded Wall Street analyst earlier

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Speaker 1: says this is like the nineteen eighty five Internet Aha moment,

440
00:25:08,920 --> 00:25:11,680
Speaker 1: or you know, kind of like when a Google came

441
00:25:11,680 --> 00:25:13,440
Speaker 1: on and we said Oh boy, this is a big

442
00:25:14,080 --> 00:25:17,480
Speaker 1: moment for the internet. He said, it's something akin to that.

443
00:25:18,200 --> 00:25:21,080
Speaker 1: How do you view this thing called artificial intelligence AI?

444
00:25:21,520 --> 00:25:24,600
Speaker 1: It really seems to be the theme of the tech

445
00:25:24,640 --> 00:25:26,960
Speaker 1: world these days.

446
00:25:27,680 --> 00:25:29,639
Speaker 9: I'm going to sound like an old fogy and that

447
00:25:30,600 --> 00:25:32,320
Speaker 9: really bothers me. They don't want to do it. I'd

448
00:25:32,359 --> 00:25:37,159
Speaker 9: rather take a different view. Yeah, AAI is a marvelous

449
00:25:37,200 --> 00:25:41,199
Speaker 9: step forward because it's going to give people access in

450
00:25:41,640 --> 00:25:45,200
Speaker 9: multi dimensions to all of the wealth of knowledge of

451
00:25:47,320 --> 00:25:51,639
Speaker 9: the world, and it will write things that will seek

452
00:25:51,720 --> 00:25:56,240
Speaker 9: things that will display things brilliantly and very helpfully. But

453
00:25:57,680 --> 00:26:03,560
Speaker 9: it is not predictive. It is a look into the

454
00:26:03,680 --> 00:26:07,119
Speaker 9: database of what's known up to now, and so I

455
00:26:07,160 --> 00:26:10,040
Speaker 9: don't think it's going to be transformative in the sense

456
00:26:10,080 --> 00:26:13,760
Speaker 9: of being the fuel cell of growth the way many

457
00:26:13,800 --> 00:26:17,600
Speaker 9: people are predicting. I don't think it's a fat or

458
00:26:17,600 --> 00:26:22,280
Speaker 9: a bubble. But I think it's importance in the economic

459
00:26:22,280 --> 00:26:25,720
Speaker 9: future of our country and of the world is overstated.

460
00:26:26,720 --> 00:26:31,280
Speaker 9: You know, the past is destructive, but it's not predictive,

461
00:26:32,000 --> 00:26:35,600
Speaker 9: and so AI is important but not transformative in my

462
00:26:36,040 --> 00:26:37,400
Speaker 9: perhaps geriatric view.

463
00:26:37,640 --> 00:26:40,679
Speaker 1: No, it's a perfectly relevant view. And but lastly, George,

464
00:26:40,720 --> 00:26:42,920
Speaker 1: I have to ask, are you an Aspen Colorado right now?

465
00:26:45,000 --> 00:26:47,720
Speaker 9: No, I'm in the Salt Colorado. If you can't quite

466
00:26:47,760 --> 00:26:50,480
Speaker 9: afford to be an Aspen, you've got ten miles down there.

467
00:26:50,880 --> 00:26:53,000
Speaker 1: All right, wherever you are in Colorado, I'm just where

468
00:26:53,000 --> 00:26:53,239
Speaker 1: I am.

469
00:26:53,280 --> 00:26:53,640
Speaker 5: All right.

470
00:26:53,680 --> 00:26:55,480
Speaker 1: Wherever you are in Colorado, I'm just going to suggest

471
00:26:55,480 --> 00:26:57,639
Speaker 1: you might be a little overdressed. You look very solid

472
00:26:57,680 --> 00:27:00,679
Speaker 1: for Wall Street, but I think the Colorado and for

473
00:27:00,760 --> 00:27:03,960
Speaker 1: Bloomberg Markets next time you can certainly go a little

474
00:27:04,000 --> 00:27:06,679
Speaker 1: bit more casual if you're in Colorado. George Ball everyone

475
00:27:07,160 --> 00:27:12,280
Speaker 1: just literally an iconic person on Wall Street. He's the

476
00:27:12,320 --> 00:27:15,080
Speaker 1: chairman of Sanders Mars Harris, but has really been a

477
00:27:15,200 --> 00:27:19,560
Speaker 1: leader on Wall Street for decades, I mean CEO level leader,

478
00:27:20,000 --> 00:27:22,840
Speaker 1: and it's just amazing the career he's had. Again, it

479
00:27:22,880 --> 00:27:24,399
Speaker 1: goes all the way back to E. F. Hutton and

480
00:27:24,440 --> 00:27:28,200
Speaker 1: you kids can look that up. Base Company another fantastic firm,

481
00:27:28,240 --> 00:27:31,159
Speaker 1: bought by Prudential Securities when it became Prudential Base and

482
00:27:31,160 --> 00:27:33,919
Speaker 1: then Potential Securities and again one of the leading firms

483
00:27:34,000 --> 00:27:35,879
Speaker 1: on the street. So George has seen it all. We

484
00:27:35,920 --> 00:27:38,960
Speaker 1: appreciate getting a few minutes of his time.

485
00:27:40,119 --> 00:27:43,520
Speaker 4: You're listening to the team Ken's are live program Bloomberg

486
00:27:43,560 --> 00:27:46,959
Speaker 4: Markets weekdays at ten am Eastern on Bloomberg dot Com,

487
00:27:47,000 --> 00:27:50,160
Speaker 4: the iHeartRadio app, and the Bloomberg Business App, or listen

488
00:27:50,240 --> 00:27:52,360
Speaker 4: on demand wherever you get your podcasts.

489
00:27:54,280 --> 00:27:58,080
Speaker 1: All right, let's talk about our good friends at Amazon.

490
00:27:58,119 --> 00:28:00,000
Speaker 1: We've all got boxes probably sitting in front of our

491
00:28:00,080 --> 00:28:03,199
Speaker 1: doors as we speak, are good friends at Amazon. And

492
00:28:03,240 --> 00:28:05,840
Speaker 1: then there's a little thing called Prime. I'm a huge

493
00:28:05,840 --> 00:28:09,720
Speaker 1: fan of Prime. Free delivery, gets created, the audio video service,

494
00:28:09,760 --> 00:28:11,240
Speaker 1: all kinds of stuff kind of wrapped up in there.

495
00:28:11,720 --> 00:28:13,920
Speaker 1: But apparently the FTC has taken a look at this thing.

496
00:28:13,960 --> 00:28:17,200
Speaker 1: Matt Sheltenham, he's a senior litigation analyst. That means he's

497
00:28:17,240 --> 00:28:20,200
Speaker 1: an attorney, but he's come over from the dark side

498
00:28:20,240 --> 00:28:22,159
Speaker 1: and he's working on Wall Street and giving us some

499
00:28:22,200 --> 00:28:26,040
Speaker 1: good research for Bloomberg Intelligence. Here, Matt talked to just

500
00:28:26,119 --> 00:28:29,960
Speaker 1: describe what the issue f the FTC has with Amazon

501
00:28:30,320 --> 00:28:31,640
Speaker 1: and its Prime business.

502
00:28:32,400 --> 00:28:35,760
Speaker 10: Yeah, Paul, we saw this this emerge somewhat surprisingly last

503
00:28:35,800 --> 00:28:39,400
Speaker 10: week when when the FDC filed this lawsuit on Wednesday

504
00:28:39,600 --> 00:28:44,840
Speaker 10: alleging that Amazon has tricks or duped millions of consumers

505
00:28:44,880 --> 00:28:50,040
Speaker 10: into signing up for Prime and and and in particular

506
00:28:50,120 --> 00:28:55,600
Speaker 10: for the automatic renewal of those Prime membership and and

507
00:28:55,600 --> 00:28:59,520
Speaker 10: and the allegation is that that violates a federal law

508
00:29:00,240 --> 00:29:05,000
Speaker 10: and that Amazon's potentially on the hook for, you know,

509
00:29:05,200 --> 00:29:08,800
Speaker 10: hundreds of millions of dollars or more in damages for

510
00:29:09,320 --> 00:29:12,280
Speaker 10: or tricking consumers into staying with the program that they

511
00:29:12,280 --> 00:29:13,600
Speaker 10: don't really want to be a part of.

