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Speaker 1: This is Bloomberg Business Wait inside from the reporters and

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Speaker 1: editors who bring you America's most trusted business magazine, plus

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Speaker 1: global business finance and tech news. The Bloomberg Business Week

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Speaker 1: Podcast with Carol Messer and Tim Stenebeck from Bloomberg Radio.

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Speaker 2: All right, I knew when Netflix reported last night and

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Speaker 2: we were breaking down the numbers, I knew that. Ultimately

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Speaker 2: I wanted to get to Keeta Ringanathon, who follows this

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Speaker 2: name so closely and picks it apart, goes through the

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Speaker 2: details and really explains the story. She has Bloomberg Intelligence,

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Speaker 2: Technology and media analyst. As I said, Geeta Ringanathon, she

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Speaker 2: is with us on zoom from our Princeton, New Jersey bureau. Hey,

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Speaker 2: geta stock's a little bit lower today, but if you

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Speaker 2: looked at the aftermarket last night, it was getting killed

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Speaker 2: Netflix like down ten to eleven percent, now down about

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Speaker 2: four percenters. So initial stock reaction, what were they reacting to?

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Speaker 2: And then maybe why did investors maybe rethink it a

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Speaker 2: little bit?

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Speaker 3: Yeah, thank you so much Carolyn Madison for having me so. Obviously,

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Speaker 3: the nee jerk reaction, as always has been to the

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Speaker 3: subscriber numbers, right, and so the one Q subscriber numbers

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Speaker 3: came in a little bit lighter. The two Q revenue

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Speaker 3: guidance wasn't looking too great either, and you know, that

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Speaker 3: was kind of what prompted that initial sell off in

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Speaker 3: the stock. I think as investors kind of had time to,

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Speaker 3: you know, go back a little bit into the report

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Speaker 3: and digest some of the other details. Remember, Netflix itself

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Speaker 3: is pivoting away from, you know, just being a subscriber

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Speaker 3: story to becoming more of a profitability story, becoming more

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Speaker 3: of a free cash flow story, and I think they

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Speaker 3: did absolutely spectacularly on that front. You know, if you

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Speaker 3: look at the operating margins, they came in well above

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Speaker 3: what they had projected. If you look at operating profits again,

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Speaker 3: they came in very well. And where they really kind

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Speaker 3: of stunned, you know, the street was with their free

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Speaker 3: cash flow numbers. So over two billion dollars just in

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Speaker 3: first quarter free cash flow. They upped their guidance for

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Speaker 3: the full year to three and a half billion, and

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Speaker 3: we think that that still is pretty conservative. It it

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Speaker 3: could well go to maybe about four billion dollars just

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Speaker 3: a free cash flow.

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Speaker 2: I heard Jens surveillance this marrity, so I got to

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Speaker 2: chee a little bit in terms of your thinking. But

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Speaker 2: that's amazing, right for media company essentially.

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Speaker 3: That is absolutely amazing for media, for streaming, and especially

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Speaker 3: for Netflix. Remember, this was a company that was getting

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Speaker 3: criticized left, right and center for not you know, not

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Speaker 3: generating cash, for losing, for burning three and a half

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Speaker 3: billion dollars in cash maybe just about four years ago.

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Speaker 3: And how they've turned the story around has just been,

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Speaker 3: you know, miraculous, And of course it gives I think

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Speaker 3: the whole of the media landscape now a playbook. Right,

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Speaker 3: this is what everybody wants to do. This is what

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Speaker 3: everybody's aspiring to do. Get to those twenty percent operating margins,

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Speaker 3: not only break even on their streaming business, but then

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Speaker 3: really get out to churning profits and charning free cash. Remember,

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Speaker 3: everybody right now is losing money in the streaming business.

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Speaker 3: Whether it's Disney they're going to lose about three billion

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Speaker 3: dollars this year, whether it's Paramount again two billion dollars

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Speaker 3: in losses, Peacock three billion dollars in losses. So the

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Speaker 3: fact that Netflix is going to throw out seven billion

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Speaker 3: dollars in EBITDA and three and a half billion in

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Speaker 3: free cash flow is is just marvelous.

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Speaker 2: You know, media companies have always been fun. If you

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Speaker 2: go back to kind of the likes of traditional cable companies,

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Speaker 2: and we always talked about them not being necessarily huge

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Speaker 2: money maker, Like how do we think about streaming. Should

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Speaker 2: they be making money KITA in this environment or should

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Speaker 2: they be as a class be making money?

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Speaker 3: I mean, that is such an excellent question, right. I

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Speaker 3: think after that initial dust settled from that streaming land

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Speaker 3: grab phase and everybody was just kind of in that

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Speaker 3: rush to get streaming subscribers, I think then this question

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Speaker 3: kind of really started to evolve being you know, what

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Speaker 3: is what is the endgame? What are the unit economics

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Speaker 3: in this business? Is it ever going to be as

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Speaker 3: good as the old TV model where we saw networks

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Speaker 3: linear TV networks like a USA or a CNBC generate

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Speaker 3: you know, thirty thirty five percent margins quarter after quarter,

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Speaker 3: and so what we've kind of now, you know, obviously,

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Speaker 3: with you know, companies spending handover fist when it came

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Speaker 3: to content, you know, there was this question about whether

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Speaker 3: they're ever going to be able to break even. I

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Speaker 3: think Netflix finally has provided the intry with some sort

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Speaker 3: of a template of how to go about it. I mean,

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Speaker 3: obviously they did have a lot of bumps down the road,

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Speaker 3: especially in their early innings. But I think now now

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Speaker 3: that you know they're in a slightly more mature face,

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Speaker 3: I think we do have this finally, this this kind

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Speaker 3: of playbook.

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Speaker 4: So that's my exact question for you. In terms of

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Speaker 4: the cash flow, what do you anticipate Netflix pouring that

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Speaker 4: cash into to increase profits even more? Are they going

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Speaker 4: to lean into more content creation for those programs that

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Speaker 4: are really cheap to produce.

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Speaker 3: That's a fabulous question. I think what they said last

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Speaker 3: night is yes, they're holding content. So they are curbing

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Speaker 3: their content spending a little bit this year especially and

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Speaker 3: also for twenty twenty four. They kind of gave some

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Speaker 3: guidance there. But I think after that, you know, kind

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Speaker 3: of everything becomes you know, fair game. So they talk

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Speaker 3: not just about you know, necessarily TV shows or film content.

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Speaker 3: They're going to expand into different categories. They're going to

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Speaker 3: expand into gaming. They already are. In fact, they might

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Speaker 3: look at different genres, you know, whether it's sports, pro gramming,

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Speaker 3: and all of that is going to take a lot

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Speaker 3: of money. So yes, they're definitely going to invest in

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Speaker 3: more content, in more programming. But they also talked about

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Speaker 3: return of capital. So they already bought back about four

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Speaker 3: hundred million shares in the first quarter, and they're going

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Speaker 3: to accelerate their whole share repurchase program. So this is

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Speaker 3: becoming very much like a traditional, you know, low growths

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Speaker 3: tech company.

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Speaker 4: Really well, real quick on that, because you mentioned sports

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Speaker 4: and I feel like the live stream of the Love

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Speaker 4: Is Blind reunion was a good test run for them

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Speaker 4: when it comes to live programming. Is it a big

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Speaker 4: deal that that didn't go well?

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Speaker 3: You know, and they addressed that yesterday a little bit,

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Speaker 3: so they did talk about, you know, some technical bugs.

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Speaker 3: It obviously is important for them to have all the

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Speaker 3: infrastructure in places before they embark on something as ambitious

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Speaker 3: as sports programming. I think they'll get there. I mean,

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Speaker 3: obviously they are. There are going to be some kings,

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Speaker 3: some hiccups on the way. I don't think it's as

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Speaker 3: big of a deal. They did finally get the show

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Speaker 3: up and running, and they're going to learn along the way,

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Speaker 3: so you know, eventually they will have all the infrastructure.

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Speaker 2: Isn't it funny though, Geita that you feel like the

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Speaker 2: streaming services all this highly produced content you know, you know,

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Speaker 2: you can watch it at any time, doesn't matter when

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Speaker 2: you watch it. Is now kind of going back to

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Speaker 2: the linear model of destination viewing or specific times, whether

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Speaker 2: it's through sports or something like a live feed.

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Speaker 3: Yeah, I mean that's kind of interesting, and I think

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Speaker 3: what Netflix is really trying to do here, I think

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Speaker 3: is just kind of build anticipation, create bus. I mean,

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Speaker 3: if you look at reality series and something like you know,

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Speaker 3: Love is Blind or I don't know, Nailed It or

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Speaker 3: all those making competitions. I mean that's something that you

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Speaker 3: typically have on in the background as you're folding laundry

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Speaker 3: or kind of you know, unloading your dishwasher. But I

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Speaker 3: think they want to just create the bus, create anticipation,

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Speaker 3: so that you have this audience kind of coming in

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Speaker 3: to watch it, almost like appointment television. So it's really

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Speaker 3: just more ways for them to you know, have a

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Speaker 3: captive audience, which eventually, hopefully they can sell to advertisers.

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Speaker 4: At some point on that exact note, advertisers, they were

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Speaker 4: going to roll out this ad supported model, What is

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Speaker 4: the plan for that? Because I have not seen that yet.

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Speaker 3: So the ad supported model is up and running. They

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Speaker 3: did not give us necessarily any subscriber numbers around that,

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Speaker 3: but they did give us some directional data points. So

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Speaker 3: what they did say is that it is going according

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Speaker 3: to plan. They probably I mean, by our estimates, they

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Speaker 3: probably have about two twenty half million subscribers on that

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Speaker 3: AD plan. There's still a lot more market to capture.

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Speaker 3: But I think the really interesting point that they gave

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Speaker 3: us is even though the plan is priced at seven dollars,

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Speaker 3: they are actually making sixteen dollars per subscriber because they're

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Speaker 3: able to attract all those advertising dollars. So essentially they're

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Speaker 3: agnostic between an AD supported consumer or an AD free consumer.

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Speaker 3: Because you remember the standard plan, the standard Netflix add

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Speaker 3: free plan costs about fifteen and a half dollars.

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Speaker 2: So the AD supported tier and the cracking down on

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Speaker 2: password sharing, that's catalysts for the stock to move to

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Speaker 2: the upside ultimately when they get it all in place.

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Speaker 2: And I'm just worry coutely and should we be concerned

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Speaker 2: about the delays here and kind of rolling this out

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Speaker 2: or nah, not.

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Speaker 3: Really, I picket it in the scheme of things. I mean,

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Speaker 3: it's probably a few months out and they needed to

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Speaker 3: kind of they need to execute well, and it was

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Speaker 3: essential for them to kind of roll it out in

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Speaker 3: a few test markets, get those learnings, and then kind

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Speaker 3: of implement it. Was it was really interesting that they

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Speaker 3: first rolled it out in Canada, and they pretty much

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Speaker 3: look at Canada as kind of this proxy for the US.

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Speaker 3: So they've got all their learnings from Canada. They know

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Speaker 3: how exactly the audience reacted, the users reacted. They kind

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Speaker 3: of know what to expect now when they do a

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Speaker 3: much broader rollout in the US, which is by far

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Speaker 3: their biggest market and where they need to execute really flawlessly.

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Speaker 2: Hey, one last question. You know Netflix has been telling

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Speaker 2: investors stop fixating on my subscriber model, take a look

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Speaker 2: at like sales and profit. Are they right to do

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Speaker 2: so in your view?

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Speaker 3: I think so? I think investors and Wall Street has

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Speaker 3: to pick a lane, right if you wanted. I mean,

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Speaker 3: all along it was the subscriber story. Then last year

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Speaker 3: you had the great Netflix meltdown, the great Netflix correction,

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Speaker 3: and you know they said, okay, subscriber growth has hit

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Speaker 3: a wall. Start focusing on our aftability metrics. And you know,

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Speaker 3: investors rightly started focusing on profitability metrics. Nobody is chasing

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Speaker 3: subscribers anymore, and so now when they are reporting plug

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Speaker 3: you know, good profits and good free cash flow, we

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Speaker 3: got to give them credit.

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Speaker 2: This is why we want to talk to you. Gita

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Speaker 2: ring Anathon. She's technology and media analyst at Bloomberg Intelligence,

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Speaker 2: knowing all when it comes to Netflix. Joining us from

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Speaker 2: our Princeton, New Jersey bureau. Gita, thank you.

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Speaker 2: All right, let's get back to Tesla because that is

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Speaker 2: certainly a big focus for investors. Dot down just about

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Speaker 2: one percent. Here in the aftermarket, we talked about the valuation,

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Speaker 2: especially as the company continues to cut prices. We talked

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Speaker 2: a little bit about margins, a little bit light based

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Speaker 2: on what the street was expecting. So let's get to

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Speaker 2: the valuation side of the story and the fundamental Esha

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Speaker 2: Day is Bloomberg News Equity Markets reporter here in our

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Speaker 2: Bloomberg Interactive Broker's studio along with gay couple Lot. She

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Speaker 2: is Bloomberg News Auto reporter. She's on the phone in

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Speaker 2: our Detroit bureau, and I do believe you know, Gabby,

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Speaker 2: We've got to start with the fundamental story. What are

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Speaker 2: some of the key takeaways that you see? We just

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Speaker 2: were kind of running through the numbers with our TV colleagues,

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Speaker 2: But what jumps out for you, hey, Carol?

