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

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Speaker 1: my co host Matt Miller.

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

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

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

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

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Speaker 1: Ridge Borky covers all the steel in speculating a little

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Speaker 1: bit here. You were just speculating, you know, just spitballing here. Boy,

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Speaker 1: I never thought we'd see a deal in there. I

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Speaker 1: just I guess I wasn't expecting the deal in the

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Speaker 1: steel industry. I don't know why, but it was an

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Speaker 1: interesting transaction. What is Cleveland Cliffs really looking to do here?

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Speaker 1: Why are they doing this steal? Why would they like

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Speaker 1: to do this deal?

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Speaker 3: I think they'd like to do this deal again. Like

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Speaker 3: you touched on, it's back to nineteen twenties. Steel is

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Speaker 3: cool again, right, let's make steel cool. Fortunately, Cleveland Cliffs

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Speaker 3: most of their plants are from the nineteen twenties.

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Speaker 4: Okay, so what.

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Speaker 3: They're looking for is, obviously they're looking to get exposure

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Speaker 3: Big River Steel, which is the EAF to US Steel.

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Speaker 1: Is building what's what's e AF. EAF is electric arc furnace,

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Speaker 1: electric arc furnace.

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Speaker 3: Rather than the blast ferns.

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Speaker 5: So this is newer technology to make steel.

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Speaker 3: Newer technology, more flexible technology usually also non union technology,

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Speaker 3: non union run. So if you look at kind of

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Speaker 3: the math they're paying, they offer thirty five dollars this year,

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Speaker 3: half cash, half stock. The cash portion comes to a

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Speaker 3: little under four billion dollars. US Steel has three point

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Speaker 3: one billion dollars of cash on the balance sheet in

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Speaker 3: order to finish big River steel they bought. They bought

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Speaker 3: Bigger River Steel when it was half built three lower

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Speaker 3: three million tons of year. They're expanding to another three

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Speaker 3: million tons of year to bought. To get three million

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Speaker 3: tons a year of EAF capacity is around three to

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Speaker 3: four billion dollars. So if you look at this way,

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Speaker 3: we just offer stock and four billion dollars of cash

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Speaker 3: to get six over six million tons of y capacity.

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Speaker 3: That probably cost US seventy eight billion to go bill.

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Speaker 6: And that's basically what they're what they're bidding, right.

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Speaker 3: That's basically what they're bidding.

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Speaker 4: Right on the EQUI.

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Speaker 2: But US Steel apparently thinks they're worth more than that,

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Speaker 2: although they were trading for twenty two dollars a share

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Speaker 2: on Friday. Why do they Why do they fight this?

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Speaker 2: It's a it's a decent premium. Can they get more elsewhere?

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Speaker 3: I think you take the deal. You have forty three

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Speaker 3: percent premium today you have out They US Steel House

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Speaker 3: outlining is what they call their best for all plant.

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Speaker 3: So part of it one one part of it is

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Speaker 3: finished billion out Big Steel. Other parts are to upgrade

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Speaker 3: our finishing capabilities. The other part is they're looking to

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Speaker 3: expand in elect sure because electrical steals which flowing to evs,

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Speaker 3: which you're gentlemen, we're talking a little about prior. So

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Speaker 3: they've outlined this plan, but it's a multi year capital

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Speaker 3: spend program and we don't see the success. You know,

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Speaker 3: whether it would be successful doesn't play out for a

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Speaker 3: few years now.

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Speaker 2: Paul, we've been, uh, you know, we've been invited out

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Speaker 2: to see Big River. I'd love to go see because

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Speaker 2: David Stickler came in here. He sat right in that

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Speaker 2: chair where you're sitting, and he said, you guys should

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Speaker 2: come out and check out our new plant.

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Speaker 1: So I guess the issue is, I mean, I'm looking

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Speaker 1: at the capex I mean, for you a steal. They

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Speaker 1: spend you know, a couple of billion dollars a year,

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Speaker 1: two and a half billion dollars a year in capex.

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Speaker 5: Is that money going just for the.

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Speaker 1: New technology or are they still putting money into somebody's

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Speaker 1: plants in Pittsburgh and Garyan Dan and Allentown, Allentown.

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Speaker 3: Right, it's about two thirds one thirds, two thirds for

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Speaker 3: the new technology, one third for old technology.

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Speaker 5: And the old technology used to kind of keep it running.

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Speaker 3: Try and keep it running.

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Speaker 4: Wow.

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Speaker 3: Correct, So the the thing here is you you have

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Speaker 3: this plan to you know, both US Steel and Cliffs

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Speaker 3: have been shining down, rationalizing capacity, you know, closing the

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Speaker 3: old blastwarenesses and things like that.

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Speaker 2: That's why environmental purposes are just because it costs too

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Speaker 2: much to run.

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Speaker 6: They're not getting enough.

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Speaker 3: A product out of it really costs too much to run, right,

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Speaker 3: and the technology in order to reinvest in the technology

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Speaker 3: is just too expensive.

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Speaker 2: Well, well, this is what Cleveland Cliffs is looking to

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Speaker 2: do essentially, right, this is what US deal is doing

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Speaker 2: at Big River, and Cleveland Cliffs wants to buy that

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Speaker 2: from them.

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Speaker 3: Right, if you look at this will take the pro

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Speaker 3: former company clean cliffs probably is ninety three percent blast

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Speaker 3: furnesses today seven percent eafs. The new technology this would

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Speaker 3: take them to eighty twenty beyond that. You say, well, gee,

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Speaker 3: that seems not that big of a deal, But it

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Speaker 3: gives them a lot of flexibility because one of the

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Speaker 3: other things about eafs is you can shut them off,

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Speaker 3: turn them up, and turn them down a lot easier

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Speaker 3: than you can't wait blastphemers.

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Speaker 2: By the way, who who are there competitors? Who else

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Speaker 2: in this market could make a bid?

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Speaker 6: If you know US steel could be worth more.

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Speaker 3: Well, you would have Newcorn Steel Dynamics, which are two

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Speaker 3: other big players in the US. They're one hundred percent

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Speaker 3: technology there, so they probably don't care for the blast

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Speaker 3: furnace route.

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Speaker 4: Right.

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Speaker 3: You know you mentioned that David Stickler built inviting you

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Speaker 3: guys out to see bigger or steel. He built bigger steel.

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Speaker 3: Now he's moved on to another project, high Bar, which

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Speaker 3: a rebar plant. But David Steckler also built some steel

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Speaker 3: dynamics first plant or was it part of a group

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Speaker 3: that built the first eas for steel dynamics.

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Speaker 5: So are new steel mills in the United States being

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Speaker 5: built today?

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Speaker 3: Well, what's happened is Section two thirty two has really

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Speaker 3: helped these guys.

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Speaker 5: If you look at the US steel what a second two.

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Speaker 3: Section two thirty two was the the Trump laws to

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Speaker 3: prevent imports, putting a twenty five percent tariff.

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Speaker 6: On steel import to the US.

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Speaker 3: If you look at the US steel industry, US steel

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Speaker 3: industry historically is about one hundred million tons of steel

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Speaker 3: a year. Eighty percent is produced in the US, twenty

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Speaker 3: percent is historically imported. With these tariffs, that's made the

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Speaker 3: imports a little less competitive. What's happened is we've seen

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Speaker 3: over the last few years capacity being added in the

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Speaker 3: US to displace the tariffs once they finally to displace

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Speaker 3: the import steel once the tariffs finally go away.

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Speaker 5: So that so the enshoing is actually happening in the

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Speaker 5: steel business.

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Speaker 4: Okay, great, So.

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Speaker 1: Are you put these If you were to put these

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Speaker 1: two companies together, Cleveland Cliffs in the US steal, how

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Speaker 1: big would they be kind of relative to the world

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Speaker 1: order of steel makers.

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Speaker 3: It wouldn't make it would make them a top ten

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Speaker 3: in the world. Okay, I don't think scale helps you

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Speaker 3: at all, But.

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Speaker 1: That's why I thought manufacturing scale helps. I learned that

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Speaker 1: I know you went to shout to go business school.

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Speaker 1: You guys are good with the numbers, but.

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Speaker 3: It's just the industry that's you know historic. You know,

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Speaker 3: it costs a lot of ship So why would you

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Speaker 3: why would it be cheaper to build a ton steal

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Speaker 3: in the US put a boat to somewhere else?

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Speaker 7: All Right?

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Speaker 5: So what what's what's this steel call?

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Speaker 7: Today?

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Speaker 1: You talk to institutional investors in the the three guys

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Speaker 1: in Pittsburgh that still invest in this stuff. What's the

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Speaker 1: call here? Do I do I buy a new Core?

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Speaker 1: Do I buy a US steal? What's what's the investment

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Speaker 1: call for this group?

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Speaker 3: I mean if you kind of look at New Core,

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Speaker 3: Newcore and Steel Dynamics, that I'll perform these these two

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Speaker 3: guys by wide margin.

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Speaker 5: And that's the function of the technology.

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Speaker 3: That I think one function of technology. And two you've

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Speaker 3: also New Core and Steel Dynamics have over the last

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Speaker 3: few years been more aggressive in moving further downstream.

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Speaker 5: Part of this deal.

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Speaker 1: And I could go to Jenrey because she's the expert

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Speaker 1: in anti trust. What is generally telling you about any

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Speaker 1: tit trust here?

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Speaker 3: There's going to be I think there's gonna be an

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Speaker 3: anti trust review here talking with Jen too. She was

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Speaker 3: also so that's another element of YO, another reason whether

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Speaker 3: the deal goes up.

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Speaker 2: Yeah, I mean I look at the five year performance

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Speaker 2: of as we mentioned earlier. You know, US steel has

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Speaker 2: returned nothing negative one percent, but new core is has

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Speaker 2: tripled over the last five years, and and Steel Dynamics

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Speaker 2: is almost there. I mean they're up one hundred and

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Speaker 2: seventy percent.

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Speaker 5: Money and steel companies.

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Speaker 2: Yeah, what are the contracts that you look at for

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Speaker 2: the underlying and get ten seconds?

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Speaker 6: What are the what are the steel contracts that.

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Speaker 3: You look at for the price, the price if you

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Speaker 3: look at S T A, N H HC.

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Speaker 5: All right, all right, we'll get into the steel business.

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Speaker 6: That's w is this hot rold steel? I got rolled

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Speaker 6: steel steel?

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Speaker 4: All right?

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Speaker 5: Rich Richard Borg, thanks so much for joining us.

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Speaker 1: Rich Board covers all the steel companies and all that

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Speaker 1: good stuff for Bloomberg Intelligence.

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Speaker 8: You're listening to the team Ken's are live program Bloomberg

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Speaker 8: Markets weekdays at ten am Eastern on Bloomberg dot com,

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Speaker 8: the iHeartRadio app and the Bloomberg Business app, or listen

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Speaker 8: on demand wherever you get your podcasts.

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Speaker 1: Matt, we like talking to Dan Ees equity als what

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Speaker 1: Bush Security is.

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Speaker 5: Pretty smart guy.

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Speaker 1: We usually learn a thing or two when we talk

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Speaker 1: to him, but I don't feel the need to interrupt

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Speaker 1: his vacation, particularly when his vacation is increte.

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Speaker 5: He's not down at the Jersey Shore.

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Speaker 1: He's in crete, and we get a video zoom thing

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Speaker 1: going of him right here. Because Bloomberg Markets is simulcast

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Speaker 1: on YouTube, you can go to YouTube and search Bloomberg

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Speaker 1: Global News. Dan, I hope you're having a great time

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Speaker 1: and create it looks beautiful behind you. Give us a

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Speaker 1: good sense of what you've kind of just observed just

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Speaker 1: in your time over there.

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Speaker 4: Yeah.

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Speaker 9: No, it's great to be great to be with you, Matt.

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Speaker 10: I am look, I mean to me, it's it's it's

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Speaker 10: it's always great food, great music. You see some you

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Speaker 10: know some obviously some maybe electric vehicles here and there,

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Speaker 10: but it's it's been a great it's been great and uh,

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Speaker 10: you know, I think it's just a good uh.

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Speaker 9: Sort of rest going into what's gonna be an exciting fall.

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Speaker 1: All right, Dan, So I'm just looking at the bring

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Speaker 1: you back into reality here, the NASTAQ one hundred stock

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Speaker 1: index Real Tech heavy here up thirty eight percent year

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Speaker 1: to date. Put that stock price performance in context of

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Speaker 1: what we've seen from these tech companies for the first

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Speaker 1: you know, we just got through their second quarter earnings

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Speaker 1: and kind of the outlook for the remainder of the year.

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Speaker 5: Put that into into context.

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Speaker 10: For US, I Mett, I view this as just a

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Speaker 10: little sell off, a pause to what's going to be

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Speaker 10: a rally second half of the year for tech. I

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Speaker 10: think if you look at numbers overall, from cloud to

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Speaker 10: digital media to what we're seeing across enterprise, I.

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Speaker 9: Think we're actually it's stronger than expected.

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Speaker 10: Now, granted the stocks have run significant, you've seen sell

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Speaker 10: offs posed to Apple, Microsoft, Tessa.

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Speaker 9: I think it's short lived because I think.

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Speaker 10: Second half numbers were conservative and we're seeing upticks across

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Speaker 10: the board.

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Speaker 9: That's sort of our view going into the year end.

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Speaker 2: Dan, where do you think they look most conservative? When

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Speaker 2: you when you were going over these earnings reports before

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Speaker 2: you jumped on the private jet decrete, where did you

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Speaker 2: where did you think, Ah, this is these guys are

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Speaker 2: sandbagging US.

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Speaker 9: I think Microsoft is extremely conservative.