512
00:29:14,200 --> 00:29:16,320
Speaker 3: And it's interesting because going through this, it says the

513
00:29:16,360 --> 00:29:19,280
Speaker 3: FTC was talking about how consumers must click through five

514
00:29:19,440 --> 00:29:22,120
Speaker 3: pages on the desktop web store or six on the

515
00:29:22,160 --> 00:29:25,600
Speaker 3: mobile app to cancel Prime, so clearly making it pretty

516
00:29:25,600 --> 00:29:29,360
Speaker 3: difficult right for consumers to try to cancel this. I mean,

517
00:29:29,360 --> 00:29:32,000
Speaker 3: how much and how far do you think this potential

518
00:29:32,320 --> 00:29:33,560
Speaker 3: case could potentially go?

519
00:29:34,360 --> 00:29:38,240
Speaker 10: Yeah, so, I mean that's exactly the allegation. What's really

520
00:29:38,280 --> 00:29:43,240
Speaker 10: the big unknown here, though, is what is simple? The

521
00:29:43,360 --> 00:29:49,960
Speaker 10: law requires a simple mechanism for subscribers to cancel automatic

522
00:29:50,000 --> 00:29:51,080
Speaker 10: renewal plans like this.

523
00:29:51,200 --> 00:29:53,600
Speaker 1: How about the time to the cable telephone company, cable

524
00:29:53,600 --> 00:29:56,160
Speaker 1: television companies? How hard is that to cancel?

525
00:29:56,640 --> 00:29:59,120
Speaker 10: Exactly? And that's exactly what you're going to hear from

526
00:29:59,120 --> 00:30:02,520
Speaker 10: Amazon in the case is look at all these other

527
00:30:02,640 --> 00:30:05,200
Speaker 10: businesses that make it so difficult. You got you want

528
00:30:05,200 --> 00:30:07,360
Speaker 10: to cancel your gym membership, You got to go into

529
00:30:07,400 --> 00:30:10,040
Speaker 10: the gym to to you know, find somebody to talk to.

530
00:30:10,640 --> 00:30:13,600
Speaker 10: Other companies require you to call somebody on the line.

531
00:30:13,680 --> 00:30:16,520
Speaker 10: Here we're talking about six clicks and and yeah, there

532
00:30:16,520 --> 00:30:19,480
Speaker 10: are some extra hoops along the way, but there's very

533
00:30:19,520 --> 00:30:22,080
Speaker 10: little guidance in the law on what is simple and

534
00:30:22,120 --> 00:30:24,520
Speaker 10: what isn't. And so you know, I think there's a

535
00:30:24,680 --> 00:30:27,400
Speaker 10: there's a real argument for Amazon to make here that

536
00:30:27,440 --> 00:30:31,960
Speaker 10: the FDC is really straining to argue that that that

537
00:30:32,280 --> 00:30:35,000
Speaker 10: the six clicks process violates the law.

538
00:30:36,040 --> 00:30:38,640
Speaker 1: I mean, to me, first of all, I don't feel

539
00:30:38,720 --> 00:30:39,680
Speaker 1: like like I was duped.

540
00:30:40,280 --> 00:30:42,480
Speaker 3: I actually had an issue when it came to Audible

541
00:30:42,720 --> 00:30:45,800
Speaker 3: when it comes to their podcast. I actually went through

542
00:30:45,800 --> 00:30:47,520
Speaker 3: this a couple of months ago where I canceled and

543
00:30:47,560 --> 00:30:49,800
Speaker 3: then kept getting charged and had it called up and

544
00:30:49,840 --> 00:30:52,880
Speaker 3: go through this. So it's interesting now that but I

545
00:30:52,960 --> 00:30:55,280
Speaker 3: kind of felt a little miffed there when that had

546
00:30:55,320 --> 00:30:56,000
Speaker 3: happened to me.

547
00:30:56,760 --> 00:31:01,200
Speaker 1: So, Matt, what is what's Amazon's mo when dealing with regulars?

548
00:31:01,240 --> 00:31:03,200
Speaker 1: Do they just like settle these things out of court

549
00:31:03,320 --> 00:31:05,000
Speaker 1: or do they fight them to toth and nail.

550
00:31:05,560 --> 00:31:08,920
Speaker 10: Yeah. So, I mean for a company like Amazon, it's

551
00:31:08,920 --> 00:31:11,480
Speaker 10: really hard for a suit like this to to be

552
00:31:11,840 --> 00:31:15,520
Speaker 10: too threatening. But one reason it does matter is that

553
00:31:15,560 --> 00:31:20,240
Speaker 10: the FTC's power to impose penalties is very broad. In theory,

554
00:31:20,320 --> 00:31:24,959
Speaker 10: the FTC can impose fifty thousand dollars per violation, and

555
00:31:25,000 --> 00:31:28,520
Speaker 10: that can be counted by user, So you start to

556
00:31:28,520 --> 00:31:30,920
Speaker 10: go one hundred million times fifty thousand and you get

557
00:31:30,920 --> 00:31:33,720
Speaker 10: to some gigantic number with lots of zeros at the end.

558
00:31:33,800 --> 00:31:38,400
Speaker 10: So Amazon has to pay attention to this, especially when

559
00:31:38,480 --> 00:31:41,640
Speaker 10: when the leader of the FTC, Lena Khan, is looking

560
00:31:41,680 --> 00:31:46,880
Speaker 10: to be very aggressive in grow going after companies like Amazon. So,

561
00:31:47,480 --> 00:31:49,600
Speaker 10: you know, I think Amazon would be happy to settle

562
00:31:49,640 --> 00:31:52,360
Speaker 10: it for a small you know, a small figure, you know,

563
00:31:52,840 --> 00:31:56,880
Speaker 10: relatively quickly. It's it's it's unclear whether the FTC would

564
00:31:56,920 --> 00:31:59,240
Speaker 10: go on you know, be on board with that right away.

565
00:31:59,280 --> 00:32:02,560
Speaker 10: But you know, I really do think Amazon has a

566
00:32:02,560 --> 00:32:06,120
Speaker 10: good chance to prevail in this litigation, but it's going

567
00:32:06,160 --> 00:32:08,720
Speaker 10: to take time, and so I do expect it to

568
00:32:08,720 --> 00:32:11,720
Speaker 10: play out for a couple of years before we see

569
00:32:11,760 --> 00:32:14,280
Speaker 10: a resolution. But it's really hard to see the FTC

570
00:32:14,480 --> 00:32:18,840
Speaker 10: winning a material damage amount against Amazon here.

571
00:32:18,920 --> 00:32:23,000
Speaker 3: Ultimately, so the timetable, like you said, being potentially a

572
00:32:23,040 --> 00:32:24,520
Speaker 3: couple of years when it comes to this.

573
00:32:25,280 --> 00:32:27,239
Speaker 10: Yeah, that's what I think. So I you know, this

574
00:32:27,400 --> 00:32:30,960
Speaker 10: was just filed last Wednesday. I think you're likely to

575
00:32:30,960 --> 00:32:33,880
Speaker 10: see a couple rounds of this litigation. And you know,

576
00:32:33,920 --> 00:32:36,160
Speaker 10: the big risk for Amazon is this gets to a

577
00:32:36,240 --> 00:32:40,400
Speaker 10: jury that that could could potentially offer you know, a

578
00:32:40,480 --> 00:32:44,640
Speaker 10: damages award in the millions or even billions of dollars.