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Speaker 5: So yeah, I think going into this, we all knew

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Speaker 5: that margins were going to suffer because Tesla has been

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Speaker 5: implementing price cuts around the world. But I think a

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Speaker 5: floor that the street had was twenty percent gross margin,

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Speaker 5: and they came in with nineteen point three percent gross margin.

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Speaker 5: So it's not a lot of difference, but that's sort

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Speaker 5: of the floor that we thought they would try to

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Speaker 5: stay above, and they did in this quarter. And if

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Speaker 5: you look at I'm just going through the letter, Tesla's

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Speaker 5: letter to investors, and the opening line talks about in

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Speaker 5: this current macro economic environment, this is a unique opportunity

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Speaker 5: for Tesla. It talks about how basically they are going

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Speaker 5: to underprice their competitors.

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Speaker 2: We see this right with I mean, China has done

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Speaker 2: it right on steel and close and you know, so,

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Speaker 2: I mean, we understand the strategy to be fair, yes.

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Speaker 5: But I mean, I mean Tesla does. China is a

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Speaker 5: huge market for Tesla, and I do have an assembly

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Speaker 5: factory in Shanghai, but that I don't think, you know,

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Speaker 5: it's not totally the same thing as like the China.

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Speaker 5: I mean, Tesla still builds vehicles in America, I think,

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Speaker 5: and employees people in the United States, and you know,

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Speaker 5: DOES is doing this in the United States, a lot

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Speaker 5: of it, so, including the cyber truck, which they just

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Speaker 5: said also that it's on track to come lader this year,

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Speaker 5: start production later this year, I should say. So it's

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Speaker 5: really about thinking about you know, Elon Musk from the beginning,

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Speaker 5: even when he was talking about going through production. Hell,

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Speaker 5: we didn't think he was going to make it. He's

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Speaker 5: always always always stressed that he was going to drive

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Speaker 5: down cost, and now we're going to see him leverage that.

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Speaker 5: And it's just a matter of if you've been doing this,

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Speaker 5: you know, for a decade versus you know, GM or

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Speaker 5: four or Stilantis or Volkswagen or whoever, who are kind

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Speaker 5: of just now coming into this. Even though they're you know,

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Speaker 5: legacy automakers with a lot of manufacturing experience, they don't

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Speaker 5: Tesla has more experience than they do at building evs

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Speaker 5: and that gives them the advantage to do that. I mean,

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Speaker 5: that's part of it.

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Speaker 2: So, and this is what's key. We're talking about, not

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Speaker 2: just the cost when you say driving down cost, the

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Speaker 2: cost of producing it as well as the cost of

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Speaker 2: the actual vehicle both.

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Speaker 5: Yes, I think yes, yes, I mean the way he

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Speaker 5: can cut he's betting that. I mean, you see other

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Speaker 5: I mean I cover Stalantis, right, So the CEO Stalantis

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Speaker 5: will always brag about how he has a that you know,

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Speaker 5: car sales around the world could drop forty percent, he

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Speaker 5: would still make money. And that's because you have you

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Speaker 5: take a lot of cost out of your manufacturing. And

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Speaker 5: that's I mean, Tesla has sought to start producing batteries

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Speaker 5: in house. They're talking about processing their own lithium to

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Speaker 5: make batteries in Texas. They have you know, they make

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Speaker 5: a lot of parts in house. You know, they're not

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Speaker 5: like other car companies that buy all this stuff from suppliers,

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Speaker 5: you know, down the supply chain. That's what we saw

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Speaker 5: during the chip crisis, Tesla did. You know, everybody suffered

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Speaker 5: in the auto industry, but Tesla did fair better because

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Speaker 5: they did so much of this stuff in the house

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Speaker 5: that they could be nimble. They could take a semi

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Speaker 5: conductor chip and rewrite the code and make it function

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Speaker 5: for another purpose or another thing inside the vehicle.

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Speaker 6: Yeah.

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Speaker 4: So yeah, well, Asha, I want to bring you in here.

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Speaker 4: Tell me what you're thinking about the market reaction. Is

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Speaker 4: there more disappointment than you anticipated.

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Speaker 6: That's a great question. So, as Gaby mentioned, you know,

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Speaker 6: the twenty percent cross margin was sort of the hard

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Speaker 6: line that everybody was looking at. Investors are trying to understand,

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Speaker 6: you know, this land craft that Tesla is kind of

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Speaker 6: getting into, at what price is it coming for?

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Speaker 2: So let's talk valuation, right because if you pull it up,

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Speaker 2: I mean we've got Tesla at a current pe of

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Speaker 2: about fifty two, a forward looking pe of almost forty six,

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Speaker 2: and we know investors were and be focused on, especially

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Speaker 2: with a company that's been cutting prices absolutely.

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Speaker 6: So that margin is the like one of the big

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Speaker 6: corner stores of the valuation that Tesla has. And the

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Speaker 6: Tesla is valued at like, you know, forty seven fifty

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Speaker 6: times earnings forward earnings when you talk about GM or four,

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Speaker 6: that's about six seven times, So you know, the disappointment

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Speaker 6: on margin is a big disappointment when it comes to

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Speaker 6: Tesla compared to like any other automaker, because that that

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Speaker 6: is sort of the corner store that's holding up the valuation.

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Speaker 6: So if GM has to cut prices or four has

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Speaker 6: to cut prices, that's still understandable. But Tesla, even though

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Speaker 6: it has scale, doesn't have scale at that point where

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Speaker 6: it can hold up the valuation. Can it bring in

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Speaker 6: as much revenue, even more scale and really cut down

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Speaker 6: the cost of making those cars to at some point,

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Speaker 6: you know, drive these margins back up.

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Speaker 4: Yeah, So, Gabby, should we be valuing Tesla differently than

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Speaker 4: a GM or a Ford.

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Speaker 5: Ooh, that's a very controversial question. Obviously, for years we

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Speaker 5: heard Wall Street say that this was a tech company,

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Speaker 5: not a car company. I kind of feel like both

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Speaker 5: are true.

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Speaker 7: How do you report on it?

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Speaker 2: Like, how do you think about it?

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Speaker 5: I take the technology company stuff with a grain of salt,

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Speaker 5: because at the end of the day, they are making

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Speaker 5: their still metal bashers. However much they don't make you

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Speaker 5: want to escape. I mean yes, And you know, when

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Speaker 5: you see Elon and I look at this investor letter

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Speaker 5: and they talk about their strategy going forward, they're talking

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Speaker 5: about we're still making investments in autonomy and vehicle software,

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Speaker 5: so that speaks to the tech question you're talking about.

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Speaker 5: Tesla believes that they you know, obviously they're quote unquote

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Speaker 5: full self driving, which is not full self driving, but

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Speaker 5: they're you know, autonomous features in the car that have

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Speaker 5: gotten them into hot water with regulators. Nonetheless, they believe

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Speaker 5: Elon still seems to firmly believe that that is a

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Speaker 5: growing revenue source in future, along with other software services

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Speaker 5: in the car, and that's something that all the carmakers

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Speaker 5: are looking at now. They're trying to not just be

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Speaker 5: metal bashers, but they also want to sell services to

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Speaker 5: consumers through the car, which obviously pits them against Apple

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Speaker 5: and things like that. There's one other point I wanted

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Speaker 5: to make Carol. As we think about this, don't forget

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Speaker 5: about China. I'm just looking at you know, there's been

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Speaker 5: a lot of headlines about what's going on the auto

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Speaker 5: market in China. Elon had a very interesting relationship with China.

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Speaker 5: All the other automakers were forced to do joint ventures

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Speaker 5: where there would be tech transfer with their Chinese competitors,

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Speaker 5: not Tesla Tesla. Elon held out right he never did that.

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Speaker 5: He came in and built a plant, you know, once

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Speaker 5: he owned it himself quickly. Nonetheless, nonetheless, you see by

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Speaker 5: d you see these other domestic Chinese car companies gaining

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Speaker 5: share in the Chinese market, not just Tesla, but everybody

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Speaker 5: is kind of feeling the pressure there from some of

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Speaker 5: the you know, the Chinese car companies have matured and

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Speaker 5: they're making some good product now, which is another say.

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Speaker 2: To think about when we think about the valuation. And Maddie,

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Speaker 2: I knew you wanted to ask that one thing when.

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Speaker 4: Its Tesla Gabby Real Quick Inflation Reduction Act, they reported

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Speaker 4: an increase in regulatory credits this quarter. How big of

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Speaker 4: a deal is that for Tesla moving forward?

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Speaker 5: Okay, well, I think it is going to be significant.

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Speaker 5: And the CFO, Zaka Kirkhorn, talked about that last quarter

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Speaker 5: and I'm definitely going to be watching for his comments

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Speaker 5: this quarter. But just let's just make it clear in

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Speaker 5: folks minds. Like one thing is the regulatory credits that

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Speaker 5: is that's been around for years and that's basically when

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Speaker 5: another car company can't meet the EPA's fuel economy rules,

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Speaker 5: they can actually buy credit from Tesla, which has nothing

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Speaker 5: Tesla does not make combustion engines, right, there's no they're

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Speaker 5: all zero emission vehicles, right, So that has been another

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Speaker 5: kind of revenue cushion. Margin cushion for Tesla is his credits.

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Speaker 5: But as more car companies get electric vehicles, they don't

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Speaker 5: need to buy from Tesla. I mean, you know, for example,

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Speaker 5: Chrysler and now Stiland has had a huge deal with Tesla,

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Speaker 5: and they weren't the only one. So I'm trying to

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Speaker 5: look at it there the let's see regular and trying

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Speaker 5: to look for regulatory credit. But basically, let me just say, yeah,

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Speaker 5: IRA is a different deal. The IRA is about Biden administration.

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Speaker 5: The federal government is paying companies to make batteries on

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Speaker 5: US soil.

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Speaker 8: Right.

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Speaker 5: Tesla already has a completely scaled and they're going to

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Speaker 5: expand it giga factory in Nevada, and they have one

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Speaker 5: in Texa. They're building it. They have it in Texas.

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Speaker 5: They're building batteries in Texas. So that's another source of

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Speaker 5: government revenue. And it's all about how many batteries, how

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Speaker 5: many kill a lot hours of batteries you made? Guess what?

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Speaker 5: Like I said, they have first moved for advantage. Tesla

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Speaker 5: has been doing this longer, and they're further along.

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Speaker 2: This is what is like when it goes back to

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Speaker 2: Esha's story that's on the Bloomberg and you should check

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Speaker 2: it out. This is why you know, it's like, yeah,

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Speaker 2: my valuation, I got it, I'm worth it.

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Speaker 9: All right.

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Speaker 2: Well, let's see what Ross Gerber has to make out

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Speaker 2: of all of this. He's president. You have Gerber Kawasaki

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Speaker 2: Wealth and Investment Management, a Tesla shareholder, a Tesla car

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Speaker 2: owner once wanted a board seat and then did not.

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Speaker 2: He's with us, back with us on Zoom from Santa Monica, California. Hey, Ross,

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Speaker 2: good to have you here. I know you're looking at

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Speaker 2: all the news coming out of Tesla's stock is still

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Speaker 2: down I think about three or four percent here in

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Speaker 2: the aftermarket. What jumps out for you.

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Speaker 9: Well, what jumps out for me is what I've been

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Speaker 9: saying forever, which is Elon needs to spend some money

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Speaker 9: on advertising instead of just sacrifice some gross margins and

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Speaker 9: cash flow to lower prices. And he's treating the car

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Speaker 9: like a commodity, and so you know, if there's less sales,

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Speaker 9: they just lower the price, and if there's more sales,

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Speaker 9: they raise the price, but that's actually not how you

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Speaker 9: sell cars. So I think that this quarter was in

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Speaker 9: line with our expectations, so that's the positive. It wasn't worse.

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Speaker 9: But you know, when sales are up forty percent but

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Speaker 9: revenue and profits are down, there's an issue you got

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Speaker 9: to deal with. And blaming the macro environment, like a

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Speaker 9: lot of people want to do, isn't isn't accurate, you know,

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Speaker 9: So Tesla needs to address the demand side of their business,

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Speaker 9: whether they like it or not.

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Speaker 4: Well, to be fair, they also blame the weather in California.

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Speaker 7: Ross do you suy that.

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Speaker 9: That was for solar deployment though, but yeah, yeahyeah, yeah.

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Speaker 7: Yeah, fair enough.

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Speaker 4: Do you do you think on the valuation side, has

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Speaker 4: your perspective changed at all? Do you think the valuation

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Speaker 4: is fair?

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Speaker 8: Yeah?

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Speaker 9: I think the valuation is fair, and I think we've

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Speaker 9: been running at fifty times earnings and originally about six

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Speaker 9: eight months ago, our earnings expectations for this year we're

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Speaker 9: six dollars, So we were at around three hundred dollars

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Speaker 9: for Tesla and now they've been lowered to under four dollars.

