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Speaker 1: Bloomberg Audio Studios, Podcasts, radio news.

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Speaker 2: This is Bloomberg business Week inside from the reporters and

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Speaker 2: editors who bring you America's most trusted business magazine, plus

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Speaker 2: global business, finance and tech news. The Bloomberg Business Week

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Speaker 2: Podcast with Carol Messer and Tim Stenebeck from Bloomberg Radio.

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Speaker 1: We know this is a market environment that moves from

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Speaker 1: economic data point to economic data point, and we also

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Speaker 1: know that not all economic data point or data is

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Speaker 1: treated equally by investors, nor economists, nor the Fed for

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Speaker 1: that matter.

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Speaker 3: Tim Well, this week though we do have a few

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Speaker 3: that are watched by all, as we get two reads

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Speaker 3: on inflation just over the next two days. Also a

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Speaker 3: read on retail sales on Friday, throwing some jobless claims, Carol,

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Speaker 3: the Fed is watching the US labor market closely. All

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Speaker 3: this could make for an interesting week. And then Michael

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Speaker 3: McKee next week goes off to Jackson Hole. Right, so

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Speaker 3: hear from VET chair J.

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Speaker 2: Powell.

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Speaker 1: We'll see what he has to say. How that shapes

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Speaker 1: the narrative? All right, So let's get to it with

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Speaker 1: us our round table Bloomberg News Economics editor of Molly Smith,

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Speaker 1: along with Bloomberg economics US economist Stuart Paul Stewart here

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Speaker 1: in studio Molly on Zoom here in New York City,

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Speaker 1: and so let's get to it. So we do, first

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Speaker 1: of all, Molly lay it out for us and what matters,

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Speaker 1: and it's actually forgive me, I think there's three inflation reports,

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Speaker 1: although I'm not quite sure the New York Fed one

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Speaker 1: year inflation expectations it already came out. There's two more

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Speaker 1: that come in aware over the next couple of days.

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Speaker 1: What matters, Yeah, I.

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Speaker 4: Mean, it's really just going to be CPI, the Consumer

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Speaker 4: Price Index and full focus that comes out on Wednesday

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Speaker 4: this week, and we also get a look at producer prices,

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Speaker 4: so this is a level of wholesale inflation that comes

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Speaker 4: out on Tuesday. Obviously, the CPI is one of, if

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Speaker 4: not the biggest margaret moving events in any given month,

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Speaker 4: and I don't think that this will necessarily be too

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Speaker 4: much different from that. But hopefully we won't see nearly

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Speaker 4: the same reaction that we did to the jobs report

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Speaker 4: that you guys mentioned just two weeks ago, pretty fast

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Speaker 4: and furious moves and stocks and treasuries on that front.

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Speaker 4: And I think this one is I think people are

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Speaker 4: maybe just looking for Okay, let's just get through this.

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Speaker 4: Hopefully it's a fairly as expected reading. Maybe inflation is

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Speaker 4: going to pick up a little bit relative to June,

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Speaker 4: but the broader trend of disinflation is still going to

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Speaker 4: be intact. And I think something like that will just

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Speaker 4: bring hopefully a reassuring colm to the market. But that said,

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Speaker 4: it doesn't take that much to tip the scales these days,

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Speaker 4: Like everybody is just very jittery about the data right now.

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Speaker 3: Well, Stuart, come on in here. Let's talk CPI before

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Speaker 3: we talk PPI here, because you and the team over

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Speaker 3: at Bloomberg Economics argue that July CPI will likely be soft,

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Speaker 3: with the year over year changing core CPI edging further down,

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Speaker 3: take us into your thinking and what you're modeling.

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Speaker 5: So we have a few factors that are going on. First,

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Speaker 5: is that the most inter sensitive categories, things like used autos,

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Speaker 5: are exerting meaningful drag on both core and headline inflation.

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Speaker 5: We have some pretty came numbers for gasoline, so it's

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Speaker 5: not really boosting the headline all that much. And then

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Speaker 5: if we look at some of the more discretionary services categories,

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Speaker 5: things like airfares, things like hotels. We're expecting to see

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Speaker 5: some pretty meaningful drag on the core.

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Speaker 1: The thing that's going to come that from the earnings front,

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Speaker 1: the earnings picture where there was airlines, where it was

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Speaker 1: airbnbat mean Expedia. Nonetheless, there's stock rally because there was

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Speaker 1: a beat. But there everybody's talking about softness in the

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Speaker 1: travel sector.

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Speaker 5: That's right, it's really some of those more discretionary services

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Speaker 5: categories where consumers are tightening their belt. We expect to

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Speaker 5: see the same thing in the retail sales number this week.

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Speaker 5: We expect to see folks really looking for discounts and

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Speaker 5: doing some online bargain hunting as opposed to splurging on

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Speaker 5: food services and some more of the intro sensitive items

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Speaker 5: like appliances and autos, even though we will get some

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Speaker 5: auto spending after last months the June cyber attack on

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Speaker 5: auto dealers. But on the inflation front, it's interesting this

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Speaker 5: is one of those peculiar months where we get PPI

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Speaker 5: ahead of CPI and we're expecting see more softness in

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Speaker 5: the headline PPI number than in CPI. So to Mally's

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Speaker 5: points about folks wanting to take a deep breath, it's

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Speaker 5: possible that when they see the monthly pace of producer

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Speaker 5: prices ticking down. So when we see producer price inflation

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Speaker 5: which is zero point two percent on the month instead

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Speaker 5: of zero point four as they saw last month, folks

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Speaker 5: might breathe that sigh of relief. It might be a

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Speaker 5: little bit too early when they then see the headline

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Speaker 5: pace of CPI increase on the month. So this is

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Speaker 5: one of those peculiar months where the timing of the

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Speaker 5: data has the opportunity to both lull people into a

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Speaker 5: sense of complacency and then give a little bit of

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Speaker 5: a whipsaw just the next day.

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Speaker 1: Well, molly, what matters in terms of like help me

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Speaker 1: through the supply chain right PPI or the endpoint input prices?

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Speaker 2: Right?

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Speaker 1: So is that something then will show up in next

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Speaker 1: month CPI report? Like how do you kind of distinguish

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Speaker 1: those two pieces of data?

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Speaker 4: So generally, when the reason why you would, as a

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Speaker 4: typical person care about the PPI is because what's happening

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Speaker 4: at the doucer price level tends to filter through to

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Speaker 4: consumers over time. Overtime doesn't necessarily mean one month, though,

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Speaker 4: these are trends that can take more than that to

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Speaker 4: more time than that to develop, and of course movements

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Speaker 4: through the supply chain are not always that quick. But

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Speaker 4: it's of course, like really important to look at for

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Speaker 4: the overall inflation trend, especially you know, when you're looking

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Speaker 4: at what's happening with commodity prices, and so many of

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Speaker 4: those have been really wild over the summer. With looking

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Speaker 4: at what prices are for coffee and chocolate and other

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Speaker 4: kinds of metals and like things like that that just

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Speaker 4: you know, maybe you as a consumer aren't purchasing that

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Speaker 4: as a raw good, but when you purchase it as

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Speaker 4: a final product, then you start to notice how inspects

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Speaker 4: how expensive those goods can be.

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Speaker 3: Stuart, come on in, because I don't know about you guys,

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Speaker 3: but I was gone last week, and I even though

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Speaker 3: I wasn't you know, glued to.

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Speaker 1: My phone, we weren't here.

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Speaker 3: I could still hear people screaming about a fifty basis

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Speaker 3: point rate cut.

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Speaker 1: Oh wait, no, you mean the emergency rate. The emergency

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Speaker 1: is seventy five basis points. I forget who was it

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Speaker 1: that Jeremy Siegel was calling for us?

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Speaker 3: So a lot has changed in just a few days.

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Speaker 3: I don't hear those echoes right now pretty quickly. Actually,

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Speaker 3: we didn't see one of those emergency rate cuts come

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Speaker 3: from the FED, but could we get a not necessarily

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Speaker 3: a reaction, but could we get calls for some sort

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Speaker 3: of movement from the Fed based on the data moving

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Speaker 3: one way or the other this week's store.

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Speaker 5: I think that this week is going to be one

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Speaker 5: of those odd weeks where you feel a little bit

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Speaker 5: more stagflation risk as opposed to just outright recession risk.

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Speaker 5: So and I think that because equity markets ever covered,

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Speaker 5: because the feed through into credit mark into credit markets,

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Speaker 5: you know, when it comes to interest rate spreads, when

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Speaker 5: it comes to default risk, we haven't seen any sort

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Speaker 5: of material move there. So I think that when it

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Speaker 5: comes to what we're going to see this week and

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Speaker 5: how it shapes the narrative, it's going to be more

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Speaker 5: so around the idea of stagflation. We're going to see

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Speaker 5: in that isn't as inflation data that aren't as good

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Speaker 5: as they've been in the last three months. We're going

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Speaker 5: to see retail sales data that when you look deeper

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Speaker 5: in the core, it's going to be pretty dismal. I

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Speaker 5: think that looking at the control group for retail sales

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Speaker 5: it's going to be pretty rough, and we're going to

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Speaker 5: see zero point two percent decline in the month and

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Speaker 5: manufacturing data to round out the week. I also expect

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Speaker 5: that we're going to see a decline about zero point

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Speaker 5: three percent month on month decline in manufacturing output. So

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Speaker 5: we're not going to have as good inflation data, and

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Speaker 5: some of the other fundamental concepts are going to show

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Speaker 5: some weakness, and that could be enough to just keep

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Speaker 5: folks on edge having this fifty basis point twenty five

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Speaker 5: bases point debate for another month.

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Speaker 1: Which makes me want to jump to it. And I want

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Speaker 1: to ask both of you, and let me Molly, let

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Speaker 1: me start with you. So are more people starting to

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Speaker 1: talk about a recession and that if we get a recession,

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Speaker 1: that you know, if the FED possibly is behind the

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Speaker 1: curve in terms of maybe sparking some growth or catching

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Speaker 1: that we're slowing down maybe faster than we anticipated, could

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Speaker 1: it be a deeper recession.

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Speaker 4: More people definitely are talking about recession. And since that

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Speaker 4: Job's report came out two weeks ago, we've seen JP

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Speaker 4: Morgan and Golden Economists both boost their likelihood of a

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Speaker 4: recession in the next twelve months. So I'm pretty sure

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Speaker 4: both of them are still below fifty percent, so still

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Speaker 4: fairly low odds. And I think those are the only

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Speaker 4: two that I'm really aware of who have come out

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Speaker 4: and boosted those odds in recent weeks. But I'm not

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Speaker 4: really hearing that it would be that would make a

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Speaker 4: recession any deeper. I think it's just now looking at

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Speaker 4: is the FED behind the curve? Like you said that

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Speaker 4: people are wondering that, And of course if you get

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Speaker 4: more inflation data that comes in softer than expected, that's

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Speaker 4: only just going to add fuel to that argument.