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Speaker 10: I think that's probably the one that sticks out the

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Speaker 10: most relative to what we've seen post quarters, especially what

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Speaker 10: we see from a from a channel perspective, I think

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Speaker 10: cloud actually see an acceleration.

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Speaker 9: We saw that with AWS, we see I think as

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Speaker 9: with Google as well.

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Speaker 10: I think overall cybersecurity, that's probably the sub sector that

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Speaker 10: I think sandbag i'll call it the most relative to estimates,

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Speaker 10: you know, and that's one where we actually see strength

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Speaker 10: going the second half of the year. And I look

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Speaker 10: at Apple, I think that's one where everything we see

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Speaker 10: from a supply chain perspective is going to be strength

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Speaker 10: from this iPhone fifteen, not just into September, but in

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Speaker 10: the year end. So that's where I really am looking.

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Speaker 10: And I continue to think AI, and we're seeing that

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Speaker 10: the mind isetion is gonna be a lot sooner than expected.

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Speaker 1: All Right, That's kind of where I wanted to go

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Speaker 1: dan AI, because that's been one of the if not

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Speaker 1: the driving force behind some of the performers we've seen

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Speaker 1: out of these the NASDAQ one hundred and big tech names.

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Speaker 1: When you talk to the institutional investor clients, what's kind

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Speaker 1: of the key question regarding AI that you get from them.

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Speaker 10: I think the big question is within Nvidia's guidance heard

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Speaker 10: around the world last quarter, the four billion dollaries. Okay,

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Speaker 10: well we're not going to We're not seeing that ramp

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Speaker 10: as quick across software. That's maybe the knee jerk disappointment

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Speaker 10: from Microsoft to maybe some of the chip names.

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Speaker 9: I think that's that's misplaced.

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Speaker 10: I think this is really just the start of what

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Speaker 10: I've used sort of fourth Industrial Revolution, almost AI goal rush,

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Speaker 10: playing out a trillion dollars of incremental spend.

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Speaker 9: And I just hit back of clients.

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Speaker 10: You know, from everything we see next year it's eight

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Speaker 10: to ten percent of budgets in terms of AI versus

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Speaker 10: less than one percent today. That iView is really going

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Speaker 10: to be the catalyst for this tech. What I view

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Speaker 10: is really the new tech bull market that's already begun

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Speaker 10: to continue well into twenty twenty four.

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Speaker 2: We're where have markets or what have markets missed? Because

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Speaker 2: in video we have all seen and everyone's impressed, and

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Speaker 2: I think everyone even Paul has bought some.

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Speaker 6: What what have we overlooked?

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Speaker 9: Well?

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Speaker 10: I think I think what's markets maybe missing the near

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Speaker 10: term is just what a catalyst this is going to

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Speaker 10: be for software.

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Speaker 9: I think really it's the software when I look at.

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Speaker 10: Names like Salesforce, Mango, dB, you look at AI names

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Speaker 10: like Pou and Teer. I think it's the second, third,

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Speaker 10: fourth derivatives that maybe market's missing out here and just

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Speaker 10: maybe underestimating it because I believe this is a nineteen

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Speaker 10: ninety five moment Star of the Internet nine nineteen ninety nine,

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Speaker 10: two thousand moment. I think that dynamic is why we

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Speaker 10: see tech stocks of fifteen percent second half of the year.

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Speaker 1: Dan, you mentioned Palenteer, and I know you recently initiated

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Speaker 1: coverage there. Not a lot of people know what Palenteer is.

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Speaker 1: Give us just a quick description what Palenteer is, uh,

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Speaker 1: and why you like this name?

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Speaker 10: Yeah, and that's one you know, car create Palenteer really

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Speaker 10: coming out of the government.

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Speaker 9: You know, if you look at where their success has been.

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Speaker 10: Really machine learning is who's who they are, and now

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Speaker 10: it's happening from a use case perspective, they probably have

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Speaker 10: the most robust pure play AI platform and use cases

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Speaker 10: out there, so you're seeing across enterprise, across government. I

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Speaker 10: look at it as a pure PLAYAI play of course

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Speaker 10: in Nvidia, Microsoft, Google, Mango dB among others, but this

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Speaker 10: is really front and center.

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Speaker 9: I think it's a it's an.

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Speaker 10: Undiscovered gem in my opinion relative to the broader.

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Speaker 9: AI try.

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Speaker 6: Let's get to Tesla.

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Speaker 2: I want to ask you about the price cuts in China.

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Speaker 2: Paul brought this up already. Weren't they supposed to stop

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Speaker 2: doing that?

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Speaker 9: Yeah, look we've said that here and there you could

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Speaker 9: see some price cuts, but this only impacts what fifteen

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Speaker 9: percent of when you look at model wise impacted, it's

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Speaker 9: really around the edges. I mean mostly you're really not

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Speaker 9: seeing price cuts across in China.

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Speaker 4: Now.

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Speaker 10: Of course, headline it's gonna look negative, but if I

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Speaker 10: look from a demand perspective, China continues to be strong.

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Speaker 10: I think on track from one point e potentially higher

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Speaker 10: in terms of million units for the year. And right

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Speaker 10: now they are just I view, this is almost halftime

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Speaker 10: of the super Bowl. But does this they're just gearing up.

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Speaker 2: But does this kick off? Does this kick off another

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Speaker 2: price war Dan in China?

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Speaker 10: I don't think so, because Matt we've talked to, I mean,

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Speaker 10: all the work that we've done the last two three weeks,

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Speaker 10: we think ninety five percent of the price cuts are

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Speaker 10: basically done in China. In terms of that price I

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Speaker 10: don't see this igniting the next stage to the price war,

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Speaker 10: which is why we're buyers here on weakness. I think

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Speaker 10: this is Barkwurson bite.

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Speaker 1: Hey, danis just looking at the PGeo function on the

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Speaker 1: Bloomberg terminal, which gives you a breakdown of the company's

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Speaker 1: businesses by measured by geography, by segment. And I'm looking

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Speaker 1: at the bi geography and China is now up to

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Speaker 1: you more than twenty percent of revenue for Tesla. How

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Speaker 1: does Elon musk How does Tesla manage its relationship with

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Speaker 1: such an important market like China?

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Speaker 5: How do they manage it?

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Speaker 10: I mean they take a page out of the playbook

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Speaker 10: from Cook and Apple because it's ten percent political ninety

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Speaker 10: percent business. But ultimately, I mean if you look at

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Speaker 10: you President g and in China, they look at having

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Speaker 10: Tesla in terms of Giga Shanghai potentially second.

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Speaker 9: Factory as well.

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Speaker 10: That's a that's a trophy case in terms of having

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Speaker 10: Tesla as well as Apple there. It's a major part

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Speaker 10: of production, a major part of the mand It's the

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Speaker 10: hearts and lungs of the story.

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Speaker 9: And that's been that tight groupe, that balancing act.

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Speaker 10: It's been one of the key successes that has been

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Speaker 10: able to navigate despite many worrying about the geopolitical and

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Speaker 10: I think they continue to gain and share there along

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Speaker 10: with Apple, and I think that's a story that's going

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Speaker 10: to continue to play out.

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Speaker 5: But to what.

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Speaker 1: Extent do investors or do you view their exposure to

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Speaker 1: China as a meaningful risk.

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Speaker 10: I view it as a background noise, headline risk.

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Speaker 9: I think they've been able to navigate that well.

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Speaker 10: I think maybe some of the risks, the most of

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Speaker 10: that was probably in the rear view mirror. It's still

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Speaker 10: gonna you know, for here and there, just given the

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Speaker 10: geopolitical but you know, we view a more as an

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Speaker 10: opportunity than a risk for the likes of test on

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Speaker 10: an Apple, and that that continues to be our view

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Speaker 10: of China in terms of those stories.

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Speaker 2: I just want to ask about the competition you see

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Speaker 2: from GM and Ford. I was over looking at Cadillacs

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Speaker 2: new Escalade IQ, which is awesome but expensive, so it's

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Speaker 2: not going to compete really with anything Tesla makes other

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Speaker 2: than I guess the X.

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Speaker 6: I don't think that would be a fair fight.

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Speaker 2: But are they gonna are the are the incumbent automakers

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Speaker 2: going to really bring it Tesla in terms of affordable

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Speaker 2: electric vehicles?

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Speaker 9: Well, that's the key.

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Speaker 10: I mean, if you what's come out of three win

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Speaker 10: three area code more affordable evs.

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Speaker 9: I think what Farley's doing to Ford or Mary's doing it,

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Speaker 9: you know GM. I think it's the right strategy, and

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Speaker 9: it's not a zero some game.

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Speaker 10: I think they are going to really transform those companies

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Speaker 10: in terms of four and GM, but Tesla in terms

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Speaker 10: of electric vehicles, it's still Tesla's world. Everyone else is

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Speaker 10: paying rent. And I think this is just start of

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Speaker 10: this green tidle wave five trillion dour green tyway that's

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Speaker 10: really going to transform autos across the board, from Tesla.

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Speaker 9: To three win three area code Europe and of course China.

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Speaker 1: Hey Dan, thanks so much for joining us. Well appreciate

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Speaker 1: you taking a few minutes out of your holiday. Dan

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Speaker 1: ives from Crete. He's a managing director senior equity analyst

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Speaker 1: at web Busch Securities. I don't know that water behind

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Speaker 1: is at the gen C or the Mediterranean.

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Speaker 5: I mean there's both of there.

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Speaker 2: I mean, what's what, I guess that's the med Yeah,

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Speaker 2: both of them are. It's a completely random gas and

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Speaker 2: I have no way.

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Speaker 5: I'm looking at my Google maps here. It could be either.

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Speaker 5: I don't know where one starts the other one begins.

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Speaker 5: Don't see at the tree right off, right off?

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Speaker 1: I guess the southern coast there of Greece, part of

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Speaker 1: Greece's largest island and vacation spot in Greece.

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Speaker 5: How about that? So Dan I was over there getting

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Speaker 5: a little R and R. Good for him.

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Speaker 8: You're listening to the tape catch are live program Bloomberg

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00:19:22,320 --> 00:19:25,919
Speaker 8: Markets weekdays at ten am Eastern on Bloomberg Radio, the

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Speaker 8: tune in app, Bloomberg dot Com, and.

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Speaker 4: The Bloomberg Business App.

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00:19:29,240 --> 00:19:32,040
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Speaker 8: flagship New York station, Just say Alexa, play Bloomberg eleven thirty.

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Speaker 1: Lots going on today, Matt in kind of just geopolitics

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Speaker 1: around the world. Argentina, they had a primary vote. I

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Speaker 1: guess a populist leaning person, not just any populous. He

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Speaker 1: looks like a cross between Elvis Presley and Johnny Cash. Nice.

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Speaker 2: Maybe throw a little James Spader in there. U.

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Speaker 6: He has I think five.

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Speaker 2: Or six giant dogs, each one named after a different

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Speaker 2: Austrian economist or I mean like liberal free market economists.

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Speaker 2: One of them is called Milton Friedman. I know from

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Speaker 2: the Bloomberg story, and I mean there's such a great

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Speaker 2: story on the terminal that if you haven't read it,

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Speaker 2: I highly recommend going to uh Bloomberg dot com on

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Speaker 2: the on the computer or on your or on your

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Speaker 2: terminal checking it out because it's really good.

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Speaker 1: And we've got big moves in the currencies and the

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Speaker 1: bonds and the treasure treasure of the government bonds there

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Speaker 1: in Argentina as well. So let's get the latest reporting there.

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Speaker 1: Manuela Tobias she joined us. She's the economy and government

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Speaker 1: reporter for Bloomberg News. Menuel it's just let's just start

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Speaker 1: off by just reframing what happened in this primary and

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Speaker 1: and what has been the reaction in Argentina.

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Speaker 5: Menuela is in Buenos As today.

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Speaker 11: Hey, yeah, so yesterday it was just a complete upset.

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Speaker 11: The economists you guys have been talking about. Outsider presidential

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Speaker 11: bid was not expected to get more than twenty percent

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Speaker 11: in this three way race. There was the incumbent, the

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Speaker 11: main opposition block that everyone saw as winning, and the

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Speaker 11: question mark was around you know what kind of margin

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Speaker 11: they would be the incumbent buy and then Relay just

446
00:21:25,760 --> 00:21:30,200
Speaker 11: completely surprised everyone and got thirty percent of the vote,

447
00:21:30,200 --> 00:21:33,840
Speaker 11: becoming the most voted candidates as well as the most

448
00:21:33,920 --> 00:21:41,000
Speaker 11: voted block, and that just sent everyone into panic mode.

449
00:21:41,160 --> 00:21:44,080
Speaker 11: No one was expecting this on the political side, and

450
00:21:44,200 --> 00:21:48,440
Speaker 11: much less on the market side. This was the least

451
00:21:48,560 --> 00:21:52,359
Speaker 11: likely scenario and definitely the one that was going to

452
00:21:52,440 --> 00:21:56,240
Speaker 11: see the most panic, and that's exactly what we're seeing today.

453
00:21:57,880 --> 00:22:02,240
Speaker 11: The central Bank just raised its key rate from the

454
00:22:02,400 --> 00:22:05,840
Speaker 11: already very high ninety seven percent to one hundred and eighteen,

455
00:22:06,520 --> 00:22:13,640
Speaker 11: and he valued the bissal, which was treating to it's

456
00:22:13,760 --> 00:22:17,880
Speaker 11: it's an officially set rate, from eighty seven to three

457
00:22:17,920 --> 00:22:19,680
Speaker 11: point fifty today.

458
00:22:20,040 --> 00:22:23,800
Speaker 6: I love the quote from your story, Manuela.