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Speaker 9: So if you do do fifty times four dollars, you're

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Speaker 9: at two hundred dollars right now we're seeing earnings that

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Speaker 9: might not even make four dollars. So I don't think

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Speaker 9: the stock's acting irrationally. And I think for investors who

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Speaker 9: have a long term focus, you know, Tesla's trading for

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Speaker 9: what it's worth, and so if you're a long term investor,

434
00:20:38,800 --> 00:20:40,959
Speaker 9: it's a good value. But if you're a long term

435
00:20:41,000 --> 00:20:44,800
Speaker 9: Tesla shareholder like myself, it's very disappointing. You see earnings

436
00:20:44,800 --> 00:20:45,960
Speaker 9: going down, right.

437
00:20:46,119 --> 00:20:48,679
Speaker 2: You buy, you sell shares of Tesla. We know that

438
00:20:48,680 --> 00:20:50,640
Speaker 2: we've talked with you a lot. Would you buy at

439
00:20:50,680 --> 00:20:52,960
Speaker 2: this valuation? And as a stock is going down about

440
00:20:52,960 --> 00:20:55,040
Speaker 2: three or four percent here, at least in the aftermarket.

441
00:20:55,640 --> 00:20:56,960
Speaker 9: Well, I think you have to look at it from

442
00:20:57,000 --> 00:20:59,399
Speaker 9: the perspective of if I'm an investor with cash and

443
00:20:59,440 --> 00:21:02,000
Speaker 9: I'm looking to make investments with Tesla still be one

444
00:21:02,040 --> 00:21:05,040
Speaker 9: of our top investments, and absolutely it would be. But

445
00:21:05,119 --> 00:21:07,560
Speaker 9: the question is is if you're heavily invested in Tesla

446
00:21:07,600 --> 00:21:10,159
Speaker 9: today and that's all you own is Tesla, is this

447
00:21:10,200 --> 00:21:12,760
Speaker 9: a smart investment strategy? And I would say no, there's

448
00:21:12,760 --> 00:21:15,440
Speaker 9: a considerable amount of risk that wasn't in Tesla a

449
00:21:15,520 --> 00:21:15,920
Speaker 9: year ago.

450
00:21:16,200 --> 00:21:20,560
Speaker 2: So are you upbeat though that they're saying they are

451
00:21:20,600 --> 00:21:23,040
Speaker 2: seeing an ongoing cost reduction of its vehicles. You know

452
00:21:23,080 --> 00:21:25,239
Speaker 2: what they're doing, right, They're cutting prices to grab more

453
00:21:25,240 --> 00:21:27,080
Speaker 2: of the market. But they're also saying, we're gonna be

454
00:21:27,080 --> 00:21:29,520
Speaker 2: able to cut the cost of production. Do you believe it?

455
00:21:29,560 --> 00:21:30,360
Speaker 2: Can they do it?

456
00:21:31,000 --> 00:21:31,160
Speaker 7: Well?

457
00:21:31,160 --> 00:21:33,840
Speaker 9: I was just there. Absolutely they can do it because

458
00:21:33,840 --> 00:21:36,560
Speaker 9: they got a massive factory that's only producing a couple

459
00:21:36,640 --> 00:21:39,040
Speaker 9: hundred thousand cars that can produce over a million cars.

460
00:21:39,200 --> 00:21:41,280
Speaker 9: So when you think about the point of scale that

461
00:21:41,320 --> 00:21:44,040
Speaker 9: they're in, there's no doubt that they can cut the

462
00:21:44,080 --> 00:21:46,000
Speaker 9: cost of the car. But the problems is as they

463
00:21:46,040 --> 00:21:49,680
Speaker 9: increase production, can they sell all these cars? And if

464
00:21:49,680 --> 00:21:53,119
Speaker 9: they continue to lower prices faster than costs are going down,

465
00:21:53,240 --> 00:21:56,560
Speaker 9: then we've got a real bad situation for Tesla. And

466
00:21:57,119 --> 00:21:59,560
Speaker 9: that's what my concern is when you haven't tried all

467
00:21:59,600 --> 00:22:02,080
Speaker 9: the leave to sell cars. But one thing I think

468
00:22:02,080 --> 00:22:04,879
Speaker 9: we can safely say that Elon's new job at Twitter

469
00:22:05,000 --> 00:22:07,199
Speaker 9: is not helping Tesla sell cars.

470
00:22:08,440 --> 00:22:10,240
Speaker 2: Yeah, and if they're going to do some advertising cuts

471
00:22:10,240 --> 00:22:12,159
Speaker 2: into margins, do you hate that he's cutting prices? Just

472
00:22:12,160 --> 00:22:14,280
Speaker 2: got thirty seconds twenty five? Absolutely?

473
00:22:14,520 --> 00:22:17,439
Speaker 9: I think Tesla's a premium product, and I think it

474
00:22:17,720 --> 00:22:21,280
Speaker 9: serves what we call the mass affluent marketplace, which is massive.

475
00:22:21,680 --> 00:22:25,560
Speaker 9: And I think the continuing and cutting prices actually hurts

476
00:22:25,600 --> 00:22:28,640
Speaker 9: the brand, and so I'd love to see Tesla try

477
00:22:28,640 --> 00:22:31,160
Speaker 9: to hold prices here and try to sell cars any

478
00:22:31,200 --> 00:22:31,560
Speaker 9: other way.

479
00:22:31,680 --> 00:22:33,600
Speaker 2: All Right, Gonna leave it on that note. We know

480
00:22:33,640 --> 00:22:36,040
Speaker 2: a busy afternoon, so so appreciate you weigh Have you

481
00:22:36,040 --> 00:22:39,560
Speaker 2: been buying prices any Tesla's on the price cuts ten seconds?

482
00:22:39,560 --> 00:22:42,399
Speaker 9: Well, no, I already have to Tesla's and they've lost value,

483
00:22:42,440 --> 00:22:43,200
Speaker 9: so I'm not happy.

484
00:22:44,160 --> 00:22:46,760
Speaker 2: All right, Well said, Well said Ross Gerber. Thank you

485
00:22:46,800 --> 00:22:49,760
Speaker 2: so much, President and Chief executive Officer Kerber Kawasaki Wealth

486
00:22:49,800 --> 00:22:50,800
Speaker 2: and Investment Management.

487
00:22:51,480 --> 00:22:55,080
Speaker 1: You're listening to the Bloomberg Business Week podcast. Catch us

488
00:22:55,119 --> 00:22:59,119
Speaker 1: live weekday afternoons from three to six Easter on Bloomberg Radio,

489
00:22:59,280 --> 00:23:02,600
Speaker 1: the Bloomberg Business App, and YouTube. You can also listen

490
00:23:02,680 --> 00:23:05,800
Speaker 1: live on Amazon Alexa from our flagship New York station,

491
00:23:06,240 --> 00:23:09,040
Speaker 1: Just say Alexa play Bloomberg eleven thirty.

492
00:23:09,800 --> 00:23:13,280
Speaker 2: Continue to track developments daily when it comes to AI,

493
00:23:13,320 --> 00:23:18,199
Speaker 2: in particular, generative artificial intelligence, increasingly creeping into the conversation

494
00:23:19,000 --> 00:23:20,760
Speaker 2: is also the creepy thought that AI is like a

495
00:23:20,800 --> 00:23:24,080
Speaker 2: person sentient. There's a great story on the Bloomberg about

496
00:23:24,119 --> 00:23:27,040
Speaker 2: that and reminding us that machines are not conscious. So

497
00:23:27,440 --> 00:23:29,360
Speaker 2: let's get to our next guest. See what he has

498
00:23:29,400 --> 00:23:31,640
Speaker 2: to say about what's going on when it comes to AI.

499
00:23:32,240 --> 00:23:36,280
Speaker 2: He is head of and co founder of a company

500
00:23:36,280 --> 00:23:38,840
Speaker 2: that has been named to Fast Companies prestigious annual list

501
00:23:38,840 --> 00:23:41,800
Speaker 2: of the world's most innovative companies for twenty twenty three

502
00:23:41,840 --> 00:23:44,879
Speaker 2: when it comes to the AI category. With more on that,

503
00:23:45,840 --> 00:23:48,320
Speaker 2: and let's get into the company. It's an AI powered

504
00:23:48,520 --> 00:23:52,160
Speaker 2: app development platform builder dot Ai. I think I need

505
00:23:52,359 --> 00:23:55,240
Speaker 2: AI right now to help me read. We welcome co

506
00:23:55,320 --> 00:24:00,280
Speaker 2: founder Satan Doogal. He's Dev Doogal, co founder and chief

507
00:24:00,320 --> 00:24:03,080
Speaker 2: wizard a builder dot Ai. He's here in our interactive

508
00:24:03,080 --> 00:24:05,600
Speaker 2: broker studio. Sorry my introduction was too long. I should

509
00:24:05,600 --> 00:24:06,480
Speaker 2: have just said hello, come in.

510
00:24:06,680 --> 00:24:07,760
Speaker 8: Thank you so much for having me.

511
00:24:08,200 --> 00:24:10,480
Speaker 2: Good to have you here with us. Tell us, first

512
00:24:10,480 --> 00:24:12,879
Speaker 2: of all, what your company does specifically.

513
00:24:12,720 --> 00:24:15,199
Speaker 8: So we allow people who don't understand technology to be

514
00:24:15,200 --> 00:24:15,439
Speaker 8: able to.

515
00:24:15,400 --> 00:24:17,360
Speaker 2: Build software, so anybody can build.

516
00:24:17,160 --> 00:24:18,439
Speaker 8: Anyone And why is that important?

517
00:24:18,480 --> 00:24:18,600
Speaker 1: Right?

518
00:24:18,600 --> 00:24:21,119
Speaker 8: If we think about every small business every department in

519
00:24:21,160 --> 00:24:23,920
Speaker 8: a large company, every entrepreneur with an idea they need

520
00:24:23,960 --> 00:24:25,920
Speaker 8: to be digitally native, but they have no idea how

521
00:24:25,960 --> 00:24:28,679
Speaker 8: to be digital native. Like the average person when you

522
00:24:28,720 --> 00:24:31,879
Speaker 8: say Java, thinks coffee being at Starbucks. And when you

523
00:24:31,920 --> 00:24:33,679
Speaker 8: ask them how do they use Excel? They use a

524
00:24:33,720 --> 00:24:36,400
Speaker 8: calculator with it. And that's the average audience. And how

525
00:24:36,440 --> 00:24:39,040
Speaker 8: suddenly do they become relevant in a world that's so digital?

526
00:24:39,240 --> 00:24:41,359
Speaker 2: Can they just hire somebody to be digitally native?

527
00:24:41,480 --> 00:24:44,280
Speaker 8: Yeah, and you have five hundred million developers worth a

528
00:24:44,320 --> 00:24:47,960
Speaker 8: demand and fifteen million developers with a supply. But the

529
00:24:48,000 --> 00:24:49,440
Speaker 8: issue is much bigger than that. I give you a

530
00:24:49,440 --> 00:24:51,760
Speaker 8: really funny an average. So I have two little children,

531
00:24:52,000 --> 00:24:54,159
Speaker 8: and if you say to them, paint me Picasso and

532
00:24:54,280 --> 00:24:58,040
Speaker 8: art class, it's pretty bad. Then you put five pictures

533
00:24:58,040 --> 00:25:00,760
Speaker 8: of Picasso around it and say, paint me pickass So

534
00:25:00,840 --> 00:25:02,800
Speaker 8: it's much better. And then you say to them you

535
00:25:02,800 --> 00:25:05,679
Speaker 8: can cut up these five paintings and make Piassa. And

536
00:25:05,680 --> 00:25:08,000
Speaker 8: now it's something you'll put on the wall. And I think,

537
00:25:08,040 --> 00:25:09,919
Speaker 8: you know how we build software to people that are

538
00:25:09,960 --> 00:25:11,520
Speaker 8: not technically it is the same thing. They need people

539
00:25:11,560 --> 00:25:14,879
Speaker 8: to pick and choose things from things they recognize just

540
00:25:14,920 --> 00:25:17,240
Speaker 8: like they order food at lunch or they order a

541
00:25:17,320 --> 00:25:19,560
Speaker 8: kitchen and that's how software should be.

542
00:25:20,119 --> 00:25:23,240
Speaker 4: So what are you seeing in the trends of the

543
00:25:23,480 --> 00:25:27,080
Speaker 4: entrepreneurs that are coming to you to use your platform.

544
00:25:27,240 --> 00:25:30,560
Speaker 4: What's changed? What's been the single biggest change you've seen

545
00:25:30,560 --> 00:25:31,760
Speaker 4: over the past couple of years.

546
00:25:32,760 --> 00:25:37,680
Speaker 8: So I think firstly the speed at which people are

547
00:25:37,680 --> 00:25:42,360
Speaker 8: coming and the sheer of velocity COVID it was interesting.