579
00:32:44,680 --> 00:32:46,720
Speaker 10: That's what you can't let happen. But there are two

580
00:32:46,800 --> 00:32:50,120
Speaker 10: steps in the litigation and process before we get there. One,

581
00:32:50,200 --> 00:32:52,640
Speaker 10: Amazon can file a most into the myth, the whole

582
00:32:52,640 --> 00:32:55,320
Speaker 10: thing and say, look, this doesn't even state a valid claim.

583
00:32:55,680 --> 00:32:58,040
Speaker 10: I don't think that will work. It has a chance.

584
00:32:58,400 --> 00:33:01,680
Speaker 10: But then even after a year or two of discovery,

585
00:33:01,760 --> 00:33:04,240
Speaker 10: when they dive into the evidence, Amazon can say, look,

586
00:33:04,840 --> 00:33:07,680
Speaker 10: the FTC collected all this evidence and there's still not

587
00:33:08,120 --> 00:33:10,640
Speaker 10: a case that should go to a jury. That's point

588
00:33:10,680 --> 00:33:13,120
Speaker 10: I think Amazon has a really good, good argument to

589
00:33:13,160 --> 00:33:16,400
Speaker 10: the judge to say, look, Amazon's process may not have

590
00:33:16,440 --> 00:33:19,640
Speaker 10: been perfect, but you know it's probably enough to qualify

591
00:33:19,680 --> 00:33:23,440
Speaker 10: as a simple mechanism under this unclear standard under the law.

592
00:33:23,880 --> 00:33:25,520
Speaker 10: So yeah, I think that's going to take a couple

593
00:33:25,560 --> 00:33:28,680
Speaker 10: of years before we see any action, and certainly before

594
00:33:28,720 --> 00:33:31,360
Speaker 10: it goes to a jury in a trial like setting.

595
00:33:31,760 --> 00:33:34,600
Speaker 1: Hey, Matt, We've been talking to jenre who covers antitrust

596
00:33:34,600 --> 00:33:36,920
Speaker 1: for Bloomberg Intelligence, and just talking about kind of the

597
00:33:37,440 --> 00:33:40,880
Speaker 1: regulatory environment out there for businesses, particularly big tech, and

598
00:33:40,920 --> 00:33:42,880
Speaker 1: it's really tough for big tech companies to really do

599
00:33:42,920 --> 00:33:46,760
Speaker 1: anything here from your perspective, from the litigation side, what's

600
00:33:46,800 --> 00:33:50,160
Speaker 1: the environment like out there for business? Is it tougher

601
00:33:50,200 --> 00:33:53,720
Speaker 1: to do business as a government really taking a harder line.

602
00:33:54,600 --> 00:33:58,400
Speaker 10: It's certainly there's been a change, certainly in you know,

603
00:33:58,480 --> 00:34:01,240
Speaker 10: the past five to ten years in Washington, d C.

604
00:34:01,400 --> 00:34:04,040
Speaker 10: On the regulation side, you know, we had this idea

605
00:34:04,080 --> 00:34:06,800
Speaker 10: that for for a couple of decades, look, it's best

606
00:34:06,800 --> 00:34:08,759
Speaker 10: to get out of the way of the Internet, let

607
00:34:08,840 --> 00:34:12,480
Speaker 10: the Internet thrive. Everything is sort of changed in terms

608
00:34:12,520 --> 00:34:15,600
Speaker 10: of policy in terms of okay, maybe we went too

609
00:34:15,640 --> 00:34:18,000
Speaker 10: far in that direction, And you're seeing the same thing

610
00:34:18,080 --> 00:34:21,560
Speaker 10: on the litigation side, where class act and lawyers the

611
00:34:21,840 --> 00:34:27,000
Speaker 10: opportunities to go after companies like like Meta and Google

612
00:34:27,239 --> 00:34:31,200
Speaker 10: for you know, potentially addictive social media you're seeing second

613
00:34:31,239 --> 00:34:34,520
Speaker 10: in two thirty, the liability field that has long protected

614
00:34:34,560 --> 00:34:38,839
Speaker 10: these companies suddenly people suggesting, maybe we've we've read that

615
00:34:38,880 --> 00:34:41,560
Speaker 10: liability field too broadly, maybe we should be able to

616
00:34:41,560 --> 00:34:45,759
Speaker 10: do these companies about harms and so it's it's it's

617
00:34:45,760 --> 00:34:50,080
Speaker 10: an on thlought of different attacks that they didn't face

618
00:34:50,239 --> 00:34:52,520
Speaker 10: five or ten years ago. That doesn't mean they're going

619
00:34:52,600 --> 00:34:56,360
Speaker 10: to lose these cases. The companies have strong defenses to

620
00:34:56,440 --> 00:34:59,319
Speaker 10: a lot of the claims, but it's definitely a more

621
00:34:59,360 --> 00:35:01,799
Speaker 10: difficult and vironment than it was a couple of years back.

622
00:35:02,200 --> 00:35:05,239
Speaker 1: And that's why Bloomberg Intelligence has a bunch of analysts

623
00:35:05,239 --> 00:35:08,480
Speaker 1: and lawyers and smart people down in Washington with their

624
00:35:08,480 --> 00:35:10,560
Speaker 1: fingers on the pulse of what's happening down there, so

625
00:35:10,840 --> 00:35:14,000
Speaker 1: they can provide some very valuable research to Bloomberg customers.

626
00:35:14,000 --> 00:35:15,920
Speaker 1: So Matt Shunholm is certainly one of them. He's a

627
00:35:15,960 --> 00:35:20,279
Speaker 1: senior litigation analyst for Bloomberg Intelligence space down in Washington, DC.

628
00:35:20,480 --> 00:35:22,520
Speaker 1: So we'll have to follow that. For Amazon, the numbers,

629
00:35:22,760 --> 00:35:24,840
Speaker 1: you know, relative to its market cap arn't material, but

630
00:35:24,880 --> 00:35:27,160
Speaker 1: it goes to one of the one of their core

631
00:35:27,200 --> 00:35:30,400
Speaker 1: business practices here. So we'll see how they continue to

632
00:35:30,400 --> 00:35:32,320
Speaker 1: fight this. But again, it's tough to fight the government

633
00:35:32,360 --> 00:35:34,680
Speaker 1: and the Federal Trade Commission. But I guess if anybody

634
00:35:34,680 --> 00:35:36,240
Speaker 1: can do it, it's Amazon.

635
00:35:36,640 --> 00:35:39,759
Speaker 4: You're listening to the tape Cat's are live program Bloomberg

636
00:35:39,840 --> 00:35:43,719
Speaker 4: Markets weekdays at ten am Eastern on Bloomberg Radio, tune

637
00:35:43,760 --> 00:35:46,719
Speaker 4: in app, Bloomberg dot Com, and the Bloomberg Business app.

638
00:35:46,760 --> 00:35:49,560
Speaker 4: You can also listen live on Amazon Alexa from our

639
00:35:49,600 --> 00:35:54,719
Speaker 4: flagship New York station. Just say Alexa play Bloomberg eleven thirty.

640
00:35:55,320 --> 00:35:56,920
Speaker 1: Let's get right to our next guest because we get

641
00:35:56,960 --> 00:35:57,800
Speaker 1: to live to the instrument.

642
00:35:57,880 --> 00:35:58,720
Speaker 3: Yes in studio.

643
00:35:58,800 --> 00:35:59,239
Speaker 8: I love this.

644
00:35:59,280 --> 00:36:02,600
Speaker 1: I know Kevin Time and senior automotive analyst for Bloomberg Intelligence.