459
00:22:24,119 --> 00:22:26,919
Speaker 2: He says, remember, a different Argentina is impossible with the

460
00:22:26,960 --> 00:22:29,000
Speaker 2: same people as always, with the same people that have

461
00:22:29,119 --> 00:22:32,080
Speaker 2: always failed. Uh, you got to get rid of all

462
00:22:32,080 --> 00:22:36,240
Speaker 2: that old blood, right, I mean, does he really want

463
00:22:36,280 --> 00:22:39,160
Speaker 2: to throw out everybody who's been there?

464
00:22:39,200 --> 00:22:39,400
Speaker 6: Well?

465
00:22:39,560 --> 00:22:42,160
Speaker 2: Does he really want to start a revolution here?

466
00:22:44,200 --> 00:22:50,000
Speaker 11: That is that's his proposal, and that's exactly why people

467
00:22:50,040 --> 00:22:54,080
Speaker 11: support him, you know, talking to his voters, this was,

468
00:22:54,960 --> 00:22:58,280
Speaker 11: you know, very much a question mark collection. No one

469
00:22:58,359 --> 00:23:02,400
Speaker 11: knew what was coming, and all the Post has described

470
00:23:02,440 --> 00:23:06,240
Speaker 11: it as one with high levels of apathy among voters.

471
00:23:06,240 --> 00:23:08,520
Speaker 11: I mean, you know, we have inflation running at one

472
00:23:08,640 --> 00:23:12,840
Speaker 11: hundred and sixteen percent, nearly forty percent of the population

473
00:23:13,080 --> 00:23:17,359
Speaker 11: is below the poverty line. Things are pretty desperate here,

474
00:23:17,440 --> 00:23:23,600
Speaker 11: and you know you need the thinking among his voters,

475
00:23:23,600 --> 00:23:26,800
Speaker 11: which you know who turned out and droves, is you

476
00:23:26,880 --> 00:23:29,800
Speaker 11: need someone just as crazy as a situation that that

477
00:23:29,920 --> 00:23:33,400
Speaker 11: we're experiencing to get us out of this mess. And

478
00:23:33,600 --> 00:23:36,400
Speaker 11: you know that was his campaign message. He said, I'm

479
00:23:36,440 --> 00:23:41,280
Speaker 11: gonna blow it all up. In fact, during his the

480
00:23:41,640 --> 00:23:47,880
Speaker 11: campaign closing event to get you know, his his fans

481
00:23:48,080 --> 00:23:52,640
Speaker 11: riled up. Before coming on stage, he had a of

482
00:23:52,720 --> 00:23:57,399
Speaker 11: videos of buildings blowing up h literally and people you know,

483
00:23:57,480 --> 00:24:00,720
Speaker 11: going crazy in the crowd, you know, before he came

484
00:24:00,760 --> 00:24:04,760
Speaker 11: on and just started singing his theme song and you

485
00:24:04,800 --> 00:24:14,720
Speaker 11: know his his his big bontra is you know uh uh.

486
00:24:14,600 --> 00:24:17,480
Speaker 6: So yeah, wait, what what is that? What's the translation

487
00:24:17,560 --> 00:24:17,880
Speaker 6: of that?

488
00:24:21,600 --> 00:24:27,159
Speaker 11: It basically means, uh sorry, I'm trying to translate in

489
00:24:27,200 --> 00:24:29,760
Speaker 11: my head now we have it at the on the

490
00:24:29,840 --> 00:24:34,760
Speaker 11: lead of the story. It's well, let's go liberty.

491
00:24:34,720 --> 00:24:40,240
Speaker 2: Dam I see long lived freedom damn it exactly the translation.

492
00:24:40,320 --> 00:24:43,040
Speaker 1: All right, So Menmuel, what's the next steps out here

493
00:24:43,280 --> 00:24:45,760
Speaker 1: in Argentina on this political process?

494
00:24:47,440 --> 00:24:47,760
Speaker 8: Uh?

495
00:24:48,280 --> 00:24:52,159
Speaker 11: So, okay, So this was a primary election, very different

496
00:24:52,240 --> 00:24:54,840
Speaker 11: from from the primaries you guys are probably used to,

497
00:24:56,080 --> 00:24:59,639
Speaker 11: where you know, there's different different parties have their own votes.

498
00:24:59,760 --> 00:25:03,160
Speaker 11: This is kind of like a general because everyone, all

499
00:25:03,200 --> 00:25:08,320
Speaker 11: the candidates are on the ballot and so and polls,

500
00:25:08,520 --> 00:25:13,360
Speaker 11: as we just went over, are completely unreliable, and so

501
00:25:13,560 --> 00:25:16,359
Speaker 11: this was kind of a thermometer of what's to come

502
00:25:16,720 --> 00:25:21,240
Speaker 11: in the general. This knocked out one of the two

503
00:25:22,760 --> 00:25:28,239
Speaker 11: candidates in the pro market business coalition. Uh, and you know,

504
00:25:28,440 --> 00:25:33,560
Speaker 11: left the incumbent party pretty knocked down. It's a historically

505
00:25:34,480 --> 00:25:39,000
Speaker 11: poor election for the parentist left wing party. So what

506
00:25:39,040 --> 00:25:42,760
Speaker 11: this means is we have an October election coming up

507
00:25:42,840 --> 00:25:47,800
Speaker 11: that's a general. In order to win that election, the

508
00:25:48,119 --> 00:25:50,760
Speaker 11: winning candidate needs to get forty five percent of the

509
00:25:50,840 --> 00:25:56,720
Speaker 11: vote or forty percent with a ten percentage point ten

510
00:25:56,840 --> 00:26:00,320
Speaker 11: ten points above you know, the runner up. If we

511
00:26:00,600 --> 00:26:04,160
Speaker 11: don't get to that, which is looking increasingly likely. With

512
00:26:04,640 --> 00:26:09,000
Speaker 11: you know this this free literally divided in three, we

513
00:26:09,080 --> 00:26:12,639
Speaker 11: could go to a runoff, which would come in November.

514
00:26:13,520 --> 00:26:16,440
Speaker 11: So uncertainty is is certainly the.

515
00:26:17,400 --> 00:26:20,680
Speaker 2: So just just to circle back to Javier Malai here

516
00:26:22,640 --> 00:26:26,920
Speaker 2: he I mean, in the story we say that he's

517
00:26:27,200 --> 00:26:31,240
Speaker 2: kind of radical with some far out ideas, and I

518
00:26:31,280 --> 00:26:33,520
Speaker 2: think the term crazy was was thrown in there.

519
00:26:33,920 --> 00:26:36,360
Speaker 6: But his dog's name Milton Friedman.

520
00:26:36,560 --> 00:26:39,679
Speaker 2: So for those of us in this country, Manuela Milton

521
00:26:39,720 --> 00:26:47,360
Speaker 2: Friedman is a pretty sound thinking free market capitalist economists, Like, uh,

522
00:26:47,880 --> 00:26:54,080
Speaker 2: what's the crazy part of Milly's propositions here, because it

523
00:26:54,119 --> 00:26:56,760
Speaker 2: seems like I don't see anything that that far out

524
00:26:56,760 --> 00:26:57,720
Speaker 2: there for us.

525
00:26:57,920 --> 00:27:02,840
Speaker 11: Yeah, of course, okay, So that you know, I think

526
00:27:02,840 --> 00:27:07,760
Speaker 11: that's part of the nuance here is that his his reforms,

527
00:27:07,800 --> 00:27:10,040
Speaker 11: you know, the policy ideas that he has, which is,

528
00:27:10,320 --> 00:27:12,960
Speaker 11: you know, cutting public spending. In fact, he says, you know,

529
00:27:13,040 --> 00:27:17,560
Speaker 11: bringing a chain saw to public spending. These are you know,

530
00:27:17,680 --> 00:27:20,560
Speaker 11: the kind of ideas that that could support that the

531
00:27:20,600 --> 00:27:25,800
Speaker 11: Pro Business Coalition is proposing. But he has a certain

532
00:27:26,240 --> 00:27:29,920
Speaker 11: in you know, eccentricity to him. We're talking about his dog.

533
00:27:30,440 --> 00:27:34,359
Speaker 11: This uh, this dog, he refers to him as his son.

534
00:27:34,880 --> 00:27:38,840
Speaker 11: And then the four the four kids that this dog

535
00:27:38,920 --> 00:27:44,680
Speaker 11: had are his grandkids. That that that oldest dog died

536
00:27:45,000 --> 00:27:49,719
Speaker 11: and he actually had it cloned. He had another another

537
00:27:49,800 --> 00:27:54,000
Speaker 11: dog and was you know, talking about him again.

538
00:27:54,040 --> 00:27:56,040
Speaker 6: I'm again, I'm totally understanding all of this.

539
00:27:57,400 --> 00:27:59,520
Speaker 1: All right, Matt Manuel. You've got a lot going on

540
00:27:59,600 --> 00:28:02,680
Speaker 1: down there. We're gonna look forward to some more reporting

541
00:28:02,720 --> 00:28:07,080
Speaker 1: down there, but a big, big surprise in the primary

542
00:28:07,080 --> 00:28:09,680
Speaker 1: election down there, which is royling the financial markets down

543
00:28:10,200 --> 00:28:11,800
Speaker 1: in Argentina.

544
00:28:12,600 --> 00:28:16,040
Speaker 8: You're listening to the team Ken's our live program, Bloomberg

545
00:28:16,080 --> 00:28:19,439
Speaker 8: Markets weekdays at ten am eastering on Bloomberg dot com,

546
00:28:19,520 --> 00:28:22,680
Speaker 8: the iHeartRadio app and the Bloomberg Business App, or listen

547
00:28:22,760 --> 00:28:24,880
Speaker 8: on demand wherever you get your podcasts.

548
00:28:26,800 --> 00:28:28,560
Speaker 5: A hotspot to another, that being China.

549
00:28:28,560 --> 00:28:30,280
Speaker 1: We got a lot of economic data coming out of

550
00:28:30,359 --> 00:28:33,280
Speaker 1: China which was we're not economic but more just some

551
00:28:33,280 --> 00:28:35,919
Speaker 1: financial data going on about the economy and some of

552
00:28:35,920 --> 00:28:39,040
Speaker 1: the businesses there. Tom Orlock joins his chief economists and

553
00:28:39,120 --> 00:28:43,200
Speaker 1: chief ASA economists for Bloomberg Economics. Tom, you lived and

554
00:28:43,240 --> 00:28:46,120
Speaker 1: worked in Beijing four years. You know what's going on there.

555
00:28:47,360 --> 00:28:49,240
Speaker 1: You know, I think twenty twenty three people went into

556
00:28:49,320 --> 00:28:51,240
Speaker 1: saying this is gonna be a good year for China.

557
00:28:51,280 --> 00:28:55,040
Speaker 1: The economy's reopening, We're gonna be a good year for China.

558
00:28:55,040 --> 00:28:56,840
Speaker 5: It's not turning out to be that way.

559
00:28:56,840 --> 00:28:59,560
Speaker 1: What are some of the key headwinds that we've been

560
00:28:59,600 --> 00:29:01,920
Speaker 1: reading in the last couple of days.

561
00:29:02,720 --> 00:29:06,840
Speaker 12: So the really big challenge for China's economy this year, Paul,

562
00:29:07,000 --> 00:29:09,880
Speaker 12: is what's happening in real estate. Real estate is a

563
00:29:10,000 --> 00:29:15,120
Speaker 12: critically important driver of China's economic growth. It's also where

564
00:29:15,160 --> 00:29:18,960
Speaker 12: households have most of their wealth. Chinese households, they haven't

565
00:29:18,960 --> 00:29:20,600
Speaker 12: got a lot in stocks, they haven't got a lot

566
00:29:20,600 --> 00:29:25,040
Speaker 12: in bonds. It's all in their houses. And what's happening

567
00:29:25,120 --> 00:29:29,200
Speaker 12: right now is there's just been massive overbuilding in the

568
00:29:29,200 --> 00:29:31,840
Speaker 12: real estate sector, and there as a consequence of that,

569
00:29:31,920 --> 00:29:36,640
Speaker 12: we're now seeing property sales falling, property investment falling, and

570
00:29:36,720 --> 00:29:41,520
Speaker 12: property prices, especially in some smaller cities falling. Now, that

571
00:29:41,720 --> 00:29:44,400
Speaker 12: was already part of the narrative in twenty twenty two,

572
00:29:44,920 --> 00:29:48,520
Speaker 12: what's happening in twenty twenty three is getting worse. We've

573
00:29:48,560 --> 00:29:51,880
Speaker 12: now got news that Country Garden, one of China's biggest

574
00:29:51,920 --> 00:29:56,280
Speaker 12: property developers, is struggling to repair its debt, and we've

575
00:29:56,320 --> 00:29:59,360
Speaker 12: got some alarming signs that this is now tipping over

576
00:29:59,640 --> 00:30:03,520
Speaker 12: into the financial sector. One of China's biggest trust companies

577
00:30:03,760 --> 00:30:05,760
Speaker 12: struggling to make its investors whole.

578
00:30:06,960 --> 00:30:12,320
Speaker 2: So what happens here, I mean, if one of their

579
00:30:12,360 --> 00:30:16,000
Speaker 2: biggest trust companies starts defaulting on payments, doesn't the government

580
00:30:16,120 --> 00:30:19,200
Speaker 2: just come in take it over and make those payments

581
00:30:19,240 --> 00:30:21,480
Speaker 2: in lieu of the failed company.