548
00:25:42,400 --> 00:25:46,320
Speaker 8: It was like a massive tailwind, but it was a

549
00:25:46,320 --> 00:25:48,199
Speaker 8: gating function. It's like, we're not going back to the

550
00:25:48,240 --> 00:25:50,879
Speaker 8: old way. I think the second thing we've seen is

551
00:25:51,000 --> 00:25:52,920
Speaker 8: it went from a nice to have where I might

552
00:25:52,920 --> 00:25:55,520
Speaker 8: speak to my cousin or the person down the road

553
00:25:55,520 --> 00:25:57,240
Speaker 8: to help me build a website or help me build

554
00:25:57,240 --> 00:25:59,160
Speaker 8: an up to my business is not going to work

555
00:25:59,400 --> 00:26:01,359
Speaker 8: unless I have this working or this problem solved. Right,

556
00:26:01,359 --> 00:26:02,720
Speaker 8: I'm not going to succeed in my job in the

557
00:26:02,760 --> 00:26:04,040
Speaker 8: finance department in a company.

558
00:26:04,119 --> 00:26:06,119
Speaker 2: Yeah. That two year plan to be really all in

559
00:26:06,160 --> 00:26:07,000
Speaker 2: on digital.

560
00:26:06,720 --> 00:26:08,879
Speaker 8: Yes, got to do it now. Yeah, it's accelerated. And

561
00:26:08,920 --> 00:26:12,000
Speaker 8: I think that the third has been for us is

562
00:26:12,359 --> 00:26:16,159
Speaker 8: people have basically let go of the idea that I

563
00:26:16,200 --> 00:26:17,960
Speaker 8: need to be a developer or I need to be

564
00:26:18,000 --> 00:26:21,199
Speaker 8: a programmer, and they are all in and saying I

565
00:26:21,280 --> 00:26:23,199
Speaker 8: just need to get on with this now. So to

566
00:26:23,240 --> 00:26:25,400
Speaker 8: the extent that someone can help me, I will take it.

567
00:26:25,760 --> 00:26:28,120
Speaker 2: You guys are powered, as we said, or I tried

568
00:26:28,119 --> 00:26:31,879
Speaker 2: to say the introduction AI powered app development platform. So

569
00:26:31,960 --> 00:26:33,920
Speaker 2: what is the role of AI in this?

570
00:26:34,320 --> 00:26:38,240
Speaker 8: So there's a couple of things. Firstly, our user experience

571
00:26:38,280 --> 00:26:41,080
Speaker 8: with the customer is like a Domino's Pizza experience plus

572
00:26:41,119 --> 00:26:43,960
Speaker 8: conversational AI. So you talk to the system and as

573
00:26:43,960 --> 00:26:47,520
Speaker 8: you talk, NATASHAI I will understand what you're saying and

574
00:26:47,560 --> 00:26:49,199
Speaker 8: we'll say, okay, let me ask this question. But she

575
00:26:49,280 --> 00:26:51,240
Speaker 8: may not ask the question that the human might ask

576
00:26:51,280 --> 00:26:54,359
Speaker 8: the question. And that's important because we're sill at a

577
00:26:54,400 --> 00:26:57,480
Speaker 8: stage where AI is a toddler, not an adult, and

578
00:26:57,520 --> 00:26:59,240
Speaker 8: so having a human in the loop is really important

579
00:26:59,280 --> 00:27:01,879
Speaker 8: and we realize this our own experience. The second is

580
00:27:02,720 --> 00:27:05,520
Speaker 8: there are things that a machine can do, for example,

581
00:27:05,840 --> 00:27:08,879
Speaker 8: like does the app look like the design? It's a

582
00:27:08,920 --> 00:27:11,399
Speaker 8: computer vision problem. We don't need a human to look

583
00:27:11,440 --> 00:27:13,359
Speaker 8: and go through every single screen on every single device

584
00:27:13,359 --> 00:27:15,800
Speaker 8: and highlight you know what's wrong. And so those are

585
00:27:15,840 --> 00:27:18,720
Speaker 8: really good examples. Our entire platform is powered by knowledge graphs.

586
00:27:18,760 --> 00:27:21,359
Speaker 8: I think about a knowledge graphs like a synthetic brain.

587
00:27:22,119 --> 00:27:23,960
Speaker 8: It knows what you want to build before you know,

588
00:27:25,080 --> 00:27:27,640
Speaker 8: because it's got so much data and so many relationships

589
00:27:27,680 --> 00:27:30,920
Speaker 8: between them. But the most important thing is what we're

590
00:27:30,960 --> 00:27:33,600
Speaker 8: what we're really seeing as an industry trend is the

591
00:27:33,680 --> 00:27:36,480
Speaker 8: shift towards making humans more creative so you don't have

592
00:27:36,480 --> 00:27:37,359
Speaker 8: to do the benign work?

593
00:27:37,560 --> 00:27:39,960
Speaker 2: Is it kind of like I love editing, so like

594
00:27:40,040 --> 00:27:42,680
Speaker 2: somebody writes something and then you go off and edit it.

595
00:27:42,720 --> 00:27:44,919
Speaker 8: Is that kind of how we even further?

596
00:27:45,359 --> 00:27:45,520
Speaker 10: Yeah?

597
00:27:45,640 --> 00:27:47,240
Speaker 8: Look, so I think the editing is even I think

598
00:27:47,280 --> 00:27:50,160
Speaker 8: the editing is a great analogy, right, And I think

599
00:27:50,160 --> 00:27:52,399
Speaker 8: that's what you're getting. What's what we're seeing now is

600
00:27:52,440 --> 00:27:54,639
Speaker 8: eighty percent of the work can be done by machine.

601
00:27:54,880 --> 00:27:55,160
Speaker 6: Yeah.

602
00:27:55,240 --> 00:27:58,280
Speaker 8: The last twenty percent is the human power of creativity? Right?

603
00:27:58,680 --> 00:28:01,720
Speaker 4: Is chat GPT then? And I'm calling it that, but

604
00:28:01,760 --> 00:28:03,280
Speaker 4: I know it's a lot bigger than that. Is that

605
00:28:03,359 --> 00:28:05,440
Speaker 4: a tailwind or a headwind for you?

606
00:28:06,040 --> 00:28:08,520
Speaker 8: I think it's a tailwind, you know. I was joking

607
00:28:08,520 --> 00:28:11,119
Speaker 8: with someone this morning that suddenly llms are things that

608
00:28:11,160 --> 00:28:14,480
Speaker 8: you hear on the tube. The large language models, which

609
00:28:14,720 --> 00:28:18,600
Speaker 8: the acronym that obviously what chat chiped's underlying is six

610
00:28:18,640 --> 00:28:20,600
Speaker 8: months ago, no one talked about llms in the tube

611
00:28:20,760 --> 00:28:23,120
Speaker 8: or chatchiped in the tube. Now suddenly everybody talks about.

612
00:28:22,880 --> 00:28:25,199
Speaker 2: Airbody this, and this is something we were trying to

613
00:28:25,200 --> 00:28:27,399
Speaker 2: talk with. I was with our Rachel Matt, who covers

614
00:28:27,440 --> 00:28:29,880
Speaker 2: AI for Bloomberg News. I mean, what is it what

615
00:28:30,160 --> 00:28:34,919
Speaker 2: happened that all of a sudden this is dominating our conversation.

616
00:28:35,080 --> 00:28:38,640
Speaker 2: AI is not a new thing. We've been using it already.

617
00:28:38,760 --> 00:28:40,680
Speaker 2: We were talking about before you or as you came

618
00:28:40,720 --> 00:28:42,760
Speaker 2: in that I read a sentence and you know, whatever

619
00:28:43,040 --> 00:28:45,880
Speaker 2: program and it helps me finish the sentence. What is

620
00:28:45,920 --> 00:28:49,239
Speaker 2: it that happened? Was it just Microsoft buying something or

621
00:28:49,560 --> 00:28:52,760
Speaker 2: what was out there? What was technologically that happened?

622
00:28:52,760 --> 00:28:56,160
Speaker 8: So I think it was like the perfect storm, and

623
00:28:56,480 --> 00:28:59,640
Speaker 8: it's it's like, you know, you had a piece of

624
00:28:59,600 --> 00:29:02,440
Speaker 8: technolog that had just reached the light level of inflection.

625
00:29:03,080 --> 00:29:05,760
Speaker 8: It got in the hands of general public, they could

626
00:29:05,760 --> 00:29:08,360
Speaker 8: actually do things with it. You had enough people writing

627
00:29:08,400 --> 00:29:11,479
Speaker 8: about it, and it drove into mainstream news so quickly.

628
00:29:11,880 --> 00:29:13,920
Speaker 8: Keep in mind that the reason has become so prolific

629
00:29:14,000 --> 00:29:16,920
Speaker 8: is a part of also how news percolates today compared

630
00:29:16,920 --> 00:29:19,240
Speaker 8: to say when AOL came out with the internet way

631
00:29:19,240 --> 00:29:21,880
Speaker 8: back when, right in the early intern days. Easy to quickly,

632
00:29:21,880 --> 00:29:23,440
Speaker 8: it's very easy to go very quickly.

633
00:29:23,760 --> 00:29:26,800
Speaker 2: It's all bite size your business. Tell us about growth rates.

634
00:29:26,800 --> 00:29:28,520
Speaker 2: We're Bloomberg. We like numbers. Tell us a little bit

635
00:29:28,560 --> 00:29:30,320
Speaker 2: about what you guys are doing, and you're profitable. What's

636
00:29:30,320 --> 00:29:30,880
Speaker 2: your growth rate?

637
00:29:31,520 --> 00:29:35,600
Speaker 8: So we grew last year at two hundred and thirty

638
00:29:35,640 --> 00:29:38,000
Speaker 8: percent year on year. The year before we grew to an.

639
00:29:37,840 --> 00:29:39,160
Speaker 2: Eighty two Are you talking toplant?

640
00:29:39,160 --> 00:29:40,200
Speaker 8: What are your top line revenue?

641
00:29:40,280 --> 00:29:40,520
Speaker 10: Okay?

642
00:29:40,600 --> 00:29:40,960
Speaker 8: And two?

643
00:29:41,280 --> 00:29:42,600
Speaker 2: Tell us what kind of numbers.

644
00:29:42,880 --> 00:29:45,240
Speaker 8: It's it's the it's in the in the in the

645
00:29:45,320 --> 00:29:47,840
Speaker 8: high hundreds of millions. It's low in the hundreds of millions, okay.

646
00:29:48,960 --> 00:29:51,000
Speaker 8: And the year before we grew it three hundred percent

647
00:29:51,040 --> 00:29:52,960
Speaker 8: year in year. We've only been out of beta for

648
00:29:52,960 --> 00:29:57,120
Speaker 8: two and a half years. Profitable, we're almost there, okay

649
00:29:57,280 --> 00:30:00,360
Speaker 8: this year on and off, I mean technology, these were

650
00:30:00,360 --> 00:30:03,600
Speaker 8: also investing a lot, right, So, but for us it's

651
00:30:03,680 --> 00:30:05,840
Speaker 8: the more important thing is we're really focused around building

652
00:30:05,840 --> 00:30:09,120
Speaker 8: a business with strong economics, and so you know, we

653
00:30:09,120 --> 00:30:12,000
Speaker 8: we return between four to six times marketing and sales,

654
00:30:12,120 --> 00:30:15,200
Speaker 8: so our customers really stick onto the platform and they

655
00:30:15,240 --> 00:30:15,760
Speaker 8: spend more.

656
00:30:15,920 --> 00:30:19,920
Speaker 2: All right, really interesting, and it really just plays into

657
00:30:20,160 --> 00:30:22,160
Speaker 2: so much that's going on in our world. Come back,

658
00:30:22,240 --> 00:30:23,320
Speaker 2: let's know how the growth is going.

659
00:30:23,320 --> 00:30:24,920
Speaker 8: Thank you really appreciate it to come back.

660
00:30:25,000 --> 00:30:30,000
Speaker 2: Yeah, really fun, really appreciate. Sachan dev Dougal, co founder

661
00:30:30,000 --> 00:30:31,720
Speaker 2: and chief wizard. What does a chief wizard do?

662
00:30:32,000 --> 00:30:32,760
Speaker 10: Just you got ten.

663
00:30:32,600 --> 00:30:34,600
Speaker 8: Seconds learning to be a CEO?

664
00:30:34,880 --> 00:30:41,320
Speaker 2: Okay, that's cool, CEO adapt that's a nice cover. He

665
00:30:41,640 --> 00:30:44,960
Speaker 2: is a chief wizard and more of Builder dot Ai

666
00:30:45,160 --> 00:30:47,880
Speaker 2: in our Bloomberg Interactive Brokers studio looking forward to and

667
00:30:48,000 --> 00:30:49,880
Speaker 2: coming back and giving us an update. All right, you're

668
00:30:49,880 --> 00:30:53,200
Speaker 2: listening and watching Bloomberg Business Week right here on Bloomberg Radio.

669
00:30:55,280 --> 00:30:59,600
Speaker 1: This is Bloomberg Business Wait inside from the reporters and

670
00:30:59,800 --> 00:31:03,400
Speaker 1: editor who bring you America's most trusted business magazine, plus

671
00:31:03,480 --> 00:31:07,600
Speaker 1: global business, finance and tech news. The Bloomberg Business Week

672
00:31:07,640 --> 00:31:12,600
Speaker 1: Podcast with Carol Messer and Tim Stenebek from Bloomberg Radio.