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Speaker 1: Stuart remind us where you guys are the team on

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Speaker 1: when it comes to recession.

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Speaker 5: So we've been noting that there's been a material slow

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Speaker 5: down in some of the underlying factors driving growth for

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Speaker 5: a long time. We've also been noting that again, even

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Speaker 5: if you just look at the Q two GDP data,

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Speaker 5: about a quarter of the growth that we saw was

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Speaker 5: from inventory accumulation. That folks and firms aren't trying to

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Speaker 5: accumulate inventories, it's just that they're accidentally producing more on

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Speaker 5: the expectation of zealing consumer demand that just isn't there.

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Speaker 5: And so we think that the FED is behind the curve,

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Speaker 5: and we do think that some of the recession, some

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Speaker 5: of the recession probabilities that have been assigned are probably

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Speaker 5: a smidge too low, but we've always been a little

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Speaker 5: bit more bearish expected and a half percent unemployment rate

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Speaker 5: by year end, and if we continue along this trajectory

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Speaker 5: that we saw in the last month, we'll hit it

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Speaker 5: or exceed it.

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Speaker 1: It's interesting because you guys have been for a long

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Speaker 1: time and at a time when it almost felt like, really,

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Speaker 1: can you still be calling this? And now it starts

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Speaker 1: to feel like things are falling into place. Guys, perfect

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Speaker 1: setup for us. On this Monday, Bloomberg News Economics Center

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Speaker 1: Molly Smith along with Bloomberg Economics US economist right here

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Speaker 1: in studio.

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Speaker 2: You're listening to the Bloomberg Business Week podcast. Catch us

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Speaker 2: live weekday afternoons from two to five pm Eastern. Listen

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Speaker 2: on Apple car Play and then Brout Auto with a

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Speaker 2: Bloomberg Business app, or watch us live on YouTube.

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Speaker 1: Now we got to talk take all of this together,

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Speaker 1: Tim and talk about the treasury and rates market trades.

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Speaker 6: Yeah.

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Speaker 3: Among our most read stories on the Bloomberg on this

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Speaker 3: Monday in August, how bonds are back as a hedge

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Speaker 3: after failing investors for years, finally contribute to the story.

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Speaker 3: Bloomberg News rates reporter Michael McKenzie. He ended last week

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Speaker 3: with us and now he kicks off Carol the week for.

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Speaker 1: Us, welcome back. You can't get away from us.

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Speaker 3: He tries, he works from home to try to get

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Speaker 3: away from us, and we still make him turn on

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Speaker 3: the camera and come hang out with us.

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Speaker 1: So Tim was away and he did read it.

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Speaker 3: Oh yeah, but so well up, he missed nothing right.

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Speaker 7: Yeah, we're violently unchanged.

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Speaker 3: Violently unchanged. That's a good way to describe.

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Speaker 1: How are you thinking about things on this Monday here?

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Speaker 3: Well, pretty much.

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Speaker 7: It's interesting too. So we've got a two year yeeld

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Speaker 7: right there on four percent, ten years at what three ninety.

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Speaker 7: So if you think last week we got as low,

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Speaker 7: we got below three eighty on tens, and we bounced

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Speaker 7: back to four to just shire four percent on Thursday

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Speaker 7: after that very week tenure auction. So we're back in

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Speaker 7: the middle of the range, and I think market's just

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Speaker 7: parked here waiting for what will be I think the

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Speaker 7: most important week for data until we get the payrolls report.

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Speaker 7: First week of September, we've got PPI, tomorrow's CPI and

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Speaker 7: then retail sales and jobbers claims. So we're going to

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Speaker 7: see whether or not inflation is trending down or whether

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Speaker 7: it's actually going to be proved to be sticky than

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Speaker 7: people are expecting the bob market. That will put pressure

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Speaker 7: on that front end. You should see high yields if

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Speaker 7: inflation is stickier when people anticipate that said, if inflation

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Speaker 7: comes in online bang in line with expectations, then I

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Speaker 7: think that the focus will turn to Okay, how is

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Speaker 7: the consumer looking for retail sales and are we seeing

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Speaker 7: any signs of weakness in those initial weekly choppers claims

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Speaker 7: on Thursday.

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Speaker 3: Of the data points that you just mentioned, of all

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Speaker 3: the economic data that we're getting this week, Michael, which

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Speaker 3: one do you think has the biggest implications for the

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Speaker 3: rates curve?

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Speaker 7: I'd say CPI. If CPI is trending in the right direction,

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Speaker 7: that means the bomb market and go right, this is

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Speaker 7: yesterday's story and let's just focus on the consumer and

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Speaker 7: the labor market. You get that all clear on CPI

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Speaker 7: this month. I actually loved the last month and it's

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Speaker 7: coming out this month. That means to focus on the

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Speaker 7: Fed's dual mandate. The labor side of things becomes really

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Speaker 7: really important now for the bomb market, and they will

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Speaker 7: continue then to really watch carefully for any scigns of

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Speaker 7: sort of labor deterioration.

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Speaker 1: You know, we mentioned in the Leader and to you, Michael,

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Speaker 1: about this story that's among the most read on the

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Speaker 1: Bloomberg about bonds or back as a hedge after failing

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Speaker 1: investors for years. We mentioned on Friday in our conversation

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Speaker 1: with you how you had caught up with Dan iverson

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Speaker 1: the world's biggest active bond fund manager, who says he's

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Speaker 1: just about ready to start adding to his treasury positions. Again.

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Speaker 1: You talk to a lot of investors, big bond investors,

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Speaker 1: are you increasingly seeing that among them that they're getting

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Speaker 1: ready to buy or are already buying.

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Speaker 7: They're already long. I mean, if you look at all

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Speaker 7: the positioning surveys, and in Himko's defense, when two year

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Speaker 7: was back above five percent briefly late April, they were buying.

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Speaker 7: So they all they've done is they've moderate their duration,

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Speaker 7: They've pulled it back a bit. They're waiting for a backup.

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Speaker 7: So if you get sticky inflation, I think they're going

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Speaker 7: to people come in and I'll cap the rise and yields,

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Speaker 7: but longer term, I mean himko take tend to take

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Speaker 7: a three to five year viewpoint of things. So Dan

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Speaker 7: Iverson's point to me was, if you're a long term investor,

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Speaker 7: you step back, four percent on a ten year is

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Speaker 7: very attractive. You know, you've got to go back to

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Speaker 7: the middle of the first decade of this century when

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Speaker 7: you had a consistent ten year around that four percent level.

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Speaker 7: So these are attractive yields for longer term investors. And

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Speaker 7: it means when you do get a wobble in the

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Speaker 7: equity market, we do get something happening in credit, then

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Speaker 7: that the trophy market, you know, reclaims its status as

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Speaker 7: being the defensive asset to hold, and it will rally.

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Speaker 7: And I think one of the problems the market's got

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Speaker 7: at the moment is that we had a very extensive

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Speaker 7: have and rally last a week ago Monday, when the

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Speaker 7: carry trade was being carried out to some extent, and

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Speaker 7: that's why yields fell so quickly and so sharply, and

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Speaker 7: you're now having to come back and sort of fired

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Speaker 7: an equilibrium. And it seems to me four percent on

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Speaker 7: twos and three nineties, maybe four percent on ten seemed

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Speaker 7: to be the right levels here.

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Speaker 3: Hey Michael, before we let you go, as we mentioned

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Speaker 3: at the top, you contributed to a story that is

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Speaker 3: among the most read on the Bloomberg terminal about how

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Speaker 3: bonds are back and increasingly money managers are hearing from

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Speaker 3: clients about investing in bonds. You talked to professional bond

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Speaker 3: investors each and every day, but talk a little bit

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Speaker 3: about how that's sort of going back to the everyday

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Speaker 3: investor who has their money managed by professionals.

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Speaker 7: Well, I mean the important thing form invest I think

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Speaker 7: a lot of people have liked the idea of putting

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Speaker 7: their money in tea bills.

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Speaker 8: You know what was it?

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Speaker 7: We had t bill and chill. That was the Jeff

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Speaker 7: Gunlack expression last year. But a lot of big bond

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Speaker 7: investors were telling me back in November December, Look, you've

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Speaker 7: got to take advantage of these treasure yields at five

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Speaker 7: percent because it won't cash rates won't stay at five

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Speaker 7: percent once the fair starts to cut again. And I

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Speaker 7: think they've been born out here, that thesis has been validated.

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Speaker 7: It's taken longer than people thought it would. Don't forget

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Speaker 7: everyone at the beginning of the year thought the SAT

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Speaker 7: would be cutting in March, and to a certain extent,

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Speaker 7: Chairpale did kind of hint that was coming. But that said,

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Speaker 7: you've now got treasure yields with four you know, four percent,

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Speaker 7: two s and below four percent and tens and fives.

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Speaker 7: So if you're still stuck in cash you're now sitting

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Speaker 7: on you know, you've got an opportunity loss here because

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Speaker 7: you could have rolled into a five percent two year

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Speaker 7: back in April, right, three four seventy five on tens

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Speaker 7: back in April. Okay, and again these bond managers saying,

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Speaker 7: you've got to start thinking about moving out. So I

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Speaker 7: think it now is time. So we had a record high.

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Speaker 1: And Michael, we got to run. Michael McKenzie, forgive me.

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Speaker 6: This is.

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Speaker 2: You're listening to the Bloomberg Business Week podcast. Listen live

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Speaker 2: each weekday starting at two pm Eastern on Apple car

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Speaker 2: Play and Android Auto with the Bloomberg Business App. You

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Speaker 2: can also listen live on Amazon Alexa from our flagship

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Speaker 2: New York station, Just say Alexa play Bloomberg eleven thirty.

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Speaker 1: Check your calendars. It was one week ago that we

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Speaker 1: had the market sell off, But it was also one

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Speaker 1: week ago that US District Court Judge am At Meta

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Speaker 1: ruled that Google had illegally monopolized the online search market.

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Speaker 1: The decision has serious ramifications for Alphabet's flagship business and

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Speaker 1: tremendous connotations for Apple as well. It's something we've been

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Speaker 1: talking about tim over the past week or so.

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Speaker 3: Yeah, much of the focus has been on what this

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Speaker 3: outcome means for Google's multi billion dollar payments to Apple

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Speaker 3: that ensure Google is the default search engine across iPhones

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Speaker 3: and iPads. But more pressing question may be what the

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Speaker 3: case itself signals for the US Department of Justices separate

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Speaker 3: anti trust lawsuit against Apple.

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Speaker 1: Bottom line, there's always a lot going on when it

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Speaker 1: comes to Apple and writing about Apple and it's cash prospects.