582
00:30:22,120 --> 00:30:26,400
Speaker 12: So the strength that China still has is that the

583
00:30:26,480 --> 00:30:30,640
Speaker 12: savings rate within China is really high. It's hard to

584
00:30:30,680 --> 00:30:33,280
Speaker 12: take money out of the country. And if you look

585
00:30:33,320 --> 00:30:36,000
Speaker 12: at not the shadow banking system, not the shadow banks

586
00:30:36,000 --> 00:30:39,480
Speaker 12: like the trusts, but the banking system proper, it's all

587
00:30:39,520 --> 00:30:42,800
Speaker 12: owned by the government. And what that means is that

588
00:30:42,880 --> 00:30:46,360
Speaker 12: China's government has a lot of tools they can use

589
00:30:46,640 --> 00:30:51,920
Speaker 12: to prevent real estate stress, even extreme real estate stress,

590
00:30:52,120 --> 00:30:56,280
Speaker 12: tipping over into a financial crisis. The question is where

591
00:30:56,320 --> 00:31:00,640
Speaker 12: do they draw the light right, A trust company, even

592
00:31:00,680 --> 00:31:03,480
Speaker 12: a really big trust company, is not what you would

593
00:31:03,520 --> 00:31:07,360
Speaker 12: consider a core part of China's financial system. If a

594
00:31:07,360 --> 00:31:11,040
Speaker 12: trust company goes down, some rich investors are going to

595
00:31:11,080 --> 00:31:14,000
Speaker 12: lose some money. It's going to be a bit painful

596
00:31:14,040 --> 00:31:16,200
Speaker 12: for a sort of that group, but it's not going

597
00:31:16,280 --> 00:31:19,480
Speaker 12: to be a trigger for a systemic crisis. The question

598
00:31:19,560 --> 00:31:22,760
Speaker 12: for Beijing in the days and weeks ahead is are

599
00:31:22,760 --> 00:31:26,320
Speaker 12: they okay with big trust companies going down. If big

600
00:31:26,320 --> 00:31:30,040
Speaker 12: trust companies go down, are they okay with small banks

601
00:31:30,080 --> 00:31:33,200
Speaker 12: going down? If small banks go down? Are they okay

602
00:31:33,200 --> 00:31:35,760
Speaker 12: with city banks going down? Where do they draw the line?

603
00:31:36,280 --> 00:31:40,320
Speaker 12: My instinct is the trusts, the shadow banks, they might

604
00:31:40,360 --> 00:31:42,520
Speaker 12: have to get through this on their own. When we

605
00:31:42,560 --> 00:31:46,360
Speaker 12: start to see problems trickling into the banking system, even

606
00:31:46,400 --> 00:31:49,200
Speaker 12: the smaller city level banks, that's where Beijing steps in.

607
00:31:50,280 --> 00:31:53,480
Speaker 1: So Tom, how much of a political headwind is this

608
00:31:53,640 --> 00:31:56,880
Speaker 1: for President Xi, who just recently secured another I don't

609
00:31:56,920 --> 00:32:00,400
Speaker 1: think it's five year term, if not more of an

610
00:32:00,440 --> 00:32:01,960
Speaker 1: issues is for him politically.

611
00:32:02,800 --> 00:32:08,480
Speaker 12: So China does not have elections, but China does have politics.

612
00:32:08,600 --> 00:32:11,400
Speaker 12: And if you're in charge of an economy with a

613
00:32:11,440 --> 00:32:16,360
Speaker 12: youth unemployment rate of twenty percent and with prices for housing,

614
00:32:16,600 --> 00:32:19,120
Speaker 12: which is where most Chinese people have stored their wealth

615
00:32:19,360 --> 00:32:23,760
Speaker 12: either flat or falling, then you've got a political problem.

616
00:32:24,120 --> 00:32:27,480
Speaker 12: How does that play out in a single party state. Well,

617
00:32:27,920 --> 00:32:30,479
Speaker 12: the first thing to say is it plays out behind

618
00:32:30,560 --> 00:32:31,120
Speaker 12: the scenes.

619
00:32:31,440 --> 00:32:31,640
Speaker 4: Right.

620
00:32:32,000 --> 00:32:34,960
Speaker 12: If She's hand is weakened, that's going to be something

621
00:32:34,960 --> 00:32:37,840
Speaker 12: which is evident to people in the backroom deals in Beijing.

622
00:32:38,200 --> 00:32:41,120
Speaker 12: It's not going to be so evident to us here

623
00:32:41,160 --> 00:32:45,640
Speaker 12: in the United States. Still, that additional political stress is

624
00:32:45,680 --> 00:32:48,600
Speaker 12: an additional constraint on policy makers room for maneuver.

625
00:32:49,840 --> 00:32:53,960
Speaker 1: Thomas Sir, I know there's not an easy fix here,

626
00:32:54,000 --> 00:32:57,120
Speaker 1: But is there is there a solution that a lot

627
00:32:57,160 --> 00:33:00,680
Speaker 1: of economists like yourself see there is, or is this

628
00:33:00,800 --> 00:33:02,880
Speaker 1: just something that the Chinese ecindress can have to deal

629
00:33:02,880 --> 00:33:05,320
Speaker 1: with for a long time let a play out.

630
00:33:06,120 --> 00:33:11,480
Speaker 12: So there's a big structural problem here right. China has

631
00:33:11,600 --> 00:33:16,239
Speaker 12: massively overbuilt its real estate sector, and that means there

632
00:33:16,280 --> 00:33:18,960
Speaker 12: isn't an easy fix. There just has to be a

633
00:33:19,080 --> 00:33:23,800
Speaker 12: period where real estate construction, real estate prices come down

634
00:33:24,120 --> 00:33:28,000
Speaker 12: in order to realign supply and demand. There's no getting

635
00:33:28,000 --> 00:33:31,880
Speaker 12: away from that. Now, what does a kind of a

636
00:33:31,920 --> 00:33:36,120
Speaker 12: big fix, a kind of grand solution look like. Well, firstly,

637
00:33:36,400 --> 00:33:39,640
Speaker 12: it's a grand solution which doesn't make things better. It's

638
00:33:39,640 --> 00:33:43,360
Speaker 12: a grand solution which stops things tipping over into catastrophe.

639
00:33:44,360 --> 00:33:46,520
Speaker 12: What are the moving parts of it? Well, I think

640
00:33:46,600 --> 00:33:50,600
Speaker 12: it has to combine an element of reform, an element

641
00:33:50,640 --> 00:33:54,960
Speaker 12: of increased transparency, an element of increased market control. So

642
00:33:55,040 --> 00:33:58,280
Speaker 12: investors think that the underlying problems here aren't going to

643
00:33:58,320 --> 00:34:02,880
Speaker 12: repeat and an l of stimulus, more significant stimulus than

644
00:34:02,920 --> 00:34:04,640
Speaker 12: the government has so far been willing to put on

645
00:34:04,720 --> 00:34:05,120
Speaker 12: the table.

646
00:34:05,680 --> 00:34:08,480
Speaker 2: And that's we still expect that base case that China's

647
00:34:08,520 --> 00:34:11,840
Speaker 2: going to stimulate the economy, so they're.

648
00:34:11,719 --> 00:34:13,040
Speaker 12: Drip feeding the stimulus.

649
00:34:13,160 --> 00:34:13,359
Speaker 4: Right.

650
00:34:13,440 --> 00:34:16,680
Speaker 12: We've had a mini rate cut by the PBAC. We've

651
00:34:16,680 --> 00:34:20,480
Speaker 12: had the PBAC guiding banks to move mortgage rates lower.

652
00:34:20,840 --> 00:34:24,680
Speaker 12: We've had Lee Chang the premiere saying to China cities, Okay,

653
00:34:25,000 --> 00:34:28,359
Speaker 12: we want you to reignite the property sector. We want

654
00:34:28,360 --> 00:34:31,960
Speaker 12: you to stoke the healthy development of the property sector.

655
00:34:32,440 --> 00:34:33,279
Speaker 12: They need to do more.

656
00:34:33,400 --> 00:34:35,759
Speaker 1: All right, Tom, thank you very much for taking a

657
00:34:35,800 --> 00:34:39,600
Speaker 1: few minutes. Tom Orlick. He's a chief economist for Bloomberg Economics.

658
00:34:39,920 --> 00:34:44,520
Speaker 1: He's also author of the book Understanding China's Economic Indicators

659
00:34:44,560 --> 00:34:47,160
Speaker 1: and China The Bubble That Never Pops.

660
00:34:47,520 --> 00:34:50,640
Speaker 8: You're listening to the tape Cancher our live program, Bloomberg

661
00:34:50,680 --> 00:34:54,279
Speaker 8: Markets weekdays at ten am Eastern on Bloomberg Radio, the

662
00:34:54,320 --> 00:34:56,279
Speaker 8: tune in app, Bloomberg dot Com, and.

663
00:34:56,239 --> 00:34:57,560
Speaker 4: The Bloomberg Business App.

664
00:34:57,600 --> 00:35:00,440
Speaker 8: You can also listen live on Amazon Alexi from our

665
00:35:00,480 --> 00:35:04,799
Speaker 8: flagship New York station. Just Say Alexa playing Bloomberg eleven.

666
00:35:06,600 --> 00:35:08,480
Speaker 1: Matt much to bring on a next quest and hopefully

667
00:35:08,480 --> 00:35:09,759
Speaker 1: we'll get the sunit back on that track.

668
00:35:09,800 --> 00:35:10,080
Speaker 4: All right.

669
00:35:10,120 --> 00:35:13,279
Speaker 2: So my old producer, Matt Siegel is here. For a

670
00:35:13,320 --> 00:35:15,480
Speaker 2: few ill fated years.

671
00:35:15,280 --> 00:35:17,000
Speaker 6: He worked at Bloomberg, but then he moved.

672
00:35:16,719 --> 00:35:23,640
Speaker 2: On and is now covering crypto, running crypto coverage for

673
00:35:23,760 --> 00:35:26,520
Speaker 2: Van Eck. And we're glad to have him here in

674
00:35:26,560 --> 00:35:31,640
Speaker 2: the studio to talk about all things bitcoin ETF. Really,

675
00:35:31,680 --> 00:35:37,840
Speaker 2: because on Friday, the twenty one shares and arc ETF

676
00:35:37,920 --> 00:35:41,200
Speaker 2: proposal was punted by the SEC. They were due to

677
00:35:41,239 --> 00:35:44,279
Speaker 2: decide I think by today was the deadline, and they

678
00:35:44,320 --> 00:35:49,440
Speaker 2: pushed that off. Now they've got even more bitcoin proposals

679
00:35:49,520 --> 00:35:52,319
Speaker 2: to decide on. One of them is black Rock. So

680
00:35:52,960 --> 00:35:57,840
Speaker 2: everybody is paying attention on the street, and Matt Siegel

681
00:35:57,960 --> 00:36:01,040
Speaker 2: is the man to ask what's going on at the SEC.

682
00:36:02,880 --> 00:36:05,000
Speaker 13: What's going on is there's it seems to be a

683
00:36:05,000 --> 00:36:09,720
Speaker 13: political objection to a bitcoin ETF, which is coming from

684
00:36:10,040 --> 00:36:12,759
Speaker 13: the very top. President Biden, if you recall January twenty

685
00:36:12,760 --> 00:36:16,279
Speaker 13: twenty two, put out an executive order instructing all agencies

686
00:36:16,440 --> 00:36:21,719
Speaker 13: to use kind of maximum levels of enforcement to bring

687
00:36:21,760 --> 00:36:25,880
Speaker 13: this market to heal, and the arguments against a bitcoin

688
00:36:26,160 --> 00:36:32,040
Speaker 13: ETF have become increasingly haphazard and incoherent, and now there

689
00:36:32,080 --> 00:36:34,879
Speaker 13: are there's a gray scale lawsuit against the SEC. We're

690
00:36:34,880 --> 00:36:39,000
Speaker 13: expecting a decision any day on that which could kind

691
00:36:39,000 --> 00:36:44,680
Speaker 13: of reveal the illogical the illogical nature of the SEC's objections.

692
00:36:44,800 --> 00:36:47,239
Speaker 13: So now it's a delay. No bitcoin ETF for now.

693
00:36:47,320 --> 00:36:49,120
Speaker 6: So what you know.

694
00:36:49,840 --> 00:36:54,560
Speaker 2: Proponents of the SEC will say the concerns are around

695
00:36:54,680 --> 00:36:58,319
Speaker 2: fraud and manipulation, and certainly there's no shortage of that

696
00:36:58,520 --> 00:37:01,080
Speaker 2: in the crypto world, not that there's any shortage of

697
00:37:01,120 --> 00:37:05,799
Speaker 2: that in the plain vanilla Wall Street world, but you've

698
00:37:05,840 --> 00:37:10,120
Speaker 2: seen some firms make moves to try and alleviate those concerns, right,

699
00:37:10,239 --> 00:37:13,640
Speaker 2: including with surveillance sharing agreements.

700
00:37:13,880 --> 00:37:15,399
Speaker 6: Is that not far enough?

701
00:37:15,600 --> 00:37:15,800
Speaker 7: Has it?

702
00:37:15,920 --> 00:37:15,960
Speaker 14: Not?

703
00:37:16,080 --> 00:37:16,319
Speaker 5: One far?

704
00:37:16,560 --> 00:37:19,000
Speaker 13: Let's start with the original objection, as you note, fraud

705
00:37:19,040 --> 00:37:22,480
Speaker 13: and manipulation and the underlying market. Well, there are plenty

706
00:37:22,520 --> 00:37:26,520
Speaker 13: of commodities like gold and silver and oil where the

707
00:37:26,719 --> 00:37:30,520
Speaker 13: underlying commodity trading is not regulated. But once you wrap

708
00:37:30,560 --> 00:37:34,640
Speaker 13: that commodity in a vehicle like an ETF, then that

709
00:37:34,880 --> 00:37:37,960
Speaker 13: vehicle needs to trade on a regulated exchange. So we

710
00:37:38,000 --> 00:37:42,080
Speaker 13: don't think SEC's being consistent in how they're applying that logic.

711
00:37:42,840 --> 00:37:48,680
Speaker 1: Why what's the basis from your understanding of this decision

712
00:37:48,680 --> 00:37:51,200
Speaker 1: coming now from the top from President Biden about crypto?