673
00:31:14,680 --> 00:31:19,400
Speaker 2: All Right, everybody, Well, the repo man is, why.

674
00:31:19,240 --> 00:31:24,000
Speaker 4: Are you I'm so sorry that I can't control.

675
00:31:24,320 --> 00:31:25,040
Speaker 7: It's kind of funny.

676
00:31:25,080 --> 00:31:29,840
Speaker 2: Well, this whole idea is just really charming. He's back,

677
00:31:29,880 --> 00:31:31,600
Speaker 2: perhaps a sign of our times. We're talking about the

678
00:31:31,600 --> 00:31:33,920
Speaker 2: Repo man. Yeah, it was not just a movie. It's

679
00:31:33,960 --> 00:31:37,160
Speaker 2: actually a real man or woman who's out there repossessing

680
00:31:37,200 --> 00:31:39,760
Speaker 2: our cars. Let's get into it with Bloomberg News Personal

681
00:31:39,760 --> 00:31:42,800
Speaker 2: Finance reporter Claire Valentine, who tracked him or her down

682
00:31:43,800 --> 00:31:46,720
Speaker 2: and a convention. Who knew there's actually a convention out there?

683
00:31:47,120 --> 00:31:49,400
Speaker 2: The stories of the upcoming new issue of Bloomberg Business

684
00:31:49,400 --> 00:31:53,200
Speaker 2: Week on newstands starting tomorrow, already online at Bloomberg dot com,

685
00:31:53,240 --> 00:31:55,680
Speaker 2: slash Businessweekend on the Bloomberg terminal. So let's get to

686
00:31:55,760 --> 00:31:58,000
Speaker 2: it and more on her reporting, Claire joining us in

687
00:31:58,000 --> 00:32:00,920
Speaker 2: her Bloomberg Interactive Broker studio al the editor of Bloomberg

688
00:32:00,920 --> 00:32:04,600
Speaker 2: Business Retil Weber, and Jill I have to say when

689
00:32:04,600 --> 00:32:06,360
Speaker 2: Claire I was in the makeup room a while ago

690
00:32:06,440 --> 00:32:08,080
Speaker 2: and she's like, you don't even believe what I'm doing.

691
00:32:08,120 --> 00:32:10,920
Speaker 2: I'm like, Repo, what what's going on? So so glad

692
00:32:10,920 --> 00:32:12,000
Speaker 2: we can talk about this story.

693
00:32:12,560 --> 00:32:14,160
Speaker 10: Are we not going to talk about Boys to Men?

694
00:32:14,480 --> 00:32:16,240
Speaker 10: Because the Boys to Men was.

695
00:32:18,000 --> 00:32:18,520
Speaker 7: Movie?

696
00:32:19,800 --> 00:32:21,360
Speaker 10: I mean, it's a certain movie. I don't know if

697
00:32:21,360 --> 00:32:24,040
Speaker 10: it's going to be this one. But somebody got to

698
00:32:24,040 --> 00:32:27,240
Speaker 10: the end of the road. All right, Okay, the connection,

699
00:32:27,720 --> 00:32:30,880
Speaker 10: so you get yeah, you get that, there's the road there. Yeah,

700
00:32:31,320 --> 00:32:35,360
Speaker 10: the producers all over that one. Okay. So this immediate

701
00:32:35,360 --> 00:32:37,320
Speaker 10: with the moment Claire started talking about that story about

702
00:32:37,360 --> 00:32:38,840
Speaker 10: the return of the rep a man, I was like,

703
00:32:38,880 --> 00:32:43,600
Speaker 10: oh man, you had me at the But the repost

704
00:32:43,600 --> 00:32:47,080
Speaker 10: story is a really significant one. The car is so

705
00:32:47,120 --> 00:32:51,960
Speaker 10: central to the American economy, and when people start to

706
00:32:52,040 --> 00:32:55,320
Speaker 10: lose that car, I think it has some bigger implications

707
00:32:55,320 --> 00:32:59,120
Speaker 10: for the US economy. But I do want to start

708
00:32:59,120 --> 00:33:03,080
Speaker 10: with this convention because lo and behold, there's a convention

709
00:33:03,200 --> 00:33:07,360
Speaker 10: for everything, and of course there's one for REPO men.

710
00:33:07,640 --> 00:33:11,360
Speaker 10: What goes down at the REPO convention? Claire, Well, as.

711
00:33:11,200 --> 00:33:14,160
Speaker 7: Soon as I heard that this existed, I told my

712
00:33:14,160 --> 00:33:16,480
Speaker 7: boss I have got to go to this and just

713
00:33:16,520 --> 00:33:19,080
Speaker 7: see what it's all about. And it did not disappoint.

714
00:33:19,880 --> 00:33:23,320
Speaker 7: It was a two day convention in Orlando, Florida. So

715
00:33:23,840 --> 00:33:27,120
Speaker 7: I went down on an airplane full of screaming children

716
00:33:27,240 --> 00:33:31,360
Speaker 7: going to Disney World and college students on their way

717
00:33:31,400 --> 00:33:36,120
Speaker 7: to spring break, and got this convention and the first

718
00:33:36,160 --> 00:33:39,360
Speaker 7: thing that really struck me was how many tow trucks

719
00:33:39,360 --> 00:33:42,240
Speaker 7: they managed to get in this hotel. They had displayed

720
00:33:42,360 --> 00:33:46,440
Speaker 7: all their tow trucks. There was all these vendors, all

721
00:33:46,440 --> 00:33:49,640
Speaker 7: this technology on the best way to repo cars. And

722
00:33:49,680 --> 00:33:53,959
Speaker 7: it really got real when one convention attendant looked at

723
00:33:53,960 --> 00:33:56,280
Speaker 7: me and said, yeah, everyone in this room has been

724
00:33:56,320 --> 00:33:57,600
Speaker 7: shot at at least once.

725
00:33:59,440 --> 00:34:01,400
Speaker 2: Why these individually? Is it largely men?

726
00:34:02,360 --> 00:34:04,680
Speaker 7: A lot of men? But it's you know, it really

727
00:34:04,800 --> 00:34:07,840
Speaker 7: is all small business. And I'm really glad I went

728
00:34:07,920 --> 00:34:11,680
Speaker 7: because in doing stories about the repoman and the consumer,

729
00:34:12,400 --> 00:34:14,920
Speaker 7: it was nice to put actual phases to this. And

730
00:34:14,960 --> 00:34:17,399
Speaker 7: what really struck me was that, you know, there is

731
00:34:17,440 --> 00:34:20,480
Speaker 7: this narrative of the evil repo man, and I'm sure

732
00:34:20,480 --> 00:34:23,279
Speaker 7: it's true in some cases, but no one here is

733
00:34:23,280 --> 00:34:26,279
Speaker 7: getting super rich off repoeing cars. He's a very down

734
00:34:26,280 --> 00:34:30,440
Speaker 7: to earth, regular Americans that just happened to make their

735
00:34:30,480 --> 00:34:32,440
Speaker 7: money taking cars from people.

736
00:34:33,080 --> 00:34:37,000
Speaker 10: Okay, so how is that business trending? And by business

737
00:34:37,040 --> 00:34:43,200
Speaker 10: I mean repos Right, this is not something that you know,

738
00:34:43,239 --> 00:34:46,120
Speaker 10: we've really seen for a while, right is it? How

739
00:34:46,600 --> 00:34:47,799
Speaker 10: is it back? And how big is it?

740
00:34:48,080 --> 00:34:48,279
Speaker 6: Yeah?

741
00:34:48,320 --> 00:34:52,680
Speaker 7: So during the pandemic, repos largely dried up. There was

742
00:34:52,840 --> 00:34:56,879
Speaker 7: stimulus checks, a lot of leniency from lenders on pain

743
00:34:57,000 --> 00:35:00,400
Speaker 7: auto loans. Now things are kind of starting to turn,

744
00:35:00,680 --> 00:35:04,520
Speaker 7: and I think this convention really marks almost the start

745
00:35:04,760 --> 00:35:08,120
Speaker 7: of what we're going to see as a surge in repossessions.

746
00:35:09,040 --> 00:35:11,560
Speaker 7: The estimates are that they were about one point two

747
00:35:11,680 --> 00:35:15,600
Speaker 7: million in twenty twenty two. It's up from last year,

748
00:35:15,760 --> 00:35:19,200
Speaker 7: but still down from about one point seven million in

749
00:35:19,280 --> 00:35:23,120
Speaker 7: twenty nineteen. So these are really picking up, and I

750
00:35:23,160 --> 00:35:25,920
Speaker 7: think the convention as a whole was this great window

751
00:35:26,040 --> 00:35:28,319
Speaker 7: into the economy. Right now, do they.

752
00:35:28,200 --> 00:35:30,840
Speaker 10: Play any jokes on each other at a convention like this?

753
00:35:32,840 --> 00:35:38,400
Speaker 7: The jokes that I saw, Let's see who's there. The

754
00:35:38,480 --> 00:35:41,800
Speaker 7: highlight was definitely the repo demo. There's some jokes thrown around.

755
00:35:41,560 --> 00:35:42,760
Speaker 10: There with the demo.

756
00:35:42,840 --> 00:35:46,400
Speaker 7: The repot demo was all of the convention attendees filed

757
00:35:46,440 --> 00:35:49,120
Speaker 7: out to the parking lot, where I thought this was

758
00:35:49,239 --> 00:35:50,120
Speaker 7: very poetic.

759
00:35:50,480 --> 00:35:50,920
Speaker 6: It was a.

760
00:35:50,920 --> 00:35:56,719
Speaker 7: Giant tow truck that was repossessing a Toyota Hybrid. So

761
00:35:56,760 --> 00:35:59,480
Speaker 7: it was like this very liberal car that they were

762
00:35:59,520 --> 00:36:04,440
Speaker 7: like re possessing, and they everyone was crying around the

763
00:36:04,920 --> 00:36:08,280
Speaker 7: tow truck was getting hooked up to take this car away,

764
00:36:08,440 --> 00:36:11,680
Speaker 7: and they were demonstrating all the best techniques and how

765
00:36:11,760 --> 00:36:14,400
Speaker 7: you know what the repro agent honing techniques, towing techniques,

766
00:36:14,680 --> 00:36:17,640
Speaker 7: That's what I thought that was the best one. Well,

767
00:36:17,800 --> 00:36:19,359
Speaker 7: the thing that that stood out to me the most

768
00:36:19,440 --> 00:36:21,880
Speaker 7: is that they really emphasized that when your back is

769
00:36:22,080 --> 00:36:26,400
Speaker 7: turned to the situation behind you, that's the most vulnerable

770
00:36:26,920 --> 00:36:29,040
Speaker 7: because you're at risk of violence.

771
00:36:28,640 --> 00:36:30,439
Speaker 2: Because you have to do it right as somebody who's

772
00:36:30,480 --> 00:36:32,600
Speaker 2: repossessing right. You have to physically do it so like

773
00:36:32,640 --> 00:36:32,920
Speaker 2: you're not.

774
00:36:33,160 --> 00:36:35,879
Speaker 7: Yeah, and it can get really dicey. That was another

775
00:36:35,880 --> 00:36:38,480
Speaker 7: big takeaway. I had no idea how dangerous this industry was.

776
00:36:40,000 --> 00:36:42,080
Speaker 10: There's the other side of this. You talk to people

777
00:36:42,200 --> 00:36:45,360
Speaker 10: who have had their vehicles repossessed, and I'm curious what

778
00:36:45,760 --> 00:36:47,160
Speaker 10: came out of those conversations.

779
00:36:47,920 --> 00:36:50,160
Speaker 7: A lot of people really struggling right now to afford

780
00:36:50,200 --> 00:36:55,000
Speaker 7: their car loans. We saw car prices skyrocket during the pandemic,

781
00:36:55,040 --> 00:36:57,799
Speaker 7: a lot of supply chain issues. A lot of people

782
00:36:57,840 --> 00:37:00,359
Speaker 7: in America just have to have a car, no way

783
00:37:00,400 --> 00:37:02,279
Speaker 7: to get around if you're you know, don't live in

784
00:37:02,280 --> 00:37:06,000
Speaker 7: a city with good public transportation. So people are taking

785
00:37:06,040 --> 00:37:11,400
Speaker 7: out loans with insane interest rates, I mean nineteen twenty percent,

786
00:37:12,239 --> 00:37:16,080
Speaker 7: and their car payments are just so high. There's not

787
00:37:16,200 --> 00:37:18,320
Speaker 7: enough money to go around, especially with inflation.

788
00:37:18,520 --> 00:37:20,480
Speaker 2: You talk about Sarah Fader in your story, and she's

789
00:37:20,520 --> 00:37:23,160
Speaker 2: the one right who's got a nineteen percent interest loan

790
00:37:23,239 --> 00:37:25,120
Speaker 2: from American Credit Acceptance.