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Speaker 1: A new research that is out by him is Bloomberg

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Speaker 1: Intelligence Senior technology analyst to Ana Agrana. He joins us

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Speaker 1: from Chicago. Happy Monday, an A, rag you were busy

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Speaker 1: got some research out over the weekend. Help us make

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Speaker 1: some sense. First of all, in terms of if Alphabet

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Speaker 1: isn't making these payments to Apple, what does it mean

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Speaker 1: in terms of their cash flow and their cash because

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Speaker 1: you guys seem or you seem pretty upbeat about it

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Speaker 1: or at least their prospects.

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Speaker 9: Yeah, so if you were to, you know, wait and

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Speaker 9: see what happens with this particular case. When you look

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Speaker 9: at Apple's score business, it's doing very well, even though

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Speaker 9: the top line is not growing in double digits like

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Speaker 9: it's used to a few years ago. But even with

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Speaker 9: five to seven percent growth type cost control, you know,

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Speaker 9: this company is generating enough free cash. I think the

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Speaker 9: biggest thing what we are trying to point out is

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Speaker 9: when you look at the other large tech companies. You know,

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Speaker 9: somebody like a Microsoft or an AWS, they are going

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Speaker 9: to spend billions of dollars over the next few week

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Speaker 9: years in order to expand their cloud data centers, invest

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Speaker 9: in AI. But in the case of Apple, I think

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Speaker 9: they're the ones they're going to stand out over the

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Speaker 9: next twelve to twenty four months because they are not

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Speaker 9: doing that same level of spending because they.

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Speaker 3: Don't have that kind of business.

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Speaker 1: Now.

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Speaker 9: The Google case you brought up is an important point,

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Speaker 9: and that's a big risk for Apple, But we're still

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Speaker 9: far away to figure out what the remedies are going

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Speaker 9: to be as long as there is no binding. If

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Speaker 9: Google and Apple can renegotiate and figure out if people

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Speaker 9: get choices, I think there is chances that they can

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Speaker 9: salvage some of that particular twenty billion dollar revenue that

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Speaker 9: comes in now if they are able to. If the

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Speaker 9: judge comes out and say, well, you cannot pay them anything,

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Speaker 9: then that's a problem. But even in that case, I

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Speaker 9: think it will be a few years before Apple can

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Speaker 9: make up for that revenue loss.

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Speaker 3: I want to go back to this idea of Apple

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Speaker 3: not having to make those huge CAPEX investments in AI

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Speaker 3: like Microsoft and Amazon have to do when it comes

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Speaker 3: to infrastructure and actually building out these facilities on a rock.

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Speaker 3: Is it a risk for Apple that they're partnering instead

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Speaker 3: with other companies that are making these investments and not

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Speaker 3: doing them themselves. Is there a chance that they could

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Speaker 3: run into an issue with a platform taking off that's

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Speaker 3: not their own platform.

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Speaker 9: Yeah. See, they're on the device business in all honesty.

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Speaker 9: When you look at the iPhone, they're really not into

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Speaker 9: cloud computing or giving people infrastructure to build their application.

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Speaker 9: So that's a completely different equation in my view. It's

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Speaker 9: like you know, getting electricity you really don't need to

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Speaker 9: own your own generators in order to get electricity. So

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Speaker 9: when you look at Apple, they're basically saying in the

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Speaker 9: same case what they have with Google, they don't want

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Speaker 9: to be in that search business.

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Speaker 6: They're going to go.

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Speaker 9: Try to get a partnership with anybody who has the

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Speaker 9: best search business right now, and they will put that

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Speaker 9: on their phones.

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Speaker 3: That would open Ai.

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Speaker 9: They went out and talked to a bunch of people

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Speaker 9: and says, who has the best large language model that

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Speaker 9: would work well on their iPhone and they chose up to,

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Speaker 9: you know, open Ai to be one of their partners.

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Speaker 9: So that's part of their business model, and that's part

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Speaker 9: the reasons why they don't need to spend that much

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Speaker 9: in Capex.

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Speaker 1: You know, it's interesting. We talked with David Weston, who

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Speaker 1: is a lawyer and spent a lot of time doing

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Speaker 1: anti trust work specifically, and I do wonder about some

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Speaker 1: of these big technology companies ana rog that they are

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Speaker 1: successful at what they do and is that a crime

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Speaker 1: that just because you're good at what you do. And

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Speaker 1: I was actually having a conversation with one of my

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Speaker 1: sisters this week and we were talking about Google, and

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Speaker 1: I'm like, but do you use you know, do you

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Speaker 1: try any other kind of search engines? Like Nope, because

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Speaker 1: Google like just works so well. So like when is

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Speaker 1: it a crime that you are really good at what

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Speaker 1: you do and you just become big because of that?

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Speaker 1: Like how do we figure out, you know, when it

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Speaker 1: is an anti trust issue and when it's not.

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Speaker 9: Yeah, I think see, all these companies are generating billions

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Speaker 9: and billions and dollars of free cash flow, which really

441
00:20:50,840 --> 00:20:53,880
Speaker 9: will attract a lot of lawyers. As you can imagine. Now,

442
00:20:53,960 --> 00:20:57,199
Speaker 9: let's say the case of iPhone. If tomorrow when I

443
00:20:57,359 --> 00:21:00,639
Speaker 9: turn on my iOS or I install a brand new system,

444
00:21:01,000 --> 00:21:03,199
Speaker 9: it'll ask me which search indind do you want to use.

445
00:21:03,240 --> 00:21:05,960
Speaker 9: It's going to give me six choices more like most

446
00:21:06,000 --> 00:21:08,399
Speaker 9: likely or not. I'm going to pick Google, and that

447
00:21:08,720 --> 00:21:11,000
Speaker 9: is going to help Google. I think the bigger question

448
00:21:11,040 --> 00:21:13,280
Speaker 9: in this all equation is you know what happens to

449
00:21:13,320 --> 00:21:14,440
Speaker 9: that payment that's happening.

450
00:21:15,200 --> 00:21:17,919
Speaker 3: Well, that's why you know, I think sort of counterintuitively,

451
00:21:18,440 --> 00:21:21,280
Speaker 3: couldn't this actually be a great thing for Google if

452
00:21:21,600 --> 00:21:25,320
Speaker 3: people end up using Google and choosing Google because they're

453
00:21:25,359 --> 00:21:27,520
Speaker 3: not choosing a competitor. But then Google doesn't have to

454
00:21:27,560 --> 00:21:30,560
Speaker 3: make these payments to Apple that's just like icing on

455
00:21:30,560 --> 00:21:32,399
Speaker 3: the cake. Is this could be a big win for

456
00:21:32,440 --> 00:21:34,560
Speaker 3: alphabet right, yeah, yeah, exactly.

457
00:21:34,600 --> 00:21:37,800
Speaker 9: I mean that's what I'm saying that in this entire case,

458
00:21:37,800 --> 00:21:40,679
Speaker 9: it's Apple who's going to lose. But here's my counter argument.

459
00:21:41,040 --> 00:21:43,440
Speaker 9: Is my counter argument to that, Apple is a company with,

460
00:21:43,600 --> 00:21:45,760
Speaker 9: as I said, over one hundred billion in free calf flow.

461
00:21:46,080 --> 00:21:48,280
Speaker 9: If they really wanted to fight Google and Search, they

462
00:21:48,320 --> 00:21:50,040
Speaker 9: can do it. They can go and acquire a small

463
00:21:50,160 --> 00:21:53,399
Speaker 9: vendor and use that in their pipeline. Remember, they ConTroll

464
00:21:53,480 --> 00:21:56,800
Speaker 9: distribution of two point two billion devices. Google's going to

465
00:21:56,880 --> 00:21:59,560
Speaker 9: lose a lot of market share if it doesn't play

466
00:21:59,640 --> 00:22:02,760
Speaker 9: nice with Apple, and so, you know, I get it

467
00:22:02,800 --> 00:22:05,760
Speaker 9: that in the short term they will be you could say,

468
00:22:05,760 --> 00:22:08,240
Speaker 9: benefit from it, But I don't think they would want

469
00:22:08,240 --> 00:22:10,040
Speaker 9: to pick a fight with Apple on this one.

470
00:22:10,280 --> 00:22:13,560
Speaker 1: Interesting the anti trust case case though against Apple by

471
00:22:13,560 --> 00:22:16,960
Speaker 1: the Department of Justice, they say to classify Apple as

472
00:22:16,960 --> 00:22:20,160
Speaker 1: a monopolis. The government attorney's carved out its relevant product

473
00:22:20,160 --> 00:22:24,280
Speaker 1: market is only including premium smartphones, a segment apparently distinc

474
00:22:24,320 --> 00:22:27,120
Speaker 1: from entry level and less expensive gadgets. I mean, it's

475
00:22:27,160 --> 00:22:29,320
Speaker 1: kind of like you're dicing and slicing the market here.

476
00:22:29,240 --> 00:22:31,600
Speaker 9: Right, yeah, yeah, yeah, So when you look at Apple's

477
00:22:31,760 --> 00:22:35,080
Speaker 9: entire so there are roughly about five plus billion smartphones

478
00:22:35,119 --> 00:22:38,240
Speaker 9: in the world, and Apple's market chair is roughly about

479
00:22:38,280 --> 00:22:40,719
Speaker 9: twenty percent or one billion. But yeah, you could make

480
00:22:40,800 --> 00:22:43,360
Speaker 9: the argument if if it was only the expensive category.

481
00:22:43,560 --> 00:22:45,520
Speaker 9: You know, they have over fifty percent of the market share.

482
00:22:45,640 --> 00:22:47,359
Speaker 9: But there are only two players out there. Either it's

483
00:22:47,400 --> 00:22:49,640
Speaker 9: Samsung or it's Apple. There you know, there isn't really

484
00:22:49,720 --> 00:22:52,320
Speaker 9: a big third player in the premium market that anybody

485
00:22:52,320 --> 00:22:54,520
Speaker 9: wants to deal with. But you know, we think twenty

486
00:22:54,560 --> 00:22:57,560
Speaker 9: percent of the market share in the global smartphone market.

487
00:22:57,720 --> 00:23:00,160
Speaker 9: That really is the big case for Apple, which is

488
00:23:00,400 --> 00:23:03,240
Speaker 9: as people become more affluent, they will go out and

489
00:23:03,280 --> 00:23:06,159
Speaker 9: get Apple Basicus. It's an aspirational brand for people in

490
00:23:06,200 --> 00:23:08,600
Speaker 9: emerging markets. It's really the place you want to be.

491
00:23:08,920 --> 00:23:11,320
Speaker 9: But in most cases you don't even qualify to buy

492
00:23:11,359 --> 00:23:13,520
Speaker 9: that product because it's so expensive.

493
00:23:14,160 --> 00:23:16,760
Speaker 1: Well, and the thing about you know, Apple, with these

494
00:23:16,840 --> 00:23:20,560
Speaker 1: large language models and all of the AI that's being done,

495
00:23:20,760 --> 00:23:23,160
Speaker 1: I guess do they not care? Go ahead and everybody else,

496
00:23:23,280 --> 00:23:25,640
Speaker 1: you spend money, you develop it, and then we'll give

497
00:23:25,680 --> 00:23:28,000
Speaker 1: you the device that people can play with it on.