713
00:37:51,239 --> 00:37:54,239
Speaker 1: What's the what is their size's fundamental issue?

714
00:37:55,160 --> 00:37:57,720
Speaker 13: I think it's an issue of control right now, which

715
00:37:57,760 --> 00:38:01,400
Speaker 13: is playing out across the political spectrum on a range

716
00:38:01,400 --> 00:38:04,960
Speaker 13: of issues, not just crypto. But it plays into the

717
00:38:05,880 --> 00:38:09,759
Speaker 13: potential threat against the dollar as a reserve currency. You know,

718
00:38:09,760 --> 00:38:11,879
Speaker 13: the IMF had a blog just last month they say

719
00:38:11,880 --> 00:38:14,840
Speaker 13: that the way to protect against the substitution of sovereign

720
00:38:14,840 --> 00:38:18,360
Speaker 13: currencies is by having legitimate institutions, and frankly, there's just

721
00:38:18,400 --> 00:38:21,080
Speaker 13: an increasing number of people around the world who are

722
00:38:21,120 --> 00:38:23,640
Speaker 13: doubting the legitimacy of those institutions.

723
00:38:23,719 --> 00:38:26,560
Speaker 1: If Blackrock comes and says they want an ETF, aren't

724
00:38:26,560 --> 00:38:28,120
Speaker 1: they going to get an ETF it's Blackrock?

725
00:38:29,360 --> 00:38:33,080
Speaker 13: Well maybe eventually, but the final decision on the Blackrock

726
00:38:33,120 --> 00:38:36,600
Speaker 13: ETF isn't DOE until next March. And before then, there's

727
00:38:36,600 --> 00:38:38,440
Speaker 13: a lot of phone calls that are going around which

728
00:38:38,440 --> 00:38:40,960
Speaker 13: are not being done in a transparent manner, And you

729
00:38:41,080 --> 00:38:43,239
Speaker 13: have all these ETF issuers like Vanak and I think

730
00:38:43,239 --> 00:38:45,080
Speaker 13: we have some authority to speak on this matter because

731
00:38:45,080 --> 00:38:47,279
Speaker 13: we were the first TRADFY managers to first file for

732
00:38:47,280 --> 00:38:50,560
Speaker 13: a physically backed ETF in twenty seventeen, and we spent

733
00:38:50,600 --> 00:38:53,920
Speaker 13: the last six years trying to figure out what exactly

734
00:38:53,960 --> 00:38:56,480
Speaker 13: are the conditions that the regulator is looking for that

735
00:38:56,560 --> 00:38:59,239
Speaker 13: will allow for the approval of such a product. Those

736
00:38:59,239 --> 00:39:01,759
Speaker 13: conditions have not been laid out in any systematic way,

737
00:39:02,040 --> 00:39:04,200
Speaker 13: and we're going to see the result of this lawsuit

738
00:39:04,239 --> 00:39:06,800
Speaker 13: Gray Scale versus the SEC, which we think will reveal

739
00:39:07,080 --> 00:39:09,239
Speaker 13: that the SEC has been acting in an arbitrary and

740
00:39:09,280 --> 00:39:11,800
Speaker 13: capricious manner in denying Whether or not that leads to

741
00:39:11,840 --> 00:39:14,160
Speaker 13: an immediate approval, we'll have to see. But we have

742
00:39:14,239 --> 00:39:17,280
Speaker 13: until next March to get the answer to your question.

743
00:39:18,280 --> 00:39:20,920
Speaker 6: All right, So, I guess.

744
00:39:23,120 --> 00:39:27,279
Speaker 2: You're putting this at President Biden's on President Biden's desk,

745
00:39:27,360 --> 00:39:29,560
Speaker 2: But if we take it a level lower, right, Gary

746
00:39:29,560 --> 00:39:32,919
Speaker 2: Gensler seems to be the one that's holding things back,

747
00:39:32,920 --> 00:39:34,520
Speaker 2: at least from our perspective.

748
00:39:34,560 --> 00:39:35,040
Speaker 6: Is that wrong?

749
00:39:36,680 --> 00:39:39,600
Speaker 2: No, at the SEC because he has recently or the

750
00:39:39,640 --> 00:39:43,120
Speaker 2: SEC has recently I'm going to say, lost a case

751
00:39:43,360 --> 00:39:49,120
Speaker 2: in the Ripple trial. Although I think they might claim

752
00:39:49,120 --> 00:39:52,440
Speaker 2: that they partially won right because the judge declared Ripple

753
00:39:52,480 --> 00:39:55,840
Speaker 2: to be a security when it was marketed to a

754
00:39:55,880 --> 00:39:58,360
Speaker 2: bunch of institutions, but not a security when it was

755
00:39:58,400 --> 00:40:02,440
Speaker 2: sold to I guess retail investors on an exchange. Is

756
00:40:02,480 --> 00:40:06,920
Speaker 2: that maybe the first crack in the SEC's you know,

757
00:40:07,120 --> 00:40:08,320
Speaker 2: wall against crypto.

758
00:40:09,320 --> 00:40:11,040
Speaker 13: I think it very well could be. And there have

759
00:40:11,120 --> 00:40:14,000
Speaker 13: been multiple examples in the last two decades of SEC

760
00:40:14,080 --> 00:40:17,680
Speaker 13: chairs who have had to step down after an embarrassing loss,

761
00:40:17,840 --> 00:40:21,439
Speaker 13: like you know, Bloomberg's former board member Arthur Levitt comes

762
00:40:21,440 --> 00:40:25,600
Speaker 13: to mind. So there's also a great history of a

763
00:40:25,719 --> 00:40:28,240
Speaker 13: change in leadership at these agencies ahead of an election,

764
00:40:28,320 --> 00:40:30,480
Speaker 13: which we have next year as well. So my personal

765
00:40:30,520 --> 00:40:32,479
Speaker 13: call is that Gensler will be gone before the next

766
00:40:32,520 --> 00:40:35,919
Speaker 13: election and that will facilitate a change in policy here.

767
00:40:35,960 --> 00:40:38,680
Speaker 13: But there's a lot of steps between here and there.

768
00:40:39,040 --> 00:40:43,600
Speaker 1: Is there a physically traded ETF bitcoin ETF in Canada.

769
00:40:43,760 --> 00:40:47,680
Speaker 13: Yes, Canada, Europe, multiple jurisdictions on Canada.

770
00:40:47,719 --> 00:40:49,920
Speaker 1: What have we learned, what have we observed about the

771
00:40:49,920 --> 00:40:53,360
Speaker 1: pros and cons of that that maybe the SEC could

772
00:40:53,640 --> 00:40:54,080
Speaker 1: learn from.

773
00:40:54,120 --> 00:40:58,360
Speaker 13: Maybe there have been no comms because ETFs are a

774
00:40:58,880 --> 00:41:02,800
Speaker 13: time tested, well regulated, liquid and cost effective.

775
00:41:02,840 --> 00:41:05,720
Speaker 5: Whether they treated US whole security in Canada? Yes, okay?

776
00:41:06,120 --> 00:41:07,120
Speaker 5: And here the.

777
00:41:07,160 --> 00:41:09,440
Speaker 13: ETF is not the bitcoin underlying.

778
00:41:09,400 --> 00:41:12,759
Speaker 1: Okay, Right, So the ETF is not the bitcoin can

779
00:41:12,800 --> 00:41:15,080
Speaker 1: that structure and that's the structure that the SEC is

780
00:41:15,080 --> 00:41:15,960
Speaker 1: not comfortable with here?

781
00:41:16,600 --> 00:41:19,240
Speaker 13: Correct? They object to the well, they have a number

782
00:41:19,239 --> 00:41:21,160
Speaker 13: of objections and as I said, each one of them

783
00:41:21,160 --> 00:41:24,200
Speaker 13: has kind of been proven to be logically incoherent. But

784
00:41:24,560 --> 00:41:27,280
Speaker 13: we're stuck in this mandate, whether it's coming from the President,

785
00:41:27,280 --> 00:41:28,400
Speaker 13: whether it's coming from Genzler.

786
00:41:28,400 --> 00:41:31,640
Speaker 1: All right, So what does this all mean for innovation

787
00:41:31,840 --> 00:41:34,719
Speaker 1: in crypto in the US. My concern, if if I

788
00:41:34,840 --> 00:41:38,080
Speaker 1: were in senior political levels, it would be I want

789
00:41:38,120 --> 00:41:41,360
Speaker 1: to make sure that whatever this crypto thing evolves into,

790
00:41:42,400 --> 00:41:42,839
Speaker 1: I want the.

791
00:41:42,840 --> 00:41:43,680
Speaker 6: US to be a leader.

792
00:41:44,360 --> 00:41:47,200
Speaker 1: And now I feel like, just knowing what I know,

793
00:41:47,239 --> 00:41:50,520
Speaker 1: that perhaps we're not going to take a leadership position

794
00:41:50,520 --> 00:41:54,400
Speaker 1: because we don't have a well defined regulatory framework. Whether

795
00:41:54,440 --> 00:41:56,000
Speaker 1: you like it or not, it doesn't seem like we

796
00:41:56,040 --> 00:41:58,520
Speaker 1: have a one. Is there a risk that the US

797
00:41:59,000 --> 00:42:01,320
Speaker 1: maybe is not a leader in this business?

798
00:42:01,640 --> 00:42:05,600
Speaker 13: That's definitely the path of direction. So we can track

799
00:42:05,680 --> 00:42:07,879
Speaker 13: the number of developers who are working on these open

800
00:42:07,880 --> 00:42:10,000
Speaker 13: source blockchains, and we can track them by country, and

801
00:42:10,000 --> 00:42:11,840
Speaker 13: what we can see is that the US is losing

802
00:42:11,920 --> 00:42:15,160
Speaker 13: market share of crypto developers, so the innovators are looking

803
00:42:15,160 --> 00:42:17,200
Speaker 13: to work elsewhere. The other thing that we can see

804
00:42:17,239 --> 00:42:20,240
Speaker 13: is that stable coins like USDC, which is the coinbas

805
00:42:20,280 --> 00:42:23,160
Speaker 13: Circle JV, are losing market share to Tether, which is

806
00:42:23,239 --> 00:42:27,440
Speaker 13: an offshore, less regulated model on the same on the

807
00:42:27,480 --> 00:42:28,120
Speaker 13: same Now.

808
00:42:28,040 --> 00:42:31,439
Speaker 1: I feel like asking a political question, should Americans care?

809
00:42:31,520 --> 00:42:37,719
Speaker 13: And if so, why Americans should care? Because every currency

810
00:42:38,920 --> 00:42:43,240
Speaker 13: has there's been no currency that has been the global

811
00:42:43,280 --> 00:42:46,800
Speaker 13: store of value for multiple centuries, and at some point

812
00:42:47,040 --> 00:42:49,560
Speaker 13: the world is looking for an alternative. And here you

813
00:42:49,640 --> 00:42:54,920
Speaker 13: have a decentralized money where the creation schedule is transparent,

814
00:42:55,239 --> 00:42:58,120
Speaker 13: and that's very different from what's happening with the Federal Reserve.

815
00:42:58,160 --> 00:43:01,000
Speaker 13: So what's going on in our concern?

816
00:43:01,120 --> 00:43:03,160
Speaker 5: I don't want a dollar to be at risk if

817
00:43:03,200 --> 00:43:04,360
Speaker 5: I'm President Biden or anything.

818
00:43:04,480 --> 00:43:06,640
Speaker 2: I think what Matt's saying is that the dollars at risk.

819
00:43:06,800 --> 00:43:09,239
Speaker 2: So even though you don't like it, we need to

820
00:43:09,360 --> 00:43:13,160
Speaker 2: prepare for the reality that that turns around. We're still

821
00:43:13,160 --> 00:43:15,880
Speaker 2: a long way off from that, right I want to

822
00:43:16,040 --> 00:43:20,520
Speaker 2: wanted to ask you about other tokens because other than Bitcoin,

823
00:43:20,880 --> 00:43:24,799
Speaker 2: you know, people may know Ether, but beyond that they

824
00:43:24,840 --> 00:43:28,440
Speaker 2: don't really I mean dogecoin, right, what else is serious?

825
00:43:28,480 --> 00:43:30,439
Speaker 2: And you think really important in crypto that we should

826
00:43:30,480 --> 00:43:31,279
Speaker 2: be paying attention to.

827
00:43:32,000 --> 00:43:36,520
Speaker 13: Yeah, So, there are a number of smart contract protocols.

828
00:43:36,560 --> 00:43:41,399
Speaker 13: These are blockchain software that enable more complex transactions than

829
00:43:41,440 --> 00:43:44,280
Speaker 13: Bitcoin can enable. So with Bitcoin, it's just very difficult

830
00:43:44,280 --> 00:43:47,400
Speaker 13: to program in a bunch of if then conditions and

831
00:43:47,440 --> 00:43:52,000
Speaker 13: make your bitcoin move around programmatically. With ethereum, because of

832
00:43:52,040 --> 00:43:55,520
Speaker 13: the architecture of the blockchain, that is possible. And there

833
00:43:55,600 --> 00:43:59,320
Speaker 13: are you know, a handful of similar layer one smart

834
00:43:59,320 --> 00:44:03,080
Speaker 13: contract platforms to Ethereum. We're thinking of like a Solana,

835
00:44:03,719 --> 00:44:10,080
Speaker 13: which make different trade offs on decentralization and speed. The

836
00:44:10,120 --> 00:44:12,640
Speaker 13: history of these digital platforms is that they tend to

837
00:44:12,680 --> 00:44:16,280
Speaker 13: be winner take all businesses. We're seeing that in Web two, Amazon, Google,

838
00:44:16,280 --> 00:44:18,600
Speaker 13: et cetera. So one of the big challenges for digital

839
00:44:18,640 --> 00:44:22,160
Speaker 13: investors right now is balancing those winner take all characteristics

840
00:44:22,160 --> 00:44:25,960
Speaker 13: of these digital platforms with the extreme price disparity year

841
00:44:25,960 --> 00:44:28,520
Speaker 13: to date, because year to date Bitcoin and Ether are outperforming.