791
00:37:25,440 --> 00:37:29,160
Speaker 7: Yes. I went up to New Haven and met Sarah,

792
00:37:29,200 --> 00:37:32,279
Speaker 7: met her family, saw the car, really talked with her

793
00:37:32,320 --> 00:37:36,920
Speaker 7: about her situation, and it really hits home just what

794
00:37:37,040 --> 00:37:38,879
Speaker 7: a lot of people are dealing with right now. I mean,

795
00:37:38,920 --> 00:37:41,440
Speaker 7: her rent had gone up, her groceries were so expensive,

796
00:37:41,440 --> 00:37:45,480
Speaker 7: She's got two teenagers, and with a high car payment,

797
00:37:45,680 --> 00:37:47,400
Speaker 7: there's she's such a tough spot.

798
00:37:48,040 --> 00:37:52,600
Speaker 4: And lenders are excited about making these loans, even if

799
00:37:52,719 --> 00:37:54,719
Speaker 4: they're lending out that money to people who they know

800
00:37:54,880 --> 00:37:56,200
Speaker 4: can't necessarily afford it.

801
00:37:56,320 --> 00:37:59,359
Speaker 7: Right, Yeah, it's the whole way they package up these

802
00:37:59,360 --> 00:38:03,399
Speaker 7: auto loans into bonds. In some cases, investors would still

803
00:38:03,400 --> 00:38:07,000
Speaker 7: get their money back with interest if even three quarters

804
00:38:07,000 --> 00:38:09,319
Speaker 7: of barers so falls it on the loans. So it's

805
00:38:09,480 --> 00:38:11,919
Speaker 7: of course, you know, moves to Wall Street where they're

806
00:38:11,960 --> 00:38:14,319
Speaker 7: eager to package these up and sell them and make

807
00:38:14,360 --> 00:38:15,120
Speaker 7: money off of them.

808
00:38:15,440 --> 00:38:19,799
Speaker 10: Okay, So with the people that you talked to. See

809
00:38:19,800 --> 00:38:23,799
Speaker 10: your car gets repossessed and you know you've lost out

810
00:38:23,800 --> 00:38:27,120
Speaker 10: in that vehicle. Where does the story go from there?

811
00:38:27,120 --> 00:38:29,000
Speaker 10: What do they have to do to get it back,

812
00:38:29,040 --> 00:38:31,240
Speaker 10: to get back to work, to get all that stuff

813
00:38:31,239 --> 00:38:33,719
Speaker 10: that not only has impacts for them, but also the

814
00:38:33,760 --> 00:38:34,560
Speaker 10: economy as a whole.

815
00:38:34,600 --> 00:38:37,400
Speaker 7: Ring. Yeah, in many cases, it's not just the back

816
00:38:37,440 --> 00:38:40,480
Speaker 7: payments that they have to make that they haven't paid

817
00:38:40,480 --> 00:38:44,360
Speaker 7: so far. It's additional fees for repossession and for towing,

818
00:38:44,640 --> 00:38:46,919
Speaker 7: and then there's the hit to their credit score, which

819
00:38:46,960 --> 00:38:49,120
Speaker 7: is going to affect them in the future. I talked

820
00:38:49,120 --> 00:38:51,680
Speaker 7: with a couple of people who'd had their cars repossessed

821
00:38:51,680 --> 00:38:54,520
Speaker 7: that essentially just had to count it as a loss.

822
00:38:54,600 --> 00:38:56,840
Speaker 7: They didn't have the money to get the car back,

823
00:38:57,160 --> 00:38:59,279
Speaker 7: so they were going to have to just move on

824
00:38:59,360 --> 00:39:02,239
Speaker 7: with their lives in any way they can. But it

825
00:39:02,320 --> 00:39:06,000
Speaker 7: definitely you know, when people suddenly don't have a car,

826
00:39:06,280 --> 00:39:08,600
Speaker 7: they can't get to work, maybe they lose their job,

827
00:39:08,800 --> 00:39:10,720
Speaker 7: and it's just this whole spiral cycle.

828
00:39:11,520 --> 00:39:13,680
Speaker 2: So you know, what I'm also curious about is the

829
00:39:13,680 --> 00:39:17,680
Speaker 2: individuals who are the repo man or woman. Do they

830
00:39:17,719 --> 00:39:20,040
Speaker 2: think about this like in terms of that there are

831
00:39:20,239 --> 00:39:24,440
Speaker 2: people and you know, with problems and financial problems. I

832
00:39:24,520 --> 00:39:26,560
Speaker 2: just wonder, is it a job or is it a

833
00:39:26,600 --> 00:39:27,160
Speaker 2: lot more than that.

834
00:39:28,120 --> 00:39:30,279
Speaker 7: I think that's a great question, and it is, you know,

835
00:39:30,320 --> 00:39:33,400
Speaker 7: I don't think they're ignorant of that. But I do

836
00:39:33,480 --> 00:39:36,759
Speaker 7: think what came away from me from the conference was

837
00:39:36,840 --> 00:39:40,120
Speaker 7: very much I think there's this attitude that REPO men

838
00:39:40,239 --> 00:39:42,359
Speaker 7: are misunderstood, and it was a bit of a.

839
00:39:43,880 --> 00:39:46,600
Speaker 2: That's another new movie or we're talking about but go ahead,

840
00:39:46,640 --> 00:39:47,000
Speaker 2: go ahead.

841
00:39:47,800 --> 00:39:50,840
Speaker 7: That the REPO men were sort of like, we're the

842
00:39:50,840 --> 00:39:53,759
Speaker 7: good guys and we're just doing our job. No one

843
00:39:53,840 --> 00:39:57,160
Speaker 7: understands how hard or how dangerous that is. And I think,

844
00:39:57,200 --> 00:40:00,560
Speaker 7: you know, it's it's easy to point your finger at

845
00:40:00,560 --> 00:40:03,960
Speaker 7: at the villain in these situations. I do think the

846
00:40:04,000 --> 00:40:07,400
Speaker 7: truth is more nuanced. You also have in some cases

847
00:40:07,400 --> 00:40:10,080
Speaker 7: predatory lenders that are giving out loans was kind of

848
00:40:10,120 --> 00:40:12,840
Speaker 7: high interest rates. So it's a whole system that contributes

849
00:40:12,880 --> 00:40:13,920
Speaker 7: to this kind of crisis.

850
00:40:14,960 --> 00:40:19,439
Speaker 10: Okay, I'm curious about the Python. Can we talk about

851
00:40:19,480 --> 00:40:19,960
Speaker 10: the Python?

852
00:40:20,320 --> 00:40:21,320
Speaker 7: Yes, the Python.

853
00:40:21,800 --> 00:40:22,280
Speaker 10: The Python.

854
00:40:22,320 --> 00:40:24,560
Speaker 7: It's a pretty impressive truck. I'm not gonna lie.

855
00:40:25,280 --> 00:40:28,760
Speaker 10: It must just like make this room get really excited

856
00:40:29,239 --> 00:40:31,280
Speaker 10: a room full of REPO people.

857
00:40:31,360 --> 00:40:33,560
Speaker 7: I wish I had seen them get these tow trucks

858
00:40:33,600 --> 00:40:35,960
Speaker 7: into the conference room because it was a very nice

859
00:40:36,000 --> 00:40:39,319
Speaker 7: conference center. I mean this was like palm trees this one.

860
00:40:39,320 --> 00:40:41,839
Speaker 7: I mean it's Florida. It was Florida. It was very nice.

861
00:40:41,840 --> 00:40:43,040
Speaker 7: And then all of a sudden you look around and

862
00:40:43,040 --> 00:40:46,719
Speaker 7: there's five tow trucks sitting around. It's I mean, they

863
00:40:46,760 --> 00:40:49,040
Speaker 7: were huge. And what I love the most is that

864
00:40:49,160 --> 00:40:51,480
Speaker 7: to sort of demonstrate how they work, they had this

865
00:40:51,600 --> 00:40:55,360
Speaker 7: little toy car that they were towing that was just

866
00:40:55,480 --> 00:40:58,439
Speaker 7: like the size of a kid's little tricycle. It's showing

867
00:40:58,440 --> 00:40:59,000
Speaker 7: how you hook it up.

868
00:40:59,040 --> 00:40:59,920
Speaker 2: So they have a sense of humor.

869
00:41:00,640 --> 00:41:03,080
Speaker 7: There was a certain extent I eighty eight thousand dollars.

870
00:41:02,719 --> 00:41:07,520
Speaker 10: Though, and these are small businesses, you know. I found

871
00:41:07,560 --> 00:41:09,880
Speaker 10: that to be really interesting, Like it's it's not like

872
00:41:09,920 --> 00:41:11,880
Speaker 10: it's some big company. It's like you're out there on

873
00:41:11,920 --> 00:41:16,400
Speaker 10: your own reprossing vehicles. Was that true for everyone in

874
00:41:16,440 --> 00:41:18,279
Speaker 10: the room? Were they all small businesses or how many

875
00:41:18,320 --> 00:41:19,399
Speaker 10: of them work for somebody else?

876
00:41:19,480 --> 00:41:21,920
Speaker 7: I mean, I would say most of them were small businesses.

877
00:41:21,960 --> 00:41:23,960
Speaker 7: I mean you look around at the clientele and These

878
00:41:24,000 --> 00:41:27,080
Speaker 7: are not you know, millionaires, businessmen in suits. These are

879
00:41:27,400 --> 00:41:30,080
Speaker 7: very you know, blue collar kind of workers. And I'd

880
00:41:30,120 --> 00:41:33,319
Speaker 7: say maybe they have, you know, a place with at

881
00:41:33,440 --> 00:41:36,920
Speaker 7: most to thirty employees, but there are really giant firms

882
00:41:36,960 --> 00:41:38,439
Speaker 7: doing this well.

883
00:41:38,840 --> 00:41:41,000
Speaker 4: It makes me wonder, Claire, like, on a personal level,

884
00:41:41,160 --> 00:41:43,640
Speaker 4: was it ever hard for you to walk up to

885
00:41:43,640 --> 00:41:46,440
Speaker 4: people and just say like, hey, can you give me

886
00:41:47,560 --> 00:41:48,480
Speaker 4: your life story?

887
00:41:48,640 --> 00:41:51,880
Speaker 7: Were you like nervous at all? I was only because

888
00:41:51,920 --> 00:41:54,680
Speaker 7: I'm pretty sure I was the only journalist there. I'm

889
00:41:54,760 --> 00:41:57,279
Speaker 7: sure I am one of the only women, one of

890
00:41:57,280 --> 00:42:01,400
Speaker 7: the only women. These people are not huge fans, so

891
00:42:01,680 --> 00:42:03,040
Speaker 7: got a lot of that, got a lot of people

892
00:42:03,080 --> 00:42:06,400
Speaker 7: coming up to me and being like, oh, hey, I

893
00:42:06,400 --> 00:42:09,040
Speaker 7: heard you're the journalist. Yeah, so it's me.

894
00:42:09,320 --> 00:42:12,080
Speaker 4: Honestly, I went to a men's rights conference for Bloomberg

895
00:42:12,120 --> 00:42:12,719
Speaker 4: a few years ago.

896
00:42:12,840 --> 00:42:14,000
Speaker 7: It sounds very similar.

897
00:42:14,200 --> 00:42:17,800
Speaker 4: You're like, hey, yeah, sorry it was Did.

898
00:42:17,719 --> 00:42:18,920
Speaker 2: You talk to the journalist?

899
00:42:19,120 --> 00:42:21,279
Speaker 7: Yeah? I do think. You know, there are pros and

900
00:42:21,320 --> 00:42:23,839
Speaker 7: cons to being a young woman in this industry, and

901
00:42:24,400 --> 00:42:26,960
Speaker 7: you know, lots of cons, as you and I know,

902
00:42:27,120 --> 00:42:30,000
Speaker 7: but one of the pros is that kind of non threatening.

903
00:42:30,400 --> 00:42:30,560
Speaker 1: Yeah.

904
00:42:30,600 --> 00:42:32,680
Speaker 7: I think if i'd been a big guy coming in

905
00:42:32,719 --> 00:42:34,400
Speaker 7: there talking to the reputman. It may have been a

906
00:42:34,440 --> 00:42:39,200
Speaker 7: little different. So it was a very unique situation recording

907
00:42:39,200 --> 00:42:40,080
Speaker 7: wise that I've been in.

908
00:42:41,360 --> 00:42:43,319
Speaker 10: Does make me wonder who do these people work for?

909
00:42:43,880 --> 00:42:46,000
Speaker 10: You know, like and like how do they get their

910
00:42:46,040 --> 00:42:47,480
Speaker 10: next job?

911
00:42:47,600 --> 00:42:47,799
Speaker 4: Oh?

912
00:42:47,840 --> 00:42:51,000
Speaker 7: This is fascinating. I had no idea the technology behind

913
00:42:51,040 --> 00:42:54,000
Speaker 7: all of this. There were so many firms that were

914
00:42:54,040 --> 00:43:00,960
Speaker 7: advertising their databases. So in some cases these small businesses

915
00:43:01,000 --> 00:43:04,680
Speaker 7: sign up to have access to databases with all this

916
00:43:04,920 --> 00:43:09,279
Speaker 7: license plate data. Oh, it's all proprietary, right, And it

917
00:43:09,320 --> 00:43:10,920
Speaker 7: was fascinating to me. I mean, these are all this

918
00:43:11,000 --> 00:43:13,400
Speaker 7: is public sector, this is or private sector. This is

919
00:43:13,400 --> 00:43:14,760
Speaker 7: not government data.