498
00:23:28,160 --> 00:23:29,159
Speaker 1: Is that what it's all about.

499
00:23:30,000 --> 00:23:31,280
Speaker 9: Yeah, But in the you know, if I was to

500
00:23:31,320 --> 00:23:33,600
Speaker 9: take the other argument of it is, if they started

501
00:23:33,600 --> 00:23:36,280
Speaker 9: doing everything else, then people will say, well, they're using

502
00:23:36,280 --> 00:23:39,639
Speaker 9: their distribution to unfairly you know, push their own product. Right.

503
00:23:39,680 --> 00:23:42,560
Speaker 9: They're basically saying, if you build the best product out there,

504
00:23:42,680 --> 00:23:44,439
Speaker 9: we will use it, and we will use it on

505
00:23:44,480 --> 00:23:47,280
Speaker 9: the merit of the product. In this particular case, they

506
00:23:47,280 --> 00:23:49,720
Speaker 9: didn't go out and picked you know, Google's Gemini on

507
00:23:49,880 --> 00:23:52,520
Speaker 9: round one. I think it's there's down the road. I

508
00:23:52,520 --> 00:23:55,359
Speaker 9: think it may happen. But they chose open Ai because

509
00:23:55,359 --> 00:23:58,240
Speaker 9: they have at this point the best large language model

510
00:23:58,280 --> 00:23:58,720
Speaker 9: out there.

511
00:23:59,160 --> 00:24:02,400
Speaker 3: Okay, speaking of the Apple being a device company, it's

512
00:24:02,440 --> 00:24:06,640
Speaker 3: the iPhone company. No question, the iPhone sixteen likely will

513
00:24:06,640 --> 00:24:08,960
Speaker 3: see it unveiled in just a few weeks.

514
00:24:08,960 --> 00:24:11,760
Speaker 1: I was getting ready and thinking really hard, so was

515
00:24:11,800 --> 00:24:15,720
Speaker 1: Paul Sweeney, like replacing my phone come later this year,

516
00:24:15,720 --> 00:24:17,080
Speaker 1: And now I'm thinking maybe I shouldn't.

517
00:24:17,240 --> 00:24:19,040
Speaker 3: Well, that's the big question. Because Mark German out with

518
00:24:19,080 --> 00:24:21,040
Speaker 3: his Power on newsletter over the weekend on it rog

519
00:24:21,080 --> 00:24:23,760
Speaker 3: talking about how this is perhaps one of those stopgap

520
00:24:23,800 --> 00:24:26,680
Speaker 3: models that gives people a bridge. The next one perhaps

521
00:24:26,720 --> 00:24:28,919
Speaker 3: doesn't get people to do an upgrade super cycle. How

522
00:24:28,960 --> 00:24:31,760
Speaker 3: are you thinking about it at Bloomberg Intelligence, Yeah.

523
00:24:31,600 --> 00:24:33,520
Speaker 9: In a similar way. We don't think it's going to

524
00:24:33,680 --> 00:24:36,239
Speaker 9: have a massive layout in year one. Over a three

525
00:24:36,320 --> 00:24:38,879
Speaker 9: year period, I think it's going to help, but at

526
00:24:38,920 --> 00:24:42,240
Speaker 9: this point we don't have We are looking at maybe

527
00:24:42,280 --> 00:24:45,280
Speaker 9: a five percent bump in total unit ship and sold

528
00:24:45,560 --> 00:24:48,080
Speaker 9: next to the at FI twenty five over Fi twenty four,

529
00:24:48,280 --> 00:24:50,760
Speaker 9: and that's because the last two has been kind of weak.

530
00:24:51,240 --> 00:24:54,840
Speaker 9: The Apple's installed by Base moves at a setic certain rate,

531
00:24:55,200 --> 00:24:58,080
Speaker 9: and you refresh your phone when to be honest, when

532
00:24:58,080 --> 00:25:00,480
Speaker 9: the battery runs out. That number you to be three

533
00:25:00,520 --> 00:25:02,920
Speaker 9: point six years for a few years, but it's been

534
00:25:02,960 --> 00:25:05,680
Speaker 9: extended closer to four years at this point. So at

535
00:25:05,720 --> 00:25:09,679
Speaker 9: an average globally out of that one billion phones I

536
00:25:09,720 --> 00:25:12,159
Speaker 9: talked about, people refresh at every four year. So I

537
00:25:12,200 --> 00:25:14,359
Speaker 9: think that is what we're going to see. We're not

538
00:25:14,400 --> 00:25:16,120
Speaker 9: going to see a massive bump because of AI.

539
00:25:16,480 --> 00:25:19,159
Speaker 1: All right, just to wrap up here, as you know,

540
00:25:19,200 --> 00:25:21,480
Speaker 1: we're over the hump, if you will, in terms of

541
00:25:21,880 --> 00:25:25,399
Speaker 1: half of twenty twenty four already over. How are you

542
00:25:25,400 --> 00:25:27,200
Speaker 1: thinking about the rest of the year when it comes

543
00:25:27,240 --> 00:25:30,240
Speaker 1: to technology in terms of some of the big major themes,

544
00:25:30,240 --> 00:25:33,240
Speaker 1: whether it's AI, CAPEX expending. Are we going to see

545
00:25:33,280 --> 00:25:37,240
Speaker 1: more kind of worrisome notes or worrisome updates or do

546
00:25:37,320 --> 00:25:39,320
Speaker 1: you feel pretty confident about the rest of the year.

547
00:25:40,240 --> 00:25:42,359
Speaker 9: See, I think from a fundamental point of view, this

548
00:25:42,480 --> 00:25:44,040
Speaker 9: is going to be fine. I mean, I don't see

549
00:25:44,080 --> 00:25:47,280
Speaker 9: any reason why the big tech companies, whether it's Microsoft

550
00:25:47,320 --> 00:25:50,680
Speaker 9: or whether it's you know, Amazon Web Services, their core

551
00:25:50,760 --> 00:25:53,560
Speaker 9: business is strong and NOO will remain. The stock reaction

552
00:25:53,680 --> 00:25:56,000
Speaker 9: is a very different game because that depends on interest

553
00:25:56,080 --> 00:25:59,119
Speaker 9: rates and so many other factors, and that's really you know,

554
00:25:59,320 --> 00:26:02,080
Speaker 9: we'll see what happens with that. But at the same time,

555
00:26:02,520 --> 00:26:04,960
Speaker 9: they will spend that a lot of money in order

556
00:26:05,000 --> 00:26:08,520
Speaker 9: to beef up their capacity so that they can grow

557
00:26:08,640 --> 00:26:10,600
Speaker 9: over the next three to five years. I think that

558
00:26:10,760 --> 00:26:13,320
Speaker 9: is really, in our view, a good long term play,

559
00:26:13,560 --> 00:26:15,320
Speaker 9: but in the short term it may have its ups

560
00:26:15,320 --> 00:26:15,760
Speaker 9: and downs.

561
00:26:15,760 --> 00:26:17,880
Speaker 1: All right, got it? Listen Anerrog, thank you so much,

562
00:26:17,960 --> 00:26:20,520
Speaker 1: really appreciate it. Ana ro Grana, he's our Bloomberg Intelligence

563
00:26:20,560 --> 00:26:23,600
Speaker 1: senior technology analyst joining us from Chicago.

564
00:26:24,560 --> 00:26:28,120
Speaker 2: You're listening to the Bloomberg Business Week podcast. Catch us

565
00:26:28,119 --> 00:26:31,399
Speaker 2: live weekday afternoons from two to five pm Eastern. Listen

566
00:26:31,400 --> 00:26:33,560
Speaker 2: on Apple car Play and then Brout Auto with a

567
00:26:33,600 --> 00:26:38,760
Speaker 2: Bloomberg Business app or watch us live on YouTube.

568
00:26:40,040 --> 00:26:41,880
Speaker 3: Carol, I don't know if you caught this article from

569
00:26:42,040 --> 00:26:43,920
Speaker 3: Sagel Kashan a few weeks ago. It was about the

570
00:26:44,040 --> 00:26:46,640
Speaker 3: changing view of ESG. We've talked about this a lot

571
00:26:46,680 --> 00:26:49,879
Speaker 3: over the last eighteen minds two years or so, he

572
00:26:49,920 --> 00:26:52,960
Speaker 3: wrote that quote. The red carpet is being formally rolled

573
00:26:53,040 --> 00:26:57,000
Speaker 3: up for the three letters ESG, at least over at II.

574
00:26:57,119 --> 00:26:59,919
Speaker 3: The fifty seven year old organization has dropped the label

575
00:27:00,359 --> 00:27:04,200
Speaker 3: short for Environmental, Social and Governance from its annual analyst rankings.

576
00:27:04,280 --> 00:27:07,560
Speaker 1: Yeah, there's been such pushback against the kind of labeling.

577
00:27:07,800 --> 00:27:10,919
Speaker 1: In its place, it's sustainability, a cineym many banks and

578
00:27:10,960 --> 00:27:14,760
Speaker 1: money managers are using instead, amid the increasingly politicized debate

579
00:27:14,800 --> 00:27:18,000
Speaker 1: over climate change and corporate diversity in the United States.

580
00:27:18,359 --> 00:27:20,800
Speaker 1: What a change if you go back, I feel like

581
00:27:20,840 --> 00:27:23,520
Speaker 1: ten fifteen years where there's been so much emphasis and

582
00:27:23,560 --> 00:27:25,719
Speaker 1: a movement towards this ESG labeling.

583
00:27:25,840 --> 00:27:28,000
Speaker 3: Yeah. Curious what Bud Sturmac has to say about all this.

584
00:27:28,080 --> 00:27:30,360
Speaker 3: He's partner and head of Impact Investing over at Paragon

585
00:27:30,400 --> 00:27:33,879
Speaker 3: Wealth Management. He joins us here in the Bloomberg Interactive

586
00:27:34,000 --> 00:27:37,639
Speaker 3: Brokers studio. Paragon has about eight billion dollars in assets

587
00:27:38,000 --> 00:27:39,359
Speaker 3: under management. Bud, how are you?

588
00:27:39,560 --> 00:27:40,960
Speaker 8: I'm good? Thanks so much for having me.

589
00:27:41,119 --> 00:27:43,840
Speaker 3: So you guys are kind of leaning into ESG when

590
00:27:43,880 --> 00:27:46,680
Speaker 3: others are pulling back a little bit. Talk a little

591
00:27:46,680 --> 00:27:47,480
Speaker 3: bit about why that is.

592
00:27:48,200 --> 00:27:52,560
Speaker 8: Yeah, I think we're in an era of personalization and

593
00:27:52,760 --> 00:27:56,840
Speaker 8: whether it's how we consume television or music, or where

594
00:27:56,840 --> 00:27:58,840
Speaker 8: we buy groceries or what kind of car we drive.