645
00:36:02,600 --> 00:36:05,160
Speaker 1: He's based down on our Printson campus. But in case

646
00:36:05,239 --> 00:36:07,200
Speaker 1: he gets up to the big town, Kevin, thanks so

647
00:36:07,239 --> 00:36:10,040
Speaker 1: much for joining us. Lots of ways to go here,

648
00:36:10,040 --> 00:36:12,680
Speaker 1: but first I gotta start your Mercedes that you drive

649
00:36:12,719 --> 00:36:14,400
Speaker 1: as a V twelve it is does that means it

650
00:36:14,400 --> 00:36:17,080
Speaker 1: has twelve cylinders twelve Cylin, and I have six in

651
00:36:17,160 --> 00:36:19,360
Speaker 1: my car. That's right, So you have twice as many,

652
00:36:19,480 --> 00:36:20,120
Speaker 1: that's correct.

653
00:36:20,400 --> 00:36:23,239
Speaker 11: Why it's and it's Here's the beautiful thing is it's

654
00:36:23,280 --> 00:36:29,000
Speaker 11: a two thousand and one Roadster V twelve with I

655
00:36:29,000 --> 00:36:31,719
Speaker 11: don't know, ninety six thousand miles on it. So my

656
00:36:32,040 --> 00:36:35,680
Speaker 11: carbon footprint is actually smaller than anybody who.

657
00:36:35,840 --> 00:36:37,680
Speaker 5: Made them build a new EV.

658
00:36:39,000 --> 00:36:40,480
Speaker 1: All right, that's all I say. I had to clear

659
00:36:40,520 --> 00:36:42,480
Speaker 1: that up there. I thought I was a tough guy

660
00:36:42,520 --> 00:36:44,640
Speaker 1: with six cylinders, but you got your driving around twelve. Great,

661
00:36:44,719 --> 00:36:47,200
Speaker 1: all right, let's start with Ford. Ford cutting some jobs here.

662
00:36:48,400 --> 00:36:49,839
Speaker 5: Why yeah.

663
00:36:49,920 --> 00:36:52,840
Speaker 11: You know, what I thought was interesting about this was

664
00:36:52,920 --> 00:36:57,560
Speaker 11: that you don't have this condition in the industry, or

665
00:36:57,560 --> 00:37:01,359
Speaker 11: at least in North America, of oversupply, right like too big,

666
00:37:01,480 --> 00:37:05,040
Speaker 11: too much product, too much production capacity, and we need

667
00:37:05,080 --> 00:37:05,720
Speaker 11: to cut back.

668
00:37:06,000 --> 00:37:06,160
Speaker 7: You know.

669
00:37:06,280 --> 00:37:09,239
Speaker 11: Inventory on the ground industry wide now in the US

670
00:37:09,320 --> 00:37:12,479
Speaker 11: is about half what it was at the peak, which

671
00:37:12,480 --> 00:37:16,480
Speaker 11: would be peak volumeer was twenty sixteen, seventeen and a

672
00:37:16,480 --> 00:37:19,440
Speaker 11: half million. Yep, we'll do fifteen this year, fifteen and

673
00:37:19,480 --> 00:37:23,440
Speaker 11: a half at the outside, you know. So inventory is

674
00:37:23,520 --> 00:37:28,440
Speaker 11: really in line with demand for the most part, and

675
00:37:28,480 --> 00:37:35,120
Speaker 11: you're seeing rationalization of costs still, which my conclusion is

676
00:37:35,120 --> 00:37:37,560
Speaker 11: is that this industry is very different than what it

677
00:37:37,640 --> 00:37:40,240
Speaker 11: used to be, which was a lot of fixed costs,

678
00:37:40,360 --> 00:37:43,759
Speaker 11: a lot of overhead produce produced, produce, figure out how

679
00:37:43,760 --> 00:37:48,239
Speaker 11: to sell it tomorrow by giving it away incentives, discounting,

680
00:37:48,680 --> 00:37:51,560
Speaker 11: lease deals, whatever it is. And to me, these job

681
00:37:51,600 --> 00:37:54,840
Speaker 11: cuts really show that the mindset of the manufacturers auto

682
00:37:54,880 --> 00:37:58,200
Speaker 11: manufacturers is we're not going back to that world.

683
00:37:59,320 --> 00:38:02,560
Speaker 3: I wanted to toward Tesla because we are seeing at

684
00:38:02,600 --> 00:38:05,080
Speaker 3: stock higher more than one percent today, so that is

685
00:38:05,120 --> 00:38:08,440
Speaker 3: pushing discretionary shares higher, and obviously it's waiting in the

686
00:38:08,520 --> 00:38:10,360
Speaker 3: S and P five hundred close to about two percent.

687
00:38:10,400 --> 00:38:13,640
Speaker 3: We did see that downgrade from Goldman Sachs on Tesla

688
00:38:13,760 --> 00:38:16,800
Speaker 3: earlier this week, just because of the difficult pricing environment.

689
00:38:16,840 --> 00:38:19,520
Speaker 3: But we saw that huge run Tesla had been on

690
00:38:19,920 --> 00:38:21,960
Speaker 3: that was snapped earlier this month. Obviously it was a

691
00:38:22,000 --> 00:38:26,239
Speaker 3: thirteen session span. Stock gained over forty percent. I know

692
00:38:26,320 --> 00:38:29,640
Speaker 3: that Paul's been watching RSI levels very closely, so it

693
00:38:29,680 --> 00:38:33,439
Speaker 3: actually got to around eighty eight eighty eight earlier this month,

694
00:38:33,560 --> 00:38:36,279
Speaker 3: obviously above seventies that overbought level. But what are you

695
00:38:36,360 --> 00:38:38,840
Speaker 3: seeing when it comes to Tesla's stock there and doesn't

696
00:38:38,840 --> 00:38:40,400
Speaker 3: make sense that we've seen a little bit of a

697
00:38:40,400 --> 00:38:42,319
Speaker 3: pullback after such a strong run like that.

698
00:38:42,640 --> 00:38:42,879
Speaker 4: Yeah.

699
00:38:42,880 --> 00:38:47,520
Speaker 11: Well, the difficult part, you know, as an analyst is

700
00:38:47,520 --> 00:38:50,319
Speaker 11: is to make sense of what the Tesla stock does

701
00:38:50,360 --> 00:38:55,040
Speaker 11: it's its own product, right, compared to what the fundamentals

702
00:38:55,120 --> 00:38:59,560
Speaker 11: of that company or of the industry are. So, you know,

703
00:38:59,600 --> 00:39:02,560
Speaker 11: I think the longer we go, we see that this

704
00:39:02,760 --> 00:39:08,759
Speaker 11: is an automaker, right, there's obviously big technology competitive advantages,

705
00:39:08,800 --> 00:39:12,400
Speaker 11: at least perceived, and we saw the idea of the

706
00:39:12,560 --> 00:39:16,719
Speaker 11: charging with GM and Ford signing on with that.

707
00:39:16,719 --> 00:39:17,920
Speaker 5: That pushes the.

708
00:39:17,880 --> 00:39:22,359
Speaker 11: Stock price higher, But ultimately this is an automaker with

709
00:39:22,840 --> 00:39:25,680
Speaker 11: automaker demand issues. I was actually driving around with my

710
00:39:25,719 --> 00:39:28,960
Speaker 11: wife this weekend and we drove by one of the malls,

711
00:39:29,040 --> 00:39:32,200
Speaker 11: the Quaker Bridge Mall, and there's there in the parking

712
00:39:32,239 --> 00:39:36,120
Speaker 11: lot there's overflow Tesla inventory as far as the eye

713
00:39:36,120 --> 00:39:38,840
Speaker 11: can see. Actually made her pull over and I got out.

714
00:39:38,719 --> 00:39:40,080
Speaker 5: And took pictures for myself.