920
00:43:14,840 --> 00:43:17,640
Speaker 2: So somebody's feeding it into these these apps if you will, or.

921
00:43:17,640 --> 00:43:20,880
Speaker 7: Whatever, like basic like readers that compile all of this

922
00:43:21,000 --> 00:43:23,719
Speaker 7: and then REPO agents can access it to then track

923
00:43:23,800 --> 00:43:24,360
Speaker 7: down your.

924
00:43:24,239 --> 00:43:28,279
Speaker 10: Car AI's work. What do they make?

925
00:43:29,280 --> 00:43:30,160
Speaker 2: How can they afford that?

926
00:43:30,200 --> 00:43:30,560
Speaker 3: Eighty eight?

927
00:43:30,600 --> 00:43:31,240
Speaker 2: That's a dullar?

928
00:43:31,320 --> 00:43:31,959
Speaker 10: What is it called?

929
00:43:32,080 --> 00:43:32,560
Speaker 5: Python?

930
00:43:32,680 --> 00:43:32,879
Speaker 1: Yeah?

931
00:43:33,120 --> 00:43:34,920
Speaker 10: What I want to see is with the Python get.

932
00:43:34,719 --> 00:43:40,440
Speaker 7: Repossessed'll circle right? Yeah, definitely? Yeah. I you know, in

933
00:43:40,520 --> 00:43:42,879
Speaker 7: terms of profit margins on these, I don't have an

934
00:43:42,880 --> 00:43:45,520
Speaker 7: incredible idea. I do think they have had a tough

935
00:43:45,560 --> 00:43:46,200
Speaker 7: couple of years.

936
00:43:46,320 --> 00:43:48,400
Speaker 10: Yeah, because the numbers went down, right like the pandemic

937
00:43:48,800 --> 00:43:51,560
Speaker 10: there was you know, the chart in the story sort

938
00:43:51,600 --> 00:43:54,680
Speaker 10: of says it all. It's like it's backed because you know,

939
00:43:55,200 --> 00:43:59,120
Speaker 10: there was a lull and so and speaking to everyone there, though,

940
00:43:59,360 --> 00:44:01,880
Speaker 10: do they think that this these numbers are going to

941
00:44:01,960 --> 00:44:03,080
Speaker 10: keep going up?

942
00:44:04,239 --> 00:44:06,160
Speaker 7: I think so, yeah. I mean that's I think the

943
00:44:06,200 --> 00:44:09,279
Speaker 7: fact that they dropped so much really has contributed to

944
00:44:09,320 --> 00:44:11,960
Speaker 7: this sort of you know, our industry is in crisis

945
00:44:12,000 --> 00:44:14,399
Speaker 7: and risk kind of mentality. And now what I saw

946
00:44:14,400 --> 00:44:16,719
Speaker 7: at the conference was a lot of people getting more

947
00:44:16,760 --> 00:44:19,360
Speaker 7: excited or getting more like, our industry is on the

948
00:44:19,440 --> 00:44:21,000
Speaker 7: upswing now we are.

949
00:44:21,080 --> 00:44:22,120
Speaker 2: Bad news is good news.

950
00:44:22,120 --> 00:44:24,240
Speaker 7: Bad news is good news. Yeah. Yeah.

951
00:44:24,360 --> 00:44:25,960
Speaker 2: I also thought it was interesting that, you know, one

952
00:44:26,000 --> 00:44:27,400
Speaker 2: of the things we talk about with I feel like

953
00:44:27,400 --> 00:44:29,479
Speaker 2: everybody who comes on is like the tight labor market,

954
00:44:29,520 --> 00:44:31,560
Speaker 2: and they're dealing with that too, just quickly they are.

955
00:44:31,640 --> 00:44:36,000
Speaker 7: Yeah, they're having a lot of trouble hiring workers, especially

956
00:44:36,040 --> 00:44:40,440
Speaker 7: because many of their repo agents from pre pandemic went

957
00:44:40,480 --> 00:44:41,960
Speaker 7: to other industries.

958
00:44:41,560 --> 00:44:44,680
Speaker 2: Right, right, exactly did they try to recruit you?

959
00:44:45,040 --> 00:44:45,840
Speaker 8: Just I'm just curious.

960
00:44:46,160 --> 00:44:47,240
Speaker 7: I quite have the profile.

961
00:44:50,160 --> 00:44:52,880
Speaker 2: Unbelievable, really funny. You took us to a place that

962
00:44:52,920 --> 00:44:56,760
Speaker 2: I think nobody has really seen. Claire Valentine, she's personal

963
00:44:56,760 --> 00:44:59,319
Speaker 2: finance reporter at Bloomberg News here in studio with us,

964
00:44:59,400 --> 00:45:02,040
Speaker 2: longing till were the editor of Bloomberg Business Week, and

965
00:45:02,080 --> 00:45:04,080
Speaker 2: of course the stories we mentioned coming up in the

966
00:45:04,080 --> 00:45:06,720
Speaker 2: new issue of Bloomberg Business Week out on newsstands tomorrow,

967
00:45:07,160 --> 00:45:10,600
Speaker 2: already online at Bloomberg dot com, slash BusinessWeek and also

968
00:45:10,680 --> 00:45:11,920
Speaker 2: on the Bloomberg terminal.

969
00:45:12,480 --> 00:45:14,359
Speaker 4: Are you gonna get a python anytime soon?

970
00:45:14,560 --> 00:45:16,440
Speaker 2: Explain the song to me? Was that from the movie

971
00:45:16,719 --> 00:45:17,320
Speaker 2: It's Boys.

972
00:45:17,520 --> 00:45:19,239
Speaker 4: I was just laughing from boys to men.

973
00:45:19,320 --> 00:45:21,759
Speaker 2: I didn't anticipate boys to men playing in my ears.

974
00:45:21,920 --> 00:45:23,279
Speaker 2: Like the wedding. He wanted to get like all the

975
00:45:23,280 --> 00:45:24,360
Speaker 2: couples get up on the floor.

976
00:45:24,920 --> 00:45:25,719
Speaker 10: This is Bloomberg.

977
00:45:27,719 --> 00:45:34,080
Speaker 2: I'm brother Marc, a journal How about you let me drive?

978
00:45:34,600 --> 00:45:39,839
Speaker 8: No, no, no, no, honey, please, how do the riding gravels?

979
00:45:40,239 --> 00:45:41,600
Speaker 1: Let's wat I want to drive.

980
00:45:41,600 --> 00:45:45,759
Speaker 6: It's a good question time.

981
00:45:48,600 --> 00:45:50,799
Speaker 1: This is the drive to the Globe.

982
00:45:50,719 --> 00:45:51,520
Speaker 10: Dot com for me.

983
00:45:51,560 --> 00:45:55,000
Speaker 1: I think we'll buy around you on Bloomberg Radio.

984
00:45:55,280 --> 00:45:57,320
Speaker 2: All right, everybody, just at your eighteen minutes left in

985
00:45:57,360 --> 00:46:01,399
Speaker 2: the trading day. Really the little changed here just down

986
00:46:01,400 --> 00:46:03,360
Speaker 2: about point three percent on the DALLA. So the biggest

987
00:46:03,360 --> 00:46:05,560
Speaker 2: move on a percentage basis right now is for the

988
00:46:05,600 --> 00:46:09,279
Speaker 2: Dow Jones Industrial Average call it flat as you heard

989
00:46:09,320 --> 00:46:10,960
Speaker 2: from Charlie on the S and P and the Nasdaq

990
00:46:11,040 --> 00:46:13,120
Speaker 2: just one tenth of a percent here. Our next guest

991
00:46:13,160 --> 00:46:16,280
Speaker 2: notes that with earnings releases picking up, some companies weren't

992
00:46:16,320 --> 00:46:18,600
Speaker 2: some pretty close watching. So let's get to it with

993
00:46:18,600 --> 00:46:22,040
Speaker 2: Burns McKinney, portfolio manager at the Global Value Equity Manager

994
00:46:22,360 --> 00:46:26,319
Speaker 2: and Texas based NFJ Investment Group. He is on the

995
00:46:26,360 --> 00:46:29,120
Speaker 2: phone from Dallas, Texas. Hey, Burn's, good to have you

996
00:46:29,160 --> 00:46:32,920
Speaker 2: here with Maddie and myself. So tell me out in Texas.

997
00:46:32,960 --> 00:46:36,640
Speaker 2: What are people talking about? Yeah?

998
00:46:37,000 --> 00:46:39,160
Speaker 11: I think that people are still you know, they've we've

999
00:46:39,200 --> 00:46:42,359
Speaker 11: been talking about inflation, inflation, inflation for fuite sometime, and

1000
00:46:42,480 --> 00:46:45,319
Speaker 11: it's something that you know, that's it's probably of all

1001
00:46:45,360 --> 00:46:47,439
Speaker 11: the economic statistics you look at, you know, whether people

1002
00:46:47,440 --> 00:46:49,920
Speaker 11: focus on GDP or earnings, it's the one thing that's

1003
00:46:50,000 --> 00:46:51,200
Speaker 11: kind of right in your face and you go to

1004
00:46:51,239 --> 00:46:53,160
Speaker 11: the grocery store, you see the price of Eggs. I mean,

1005
00:46:53,239 --> 00:46:56,680
Speaker 11: the price of gasoline is is advertised in you know,

1006
00:46:57,040 --> 00:46:59,680
Speaker 11: in five foot letters on huge designs, and so people

1007
00:46:59,760 --> 00:47:02,319
Speaker 11: have been talking about inflation for quite some time. But

1008
00:47:02,680 --> 00:47:04,640
Speaker 11: you know, we are starting to see a shift such

1009
00:47:04,680 --> 00:47:07,280
Speaker 11: that I think the focus is kind of moving towards

1010
00:47:07,280 --> 00:47:09,760
Speaker 11: the fact that the economy is starting to slow.

1011
00:47:10,760 --> 00:47:14,000
Speaker 4: So talk to me Burns about the stickiness of inflation

1012
00:47:14,239 --> 00:47:17,720
Speaker 4: versus the indications that you're seeing in terms of slowing.

1013
00:47:17,760 --> 00:47:20,000
Speaker 4: Because I have to wonder, I know you're in Texas,

1014
00:47:20,040 --> 00:47:22,200
Speaker 4: big oil state, but are you worried at all about

1015
00:47:22,239 --> 00:47:25,160
Speaker 4: these OPEC cuts making inflation stickier than we might be

1016
00:47:25,200 --> 00:47:26,479
Speaker 4: seen in the data right now?

1017
00:47:28,480 --> 00:47:31,319
Speaker 11: Well, you know, OPEK, it really does appear that they've

1018
00:47:31,400 --> 00:47:34,960
Speaker 11: kind of settled on trying to establish a cost more

1019
00:47:35,160 --> 00:47:39,280
Speaker 11: for oil of around eighty dollars. That's something that probably

1020
00:47:39,600 --> 00:47:41,560
Speaker 11: in many ways makes it sticky. But at the same time,

1021
00:47:41,640 --> 00:47:44,480
Speaker 11: you know, that's something that as well, you know, at

1022
00:47:44,520 --> 00:47:47,120
Speaker 11: some point you start lapping you know, these figures and

1023
00:47:47,560 --> 00:47:50,760
Speaker 11: it no longer actually has the impact on inflation today.

1024
00:47:50,840 --> 00:47:54,520
Speaker 11: But you know, really investors are focused on inflation being sticky.

1025
00:47:54,600 --> 00:47:56,880
Speaker 11: You know, there's there's been a lot of consternation of

1026
00:47:56,920 --> 00:47:59,239
Speaker 11: the fact that it hasn't been it's been pulling back,

1027
00:47:59,280 --> 00:48:02,120
Speaker 11: but it hasn't been coming down as quickly as a

1028
00:48:02,160 --> 00:48:04,600
Speaker 11: lot of forecaster, a lot of investors would would really

1029
00:48:04,680 --> 00:48:06,360
Speaker 11: like to see it come down. And you know, I

1030
00:48:06,360 --> 00:48:09,040
Speaker 11: think that's something that just really involves patients. You know,

1031
00:48:09,080 --> 00:48:12,440
Speaker 11: they say that, you know that it took about you know,

1032
00:48:12,480 --> 00:48:14,800
Speaker 11: a year and a half or so for inflation to

1033
00:48:14,880 --> 00:48:18,800
Speaker 11: kind of peak, and expectations should really sort of remnage

1034
00:48:18,800 --> 00:48:20,360
Speaker 11: themselves such that it might take a year and a

1035
00:48:20,360 --> 00:48:22,440
Speaker 11: half for it to actually come down to prior levels,

1036
00:48:22,600 --> 00:48:25,520
Speaker 11: and even then it probably won't go all the way

1037
00:48:25,600 --> 00:48:28,479
Speaker 11: back down to that two percent sub to two percent level,

1038
00:48:28,520 --> 00:48:31,399
Speaker 11: because you know, there are some structural factors that may

1039
00:48:31,400 --> 00:48:33,960
Speaker 11: actually keep it a little bit more elevated than it's been.