595
00:27:59,040 --> 00:28:02,080
Speaker 8: I think you're seeing that at start to evolve investing.

596
00:28:02,520 --> 00:28:06,719
Speaker 8: And so what Paragon stands for is empowering our clients

597
00:28:06,720 --> 00:28:10,960
Speaker 8: with choice. And we think it's important to include in

598
00:28:11,000 --> 00:28:15,520
Speaker 8: our client onboarding process questions about understanding what values our

599
00:28:15,520 --> 00:28:16,600
Speaker 8: clients bring to the table.

600
00:28:17,320 --> 00:28:19,440
Speaker 3: These are values that clients bring to the table. Yes,

601
00:28:20,040 --> 00:28:20,760
Speaker 3: what do you mean by that?

602
00:28:21,040 --> 00:28:24,720
Speaker 8: So you know, as well as understanding client goals, financial planning,

603
00:28:24,760 --> 00:28:28,720
Speaker 8: and understanding their investments, what do they care about most?

604
00:28:29,040 --> 00:28:31,960
Speaker 8: Is it climate change? Is it gender equality? Is it

605
00:28:32,040 --> 00:28:32,679
Speaker 8: racial equality?

606
00:28:32,680 --> 00:28:34,600
Speaker 3: If those things are what if they only care about returns,

607
00:28:34,680 --> 00:28:37,240
Speaker 3: that's fine, Yeah, they don't care about any of the

608
00:28:37,280 --> 00:28:37,840
Speaker 3: other stuff.

609
00:28:38,120 --> 00:28:40,800
Speaker 8: That's totally okay, But we think it's important in this

610
00:28:40,880 --> 00:28:42,800
Speaker 8: day and age that we ask the question because it

611
00:28:42,840 --> 00:28:47,320
Speaker 8: brings a whole other layer of the ability for clients

612
00:28:47,320 --> 00:28:49,720
Speaker 8: to connect with their portfolio if that's something that they

613
00:28:49,720 --> 00:28:50,760
Speaker 8: believe passionately about.

614
00:28:51,000 --> 00:28:53,080
Speaker 1: But you know, if you build it, they will come

615
00:28:53,360 --> 00:28:56,120
Speaker 1: that kind of thinking. So I guess my question is,

616
00:28:56,480 --> 00:29:00,680
Speaker 1: are your clients still saying, Hey, we like this SG thing,

617
00:29:00,720 --> 00:29:02,800
Speaker 1: we get it, we understand, we want to commit money

618
00:29:02,800 --> 00:29:03,120
Speaker 1: to it.

619
00:29:04,040 --> 00:29:05,840
Speaker 8: The answer is yes. But I also think that you

620
00:29:05,920 --> 00:29:08,480
Speaker 8: hit on something before, which is people are moving away

621
00:29:08,480 --> 00:29:12,400
Speaker 8: from the term ESG, so sustainable investing values based investing.

622
00:29:13,520 --> 00:29:17,200
Speaker 8: We're not like regularly using the term ESG. Again, I

623
00:29:17,240 --> 00:29:19,400
Speaker 8: think it comes back to client values. What do they

624
00:29:19,400 --> 00:29:20,080
Speaker 8: care about most?

625
00:29:20,360 --> 00:29:20,560
Speaker 4: What?

626
00:29:20,840 --> 00:29:23,640
Speaker 1: So where did ESG get it wrong? Like? What is

627
00:29:23,640 --> 00:29:26,320
Speaker 1: it that? It's just kind of mind blowing how much

628
00:29:26,400 --> 00:29:30,520
Speaker 1: time and energy and money we spent talking about it

629
00:29:30,920 --> 00:29:32,960
Speaker 1: and then now it's kind of getting Although things evolve

630
00:29:33,000 --> 00:29:35,520
Speaker 1: and sometimes things come out better on the other side,

631
00:29:35,600 --> 00:29:37,120
Speaker 1: But so what happened?

632
00:29:37,240 --> 00:29:41,160
Speaker 8: It's funny because ESG really is research, it's data. Yeah,

633
00:29:41,200 --> 00:29:46,000
Speaker 8: and the term sort of got turned into investing which

634
00:29:46,040 --> 00:29:49,360
Speaker 8: it's really not. It's a tool, right, So I think.

635
00:29:49,600 --> 00:29:54,640
Speaker 1: It's vulnerabilities, isn't it, like, yeah, climate exposure? Right exactly.

636
00:29:54,880 --> 00:29:58,000
Speaker 8: Yeah. I think where it sort of went awry is

637
00:29:59,040 --> 00:30:03,520
Speaker 8: maybe two things on the investing Within the investing landscape,

638
00:30:03,520 --> 00:30:07,480
Speaker 8: you have mutual funds and ETFs that label themselves ESG

639
00:30:07,600 --> 00:30:11,560
Speaker 8: or sustainable, and when a client is buying into that fund,

640
00:30:11,640 --> 00:30:14,440
Speaker 8: they're sort of having to adopt whatever the fund manager's

641
00:30:14,560 --> 00:30:19,560
Speaker 8: view on sustainability is. The client may agree with that view,

642
00:30:19,680 --> 00:30:23,760
Speaker 8: it may not. And so then you have the ESG

643
00:30:23,960 --> 00:30:29,080
Speaker 8: data companies, Right, they're taking very complex E S and

644
00:30:29,320 --> 00:30:31,600
Speaker 8: G data and they're trying to wrap that up into

645
00:30:31,640 --> 00:30:35,200
Speaker 8: a nice score that everybody can say, oh, this is

646
00:30:35,240 --> 00:30:38,200
Speaker 8: a very sustainable company. This is not the problem with

647
00:30:38,240 --> 00:30:40,920
Speaker 8: that is is that the es and the G data,

648
00:30:41,880 --> 00:30:45,160
Speaker 8: those data points might actually be very accurate and succinct,

649
00:30:45,600 --> 00:30:49,600
Speaker 8: but wrapping them into a nice need score invites subjectivity

650
00:30:49,800 --> 00:30:51,240
Speaker 8: and so well.

651
00:30:51,120 --> 00:30:54,080
Speaker 1: Governance might be easier than environment, like some of it

652
00:30:54,160 --> 00:30:55,680
Speaker 1: might be easier to measure than others.

653
00:30:55,760 --> 00:30:58,480
Speaker 8: Right, Also, I think that's true, but trying to wrap

654
00:30:58,520 --> 00:31:01,160
Speaker 8: it up into a nice need score what you're and

655
00:31:01,200 --> 00:31:04,200
Speaker 8: the criticism that was invited from that was that, oh,

656
00:31:04,320 --> 00:31:08,120
Speaker 8: this ESG data company rates Tesla very high, while this

657
00:31:08,360 --> 00:31:11,479
Speaker 8: ESG data company rates it low, and that you know this,

658
00:31:11,880 --> 00:31:16,080
Speaker 8: and it basically brought in criticism of ESG in general.

659
00:31:16,280 --> 00:31:19,280
Speaker 3: Well, maybe the E is a positive for some for

660
00:31:19,520 --> 00:31:21,440
Speaker 3: some of these and when it comes to data, but

661
00:31:21,480 --> 00:31:24,240
Speaker 3: the G when it comes to governance and who's on

662
00:31:24,320 --> 00:31:28,040
Speaker 3: Tesla's board, for example, doesn't necessarily pass muster for this

663
00:31:28,120 --> 00:31:30,680
Speaker 3: other organization, just for example. I mean, the ES and

664
00:31:30,760 --> 00:31:32,480
Speaker 3: G are three completely different things.

665
00:31:32,680 --> 00:31:34,640
Speaker 8: That's exactly right. And I think that.

666
00:31:34,680 --> 00:31:36,440
Speaker 3: Maybe some people would have problem with the social part

667
00:31:36,440 --> 00:31:36,680
Speaker 3: of it.

668
00:31:37,440 --> 00:31:40,400
Speaker 8: Yes, And so when we're talking about bringing it back

669
00:31:40,440 --> 00:31:43,320
Speaker 8: to what the client cares about, then you have an

670
00:31:43,320 --> 00:31:46,840
Speaker 8: opportunity to say, Okay, this client cares about the environment,

671
00:31:47,200 --> 00:31:49,840
Speaker 8: so maybe Tesla would be in the in the portfolio,

672
00:31:50,240 --> 00:31:54,800
Speaker 8: or this client cares mostly about gender equality. So you know,

673
00:31:55,080 --> 00:31:58,240
Speaker 8: you're applying a different lens. It's what the client cares about,

674
00:31:58,280 --> 00:32:01,840
Speaker 8: and it's building the portfolio stock by stock around exactly

675
00:32:02,120 --> 00:32:02,520
Speaker 8: that client.

676
00:32:02,560 --> 00:32:03,719
Speaker 1: So it's a true customization.

677
00:32:03,880 --> 00:32:05,160
Speaker 8: Yes, so here's what you like.

678
00:32:05,520 --> 00:32:08,760
Speaker 1: I'm going to go find companies that meet those metrics.

679
00:32:08,920 --> 00:32:12,360
Speaker 1: If those companies don't perform, don't put them in my portfolio.

680
00:32:12,800 --> 00:32:16,160
Speaker 1: Is it as simple as that, Well, it's really like

681
00:32:16,240 --> 00:32:17,800
Speaker 1: what's the balance, and it's.

682
00:32:17,720 --> 00:32:20,680
Speaker 8: Trying to It is trying to mirror the index performance. So,

683
00:32:20,680 --> 00:32:22,640
Speaker 8: whether you're talking about an S and P five hundred

684
00:32:22,720 --> 00:32:25,840
Speaker 8: or a Russell one thousand or whatever your underlying index is,

685
00:32:26,240 --> 00:32:29,640
Speaker 8: you're still mostly owning the industries and the industry waitings,

686
00:32:29,800 --> 00:32:34,400
Speaker 8: but you're tilting away from the worst companies that the

687
00:32:34,440 --> 00:32:38,560
Speaker 8: client would disagree with. Yeah, and you're recreating the index,

688
00:32:38,640 --> 00:32:41,960
Speaker 8: so it does. Yes, you're inviting tracking error. If a

689
00:32:42,000 --> 00:32:45,560
Speaker 8: client really is like Adamant, I don't want several different

690
00:32:45,560 --> 00:32:48,360
Speaker 8: industries or exposures to you could increase the tracking air

691
00:32:48,400 --> 00:32:50,880
Speaker 8: a little bit, but most of what we've seen has

692
00:32:50,920 --> 00:32:55,040
Speaker 8: been that the underlying portfolios do closely track the performance

693
00:32:55,080 --> 00:32:55,680
Speaker 8: of the index.

694
00:32:56,080 --> 00:33:00,840
Speaker 3: Does it make it so oil companies are just not

695
00:33:00,920 --> 00:33:04,000
Speaker 3: in there because they don't have the E part of ESG.