715
00:39:40,080 --> 00:39:42,920
Speaker 11: But you know, so when you look at it in

716
00:39:43,000 --> 00:39:47,600
Speaker 11: the from the perspective of a metal bender that needs

717
00:39:47,640 --> 00:39:51,560
Speaker 11: to produce and sell, and you take some of that

718
00:39:51,920 --> 00:39:55,440
Speaker 11: technology angle away from it, you're going to have those

719
00:39:55,560 --> 00:39:59,839
Speaker 11: periods where the stock just just adjusts yep, and then

720
00:39:59,840 --> 00:40:02,319
Speaker 11: you you're gonna have periods where there's an announcement and

721
00:40:02,400 --> 00:40:04,880
Speaker 11: it makes a big difference to the upside.

722
00:40:04,960 --> 00:40:06,719
Speaker 1: And one of the things I don't I'm still trying

723
00:40:06,760 --> 00:40:08,320
Speaker 1: to get a sense of is if I were Tesla

724
00:40:08,360 --> 00:40:10,640
Speaker 1: investor or an analyst, is kind of where it fits

725
00:40:10,680 --> 00:40:14,239
Speaker 1: into a world that's going completely electric. They get all

726
00:40:14,280 --> 00:40:16,720
Speaker 1: the credit in the world for being first and being really,

727
00:40:16,760 --> 00:40:20,040
Speaker 1: really really good, But in a day, as you suggest,

728
00:40:20,080 --> 00:40:24,920
Speaker 1: it's a metal bending business and Ford gm VW. So

729
00:40:24,960 --> 00:40:28,160
Speaker 1: when I think about Tesla, Rivian, Lucid all these other ones,

730
00:40:28,560 --> 00:40:30,879
Speaker 1: how do you think this industry is gonna shake out?

731
00:40:31,000 --> 00:40:31,200
Speaker 7: Yeah?

732
00:40:31,239 --> 00:40:37,080
Speaker 11: I think we're through a period where where markets investors

733
00:40:37,120 --> 00:40:39,719
Speaker 11: talked about the auto industry like it was the tech

734
00:40:39,760 --> 00:40:42,520
Speaker 11: industry or these were tech companies, and they always have

735
00:40:42,719 --> 00:40:45,480
Speaker 11: been right. Automakers have always been ahead of the curve

736
00:40:45,480 --> 00:40:48,800
Speaker 11: of technology wise developed some great things over the years,

737
00:40:49,120 --> 00:40:52,200
Speaker 11: and I think there was this belief that because of Tesla,

738
00:40:52,280 --> 00:40:55,680
Speaker 11: that this entire industry was now, all of a sudden,

739
00:40:55,880 --> 00:40:59,080
Speaker 11: a technology industry. So what you get is you're either

740
00:40:59,120 --> 00:41:03,760
Speaker 11: going to get every other automaker that is essentially doing

741
00:41:03,840 --> 00:41:07,040
Speaker 11: and can do the same things as Tesla, Beee considered

742
00:41:07,040 --> 00:41:10,440
Speaker 11: tech companies and deserve those valuations, or you're going to

743
00:41:10,520 --> 00:41:14,239
Speaker 11: get the ones that had the wild technology valuations, you know,

744
00:41:14,920 --> 00:41:19,640
Speaker 11: recalibrated down to automakers. And I think that's the that's

745
00:41:19,719 --> 00:41:22,719
Speaker 11: the greater likelihood. Ford and GM are never going to

746
00:41:22,760 --> 00:41:25,680
Speaker 11: be and have never been valued like that. And I

747
00:41:25,719 --> 00:41:28,960
Speaker 11: think the reality would be more that the EV and

748
00:41:29,040 --> 00:41:33,120
Speaker 11: technology companies would be valued more as automakers than the reverse.

749
00:41:33,400 --> 00:41:37,960
Speaker 3: And our friend Matt Miller actually interviewed Ford's CEO Jim Farley,

750
00:41:38,640 --> 00:41:41,600
Speaker 3: about a month ago, it's actually toward late May, about

751
00:41:41,960 --> 00:41:45,680
Speaker 3: cost cuts, auto prices, evs when it comes to Ford

752
00:41:45,800 --> 00:41:48,399
Speaker 3: in position to some of these other competitors that we're

753
00:41:48,440 --> 00:41:52,040
Speaker 3: talking about, and obviously it's outlook for the next few years,

754
00:41:52,120 --> 00:41:53,880
Speaker 3: do you think that's sustainable and do you think it

755
00:41:53,920 --> 00:41:54,440
Speaker 3: could happen?

756
00:41:55,560 --> 00:41:59,799
Speaker 11: Well, yeah, I mean I think profitability is going to

757
00:41:59,840 --> 00:42:05,240
Speaker 11: drive I've adoption, right. These these are automakers, the legacy

758
00:42:05,280 --> 00:42:08,000
Speaker 11: automakers are making a lot of money doing what they're

759
00:42:08,040 --> 00:42:12,920
Speaker 11: good at, which is trucks and SUVs, and then being

760
00:42:13,040 --> 00:42:16,879
Speaker 11: asked or forced into this transition to EV when it's

761
00:42:16,920 --> 00:42:20,240
Speaker 11: not really ready. And I understand the concept of disruption,

762
00:42:20,400 --> 00:42:23,600
Speaker 11: and that's what happens, and some things are messy during

763
00:42:23,640 --> 00:42:27,080
Speaker 11: the transition. But you know, during the period where we

764
00:42:27,200 --> 00:42:30,680
Speaker 11: transition to truck from car, that was profitable on the

765
00:42:30,680 --> 00:42:35,000
Speaker 11: first day. Right So now you have you know, Tesla

766
00:42:35,239 --> 00:42:38,120
Speaker 11: leading the way, but the governments starting to get involved

767
00:42:38,239 --> 00:42:43,720
Speaker 11: with credits and infrastructure spending, motivating the consumer to demand

768
00:42:43,719 --> 00:42:46,920
Speaker 11: these things that the manufacturer can't necessarily make money.

769
00:42:46,640 --> 00:42:47,520
Speaker 5: On just yet.

770
00:42:47,880 --> 00:42:50,640
Speaker 11: And it's and it's a it's a messy transition period

771
00:42:50,719 --> 00:42:54,040
Speaker 11: right now because you don't want to give up profitable

772
00:42:54,080 --> 00:42:56,919
Speaker 11: things to do unprofitable things in this market right now.

773
00:42:57,239 --> 00:42:59,800
Speaker 1: You know, I drove the Ford F one fifty of

774
00:43:00,000 --> 00:43:02,600
Speaker 1: electric one and as first time I drove a pickup

775
00:43:02,600 --> 00:43:05,600
Speaker 1: truck probably the last. I'm a Wall Street guy, I'm

776
00:43:05,600 --> 00:43:08,840
Speaker 1: not dry. My son's got one and it was my

777
00:43:08,880 --> 00:43:11,680
Speaker 1: first electric vehicle, and boy, it blew me away in

778
00:43:11,719 --> 00:43:13,399
Speaker 1: terms of the power and it's work and things like that.

779
00:43:13,640 --> 00:43:17,520
Speaker 1: Is the belief in Detroit that the average pickup truck

780
00:43:17,600 --> 00:43:21,160
Speaker 1: driver will demand an electric version.