1040
00:48:34,000 --> 00:48:36,880
Speaker 11: You know, really, I think the primary one being just

1041
00:48:36,960 --> 00:48:40,600
Speaker 11: the you know, the reversal of the globalization and and

1042
00:48:40,840 --> 00:48:43,279
Speaker 11: the you know, near shoring of supply chains and may

1043
00:48:43,360 --> 00:48:45,440
Speaker 11: keep prices higher. But we do think that it's probably

1044
00:48:45,440 --> 00:48:47,160
Speaker 11: going to pull back and continue to pull back the

1045
00:48:47,160 --> 00:48:47,879
Speaker 11: remainder this year.

1046
00:48:47,960 --> 00:48:48,239
Speaker 3: All right.

1047
00:48:48,239 --> 00:48:50,719
Speaker 2: What we love is when a guest comes on and

1048
00:48:50,760 --> 00:48:52,560
Speaker 2: they say, we've got some names you want to talk

1049
00:48:52,600 --> 00:48:54,799
Speaker 2: about because we can get into the specifics which I

1050
00:48:54,800 --> 00:48:58,720
Speaker 2: think our audience really appreach it, appreciate, appreciates about around

1051
00:48:58,719 --> 00:49:00,880
Speaker 2: the much you know, debate that we are having on

1052
00:49:00,920 --> 00:49:04,040
Speaker 2: so many different macro issues. So, having said that, Zoetes

1053
00:49:04,400 --> 00:49:08,480
Speaker 2: zts is the ticker Animal Pharmaceuticals. If you will, this

1054
00:49:08,640 --> 00:49:10,720
Speaker 2: is a name that you like. It's up roughly twenty

1055
00:49:10,719 --> 00:49:12,960
Speaker 2: percent so far this year. I know people spend a

1056
00:49:13,000 --> 00:49:14,879
Speaker 2: lot on their pets. I know I spend a lot

1057
00:49:14,880 --> 00:49:18,320
Speaker 2: of my pets. I know private equity likes the pet space.

1058
00:49:18,480 --> 00:49:20,200
Speaker 2: What is it about zoetis so that.

1059
00:49:20,160 --> 00:49:20,799
Speaker 5: You really like?

1060
00:49:21,960 --> 00:49:22,160
Speaker 1: Sure?

1061
00:49:22,239 --> 00:49:24,320
Speaker 11: Yeah, and private equity definitely does you know there was

1062
00:49:24,560 --> 00:49:28,640
Speaker 11: one of the European competitor was just announced to an acquisition,

1063
00:49:28,719 --> 00:49:32,879
Speaker 11: Decra Pharmaceuticals. Yeah, an acquisition was announced just earlier this week.

1064
00:49:32,960 --> 00:49:34,880
Speaker 11: But yeah, I think this is the name that you know,

1065
00:49:34,920 --> 00:49:39,120
Speaker 11: you're looking for companies that have the company specific you know,

1066
00:49:39,200 --> 00:49:42,080
Speaker 11: benefits from long term structural tailwinds and they have a

1067
00:49:42,080 --> 00:49:43,560
Speaker 11: couple of big ones. You know, this is the name

1068
00:49:43,600 --> 00:49:45,760
Speaker 11: that you know for those who an't is familiar with it,

1069
00:49:45,760 --> 00:49:49,200
Speaker 11: it's you know, really the leading pure play global animal

1070
00:49:49,200 --> 00:49:51,000
Speaker 11: health company. It was spun out a few years back

1071
00:49:51,040 --> 00:49:54,200
Speaker 11: from Pfizer, and you know, they split their business pretty

1072
00:49:54,200 --> 00:49:58,600
Speaker 11: equally between what they call companion care i e. Pets

1073
00:49:58,600 --> 00:50:01,520
Speaker 11: and livestock. So the two big secular trends, one of

1074
00:50:01,560 --> 00:50:04,480
Speaker 11: which is you're just seeing increasing demand for global protein

1075
00:50:04,640 --> 00:50:07,000
Speaker 11: as you have a growing middle class. And then on

1076
00:50:07,040 --> 00:50:09,239
Speaker 11: the pet side, you're just seeing that you know, as

1077
00:50:09,280 --> 00:50:12,000
Speaker 11: you know correctly noted, you know, pets are becoming increasingly

1078
00:50:12,719 --> 00:50:16,520
Speaker 11: important members of people's families. And as that's happened.

1079
00:50:16,560 --> 00:50:20,080
Speaker 2: Costly members and costly members families, there's been more Yeah,

1080
00:50:20,120 --> 00:50:22,240
Speaker 2: there's been more penetration of medicines used for pets.

1081
00:50:22,280 --> 00:50:24,280
Speaker 11: And you know, I could say for our for our household,

1082
00:50:24,320 --> 00:50:27,040
Speaker 11: they probably have me forgo my meds before we would,

1083
00:50:27,239 --> 00:50:29,480
Speaker 11: you know, have our cat or our dog, you know,

1084
00:50:29,640 --> 00:50:31,880
Speaker 11: forego theirs. And that's something that makes it. It tends

1085
00:50:31,880 --> 00:50:34,880
Speaker 11: to be defensive for that reason, and it tends to

1086
00:50:34,880 --> 00:50:36,920
Speaker 11: give them pricing power, which is what you should look

1087
00:50:36,960 --> 00:50:38,520
Speaker 11: for in an inflationary environment.

1088
00:50:39,040 --> 00:50:41,560
Speaker 4: So talk to me about true as Financial another pick

1089
00:50:41,600 --> 00:50:44,480
Speaker 4: of yours. It's a Charlotte based bank. Why why do

1090
00:50:44,520 --> 00:50:46,840
Speaker 4: you like it? Because we're obsessed with the regional bank story.

1091
00:50:46,920 --> 00:50:47,680
Speaker 4: So tell me more.

1092
00:50:48,719 --> 00:50:50,799
Speaker 11: Yeah, this is one you know for contrarians who are

1093
00:50:50,880 --> 00:50:53,320
Speaker 11: kind of looking for, you know, the proverbial babies that

1094
00:50:53,320 --> 00:50:56,080
Speaker 11: have been thrown out with the bath water with respect

1095
00:50:56,160 --> 00:51:00,040
Speaker 11: to you know, some of the recent banking turmoil. But

1096
00:51:00,320 --> 00:51:03,400
Speaker 11: you know, for one thing, we do tell our clients

1097
00:51:03,440 --> 00:51:05,680
Speaker 11: that that a lot of the turmoil that we've seen

1098
00:51:05,680 --> 00:51:09,560
Speaker 11: around Silicon Valley Bank really was very company specific to them.

1099
00:51:09,640 --> 00:51:12,160
Speaker 11: You know, they had some issues with hedging, hedging their

1100
00:51:12,200 --> 00:51:16,720
Speaker 11: assets as well as a very concentrated deposit base, whereas

1101
00:51:16,920 --> 00:51:18,960
Speaker 11: you know Truest it's uh, you know, it's now I

1102
00:51:18,960 --> 00:51:24,359
Speaker 11: think the sixth largest US bank, So it does have

1103
00:51:24,480 --> 00:51:27,880
Speaker 11: good diverse diversity of their clients. It was born with

1104
00:51:28,120 --> 00:51:29,960
Speaker 11: the merger of bb and T and sun Trust, and

1105
00:51:30,000 --> 00:51:32,360
Speaker 11: so they're located in a lot of the great places

1106
00:51:32,600 --> 00:51:35,200
Speaker 11: the South, the sun Belt, where you do have good

1107
00:51:35,280 --> 00:51:38,440
Speaker 11: demographic and population growth. Right now, it trades at a

1108
00:51:38,520 --> 00:51:41,120
Speaker 11: discount to book value, which is a discount even to

1109
00:51:41,160 --> 00:51:44,240
Speaker 11: the regional banks. You have a six percent dividend yield

1110
00:51:44,239 --> 00:51:46,920
Speaker 11: that that does look pretty safe. They hiked it by

1111
00:51:46,960 --> 00:51:50,160
Speaker 11: eight percent over the past year, which demonstrates management confidence

1112
00:51:50,160 --> 00:51:53,480
Speaker 11: in the business. And you know, yeah, and you compare

1113
00:51:53,520 --> 00:51:54,880
Speaker 11: it to you know, where there has been some of

1114
00:51:54,920 --> 00:51:57,719
Speaker 11: the turmoil. You know, roughly, you know, only half of

1115
00:51:57,800 --> 00:52:01,120
Speaker 11: their their deposits running shirt, which does prevent them from

1116
00:52:01,160 --> 00:52:02,680
Speaker 11: having any sort of run on a bank like you

1117
00:52:02,760 --> 00:52:04,520
Speaker 11: saw in Silicon.

1118
00:52:04,239 --> 00:52:06,040
Speaker 2: Valance, Hey Burns. One last one I want to ask

1119
00:52:06,080 --> 00:52:09,080
Speaker 2: you about, certainly because real estate is on our minds,

1120
00:52:09,080 --> 00:52:11,040
Speaker 2: as you know, and wondering if that's the next shoot

1121
00:52:11,040 --> 00:52:14,240
Speaker 2: a drop coming off of the bank crisis not crisis,

1122
00:52:14,280 --> 00:52:17,600
Speaker 2: if you will. Alexandria real estate ticker is AR. It's

1123
00:52:17,680 --> 00:52:22,080
Speaker 2: up about off about fifteen percent so far in twenty

1124
00:52:22,080 --> 00:52:25,520
Speaker 2: twenty three, twenty one billion dollar market cap. They play

1125
00:52:25,520 --> 00:52:29,360
Speaker 2: in the Pharmer space personal care research thirty seconds.

1126
00:52:29,400 --> 00:52:33,040
Speaker 11: Why though this name, you know, it's you know right now,

1127
00:52:33,040 --> 00:52:36,000
Speaker 11: it's traded about thirteen and a half time spun from operations.

1128
00:52:36,040 --> 00:52:38,399
Speaker 11: You have a nice four percent dividend deals, which we love.

1129
00:52:38,840 --> 00:52:40,719
Speaker 11: And what they do is they own office and lab space.

1130
00:52:40,760 --> 00:52:43,840
Speaker 11: They least a pharma, biotech and diagnostics, and so you know,

1131
00:52:43,880 --> 00:52:46,000
Speaker 11: sometimes people sort of misclassify it. They think of it

1132
00:52:46,040 --> 00:52:48,839
Speaker 11: as being more of a real estate play, but really

1133
00:52:48,920 --> 00:52:51,840
Speaker 11: it tends to trade more with the biotechs, and you know,

1134
00:52:51,840 --> 00:52:54,759
Speaker 11: they have long term relationships with where there's academic institutions,

1135
00:52:54,800 --> 00:52:56,840
Speaker 11: all the places you want your kids to get into college,

1136
00:52:56,840 --> 00:52:59,680
Speaker 11: you know, Harvard, Stanford, Duke, as well as a lot

1137
00:52:59,680 --> 00:53:01,160
Speaker 11: of the big form of companies. And they have a

1138
00:53:01,160 --> 00:53:04,480
Speaker 11: great track record of growing their funds from operations every

1139
00:53:04,560 --> 00:53:07,640
Speaker 11: quarter for just like five years. And so it doesn't

1140
00:53:07,640 --> 00:53:09,719
Speaker 11: really trade like the reefs, and as a result it

1141
00:53:09,719 --> 00:53:12,400
Speaker 11: should give investors a little bit of defensiveness in involved

1142
00:53:12,440 --> 00:53:12,840
Speaker 11: the market.

1143
00:53:12,880 --> 00:53:14,800
Speaker 2: All right, but as we said, down about eighteen percent

1144
00:53:14,880 --> 00:53:18,040
Speaker 2: so far here in twenty twenty three, and that's after

1145
00:53:18,120 --> 00:53:20,880
Speaker 2: a year. Well, we know a lot of assets classes

1146
00:53:20,920 --> 00:53:22,600
Speaker 2: and assets where are really beating up?

1147
00:53:22,600 --> 00:53:24,000
Speaker 10: Who was down about twenty sixty percent?

1148
00:53:24,200 --> 00:53:27,040
Speaker 2: Hey Burns, thank you so much. Bernie McKinney, he's portfolio

1149
00:53:27,120 --> 00:53:30,439
Speaker 2: manager at NFJ Investment Group. Joining us on a phone

1150
00:53:30,440 --> 00:53:31,440
Speaker 2: from Dallas, Texas.

1151
00:53:31,520 --> 00:53:36,640
Speaker 1: This is Bloomberg Radio. This is the Bloomberg Business Week Podcast.

1152
00:53:36,719 --> 00:53:39,680
Speaker 1: I'll a little lot of Apple, Spotify and anywhere else

1153
00:53:39,719 --> 00:53:43,719
Speaker 1: you get your podcast. Listen live weekday afternoons from three

1154
00:53:43,719 --> 00:53:47,520
Speaker 1: to six easterning on Bloomberg dot com, the iHeartRadio app,

1155
00:53:47,600 --> 00:53:50,120
Speaker 1: tune In, and the Bloomberg Business app. You can also

1156
00:53:50,280 --> 00:53:53,680
Speaker 1: watch US live every weekday on YouTube and always on

1157
00:53:53,719 --> 00:53:55,040
Speaker 1: the Bloomberg terminal alone.