696
00:33:04,360 --> 00:33:06,640
Speaker 8: If it was a climate change focus for a client,

697
00:33:06,920 --> 00:33:09,440
Speaker 8: or the E was the most important factor, then most

698
00:33:09,520 --> 00:33:12,200
Speaker 8: likely those would not be included in the portfolio.

699
00:33:12,280 --> 00:33:15,520
Speaker 1: But an automaker, that's still it's a lot of you know,

700
00:33:15,960 --> 00:33:20,120
Speaker 1: traditional still gas burning cars, but they're increasingly moving into

701
00:33:20,160 --> 00:33:23,800
Speaker 1: ev like do you have a conversation with an investor

702
00:33:24,120 --> 00:33:26,840
Speaker 1: or yeah? Like how does something like that?

703
00:33:27,320 --> 00:33:34,080
Speaker 8: Yeah, so we're not making the investment decisions necessarily. We're

704
00:33:34,240 --> 00:33:37,640
Speaker 8: hiring outside managers to do that, and they have very

705
00:33:39,600 --> 00:33:44,000
Speaker 8: finite data that can bring more to light in that conversation,

706
00:33:44,120 --> 00:33:47,280
Speaker 8: and we can have that conversation with a client. But

707
00:33:47,400 --> 00:33:50,480
Speaker 8: the data really is quite good, and we can show clients,

708
00:33:50,840 --> 00:33:54,160
Speaker 8: you know, here's the companies that would fall into the portfolio,

709
00:33:54,240 --> 00:33:55,520
Speaker 8: and here's what would be cut out. How do you

710
00:33:55,560 --> 00:33:56,240
Speaker 8: feel about that?

711
00:33:56,360 --> 00:33:58,360
Speaker 1: So it's that specific and you can kind of pick

712
00:33:58,360 --> 00:33:58,760
Speaker 1: and choose.

713
00:33:58,840 --> 00:34:02,160
Speaker 3: Yes, is the the objective you said is to match

714
00:34:02,400 --> 00:34:05,160
Speaker 3: the major indices rather than beat them.

715
00:34:05,400 --> 00:34:08,000
Speaker 8: Yeah, it's really the track this is, this is it's

716
00:34:08,040 --> 00:34:09,200
Speaker 8: really a passive approach.

717
00:34:10,040 --> 00:34:12,280
Speaker 3: There are neta fees.

718
00:34:13,480 --> 00:34:25,200
Speaker 8: Uh so that's gross. Okay, yeah, the underlying indexes. I'm sorry,

719
00:34:25,239 --> 00:34:26,160
Speaker 8: i just lost my train of thought.

720
00:34:26,160 --> 00:34:26,560
Speaker 10: No, that's okay.

721
00:34:26,560 --> 00:34:28,359
Speaker 3: I'm just wondering about the sales pitch when you're making

722
00:34:28,360 --> 00:34:31,360
Speaker 3: it to clients. So it's like, okay, we're gonna in

723
00:34:31,360 --> 00:34:34,040
Speaker 3: your portfolio, We're going to have your your portfolio is

724
00:34:34,040 --> 00:34:35,920
Speaker 3: going to match the index. But it's going to not

725
00:34:35,960 --> 00:34:38,600
Speaker 3: include the companies that don't necessarily align with your.

726
00:34:38,480 --> 00:34:40,920
Speaker 8: Own value, right, I was gonna I was gonna mention that.

727
00:34:40,920 --> 00:34:43,759
Speaker 1: Yeah, it's a Monday in August, you are giving you

728
00:34:43,800 --> 00:34:44,960
Speaker 1: should have stated us earlier.

729
00:34:45,080 --> 00:34:46,160
Speaker 3: Is absolutely correct.

730
00:34:46,920 --> 00:34:49,040
Speaker 8: I was just gonna mention that in some cases, like

731
00:34:49,760 --> 00:34:54,560
Speaker 8: in that direct indexing portfolio, you can tilt towards companies

732
00:34:54,560 --> 00:34:59,359
Speaker 8: that maybe are the leaders on various ESG factors. Again,

733
00:34:59,400 --> 00:35:02,200
Speaker 8: that would be specific to that client, but you maybe

734
00:35:02,200 --> 00:35:05,880
Speaker 8: your portfolio'll be more tilted towards clean energy or different

735
00:35:05,920 --> 00:35:06,399
Speaker 8: things like that.

736
00:35:06,520 --> 00:35:08,480
Speaker 1: So basically an evolution of ESG.

737
00:35:09,400 --> 00:35:12,439
Speaker 8: Yeah, I think it's this. This has been a huge

738
00:35:12,480 --> 00:35:14,560
Speaker 8: evolution in the field in the last like ten or

739
00:35:14,560 --> 00:35:18,040
Speaker 8: fifteen years, especially the last five. Right, Yeah, and a pushback.

740
00:35:18,320 --> 00:35:21,040
Speaker 1: It's kind of fascinating to see. But thank you so much,

741
00:35:21,040 --> 00:35:23,600
Speaker 1: really appreciate it. Budster Macky is partner and head of

742
00:35:23,600 --> 00:35:27,160
Speaker 1: Impact Investing at Paragone Wealth and Management. Joining us right

743
00:35:27,200 --> 00:35:28,920
Speaker 1: here in our interactive broker studio.

744
00:35:29,719 --> 00:35:33,279
Speaker 2: You're listening to the Bloomberg Business Week podcast. Catch us

745
00:35:33,320 --> 00:35:36,560
Speaker 2: live weekday afternoons from two to five pm Eastern Listen

746
00:35:36,600 --> 00:35:38,759
Speaker 2: on Apple car Play, and then brout Auto with a

747
00:35:38,760 --> 00:35:43,400
Speaker 2: Bloomberg Business app or watch us live on YouTube.

748
00:35:44,719 --> 00:35:46,719
Speaker 3: You and I've been doing this together for quite a while.

749
00:35:46,800 --> 00:35:49,319
Speaker 3: We have, including Wow four years.

750
00:35:49,400 --> 00:35:52,279
Speaker 1: Yeah, if you think about it, it was the depth or

751
00:35:53,000 --> 00:35:54,640
Speaker 1: I mean of the pandemic.

752
00:35:54,760 --> 00:35:58,239
Speaker 3: Yeah, it was. Yeah, it really was. Think about back

753
00:35:58,280 --> 00:36:01,720
Speaker 3: to that environment, if you will, and the pandemic was raging.

754
00:36:02,239 --> 00:36:05,120
Speaker 3: Tensions in cities were high. People took to the streets

755
00:36:05,120 --> 00:36:08,400
Speaker 3: to protest the murder of George Floyd around that time.

756
00:36:08,640 --> 00:36:10,600
Speaker 3: We wrote a lot about this at Bloomberg News and

757
00:36:10,600 --> 00:36:13,960
Speaker 3: at Bloomberg BusinessWeek. A lot of companies and stories about

758
00:36:14,000 --> 00:36:17,160
Speaker 3: companies big and small joining the conversation making commitments of

759
00:36:17,200 --> 00:36:21,239
Speaker 3: different types. Wells Fargo one of those companies. Four years ago,

760
00:36:21,239 --> 00:36:24,960
Speaker 3: it committed more than four hundred million dollars to nonprofits.

761
00:36:25,680 --> 00:36:28,040
Speaker 3: The idea was to help local businesses that were hit

762
00:36:28,080 --> 00:36:31,040
Speaker 3: by the pandemic, so the nonprofits would deploy the cash

763
00:36:31,440 --> 00:36:34,479
Speaker 3: to local businesses, and according to the Bank, the vast

764
00:36:34,520 --> 00:36:38,880
Speaker 3: majority of those businesses they identified as racially and ethnically diverse.

765
00:36:39,200 --> 00:36:42,080
Speaker 1: Darlene Goins is head of Philanthropy and Community Impact at

766
00:36:42,120 --> 00:36:44,920
Speaker 1: Wells Fargo. Here to talk a little bit more about

767
00:36:44,920 --> 00:36:47,000
Speaker 1: what they are up to and how it is going.

768
00:36:47,040 --> 00:36:49,640
Speaker 1: She joins us from San Francisco. Darlene, nice to have

769
00:36:49,719 --> 00:36:50,719
Speaker 1: you here. How are you?

770
00:36:52,160 --> 00:36:53,640
Speaker 10: I'm great, Thanks for having me.

771
00:36:53,880 --> 00:36:57,200
Speaker 1: Yeah, it's great to check in with you. Tell us

772
00:36:57,239 --> 00:36:59,680
Speaker 1: how things are going. And you know, four years ago,

773
00:36:59,719 --> 00:37:03,120
Speaker 1: you guys committed this big chunk of change formed million

774
00:37:03,120 --> 00:37:06,680
Speaker 1: to those nonprofits that really helped out local businesses hit

775
00:37:06,719 --> 00:37:09,520
Speaker 1: by the pandemic. Where are you in that and what's

776
00:37:09,520 --> 00:37:10,520
Speaker 1: been the impact of it?

777
00:37:11,960 --> 00:37:14,759
Speaker 10: Sure, so let me start by giving some context. So

778
00:37:15,239 --> 00:37:19,719
Speaker 10: back in twenty twenty, the pandemic hit and immediately millions

779
00:37:19,719 --> 00:37:23,160
Speaker 10: of small businesses, often those small businesses that help make

780
00:37:23,200 --> 00:37:27,480
Speaker 10: communities feel like home, they immediately struggled. And so we

781
00:37:27,600 --> 00:37:33,880
Speaker 10: really wanted to create a national inclusive small business recovery effort,

782
00:37:34,480 --> 00:37:37,560
Speaker 10: and so we started by listening. We talked with our

783
00:37:37,680 --> 00:37:41,239
Speaker 10: nonprofit partners that were in communities and asked them what

784
00:37:41,360 --> 00:37:45,160
Speaker 10: they needed, and they said they needed flexible capital, flexible

785
00:37:45,200 --> 00:37:48,360
Speaker 10: capital that could enable them to pivot to meet whatever

786
00:37:48,400 --> 00:37:52,880
Speaker 10: the local community's needs were. And so our CEO, Charlie Sharf,

787
00:37:53,000 --> 00:37:57,080
Speaker 10: decided to donate all of the gross processing fees that

788
00:37:57,120 --> 00:38:00,440
Speaker 10: Wells Fargo earned from the government for administer during the

789
00:38:00,480 --> 00:38:04,400
Speaker 10: Paycheck Protection program in twenty twenty and we created the

790
00:38:04,440 --> 00:38:07,840
Speaker 10: Open for Business Fund, roughly four hundred and twenty million

791
00:38:07,880 --> 00:38:12,560
Speaker 10: dollars that we deployed through flexible grants back into communities

792
00:38:12,640 --> 00:38:16,040
Speaker 10: to serve small businesses. And if you fast forward then

793
00:38:16,239 --> 00:38:20,960
Speaker 10: four years now, those more than two hundred nonprofits and

794
00:38:21,040 --> 00:38:25,360
Speaker 10: community development financial institutions are telling us they were able

795
00:38:25,440 --> 00:38:29,320
Speaker 10: to serve over three hundred and thirty six thousand small

796
00:38:29,360 --> 00:38:33,640
Speaker 10: businesses and help create and preserve more than four hundred

797
00:38:33,680 --> 00:38:36,640
Speaker 10: and sixty one thousand jobs in our local communities.