781
00:43:21,200 --> 00:43:23,879
Speaker 11: Well, I guess it's going to depend. I mean the

782
00:43:23,920 --> 00:43:28,680
Speaker 11: towing capacity of the range. You know, there's and look

783
00:43:28,960 --> 00:43:30,960
Speaker 11: and what I would say is this is that it's

784
00:43:31,040 --> 00:43:34,200
Speaker 11: why we have segments. It's why we have vehicles with

785
00:43:34,280 --> 00:43:38,760
Speaker 11: different capabilities, you know, so different drive trains. I got

786
00:43:39,160 --> 00:43:42,200
Speaker 11: twelve cylinders, you got six, you know, like that, and

787
00:43:42,360 --> 00:43:45,680
Speaker 11: to make the world go round. So the idea that

788
00:43:45,800 --> 00:43:50,719
Speaker 11: maybe the technological singularity of internal combustion being traded for

789
00:43:51,200 --> 00:43:54,840
Speaker 11: the singularity of electrification, for me is a difficult concept

790
00:43:54,880 --> 00:43:58,360
Speaker 11: because I feel like there is a place, there's people

791
00:43:58,840 --> 00:44:01,719
Speaker 11: that need, but I'm not sure we're ready to say

792
00:44:01,760 --> 00:44:04,400
Speaker 11: one hundred this is your only option in terms of

793
00:44:04,719 --> 00:44:06,000
Speaker 11: drive train technology.

794
00:44:06,280 --> 00:44:09,600
Speaker 3: I hear you're speaking at a Bloomberg Intelligent event to

795
00:44:09,760 --> 00:44:11,080
Speaker 3: date right upstairs.

796
00:44:11,160 --> 00:44:13,320
Speaker 1: Uh right, and what are we doing there?

797
00:44:13,520 --> 00:44:17,879
Speaker 11: That's uh the bi Lithium conference. So I'm the I'm

798
00:44:17,920 --> 00:44:20,680
Speaker 11: the square you're the that's right here, the auto guy.

799
00:44:20,680 --> 00:44:23,319
Speaker 1: I'm the square peg And just what what is the

800
00:44:23,560 --> 00:44:26,239
Speaker 1: we got thirty seconds? Okay, what's the consensus out there

801
00:44:26,239 --> 00:44:28,719
Speaker 1: on the street about lithium batteries? Are Is there going

802
00:44:28,760 --> 00:44:29,960
Speaker 1: to be enough stuff? Are we going to be able

803
00:44:30,000 --> 00:44:30,560
Speaker 1: to get them all?

804
00:44:30,719 --> 00:44:31,399
Speaker 5: And no?

805
00:44:31,520 --> 00:44:33,160
Speaker 11: That's and that's one of the things that I was

806
00:44:33,280 --> 00:44:37,480
Speaker 11: out last week speaking at a couple of dealer conventions

807
00:44:37,760 --> 00:44:42,960
Speaker 11: Louisiana Dealers, Virginia dealers. And that's the big question is

808
00:44:43,000 --> 00:44:46,360
Speaker 11: you know, we hear this one hundred percent by whatever

809
00:44:46,520 --> 00:44:50,920
Speaker 11: date you know, and and it's when you when you

810
00:44:50,960 --> 00:44:54,040
Speaker 11: total up the market share and the volume, it just

811
00:44:54,120 --> 00:44:58,120
Speaker 11: doesn't compute with the materials that we have access to,

812
00:44:58,400 --> 00:45:02,640
Speaker 11: you know. And and the most cynical of them would say, look,

813
00:45:02,680 --> 00:45:06,560
Speaker 11: we're essentially trading our dependence on foreign oil for our

814
00:45:06,600 --> 00:45:08,560
Speaker 11: dependence on China materials.

815
00:45:08,640 --> 00:45:09,840
Speaker 5: Yep at this point.

816
00:45:10,520 --> 00:45:12,319
Speaker 11: But a lot of people don't want to talk about that.

817
00:45:12,840 --> 00:45:15,719
Speaker 1: But you will. Yeah, that's why we've read Kevin Log,

818
00:45:15,960 --> 00:45:19,240
Speaker 1: Kevin Tyne, and Old School Auto Kevin Tiny, senior automotive

819
00:45:19,239 --> 00:45:21,920
Speaker 1: analyst for Bloomberg Intelligence, joining us here live and our

820
00:45:22,160 --> 00:45:25,200
Speaker 1: Bloomberg Interactive Broker Studio. We appreciate that.

821
00:45:25,560 --> 00:45:28,680
Speaker 4: You're listening to the tape. Cats are live program Bloomberg

822
00:45:28,719 --> 00:45:32,319
Speaker 4: Markets weekdays at ten am Eastern on Bloomberg Radio, the

823
00:45:32,400 --> 00:45:35,640
Speaker 4: tune in app, Bloomberg dot Com, and the Bloomberg Business App.

824
00:45:35,640 --> 00:45:38,480
Speaker 4: You can also listen live on Amazon Alexa from our

825
00:45:38,480 --> 00:45:42,480
Speaker 4: flagship New York station, Just say Alexa play Bloomberg eleven

826
00:45:42,600 --> 00:45:44,759
Speaker 4: thirty one.

827
00:45:44,800 --> 00:45:47,160
Speaker 1: Thing we don't talk about we've already should is signific

828
00:45:47,239 --> 00:45:50,239
Speaker 1: it at you know asset class. If you all alternative

829
00:45:50,280 --> 00:45:52,920
Speaker 1: asset class, which is jewelry. Our next guest can help

830
00:45:53,000 --> 00:45:55,280
Speaker 1: us do that oncoor Dagga joints us he's the CEO

831
00:45:55,320 --> 00:45:58,080
Speaker 1: of Angara and he joins us here in O bloombergetting

832
00:45:58,120 --> 00:46:00,799
Speaker 1: Arrective Broker Studio. Thanks once for joining us.

833
00:46:00,880 --> 00:46:02,080
Speaker 12: Absolutely great to be here.

834
00:46:02,120 --> 00:46:04,239
Speaker 1: Tell us about your company. What do you guys do it?

835
00:46:04,280 --> 00:46:08,120
Speaker 12: And Kara, So we specialize in colored gemstones, so ruby, emerald, sapphire.

836
00:46:08,239 --> 00:46:10,560
Speaker 12: We are vertically integrated, so we do everything from buy

837
00:46:10,600 --> 00:46:14,320
Speaker 12: from the mines, cut in polish gemstones, design jewelry, manufactured jewelry,

838
00:46:14,400 --> 00:46:16,680
Speaker 12: and retail over the web in over thirty countries.

839
00:46:16,840 --> 00:46:19,880
Speaker 3: And you grew up in the jewelry industry. Talk to

840
00:46:19,960 --> 00:46:21,280
Speaker 3: us about how you got started.

841
00:46:21,680 --> 00:46:23,080
Speaker 1: Yeah, absolutely so I did.

842
00:46:23,160 --> 00:46:24,640
Speaker 12: From the age of five. I was going to my

843
00:46:24,760 --> 00:46:28,399
Speaker 12: dad's wholesale business and I inutially vowed that I would

844
00:46:28,440 --> 00:46:31,759
Speaker 12: never come back into the industry. It was quite antiquated,

845
00:46:32,640 --> 00:46:35,080
Speaker 12: and like Paula worked at Credits weeks before and then McKenzie.

846
00:46:35,200 --> 00:46:38,160
Speaker 12: My largest project at McKinzie was turning around a jewelry

847
00:46:38,239 --> 00:46:40,000
Speaker 12: retailer and I was like, wow, there's a lot of

848
00:46:40,000 --> 00:46:42,640
Speaker 12: opportunity here and then there we are, all.

849
00:46:42,560 --> 00:46:45,040
Speaker 1: Right, explain to me this thing. I know nothing about it.

850
00:46:45,560 --> 00:46:50,640
Speaker 1: You know, I'm gonna say natural mind diamonds versus lab

851
00:46:50,640 --> 00:46:52,799
Speaker 1: grown diamonds. I didn't even know there was really a

852
00:46:52,840 --> 00:46:55,520
Speaker 1: thing for lab grown diamonds. Talk to us about lab

853
00:46:55,680 --> 00:46:56,680
Speaker 1: grown diamonds.