842
00:44:28,760 --> 00:44:30,440
Speaker 13: Part of the reason why they're out performing because the

843
00:44:30,440 --> 00:44:33,799
Speaker 13: regulator is coming down on all these other innovative platforms.

844
00:44:34,000 --> 00:44:37,480
Speaker 13: The more that the regulator cracks down, the greater these winner.

845
00:44:37,200 --> 00:44:39,440
Speaker 6: Take all characteristics will be, the faster they will be.

846
00:44:39,560 --> 00:44:41,839
Speaker 13: In a way, it's government picking winners. We don't want that.

847
00:44:41,880 --> 00:44:43,520
Speaker 13: We want innovation to win out.

848
00:44:44,400 --> 00:44:46,279
Speaker 1: Sounds reasonable to me. Matt Siegel, thanks so much for

849
00:44:46,360 --> 00:44:48,120
Speaker 1: joining us here. A Matt Siegel, He's head of digital

850
00:44:48,120 --> 00:44:51,880
Speaker 1: asset research for at van Neck. He's joins us live

851
00:44:51,880 --> 00:44:54,759
Speaker 1: here in a Bloomberg Interactor broker's studio. Just looking at

852
00:44:55,280 --> 00:44:58,239
Speaker 1: bitcoin because it's on my monitor for whatever reason. It's

853
00:44:58,280 --> 00:45:00,280
Speaker 1: up six tens of one percent here twenty ninth, thousand

854
00:45:00,280 --> 00:45:02,280
Speaker 1: and five eighty one for bitcoin.

855
00:45:02,520 --> 00:45:05,640
Speaker 8: You're listening to the tape Cat's are Live program Bloomberg

856
00:45:05,680 --> 00:45:09,279
Speaker 8: Markets weekdays at ten am Eastern on Bloomberg Radio, the

857
00:45:09,360 --> 00:45:11,359
Speaker 8: tune in app, Bloomberg dot Com, and.

858
00:45:11,280 --> 00:45:12,560
Speaker 4: The Bloomberg Business app.

859
00:45:12,600 --> 00:45:15,440
Speaker 8: You can also listen live on Amazon Alexa from our

860
00:45:15,440 --> 00:45:20,480
Speaker 8: flagship New York station. Just say Alexa play Bloomberg eleven thirty.

861
00:45:21,040 --> 00:45:25,120
Speaker 1: Joining us here in our Bloomberg Interactive workers studio. Matthew Palosola.

862
00:45:25,160 --> 00:45:27,920
Speaker 1: He's a senior insurance analyst with Bloomberg Intelligence.

863
00:45:28,080 --> 00:45:29,960
Speaker 2: But whenever I see this guy, I know that a

864
00:45:30,040 --> 00:45:31,360
Speaker 2: disaster has happened.

865
00:45:32,080 --> 00:45:34,719
Speaker 1: Is exactly ifa things have happened. So we call on

866
00:45:34,960 --> 00:45:38,800
Speaker 1: Matthew Palazola. So, Matthew, we seen the destruction in Bermuda,

867
00:45:38,840 --> 00:45:41,520
Speaker 1: the loss of life. It's just horrific as some of

868
00:45:41,560 --> 00:45:46,040
Speaker 1: the images that we saw. From the insurance perspective, What

869
00:45:46,080 --> 00:45:49,400
Speaker 1: are you hearing from the companies as to perhaps exposure

870
00:45:49,440 --> 00:45:50,440
Speaker 1: and all that type of thing.

871
00:45:50,440 --> 00:45:53,799
Speaker 15: Right, So, nothing from the company specifically. We're looking at

872
00:45:54,520 --> 00:45:59,200
Speaker 15: industry data data from the ground. In these cases, you'll

873
00:45:59,200 --> 00:46:02,279
Speaker 15: hear about economic losses first, So we're hearing numbers like

874
00:46:02,480 --> 00:46:04,719
Speaker 15: five six it can I just saw one for eight

875
00:46:04,760 --> 00:46:06,760
Speaker 15: billion dollars economic damages?

876
00:46:07,120 --> 00:46:08,120
Speaker 5: What's that actually?

877
00:46:08,400 --> 00:46:09,080
Speaker 4: So that could be.

878
00:46:09,239 --> 00:46:14,719
Speaker 15: Lost wages that it encompasses insured values, but insured values

879
00:46:14,719 --> 00:46:16,680
Speaker 15: would be much less, so they could be even half

880
00:46:16,719 --> 00:46:19,000
Speaker 15: of that. So the work that I'm doing now is

881
00:46:19,120 --> 00:46:23,040
Speaker 15: kind of coming around low single digit billions for the

882
00:46:23,080 --> 00:46:25,920
Speaker 15: insurance companies, which is a pretty manageable.

883
00:46:25,560 --> 00:46:26,000
Speaker 4: Number, right.

884
00:46:26,840 --> 00:46:31,080
Speaker 2: So but the thing is, these payouts are probably going

885
00:46:31,120 --> 00:46:33,200
Speaker 2: to get bigger and bigger every year, right, not just

886
00:46:33,440 --> 00:46:37,160
Speaker 2: what's happening in Hawaii. Not just you know what the

887
00:46:37,200 --> 00:46:41,359
Speaker 2: Anino's brought, but every year did natural disaster payouts climb

888
00:46:41,440 --> 00:46:42,160
Speaker 2: higher and higher.

889
00:46:42,480 --> 00:46:43,680
Speaker 4: That has been happening, right.

890
00:46:43,760 --> 00:46:46,560
Speaker 15: So it was thought of years ago that one hundred

891
00:46:46,600 --> 00:46:49,479
Speaker 15: billion dollars and insured losses was something we'd never see,

892
00:46:49,560 --> 00:46:53,400
Speaker 15: and now we've seen it for several years. So this

893
00:46:53,400 --> 00:46:57,200
Speaker 15: this year so far is running above average catastrophe losses.

894
00:46:58,239 --> 00:47:01,080
Speaker 15: It's climate change is kind of the big boogeyman in

895
00:47:01,120 --> 00:47:03,799
Speaker 15: the whole thing. No one's saying it's not happening, but

896
00:47:04,160 --> 00:47:06,160
Speaker 15: the insurance companies will tell you that a lot of

897
00:47:06,200 --> 00:47:11,319
Speaker 15: this is driven by density of population building in catastrophe

898
00:47:11,360 --> 00:47:14,600
Speaker 15: prone areas. It's kind of moral hazard where we keep building,

899
00:47:14,680 --> 00:47:17,280
Speaker 15: we keep rebuilding, and you know it did. The cycle

900
00:47:17,320 --> 00:47:19,840
Speaker 15: goes on and on. So it's not all climate change.

901
00:47:19,960 --> 00:47:21,719
Speaker 15: I wrote down the stat I did want to tell

902
00:47:21,760 --> 00:47:25,480
Speaker 15: you though. This is according to a catastree modeling company.

903
00:47:25,680 --> 00:47:31,840
Speaker 15: They said, over the past century Hawaii, the average burning

904
00:47:31,880 --> 00:47:35,160
Speaker 15: there has increased four hundred percent due to man made factors.

905
00:47:35,239 --> 00:47:38,880
Speaker 15: So obviously something's happening there's I also thought this was interesting.

906
00:47:38,920 --> 00:47:44,120
Speaker 15: There's a non native grass species that was introduced to Hawaii,

907
00:47:44,239 --> 00:47:46,799
Speaker 15: which actually is feeding a lot of this because it's

908
00:47:47,520 --> 00:47:48,120
Speaker 15: fire prone.

909
00:47:48,440 --> 00:47:51,399
Speaker 1: Right, So, I mean there's all kinds of drivers here

910
00:47:51,560 --> 00:47:53,799
Speaker 1: go into that issue of the kind of the moral hazard.

911
00:47:54,160 --> 00:47:58,320
Speaker 1: I mean, a hurricane comes along, hits take your place, Florida,

912
00:47:58,440 --> 00:48:02,000
Speaker 1: North Carolina said, you know where reverer it hits wipes.

913
00:48:01,640 --> 00:48:06,040
Speaker 2: Out a part of the coast, The insurance guys pay

914
00:48:06,120 --> 00:48:07,919
Speaker 2: up and people build houses right back there.

915
00:48:07,920 --> 00:48:09,319
Speaker 1: Again, I forget about that. I'll take that to my

916
00:48:09,840 --> 00:48:12,759
Speaker 1: Jersey Shore super sore Sandy. You get down a man

917
00:48:12,920 --> 00:48:14,640
Speaker 1: looking which have some of the nicest homes on a

918
00:48:14,719 --> 00:48:18,920
Speaker 1: Jersey Shore, they have rebuilt monster homes. And then the

919
00:48:18,960 --> 00:48:23,080
Speaker 1: next lot, for whatever reason, completely blank, nothing's been rebuilt.

920
00:48:23,120 --> 00:48:25,040
Speaker 1: So I don't know if there's a money's held up

921
00:48:25,040 --> 00:48:25,200
Speaker 1: with the.

922
00:48:25,200 --> 00:48:28,600
Speaker 5: Family or whatever. But is that person who rebuilt the

923
00:48:28,760 --> 00:48:32,440
Speaker 5: monster house again, is he or she getting insured?

924
00:48:34,080 --> 00:48:37,279
Speaker 15: Look, depending on the monstrosity of the house, may you

925
00:48:37,520 --> 00:48:38,640
Speaker 15: might not be insured.

926
00:48:38,680 --> 00:48:40,920
Speaker 2: And yeah, my experience with Chubb is after you take

927
00:48:40,960 --> 00:48:42,279
Speaker 2: a big hit, they don't come back.

928
00:48:42,480 --> 00:48:42,680
Speaker 4: Yeah.

929
00:48:42,719 --> 00:48:44,640
Speaker 15: So I mean, if you have enough money where you

930
00:48:44,680 --> 00:48:46,560
Speaker 15: built that house on your own, you don't have a mortgage,

931
00:48:46,560 --> 00:48:48,640
Speaker 15: you don't have anything. I guess theoretically you could not

932
00:48:48,680 --> 00:48:51,600
Speaker 15: have insurance. What's happening though in these states is a

933
00:48:51,640 --> 00:48:54,680
Speaker 15: lot of it's falling on the state. So when you

934
00:48:54,719 --> 00:48:58,439
Speaker 15: can't get insurance in Florida, in California, there's a state

935
00:48:58,520 --> 00:49:01,120
Speaker 15: fund that you get insurance from. So ultimately it comes

936
00:49:01,160 --> 00:49:04,040
Speaker 15: back to the taxpayer. So it's not the insurance companies

937
00:49:04,800 --> 00:49:05,520
Speaker 15: have wised up.

938
00:49:05,560 --> 00:49:06,879
Speaker 4: They don't just keep paying out.

939
00:49:06,960 --> 00:49:09,520
Speaker 15: They will step back and drop these people. But what

940
00:49:09,600 --> 00:49:12,799
Speaker 15: happens is or charge higher rates. They'll definitely charge higher rates,

941
00:49:12,800 --> 00:49:14,799
Speaker 15: so they'll they'll charge you such a high rate that

942
00:49:14,880 --> 00:49:16,440
Speaker 15: you'll go to the state fund.

943
00:49:17,040 --> 00:49:18,440
Speaker 6: Does the state fund lose money?

944
00:49:19,320 --> 00:49:20,160
Speaker 4: Hand over fists?

945
00:49:20,880 --> 00:49:21,400
Speaker 6: I see?

946
00:49:21,600 --> 00:49:24,719
Speaker 1: And funds that we do you okay, okay, yeah, you

947
00:49:24,800 --> 00:49:28,600
Speaker 1: gut did. And but that's again at some point. Again,

948
00:49:28,680 --> 00:49:30,640
Speaker 1: my good friends at the Jersey Shore, I'm just picking

949
00:49:30,680 --> 00:49:33,120
Speaker 1: on them because that that that's where I crip these days.

950
00:49:33,680 --> 00:49:37,800
Speaker 5: I mean, I don't feel like they should be insured.

951
00:49:37,920 --> 00:49:40,320
Speaker 1: I mean, if it's given what's happened, But I guess

952
00:49:40,960 --> 00:49:42,640
Speaker 1: it's up to the companies and if maybe they can

953
00:49:42,840 --> 00:49:44,040
Speaker 1: decline to have insurance.

954
00:49:44,080 --> 00:49:46,040
Speaker 2: Right And this question may be beyond the scope of

955
00:49:46,040 --> 00:49:47,480
Speaker 2: Matthew Powis no, no.

956
00:49:47,320 --> 00:49:49,920
Speaker 15: No, I don't know if we can get the situation

957
00:49:50,040 --> 00:49:54,479
Speaker 15: in here from Jersey. But no, I mean, like I said,

958
00:49:54,520 --> 00:49:56,319
Speaker 15: you if you have a mortgage on your property, you

959
00:49:56,360 --> 00:49:57,719
Speaker 15: need you have to have insurance.

960
00:49:57,800 --> 00:49:59,600
Speaker 5: Yes, and that's it right.

961
00:50:00,000 --> 00:50:03,320
Speaker 15: The state is not going to let people be completely

962
00:50:03,360 --> 00:50:05,560
Speaker 15: unhappy and not have insurance and it'd be too expensive.