798
00:38:37,200 --> 00:38:39,520
Speaker 3: Wow, what does a flexible grant mean?

799
00:38:41,480 --> 00:38:45,040
Speaker 10: So, it means that the nonprofits can use the money

800
00:38:45,480 --> 00:38:48,319
Speaker 10: in the best way that they need to use it. So,

801
00:38:48,560 --> 00:38:51,319
Speaker 10: for example, they may use it to shore up loan

802
00:38:51,400 --> 00:38:54,520
Speaker 10: loss reserves so that they can expand their credit box.

803
00:38:54,920 --> 00:38:58,080
Speaker 10: They may use it to create new products such as

804
00:38:58,960 --> 00:39:05,160
Speaker 10: no and low cost rants or loans that don't require

805
00:39:05,200 --> 00:39:10,600
Speaker 10: collateral or loan modification products. So they really could use

806
00:39:10,640 --> 00:39:14,920
Speaker 10: it very flexibly. And I would say the what we

807
00:39:15,200 --> 00:39:20,000
Speaker 10: found successful was in addition to making that capital affordable

808
00:39:20,440 --> 00:39:24,759
Speaker 10: for small businesses, where we had caps interest rates of

809
00:39:24,840 --> 00:39:30,920
Speaker 10: three percent, they paired it with technical assistance. Those nonprofits

810
00:39:30,920 --> 00:39:36,400
Speaker 10: in CDFI's delivered one point one million hours of technical assistance,

811
00:39:36,920 --> 00:39:38,960
Speaker 10: more than half of that in a one on one

812
00:39:39,040 --> 00:39:43,400
Speaker 10: format to those small businesses, many of whom were having

813
00:39:43,480 --> 00:39:47,520
Speaker 10: to learn how to pivot from providing in person services

814
00:39:47,560 --> 00:39:51,360
Speaker 10: to something virtual, and so it really was instrumental in

815
00:39:52,200 --> 00:39:56,480
Speaker 10: being able to help these small businesses through these turbula.

816
00:39:56,760 --> 00:39:59,280
Speaker 3: I'm wondering, you know, we're Bloomberg. We care about metrics.

817
00:39:59,320 --> 00:40:02,440
Speaker 3: We follow the money and how it's doing, even when

818
00:40:02,480 --> 00:40:06,160
Speaker 3: it comes to especially when it comes to philanthropy. If

819
00:40:06,160 --> 00:40:07,880
Speaker 3: you could, I mean, is it fair to call this philanthropy?

820
00:40:07,920 --> 00:40:08,960
Speaker 3: Is that how you would describe it?

821
00:40:10,480 --> 00:40:12,120
Speaker 10: Yes, okay's philanthropy.

822
00:40:12,719 --> 00:40:16,120
Speaker 3: So but but sorry, I just we'd only have a

823
00:40:16,160 --> 00:40:17,319
Speaker 3: little bit of time, and I want to make sure

824
00:40:17,480 --> 00:40:18,640
Speaker 3: we get to this. I want to know about the

825
00:40:18,680 --> 00:40:21,120
Speaker 3: metrics when you follow it over the last four years,

826
00:40:21,520 --> 00:40:24,920
Speaker 3: how it's been deployed, and how it's helped these businesses.

827
00:40:24,960 --> 00:40:25,880
Speaker 3: Has it been effective?

828
00:40:27,520 --> 00:40:32,200
Speaker 10: It absolutely has been effective. The great thing was because

829
00:40:32,280 --> 00:40:35,120
Speaker 10: the nonprofits had this flexible capital and they could shore

830
00:40:35,200 --> 00:40:38,040
Speaker 10: up their balance sheets, they were able to leverage our

831
00:40:38,160 --> 00:40:42,319
Speaker 10: funding to attract other public and private investment to the

832
00:40:42,360 --> 00:40:46,319
Speaker 10: tune of two point one billion dollars. And the other

833
00:40:46,440 --> 00:40:50,480
Speaker 10: great thing is we saw from the data that the

834
00:40:50,520 --> 00:40:53,480
Speaker 10: most in need small businesses were able to stay open.

835
00:40:53,640 --> 00:40:57,640
Speaker 10: So seventy nine percent of the small business owners identified

836
00:40:57,760 --> 00:41:02,160
Speaker 10: as racially or ethnically di verse, seventy two percent identified

837
00:41:02,200 --> 00:41:04,839
Speaker 10: as low and moderate income, and fifty three percent were

838
00:41:04,840 --> 00:41:06,160
Speaker 10: women owned small businesses.

839
00:41:06,920 --> 00:41:08,560
Speaker 1: You know, one of the things I wanted to ask you,

840
00:41:08,760 --> 00:41:11,160
Speaker 1: and like a lot of financial institutions or a lot

841
00:41:11,160 --> 00:41:13,040
Speaker 1: of companies, like they go through cycles and they have

842
00:41:13,080 --> 00:41:16,319
Speaker 1: their ups and downs, and certainly Wells Fargo's has been

843
00:41:16,520 --> 00:41:19,600
Speaker 1: well documented over the past years and prior to Charlie

844
00:41:19,640 --> 00:41:22,080
Speaker 1: Sharf taking over. But one of the things he said

845
00:41:22,200 --> 00:41:23,480
Speaker 1: is that he wanted to, you know, clean up the

846
00:41:23,520 --> 00:41:26,960
Speaker 1: banks many messes. Having said that, what have you guys

847
00:41:27,160 --> 00:41:30,560
Speaker 1: learned as an institution, how have you evolved in terms

848
00:41:30,600 --> 00:41:35,200
Speaker 1: of reaching out to, you know, those who need to

849
00:41:35,239 --> 00:41:37,080
Speaker 1: be banked and are underbanked.

850
00:41:39,239 --> 00:41:42,080
Speaker 10: Well, I think the Open for Business Fund is a

851
00:41:42,160 --> 00:41:47,080
Speaker 10: perfect example of how we are investing in the entire ecosystem.

852
00:41:47,600 --> 00:41:50,640
Speaker 10: So when we think about different small businesses, it's important

853
00:41:50,680 --> 00:41:53,680
Speaker 10: to meet them where they are. And some may need

854
00:41:53,760 --> 00:41:57,680
Speaker 10: a micro loan, Some may need a loan from a

855
00:41:57,960 --> 00:42:02,840
Speaker 10: community development financial Institution or CDFI. Others may be ready

856
00:42:02,880 --> 00:42:07,400
Speaker 10: to engage with a more traditional bank. But our investment

857
00:42:07,480 --> 00:42:11,120
Speaker 10: spans all of those dimensions so that we can best

858
00:42:11,320 --> 00:42:13,279
Speaker 10: meet small businesses where they are.

859
00:42:13,640 --> 00:42:16,560
Speaker 1: How do you make sure you reach enough? Like, what's

860
00:42:16,600 --> 00:42:18,000
Speaker 1: the metrics for measuring that?

861
00:42:20,040 --> 00:42:26,440
Speaker 10: Well, we actually exceeded our expectations and going into this significantly.

862
00:42:27,000 --> 00:42:30,280
Speaker 10: So we knew that we wanted it to be national,

863
00:42:30,400 --> 00:42:32,480
Speaker 10: We wanted to be able to touch small businesses in

864
00:42:32,600 --> 00:42:36,239
Speaker 10: every state. We knew that we wanted to be able

865
00:42:36,280 --> 00:42:39,759
Speaker 10: to reach over one hundred and fifty thousand businesses, but

866
00:42:39,800 --> 00:42:42,759
Speaker 10: we exceeded those expectations and being able to reach over

867
00:42:42,800 --> 00:42:47,239
Speaker 10: three hundred and thirty six thousand. So we are definitely

868
00:42:47,280 --> 00:42:51,320
Speaker 10: looking at ways that philanthropy can be a catalyst for

869
00:42:52,040 --> 00:42:56,240
Speaker 10: attracting other investment as well as creating and preserving jobs

870
00:42:56,239 --> 00:42:57,240
Speaker 10: in the local economy.

871
00:42:57,600 --> 00:43:01,160
Speaker 3: Does it make you think about out making this an

872
00:43:01,200 --> 00:43:05,719
Speaker 3: ongoing program rather than a one off program.

873
00:43:06,200 --> 00:43:11,160
Speaker 10: Definitely, we are focused on what's next right now. In fact,

874
00:43:11,280 --> 00:43:14,280
Speaker 10: Round three of the Open for Business Fund is still

875
00:43:14,320 --> 00:43:18,799
Speaker 10: ongoing and it's about asset ownership. We recognize that as

876
00:43:18,840 --> 00:43:22,880
Speaker 10: small businesses are really trying to scale to grow, asset

877
00:43:23,320 --> 00:43:26,120
Speaker 10: acquisition is going to be really important, whether that is

878
00:43:26,560 --> 00:43:33,080
Speaker 10: commercial property or equipment or technological infrastructure for their businesses.

879
00:43:33,520 --> 00:43:36,440
Speaker 10: And so that piece is ongoing and it will continue

880
00:43:36,480 --> 00:43:40,200
Speaker 10: through mid next year, and then hopefully soon we will

881
00:43:40,239 --> 00:43:44,600
Speaker 10: have some new news to share about what's next in

882
00:43:45,160 --> 00:43:46,840
Speaker 10: following the Open for Business Fund.

883
00:43:47,040 --> 00:43:48,799
Speaker 1: Well, we look forward to that and checking in with

884
00:43:48,840 --> 00:43:52,359
Speaker 1: you because it's certainly programs like this that help out

885
00:43:52,360 --> 00:43:55,280
Speaker 1: communities and those that, like we say, that are unbanked

886
00:43:55,360 --> 00:43:58,120
Speaker 1: or underbanked. I always important to have Darline. Thank you

887
00:43:58,120 --> 00:44:00,200
Speaker 1: so much, really appreciate you checking in with us on

888
00:44:00,239 --> 00:44:03,240
Speaker 1: this Monday. Darling Goyn's head of philanthropy and community impact

889
00:44:03,280 --> 00:44:06,040
Speaker 1: over at Wells Fargo, joining us from San Francisco.

890
00:44:06,719 --> 00:44:10,800
Speaker 2: Bromack a journal.

891
00:44:11,840 --> 00:44:12,799
Speaker 1: How about you let me drive?

892
00:44:13,080 --> 00:44:18,640
Speaker 3: Oh no, no, no, no, honey, please, I'll do the riding gravels.