854
00:46:56,840 --> 00:46:59,759
Speaker 12: Yeah, So they've been gaining popularity over the last three years.

855
00:47:00,040 --> 00:47:03,799
Speaker 12: Twenty eighteen, the FTC said a lab grown diamond is

856
00:47:03,880 --> 00:47:06,480
Speaker 12: a diamond, just like a natural diamond. Is the only

857
00:47:06,560 --> 00:47:08,719
Speaker 12: caveat is you have to disclose that as lab grown.

858
00:47:09,200 --> 00:47:11,960
Speaker 12: Since then, the industry has really taken off. So in

859
00:47:12,000 --> 00:47:14,960
Speaker 12: twenty twenty they were roughly two point three percent of

860
00:47:15,040 --> 00:47:18,799
Speaker 12: all diamonds sold. Now they're over ten percent, and the

861
00:47:18,800 --> 00:47:21,680
Speaker 12: growth rate obviously is fantastic, but in terms of unit

862
00:47:21,719 --> 00:47:23,000
Speaker 12: volumes even more so.

863
00:47:23,200 --> 00:47:23,480
Speaker 1: Now.

864
00:47:24,360 --> 00:47:27,799
Speaker 12: I think part of why they are so attractive is

865
00:47:27,840 --> 00:47:30,120
Speaker 12: now they are trading at seventy five percent to ninety

866
00:47:30,160 --> 00:47:32,000
Speaker 12: percent cheaper than their natural mind equipment.

867
00:47:32,040 --> 00:47:34,319
Speaker 1: Really, and what do they look like?

868
00:47:34,400 --> 00:47:39,239
Speaker 12: How did they So they are optically, physically, chemically identical

869
00:47:39,520 --> 00:47:42,960
Speaker 12: to natural so they really are a perfect substitute.

870
00:47:43,040 --> 00:47:45,560
Speaker 1: So what's that done for the price of natural.

871
00:47:45,200 --> 00:47:48,359
Speaker 12: Diamonds, that is, that is a significant issue. So our

872
00:47:48,560 --> 00:47:53,799
Speaker 12: industry generally has not been prone to technological disruption. This

873
00:47:53,880 --> 00:47:56,760
Speaker 12: is the first time. So over the last twelve months,

874
00:47:56,800 --> 00:47:59,479
Speaker 12: diamond prices have gone down anywhere between eighteen and twenty

875
00:47:59,480 --> 00:48:02,480
Speaker 12: four percent, and I think they're going to go down

876
00:48:02,560 --> 00:48:04,800
Speaker 12: by another twenty to twenty five percent over the next.

877
00:48:04,600 --> 00:48:07,400
Speaker 3: Twelve mile And how did COVID play into that in

878
00:48:07,440 --> 00:48:09,200
Speaker 3: the direction of diamond prices.

879
00:48:09,560 --> 00:48:13,040
Speaker 12: So COVID was another anominally for US, as many other industries.

880
00:48:13,640 --> 00:48:16,359
Speaker 12: Acid prices for diamonds specifically went up by twenty five

881
00:48:16,400 --> 00:48:19,960
Speaker 12: percent between let's say June twenty twenty and June twenty

882
00:48:20,000 --> 00:48:23,800
Speaker 12: twenty two. But that demand has now moderated. During COVID,

883
00:48:23,840 --> 00:48:26,760
Speaker 12: people couldn't go out, they couldn't eat out, they couldn't travel.

884
00:48:26,840 --> 00:48:32,000
Speaker 12: A lot of that stimulus money went into luxury, especially jewelry,

885
00:48:32,280 --> 00:48:34,480
Speaker 12: and now we're seeing a moderation of that. But what's

886
00:48:34,560 --> 00:48:37,759
Speaker 12: now causing the further dip is really technology and lab

887
00:48:37,760 --> 00:48:38,960
Speaker 12: grown diamonds.

888
00:48:38,520 --> 00:48:42,400
Speaker 3: And what exactly was the catalyst for those prices moderating.

889
00:48:43,680 --> 00:48:47,880
Speaker 12: So demand has receded a little bit after the pandemic

890
00:48:48,040 --> 00:48:50,359
Speaker 12: has come off because now people are traveling a lot more,

891
00:48:50,360 --> 00:48:53,200
Speaker 12: they're eating out a lot more, so they're balancing where

892
00:48:53,200 --> 00:48:54,160
Speaker 12: they're spending their money.

893
00:48:54,160 --> 00:48:58,640
Speaker 1: Again, So your company over four hundred employees, where are

894
00:48:58,680 --> 00:49:00,600
Speaker 1: they and what do they do? Sure?

895
00:49:00,680 --> 00:49:04,520
Speaker 12: So we have now ten offices across the world. LA

896
00:49:05,320 --> 00:49:09,080
Speaker 12: is our headquarters. India's our largest operation, followed by Thailand

897
00:49:09,520 --> 00:49:13,640
Speaker 12: and then Ireland, UK, Australia, Canada to add interesting.

898
00:49:13,719 --> 00:49:16,239
Speaker 1: Okay, so talk to us about kind of some other

899
00:49:17,120 --> 00:49:20,640
Speaker 1: gemstones and how do they compare us investments? Because I

900
00:49:20,640 --> 00:49:22,120
Speaker 1: know a lot of people think about them. It's not

901
00:49:22,200 --> 00:49:24,480
Speaker 1: just what I wear around on my finger or around

902
00:49:24,480 --> 00:49:27,160
Speaker 1: my neck. It's an investment for me. How does how

903
00:49:27,160 --> 00:49:29,839
Speaker 1: does those gemstones perform? That's right.

904
00:49:29,880 --> 00:49:32,799
Speaker 12: So colored gemstones as opposed to diamonds, are seeing a

905
00:49:32,840 --> 00:49:35,360
Speaker 12: great boom over the last three years. So the S

906
00:49:35,400 --> 00:49:37,560
Speaker 12: and P three year kieger is around eleven and a

907
00:49:37,600 --> 00:49:41,399
Speaker 12: half percent. Rubies have gone up by seventeen percent per year,

908
00:49:41,800 --> 00:49:44,920
Speaker 12: sapphires by twelve percent a year, emeralds by thirteen percent.

909
00:49:45,239 --> 00:49:47,960
Speaker 12: Some of the more niche gemstones like opals and turmalines

910
00:49:48,000 --> 00:49:50,040
Speaker 12: are up anywhere from twenty percent a year to thirty

911
00:49:50,080 --> 00:49:54,240
Speaker 12: six percent a year, so they are crushing the S andp.

912
00:49:53,760 --> 00:49:56,680
Speaker 3: So there's this growing appreciation for more colored stones as

913
00:49:56,719 --> 00:49:57,680
Speaker 3: an asset class.

914
00:49:57,840 --> 00:49:58,239
Speaker 5: That's right.

915
00:49:58,320 --> 00:50:01,640
Speaker 12: Yeah, So there's a few tailwinds that colorstones are experiencing.

916
00:50:02,120 --> 00:50:04,680
Speaker 12: I think one is we're kind of going from this

917
00:50:04,840 --> 00:50:07,399
Speaker 12: unipolar world where the US dominated. So ten years ago,

918
00:50:07,440 --> 00:50:10,840
Speaker 12: the US was by far the largest consumer of color gemstones.

919
00:50:10,920 --> 00:50:13,880
Speaker 12: Now we have India, which has historically been what I

920
00:50:13,920 --> 00:50:18,160
Speaker 12: would describe as a as a gold jewelry market. Now

921
00:50:18,320 --> 00:50:21,440
Speaker 12: as they get more sophisticated, diamonds and gemstones are catching up,

922
00:50:21,800 --> 00:50:25,120
Speaker 12: and China gemstone consumption is even more so than in

923
00:50:25,200 --> 00:50:28,400
Speaker 12: the US. So we have other tent poles across the

924
00:50:28,440 --> 00:50:32,000
Speaker 12: world that are consuming more. So there's a supply demand imbalance.