963
00:50:05,600 --> 00:50:07,439
Speaker 15: So that's where this kind of comes in, and people

964
00:50:07,480 --> 00:50:09,759
Speaker 15: don't realize that the taxpayers are ending up funding that.

965
00:50:09,880 --> 00:50:11,719
Speaker 2: It's also it's not the Jersey shore that takes the

966
00:50:11,719 --> 00:50:13,720
Speaker 2: big losses, right, it's Florida's Florida.

967
00:50:13,800 --> 00:50:17,240
Speaker 15: Yeah, it's Florida, and it's California. It's Texas to some degree.

968
00:50:17,280 --> 00:50:20,120
Speaker 15: I mean, we had Sandy that was kind of an

969
00:50:20,120 --> 00:50:22,919
Speaker 15: odd off event, but I mean we also had last

970
00:50:23,040 --> 00:50:24,520
Speaker 15: was I don't remember if it was last year where

971
00:50:24,520 --> 00:50:27,440
Speaker 15: we had these significant rain events repeatedly and it was

972
00:50:27,520 --> 00:50:29,920
Speaker 15: the most rain ever. And then the next week it

973
00:50:29,920 --> 00:50:31,920
Speaker 15: was the most rain ever again in you know, the

974
00:50:31,960 --> 00:50:32,680
Speaker 15: tri state area.

975
00:50:32,719 --> 00:50:33,600
Speaker 7: So it is happening, all right.

976
00:50:33,640 --> 00:50:38,520
Speaker 1: So we're August fourteenth here, what's the hurricane call this year?

977
00:50:38,640 --> 00:50:41,520
Speaker 1: I mean, so far, I don't recall nothing big as

978
00:50:41,600 --> 00:50:42,399
Speaker 1: hit here has it?

979
00:50:42,440 --> 00:50:42,960
Speaker 4: So it hasn't been.

980
00:50:43,000 --> 00:50:46,719
Speaker 15: It started off quick and hot, right, and I was

981
00:50:46,960 --> 00:50:49,240
Speaker 15: pretty worried that it was going to be above average season.

982
00:50:50,040 --> 00:50:54,040
Speaker 15: So you've got to countervailing forces. You've got the Onino

983
00:50:54,040 --> 00:50:57,680
Speaker 15: effect which you brought up before, that actually suppresses hurricane

984
00:50:57,760 --> 00:50:59,200
Speaker 15: activity in the Atlantic.

985
00:50:59,560 --> 00:51:01,400
Speaker 5: That just and it's blowing somewhere.

986
00:51:01,440 --> 00:51:02,440
Speaker 6: It's a hot current, right.

987
00:51:02,960 --> 00:51:06,040
Speaker 15: Essentially, I'm just an amateur meteor I'll just so you know,

988
00:51:06,080 --> 00:51:07,719
Speaker 15: I'll have to take a step back and say, yes,

989
00:51:07,760 --> 00:51:12,120
Speaker 15: it's basically just winds moving right that usually suppresses hurricane

990
00:51:12,120 --> 00:51:15,360
Speaker 15: activity here. But then you have these record sea surface

991
00:51:15,440 --> 00:51:19,720
Speaker 15: temperatures which feed hurricane activity. So this has netted itself

992
00:51:19,760 --> 00:51:23,600
Speaker 15: out to be from the forecasts. Forecast is to be

993
00:51:23,680 --> 00:51:26,960
Speaker 15: in above average season, but we haven't seen that much yet.

994
00:51:27,120 --> 00:51:29,680
Speaker 15: The peak is about mid September.

995
00:51:30,560 --> 00:51:33,040
Speaker 2: By the way, hell Nino is it is a warm

996
00:51:33,120 --> 00:51:36,000
Speaker 2: current of water, ocean water that develops in the central

997
00:51:36,000 --> 00:51:38,520
Speaker 2: and east central Equatorial Pacific.

998
00:51:38,600 --> 00:51:41,560
Speaker 15: And do you know the opposite La Nina.

999
00:51:41,680 --> 00:51:44,440
Speaker 2: La Nina, the girl sorr Helinos, the boys.

1000
00:51:44,680 --> 00:51:47,080
Speaker 15: We've been in La Nina for a while, which is

1001
00:51:47,120 --> 00:51:49,239
Speaker 15: actually fed hurricane activity.

1002
00:51:48,880 --> 00:51:49,160
Speaker 4: All right.

1003
00:51:49,200 --> 00:51:51,920
Speaker 1: The S and P five hundred property and casually insurance

1004
00:51:52,080 --> 00:51:54,759
Speaker 1: sub index down five percent this year.

1005
00:51:55,280 --> 00:51:59,439
Speaker 15: Yes, so insurance is gonna be suffered from. I think

1006
00:51:59,440 --> 00:52:01,920
Speaker 15: that the risk on environment in the beginning of the year,

1007
00:52:02,000 --> 00:52:05,160
Speaker 15: the defensive stocks they can sell off with that rotation.

1008
00:52:06,880 --> 00:52:10,200
Speaker 15: I think unfairly they suffered with the bank turmoil, they

1009
00:52:10,200 --> 00:52:13,960
Speaker 15: weren't really exposed. Another concern is commercial real.

1010
00:52:13,800 --> 00:52:15,560
Speaker 2: Li I mean, the concern there is that they hold

1011
00:52:15,600 --> 00:52:19,920
Speaker 2: a lot of long term for example, treasuries, you know,

1012
00:52:19,960 --> 00:52:21,719
Speaker 2: and maybe they haven't marked to market either.

1013
00:52:21,920 --> 00:52:23,920
Speaker 15: So they did that last year. That was a big impact,

1014
00:52:23,960 --> 00:52:26,440
Speaker 15: so they still do. It's it's kind of reversing this year.

1015
00:52:26,480 --> 00:52:28,239
Speaker 15: They have They hold a lot of commercial real estate,

1016
00:52:29,080 --> 00:52:31,120
Speaker 15: more specifically maybe the life insurance companies.

1017
00:52:31,680 --> 00:52:32,880
Speaker 4: We did analysis on that.

1018
00:52:32,920 --> 00:52:35,319
Speaker 15: It seemed to be very magicable exposure for them in

1019
00:52:35,360 --> 00:52:38,200
Speaker 15: a bad case. It hasn't really reared its head, but

1020
00:52:38,200 --> 00:52:40,000
Speaker 15: I think those things were dragging them down.

1021
00:52:40,040 --> 00:52:43,200
Speaker 2: And otherwise, how are they doing making returns because that's

1022
00:52:43,239 --> 00:52:45,719
Speaker 2: the idea, right we all put in our premiums and

1023
00:52:45,719 --> 00:52:47,759
Speaker 2: then they take that money and they go invest it

1024
00:52:47,800 --> 00:52:49,759
Speaker 2: and hopefully they make more than they lose on these

1025
00:52:49,800 --> 00:52:51,360
Speaker 2: natural disasters exactly.

1026
00:52:51,440 --> 00:52:53,839
Speaker 4: So fundamentals are good.

1027
00:52:53,920 --> 00:52:56,719
Speaker 15: They've been you know, earning that we're at it could

1028
00:52:56,760 --> 00:52:59,120
Speaker 15: be peak roes for this pricing cycle, could be this

1029
00:52:59,239 --> 00:53:03,880
Speaker 15: year or next year. Typically valuations peak before the rois,

1030
00:53:04,040 --> 00:53:06,600
Speaker 15: So if it's this year, then maybe they peaked already,

1031
00:53:06,640 --> 00:53:07,680
Speaker 15: but it could be next year.

1032
00:53:07,719 --> 00:53:08,960
Speaker 6: Are there any hot you know?

1033
00:53:09,120 --> 00:53:11,719
Speaker 2: We're the best managed insurance companies that you cover.

1034
00:53:13,640 --> 00:53:16,680
Speaker 15: I love Chubb, right, It's it's the biggest and it's

1035
00:53:16,719 --> 00:53:21,000
Speaker 15: still growing a lot. Evan Greenberg's CEO, I mean, of

1036
00:53:21,040 --> 00:53:23,960
Speaker 15: this giant company, and he kind of knows all the

1037
00:53:24,000 --> 00:53:25,120
Speaker 15: ins and out details.

1038
00:53:26,000 --> 00:53:29,080
Speaker 4: You know. They they have the best.

1039
00:53:28,840 --> 00:53:32,719
Speaker 15: Commercial underwriting of their peer group and they're the biggest ones.

1040
00:53:32,760 --> 00:53:36,560
Speaker 15: So I mean, I love Chubb's management and strategy.

1041
00:53:36,719 --> 00:53:38,440
Speaker 6: It will take me. It will take me as a client.

1042
00:53:38,480 --> 00:53:40,279
Speaker 5: They wouldn't take you, of course they did once.

1043
00:53:40,400 --> 00:53:42,920
Speaker 2: But I guess too many losses, too many claims.

1044
00:53:43,080 --> 00:53:47,319
Speaker 1: You have like motorcycle, there's no way may have been

1045
00:53:47,320 --> 00:53:47,759
Speaker 1: a part of that.

1046
00:53:48,239 --> 00:53:50,799
Speaker 15: Do you have your Picasso in the basement when flood.

1047
00:53:50,640 --> 00:53:54,040
Speaker 2: No, I didn't have any really big problems, just I

1048
00:53:54,080 --> 00:53:56,920
Speaker 2: think there was a small issue with a Portia nine

1049
00:53:56,920 --> 00:53:59,360
Speaker 2: to eleven and and the motorcycle accsent.

1050
00:53:59,280 --> 00:54:01,560
Speaker 1: Motorcycle acts and in the jump out of planes and

1051
00:54:01,560 --> 00:54:04,080
Speaker 1: all that kind of stuff. What's the area of insurance

1052
00:54:04,120 --> 00:54:06,319
Speaker 1: that you just are staying away from it right right

1053
00:54:06,360 --> 00:54:06,839
Speaker 1: now in your.

1054
00:54:06,760 --> 00:54:12,160
Speaker 15: Space, staying away from so you know what's interesting. Cyber insurance,

1055
00:54:12,239 --> 00:54:16,160
Speaker 15: right Cyber insurance is a big growing line of business.

1056
00:54:16,480 --> 00:54:20,319
Speaker 15: A lot of companies are getting into it. They've been

1057
00:54:20,480 --> 00:54:25,080
Speaker 15: very cautious on it. But you don't know what you're underwriting.

1058
00:54:25,080 --> 00:54:27,960
Speaker 15: And I think the world is not getting anymore any

1059
00:54:28,000 --> 00:54:30,960
Speaker 15: It's not gonna getting less risky, right, and it's getting

1060
00:54:31,000 --> 00:54:31,439
Speaker 15: more risky.

1061
00:54:31,480 --> 00:54:33,680
Speaker 4: And these are things that companies don't know about.

1062
00:54:33,719 --> 00:54:35,919
Speaker 15: So be a little wary of someone who's talking about

1063
00:54:35,960 --> 00:54:38,319
Speaker 15: growing a lot in cyber insurance right now, all.

1064
00:54:38,280 --> 00:54:40,640
Speaker 1: Right, Matt, good to have you in our Bloomberg Interactive

1065
00:54:40,640 --> 00:54:43,400
Speaker 1: Broker studio. Matthew Palosola. He's a senior al is covering

1066
00:54:43,440 --> 00:54:47,960
Speaker 1: all the property and casualty insurance for Bloomberg Intelligence.

1067
00:54:48,239 --> 00:54:51,360
Speaker 8: You're listening to the tape cans Are Live program Bloomberg

1068
00:54:51,400 --> 00:54:55,000
Speaker 8: Markets weekdays at ten am Eastern on Bloomberg Radio, the

1069
00:54:55,080 --> 00:54:57,000
Speaker 8: tune in app, Bloomberg dot Com, and.

1070
00:54:57,000 --> 00:54:58,279
Speaker 4: The Bloomberg Business App.

1071
00:54:58,320 --> 00:55:01,160
Speaker 8: You can also listen live on Mazon Alexa from our

1072
00:55:01,160 --> 00:55:05,879
Speaker 8: flagship New York station. Just say Alexa play Bloomberg eleven thirty.

1073
00:55:07,440 --> 00:55:08,160
Speaker 5: Javia Giant.

1074
00:55:08,239 --> 00:55:11,239
Speaker 1: He is the founder and CEO of Techian and he

1075
00:55:11,320 --> 00:55:15,480
Speaker 1: was the former chief information officer at Tesla. So let's

1076
00:55:15,520 --> 00:55:17,879
Speaker 1: talk a little bit what's happening in the EV space. Jay,

1077
00:55:17,880 --> 00:55:20,400
Speaker 1: Thanks so much for joining us here. First, let's just

1078
00:55:20,400 --> 00:55:22,120
Speaker 1: start off real quick. Tell us what you guys are

1079
00:55:22,120 --> 00:55:23,280
Speaker 1: doing at Techian.

1080
00:55:24,800 --> 00:55:28,560
Speaker 14: Hi, Paul, Thanks thanks for having me here. You're looking

1081
00:55:28,600 --> 00:55:32,719
Speaker 14: forward to to our conversation. Yeah, at Techian, as you know,

1082
00:55:33,000 --> 00:55:36,960
Speaker 14: the automotive industry is going through some major fans everything

1083
00:55:37,040 --> 00:55:42,360
Speaker 14: from EV to autonomous vehicles, from selling models.

1084
00:55:43,520 --> 00:55:44,880
Speaker 7: Tekan is right in the center.

1085
00:55:44,960 --> 00:55:49,120
Speaker 14: So we've created a platform to bring together the major

1086
00:55:49,200 --> 00:55:57,080
Speaker 14: stakeholders in one seamless and technology platform. Manufacturers, the retailers,

1087
00:55:58,080 --> 00:56:01,800
Speaker 14: the ecosystem players being soft where are be functional industry

1088
00:56:01,840 --> 00:56:04,640
Speaker 14: players like insurance providers, lenders and others.