925
00:50:32,040 --> 00:50:35,080
Speaker 12: At the same time. On the supply side, mines are

926
00:50:35,120 --> 00:50:38,840
Speaker 12: genuinely running out of material that they're mining. So for example,

927
00:50:38,880 --> 00:50:42,080
Speaker 12: the Burmese mine for rubies has shut down effectively, the

928
00:50:42,160 --> 00:50:46,440
Speaker 12: cash mind for sapphires has shut down. Columbia major emerald producer,

929
00:50:46,560 --> 00:50:49,319
Speaker 12: is really reducing output. So at the same time the

930
00:50:49,360 --> 00:50:52,520
Speaker 12: demand is rising, supply is reducing and we think that's

931
00:50:52,520 --> 00:50:55,759
Speaker 12: going to continue over the next few years. I think

932
00:50:55,800 --> 00:50:57,799
Speaker 12: the other thing for gemstones is when people are now

933
00:50:57,880 --> 00:51:01,759
Speaker 12: realizing that diamonds are depreciating it as an asset class. Traditionally,

934
00:51:01,760 --> 00:51:05,640
Speaker 12: both diamonds and gemstones have really been inflation hedges. But

935
00:51:05,880 --> 00:51:09,240
Speaker 12: because colorstones are appreciating, more and more money is going

936
00:51:09,360 --> 00:51:10,919
Speaker 12: there that we see.

937
00:51:11,000 --> 00:51:13,000
Speaker 1: You think about some of those spy movies where right,

938
00:51:13,160 --> 00:51:16,080
Speaker 1: you get payment in diamonds, you know, because you can

939
00:51:16,560 --> 00:51:18,879
Speaker 1: take all that can, right, But now that's not such

940
00:51:18,920 --> 00:51:19,600
Speaker 1: a good thing, right.

941
00:51:20,040 --> 00:51:22,120
Speaker 3: Something I was curious about is what happens if you

942
00:51:22,200 --> 00:51:25,879
Speaker 3: have it juxtapost where diamond prices are falling, but then

943
00:51:25,920 --> 00:51:28,960
Speaker 3: you have these gemstone prices that are rising. Is there

944
00:51:28,960 --> 00:51:31,360
Speaker 3: a triptical something that can happen there with the swings

945
00:51:31,360 --> 00:51:33,680
Speaker 3: and prices when you have them diverging like that.

946
00:51:34,239 --> 00:51:37,000
Speaker 12: Yeah, I think what we are seeing is much more

947
00:51:37,040 --> 00:51:39,120
Speaker 12: of a transition to color because it is a better

948
00:51:39,160 --> 00:51:42,120
Speaker 12: investment over time. The other thing is I think just

949
00:51:42,160 --> 00:51:46,640
Speaker 12: in terms of culturally, color is becoming much more commonplace.

950
00:51:46,680 --> 00:51:50,640
Speaker 12: So for example, Rolex Automarpiget Petek Philippe, they're higher end watches,

951
00:51:50,680 --> 00:51:55,360
Speaker 12: their most coveted watches now have gemstone bezels around the dial.

952
00:51:56,239 --> 00:51:58,160
Speaker 12: We think we also have. A part of it is

953
00:51:58,480 --> 00:52:01,000
Speaker 12: we're typically now known within the industry as the Debiers

954
00:52:01,000 --> 00:52:04,600
Speaker 12: for colorstone, and the idea is, you know, starting nineteen

955
00:52:04,680 --> 00:52:07,040
Speaker 12: forty eight, Debiers came out with the Diamond Is Forever

956
00:52:07,760 --> 00:52:09,640
Speaker 12: and the Four Seas, and there was this idea that

957
00:52:09,680 --> 00:52:13,359
Speaker 12: there is a singular perfect diamond, which is a de flawless. Now,

958
00:52:13,480 --> 00:52:17,320
Speaker 12: US as a society we've changed considerably. Where we're celebrating uniqueness,

959
00:52:17,360 --> 00:52:21,919
Speaker 12: we're celebrating diversity. We're also celebrating what we like rather

960
00:52:21,960 --> 00:52:24,640
Speaker 12: than what we're told we should like, and that transition,

961
00:52:24,840 --> 00:52:27,800
Speaker 12: we're moving the market towards color with really a focus

962
00:52:27,840 --> 00:52:30,080
Speaker 12: on education and customization of jewelry.

963
00:52:30,360 --> 00:52:32,359
Speaker 1: All right, thirty seconds left. What's the future of your

964
00:52:32,360 --> 00:52:34,040
Speaker 1: company over the next few years. You've got what one

965
00:52:34,120 --> 00:52:35,879
Speaker 1: hundred million revenue roughly, that's right.

966
00:52:35,920 --> 00:52:38,280
Speaker 12: So we've grown four x in the last three years.

967
00:52:38,880 --> 00:52:40,600
Speaker 12: Our next target is a billion dollars. I think we

968
00:52:40,600 --> 00:52:42,839
Speaker 12: can achieve in five years, so that's the goal.

969
00:52:43,120 --> 00:52:46,080
Speaker 1: Really awesome, that is good. We're going to keep in

970
00:52:46,080 --> 00:52:46,720
Speaker 1: touch with this guy.

971
00:52:47,000 --> 00:52:47,960
Speaker 3: I'd forget back.

972
00:52:48,239 --> 00:52:48,439
Speaker 4: Yeah.

973
00:52:48,520 --> 00:52:52,000
Speaker 1: Awesome, that well, congratulations, fascinating discussion. I learned a ton there,

974
00:52:52,360 --> 00:52:55,520
Speaker 1: particularly about the lab grown diamond prices. So do you

975
00:52:55,920 --> 00:52:57,279
Speaker 1: I mean, do you go to your fiancee and kind

976
00:52:57,280 --> 00:52:58,759
Speaker 1: of slip in a like real diamond but it's a

977
00:52:58,840 --> 00:53:00,680
Speaker 1: lab grown one. I don't know what do you do there?

978
00:53:00,719 --> 00:53:02,760
Speaker 3: I mean, look at these emeralds. This is my Bruce Snows.

979
00:53:02,800 --> 00:53:05,840
Speaker 3: I keep looking at that. Up about thirteen percent?

980
00:53:06,200 --> 00:53:08,840
Speaker 1: Yeah, good stuff? All right Oncrutondagah. Thanks so much. CEO

981
00:53:08,880 --> 00:53:11,560
Speaker 1: of Angara joining us here live in our Bloomberg Interactive

982
00:53:11,600 --> 00:53:14,960
Speaker 1: Brokers studio talking about the jewelry business. Fascinating discussion. We

983
00:53:15,080 --> 00:53:17,560
Speaker 1: will follow an Encore's company going forward.

984
00:53:20,040 --> 00:53:23,120
Speaker 2: Thanks for listening to the Bloomberg Markets podcast. You can

985
00:53:23,160 --> 00:53:27,279
Speaker 2: subscribe and listen to interviews at Apple Podcasts or whatever.

986
00:53:27,040 --> 00:53:29,720
Speaker 1: Podcast platform you prefer. I'm Matt Miller.

987
00:53:29,960 --> 00:53:32,879
Speaker 2: I'm on Twitter at Matt Miller nineteen seventy three.

988
00:53:33,320 --> 00:53:34,160
Speaker 10: And I'm Paul Sweeney.

989
00:53:34,239 --> 00:53:36,840
Speaker 1: I'm on Twitter at pt Sweeney. Before the podcast, you

990
00:53:36,880 --> 00:53:40,280
Speaker 1: can always catch us worldwide at Bloomberg Radio