1089
00:56:05,080 --> 00:56:07,520
Speaker 7: And then of course the beneficiary is the consumer.

1090
00:56:07,600 --> 00:56:10,880
Speaker 14: So the end of the day, the consumer really needs

1091
00:56:10,920 --> 00:56:13,799
Speaker 14: to get what they're looking for in terms of their

1092
00:56:13,800 --> 00:56:18,440
Speaker 14: automotive buying experience. We are delivering that through the technology

1093
00:56:18,640 --> 00:56:23,719
Speaker 14: as an operating platform for retailers, manufacturers, and then the

1094
00:56:23,760 --> 00:56:27,160
Speaker 14: participants are the industry software ecosystem players.

1095
00:56:27,719 --> 00:56:30,040
Speaker 1: So it gives a sense Jay, I mean if I'm

1096
00:56:30,040 --> 00:56:32,920
Speaker 1: a I mean, Tesla has obviously a different distribution model,

1097
00:56:33,120 --> 00:56:36,680
Speaker 1: does not rely on dealerships, kind of goes direct to consumer.

1098
00:56:37,440 --> 00:56:38,480
Speaker 5: How do you think.

1099
00:56:38,480 --> 00:56:43,480
Speaker 1: The the original OEMs, the Fords, the Gms of the world,

1100
00:56:43,520 --> 00:56:45,640
Speaker 1: the Volkswagens of the world, how do you think or

1101
00:56:45,640 --> 00:56:48,040
Speaker 1: how do you think they should make this evolution to

1102
00:56:48,360 --> 00:56:49,960
Speaker 1: selling these electric vehicles?

1103
00:56:51,120 --> 00:56:54,120
Speaker 14: Great question, you know it's a I don't know if

1104
00:56:54,239 --> 00:56:56,160
Speaker 14: I should say it's a trillion dollar question, right. So

1105
00:56:56,480 --> 00:57:00,200
Speaker 14: the way we see it, we are deep into the industry.

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00:57:00,600 --> 00:57:04,520
Speaker 14: Techon works with you know, many of the retailers. In fact,

1107
00:57:04,560 --> 00:57:08,480
Speaker 14: the world's largest automountive retailer runs on Techion, and we

1108
00:57:08,520 --> 00:57:12,279
Speaker 14: work with multiple manufacturers as well. So the bottom line is,

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00:57:12,320 --> 00:57:15,480
Speaker 14: as you know, trends go through different phases, and they

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00:57:15,520 --> 00:57:18,240
Speaker 14: go through a big hype face where everything is like, oh,

1111
00:57:18,320 --> 00:57:21,440
Speaker 14: for example, I can give you a simple example. Before COVID,

1112
00:57:22,040 --> 00:57:25,080
Speaker 14: everyone said the car ownership is going away, and after

1113
00:57:25,720 --> 00:57:27,479
Speaker 14: COVID we all realize it hasn't gone away.

1114
00:57:27,480 --> 00:57:30,160
Speaker 7: People are still buying cars now.

1115
00:57:30,440 --> 00:57:33,480
Speaker 14: The other trend is people are thinking about everything will

1116
00:57:33,520 --> 00:57:39,080
Speaker 14: go online, people will buy cars online. I don't ever

1117
00:57:39,160 --> 00:57:42,080
Speaker 14: see that happening for the same reason why you know

1118
00:57:42,160 --> 00:57:45,400
Speaker 14: Tesla is still opening retail stores. Apple is still opening

1119
00:57:45,440 --> 00:57:49,520
Speaker 14: retail stores. What consumers are looking for again, putting myself

1120
00:57:49,600 --> 00:57:52,200
Speaker 14: in consumer shoes, the way I shop versus the way

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00:57:52,240 --> 00:57:55,480
Speaker 14: my twenty year old daughter shops for cars versus my

1122
00:57:55,560 --> 00:57:58,760
Speaker 14: wife shops for cars would be different. What consumers are

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00:57:58,800 --> 00:58:02,960
Speaker 14: looking for is give me the choice, give me the experience.

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00:58:03,040 --> 00:58:06,480
Speaker 14: Seamless experience doesn't matter how I choose to shop. If

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00:58:06,480 --> 00:58:10,920
Speaker 14: I want to do an online, majority of the tasks

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00:58:10,960 --> 00:58:14,880
Speaker 14: which I don't like, which is signing documents or negotiating price.

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00:58:15,560 --> 00:58:19,720
Speaker 14: But there are specific tasks I want to do it in,

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00:58:20,200 --> 00:58:23,320
Speaker 14: like experiencing the product itself, test driving, Like I walk

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00:58:23,360 --> 00:58:25,960
Speaker 14: into an Apple store and touch and feel my iPhone

1130
00:58:26,040 --> 00:58:26,960
Speaker 14: or a Mac.

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00:58:27,720 --> 00:58:29,919
Speaker 7: I think that is the fundamental.

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00:58:30,080 --> 00:58:33,440
Speaker 14: So what I see the industry evolving as the retail

1133
00:58:34,480 --> 00:58:37,920
Speaker 14: model not going away, but the business will evolve to

1134
00:58:38,000 --> 00:58:41,920
Speaker 14: provide the seamless experience for consumers, be if they start

1135
00:58:42,000 --> 00:58:45,480
Speaker 14: at a manufacturer website or they start shopping at a

1136
00:58:45,520 --> 00:58:46,600
Speaker 14: retailer website.

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00:58:47,280 --> 00:58:48,960
Speaker 7: Today, the experience is disconnected.

1138
00:58:49,680 --> 00:58:52,880
Speaker 14: That's exactly what we are solving from Technion perspective. Why

1139
00:58:52,920 --> 00:58:55,800
Speaker 14: not use that as an advantage because consumers need to

1140
00:58:55,880 --> 00:58:58,680
Speaker 14: go into a retail location to shop.

1141
00:59:00,640 --> 00:59:01,040
Speaker 4: Or whatever.

1142
00:59:01,160 --> 00:59:04,400
Speaker 1: Yeah, what are you finding from some of these existing OEMs?

1143
00:59:04,440 --> 00:59:09,200
Speaker 1: Do they have that mindset to be flexible about using technology?

1144
00:59:09,200 --> 00:59:11,520
Speaker 1: Maybe in ways they haven't done it before. Number one,

1145
00:59:11,760 --> 00:59:12,400
Speaker 1: Are they flexible?

1146
00:59:12,480 --> 00:59:15,120
Speaker 5: Number two? Are they willing to make whatever investment is required?

1147
00:59:16,480 --> 00:59:18,080
Speaker 7: Yeah, you see a full spectrum.

1148
00:59:18,480 --> 00:59:20,920
Speaker 14: Some of them are quite flexible, want to do it,

1149
00:59:21,040 --> 00:59:23,720
Speaker 14: and they didn't have a very clear path to do it.

1150
00:59:24,080 --> 00:59:27,240
Speaker 14: And I think fortunately, I think Techion is helping because

1151
00:59:27,240 --> 00:59:29,360
Speaker 14: as you know, we are a new generation company started

1152
00:59:29,400 --> 00:59:33,240
Speaker 14: in twenty sixteen, so most modern tech platform from a

1153
00:59:33,280 --> 00:59:37,720
Speaker 14: cloud native versus also using machine learning and AI inherently

1154
00:59:37,760 --> 00:59:39,680
Speaker 14: part of the platform. So it was not an afterthought

1155
00:59:39,760 --> 00:59:42,080
Speaker 14: where we went ahead and built something like that.

1156
00:59:42,520 --> 00:59:43,840
Speaker 7: So now we are helping that.

1157
00:59:44,320 --> 00:59:47,320
Speaker 14: And you see there are another spectrum where not a

1158
00:59:47,360 --> 00:59:50,040
Speaker 14: lot of flexibility is there. There's willingness from the you know,

1159
00:59:50,920 --> 00:59:54,640
Speaker 14: senior management at the working level, there is not enough incentive.

1160
00:59:54,240 --> 00:59:55,440
Speaker 7: Or push to go change.

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00:59:55,480 --> 01:00:00,440
Speaker 14: So we see a full spectrum of automotive manufacturers willing

1162
01:00:00,520 --> 01:00:04,600
Speaker 14: to change again. We all know the answer is anyone

1163
01:00:04,640 --> 01:00:09,120
Speaker 14: who's truly changing will continue to be in the middle

1164
01:00:09,200 --> 01:00:11,640
Speaker 14: or be in the front in some cases, and anyone

1165
01:00:11,680 --> 01:00:14,160
Speaker 14: who's not changing will be left behind over a.

1166
01:00:14,080 --> 01:00:14,760
Speaker 7: Period of time.

1167
01:00:15,080 --> 01:00:17,160
Speaker 1: So Jay, just give us a sense of what product

1168
01:00:17,240 --> 01:00:20,120
Speaker 1: or what service that you guys are tech on provide,

1169
01:00:20,520 --> 01:00:24,320
Speaker 1: say a dealer or the OEM dealer relationship that's proving

1170
01:00:24,360 --> 01:00:25,760
Speaker 1: I guess the most popular at the moment.

1171
01:00:27,240 --> 01:00:30,960
Speaker 14: Absolutely there are you know two, so three we have

1172
01:00:31,000 --> 01:00:34,920
Speaker 14: a three offerings. Retail cloud all interconnected. Retail cloud is

1173
01:00:34,920 --> 01:00:39,920
Speaker 14: for retailers. Runs the entire retail operations of retailer everything

1174
01:00:40,160 --> 01:00:42,960
Speaker 14: you know if you look at from selling cars online,

1175
01:00:43,120 --> 01:00:49,080
Speaker 14: shopping experience, selling cars online, customer engagement, selling cars in store,

1176
01:00:49,480 --> 01:00:51,800
Speaker 14: and the complete back office if you would call it

1177
01:00:51,840 --> 01:00:55,080
Speaker 14: as a ERP like General Ledger ap AR running their

1178
01:00:55,240 --> 01:00:58,280
Speaker 14: entire business and payments because it needs to come together

1179
01:00:58,360 --> 01:01:01,040
Speaker 14: to give the best experience. So that's automotive retail cloud

1180
01:01:01,520 --> 01:01:04,920
Speaker 14: seamlessly connected to the manufacturer's back end system so to

1181
01:01:05,080 --> 01:01:09,200
Speaker 14: track vehicle inventory, to order parts and receive it seamlessly,

1182
01:01:09,280 --> 01:01:12,480
Speaker 14: have with civility. And then we have enterprise cloud, which

1183
01:01:12,520 --> 01:01:16,640
Speaker 14: is focused as an e commerce engine for manufacturers but

1184
01:01:16,760 --> 01:01:19,560
Speaker 14: having a seamless connection to its dealers. So we have

1185
01:01:19,640 --> 01:01:24,600
Speaker 14: some of the largest manufacturers in the world using Techion,

1186
01:01:24,720 --> 01:01:29,200
Speaker 14: like for example, General Motors is our customer, so we

1187
01:01:29,760 --> 01:01:33,160
Speaker 14: have a white liveled platform for them to sell their evs,

1188
01:01:33,480 --> 01:01:36,520
Speaker 14: but seamlessly really connecting to their dealers as well.

1189
01:01:36,600 --> 01:01:37,800
Speaker 7: So that's retail cloud.

1190
01:01:38,280 --> 01:01:40,600
Speaker 14: The last one is we need to bring the ecosystem

1191
01:01:40,640 --> 01:01:45,400
Speaker 14: together partner cloud where we have technology APIs and you

1192
01:01:45,440 --> 01:01:49,080
Speaker 14: have like insurance providers lenders. So if you sell a car,

1193
01:01:49,160 --> 01:01:51,080
Speaker 14: you need to sell an insurance So how do you

1194
01:01:51,120 --> 01:01:53,680
Speaker 14: make that process simple and easy? See how do you

1195
01:01:53,800 --> 01:01:59,560
Speaker 14: bring the providers through technology seamlessly exchanging data and securely?

1196
01:02:00,880 --> 01:02:01,959
Speaker 5: All right, fascinating stuff.

1197
01:02:02,000 --> 01:02:05,040
Speaker 1: I mean, it's an industry that is evolving with the

1198
01:02:05,080 --> 01:02:07,560
Speaker 1: times and with the technology, and you kind of think

1199
01:02:07,600 --> 01:02:11,400
Speaker 1: about the whole stack there. Jay Vagian is the founder

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01:02:11,440 --> 01:02:13,640
Speaker 1: and CEO of tech Young, joining us here to talk

1201
01:02:13,680 --> 01:02:15,919
Speaker 1: to us about evs and how you kind of bring

1202
01:02:15,960 --> 01:02:19,800
Speaker 1: it all together from the OEM manufacturer right down through

1203
01:02:19,800 --> 01:02:21,000
Speaker 1: the dealer and to the consumer.

1204
01:02:23,040 --> 01:02:26,080
Speaker 2: Thanks for listening to the Bloomberg Markets podcast. You can

1205
01:02:26,160 --> 01:02:29,960
Speaker 2: subscribe and listen to interviews at Apple Podcasts or whatever

1206
01:02:30,040 --> 01:02:33,760
Speaker 2: podcast platform you prefer. I'm Matt Miller. I'm on Twitter

1207
01:02:33,960 --> 01:02:35,880
Speaker 2: at Matt Miller nineteen seventy three.

1208
01:02:36,320 --> 01:02:38,760
Speaker 5: And I'm fall Sweeney. I'm on Twitter at pt Sweeney.

1209
01:02:38,840 --> 01:02:41,520
Speaker 1: Before the podcast, you can always catch us worldwide at

1210
01:02:41,520 --> 01:02:43,240
Speaker 1: Bloomberg Radio