893
00:44:18,960 --> 00:44:23,480
Speaker 1: Let's wat, I want to drive. It's a good question.

894
00:44:23,960 --> 00:44:28,080
Speaker 5: Good, this is good.

895
00:44:28,200 --> 00:44:32,080
Speaker 2: Drive to the globe. Do for me? Well, Young Don

896
00:44:32,480 --> 00:44:33,720
Speaker 2: on Bloomberg Radio.

897
00:44:33,920 --> 00:44:36,400
Speaker 1: All right, everybody, just about eighteen minutes to go until

898
00:44:36,440 --> 00:44:38,440
Speaker 1: we wrap up the trading day, the first trading day

899
00:44:38,480 --> 00:44:40,600
Speaker 1: of the week, and because things have kind of it's

900
00:44:40,640 --> 00:44:44,000
Speaker 1: not like a week ago last Monday, when everybody was

901
00:44:44,120 --> 00:44:46,600
Speaker 1: using the word panic and concerned about what was going

902
00:44:46,600 --> 00:44:49,399
Speaker 1: on in the financial markets. It's a much calmer day

903
00:44:49,800 --> 00:44:52,440
Speaker 1: and so we wanted to do something a little bit differently.

904
00:44:52,760 --> 00:44:54,520
Speaker 3: Yeah, we're joined by the CEO of a firm that

905
00:44:54,520 --> 00:44:58,240
Speaker 3: says it's building AI to help make traders faster. Toggle

906
00:44:58,239 --> 00:45:00,879
Speaker 3: AI is the company. It's backed by v Ease as

907
00:45:00,920 --> 00:45:03,680
Speaker 3: well as Stint Drunken Miller and Thomas Petterfee. We should

908
00:45:03,680 --> 00:45:07,120
Speaker 3: remind you that Thomas Petterfee is the chairman of Interactive Brokers,

909
00:45:07,120 --> 00:45:10,359
Speaker 3: the sponsor of the Interactive Brokers studio. We got with us.

910
00:45:10,719 --> 00:45:14,160
Speaker 3: Yon Silodgi, CEO of toggle AI, He joins us here

911
00:45:14,440 --> 00:45:16,640
Speaker 3: in the studio. Yon, good to have you with us.

912
00:45:16,680 --> 00:45:17,839
Speaker 3: How are you very well?

913
00:45:17,880 --> 00:45:18,880
Speaker 6: Thank you, thanks for having me.

914
00:45:19,040 --> 00:45:22,080
Speaker 3: So you're doing something pretty cool over at toggle you're

915
00:45:22,520 --> 00:45:25,120
Speaker 3: a apart from you know, doing your doing other Among

916
00:45:25,160 --> 00:45:27,040
Speaker 3: the things that you're doing is you're working with Microsoft

917
00:45:27,080 --> 00:45:31,160
Speaker 3: to build out this chat GPT like LM for traders

918
00:45:31,160 --> 00:45:33,240
Speaker 3: to use. What's the vision here.

919
00:45:34,200 --> 00:45:37,640
Speaker 6: The vision for the team that really comes from the

920
00:45:37,719 --> 00:45:41,600
Speaker 6: hedgefront background is to create a better platform to take

921
00:45:41,600 --> 00:45:44,239
Speaker 6: advantage of all the data that we have available to

922
00:45:44,239 --> 00:45:45,320
Speaker 6: make investing decisions.

923
00:45:45,360 --> 00:45:45,520
Speaker 1: Right.

924
00:45:45,600 --> 00:45:49,080
Speaker 6: You have research reports, you have time series data. There's

925
00:45:49,120 --> 00:45:51,080
Speaker 6: just a lot going on every day. For example, just

926
00:45:51,120 --> 00:45:54,200
Speaker 6: on Bloomberg, you have eighteen headlines per second. It's impossible

927
00:45:54,760 --> 00:45:57,680
Speaker 6: for humans to absorb all of that and processant. So

928
00:45:57,760 --> 00:46:02,640
Speaker 6: we've built a platform that allows a hedge fund investor

929
00:46:03,040 --> 00:46:06,359
Speaker 6: to connect the dots faster, understand how things that are

930
00:46:06,400 --> 00:46:08,920
Speaker 6: moving are impacting different assets they care about.

931
00:46:08,920 --> 00:46:11,239
Speaker 1: Based on what data. What's the data that goes into

932
00:46:11,280 --> 00:46:11,760
Speaker 1: this model.

933
00:46:11,800 --> 00:46:14,200
Speaker 6: So we have a range of different data sources. We

934
00:46:14,239 --> 00:46:17,839
Speaker 6: have data that come from company fundamentals. We get data

935
00:46:17,880 --> 00:46:20,600
Speaker 6: from the Federal Reserve. We get data from example, from

936
00:46:20,840 --> 00:46:25,319
Speaker 6: various research reports, company filings, company presentations, and so on.

937
00:46:25,719 --> 00:46:28,279
Speaker 6: It's extremely wide ranging, and that's partly the point, because

938
00:46:28,320 --> 00:46:30,560
Speaker 6: we think that with the help of AI, you're now

939
00:46:30,600 --> 00:46:32,960
Speaker 6: able to look across all of these different types of

940
00:46:33,040 --> 00:46:33,640
Speaker 6: data points.

941
00:46:34,040 --> 00:46:37,239
Speaker 3: So you would essentially create or the firms that you

942
00:46:37,360 --> 00:46:42,720
Speaker 3: sell the tool too, would create their own custom llms

943
00:46:42,719 --> 00:46:46,120
Speaker 3: for whatever trades they're working on, right, because they wouldn't

944
00:46:46,160 --> 00:46:50,040
Speaker 3: have the same inputs, because then it just becomes a

945
00:46:50,080 --> 00:46:54,120
Speaker 3: commodity whereas everybody has where everybody has the same analysis

946
00:46:54,120 --> 00:46:54,840
Speaker 3: of the information.

947
00:46:55,640 --> 00:46:59,120
Speaker 6: Actually, maybe the way I would explain this is that

948
00:46:59,160 --> 00:47:01,640
Speaker 6: you don't have to your own custom LLM. We use

949
00:47:01,800 --> 00:47:06,240
Speaker 6: llms primarily as a way to help you navigate our system,

950
00:47:06,280 --> 00:47:08,120
Speaker 6: so that instead of you having to press a button,

951
00:47:08,239 --> 00:47:10,640
Speaker 6: you can say, I would like to know whether or

952
00:47:10,680 --> 00:47:14,000
Speaker 6: not fast food restaurants do poorly when gasoline prices rise

953
00:47:14,080 --> 00:47:16,480
Speaker 6: ten percent. It's the kind of instruction that you might

954
00:47:16,480 --> 00:47:18,719
Speaker 6: give to an analyst, and then you rely on the

955
00:47:18,760 --> 00:47:21,680
Speaker 6: system to go and do the analysis, fetch the data,

956
00:47:22,120 --> 00:47:23,680
Speaker 6: and then give you the answer, and then you can

957
00:47:23,680 --> 00:47:25,520
Speaker 6: have a little bit of a back and forth that way.

958
00:47:26,000 --> 00:47:29,560
Speaker 6: But it relies on our proprietary knowledge graph to be

959
00:47:29,640 --> 00:47:32,560
Speaker 6: able to connect these dots and say like, oh, actually,

960
00:47:32,560 --> 00:47:35,560
Speaker 6: for gasoline prices, I'll use the first future and for

961
00:47:35,760 --> 00:47:39,040
Speaker 6: fast food restaurants, I know which set of tickers I require.

962
00:47:39,239 --> 00:47:40,840
Speaker 1: Yeah, And what would you have done though? During the

963
00:47:40,880 --> 00:47:46,080
Speaker 1: pandemic when of the meme stocks which most would argue

964
00:47:46,360 --> 00:47:50,680
Speaker 1: weren't necessarily trading on fundamentals, and it was the retail

965
00:47:50,719 --> 00:47:53,719
Speaker 1: investor out in a big way. Like, how would you

966
00:47:54,440 --> 00:47:56,960
Speaker 1: what data would you look at, how would you anticipate

967
00:47:57,040 --> 00:48:00,080
Speaker 1: or how would you analyze something like that?

968
00:48:00,080 --> 00:48:04,719
Speaker 6: That's a very different phenomenon to analyze because it obviously

969
00:48:04,760 --> 00:48:07,239
Speaker 6: is something that hadn't really happened before, not in that

970
00:48:07,320 --> 00:48:10,319
Speaker 6: kind of way. Right, the retail investor was really seen

971
00:48:10,360 --> 00:48:11,719
Speaker 6: to have a lot more power than I think people

972
00:48:11,760 --> 00:48:14,440
Speaker 6: had anticipated. So I would say that in that case,

973
00:48:15,000 --> 00:48:17,520
Speaker 6: a system like ours would have relied a lot more

974
00:48:18,000 --> 00:48:19,920
Speaker 6: on some of the sentiment data and some of the

975
00:48:19,960 --> 00:48:25,080
Speaker 6: news as opposed to, for example, price data from ten

976
00:48:25,160 --> 00:48:27,200
Speaker 6: years ago in so on. So if there's an event

977
00:48:27,280 --> 00:48:30,640
Speaker 6: that doesn't have a lot of repeated occurrences, you need

978
00:48:30,680 --> 00:48:35,040
Speaker 6: to be much more focused on things that are happening now,

979
00:48:35,080 --> 00:48:36,560
Speaker 6: on what people are talking about in.

980
00:48:36,520 --> 00:48:39,400
Speaker 1: Song, so like social momentum, social velocity for example.

981
00:48:39,480 --> 00:48:42,200
Speaker 6: Yeah, those would be the types of analysis that you'd

982
00:48:42,239 --> 00:48:45,960
Speaker 6: be looking at now that obviously can be a lot

983
00:48:45,960 --> 00:48:48,560
Speaker 6: more wrong because again you don't have a lot of

984
00:48:48,600 --> 00:48:50,920
Speaker 6: prior experiences to say like, oh, I can see what

985
00:48:51,000 --> 00:48:51,799
Speaker 6: the connection is.

986
00:48:51,760 --> 00:48:56,520
Speaker 3: Here, what is the LLM product different from other products

987
00:48:56,560 --> 00:48:59,800
Speaker 3: that you're building as a company.

988
00:48:59,560 --> 00:49:04,880
Speaker 6: So really we use foundational large language models, including the

989
00:49:04,880 --> 00:49:07,680
Speaker 6: ones from open AI and others in order to do

990
00:49:07,719 --> 00:49:12,440
Speaker 6: two things. We use them to extract information from a

991
00:49:12,520 --> 00:49:15,680
Speaker 6: variety of different articles, and then we use them as

992
00:49:15,680 --> 00:49:17,640
Speaker 6: a way for you to be able to use the

993
00:49:17,840 --> 00:49:22,640
Speaker 6: analytics engines that are available to all of our users.

994
00:49:23,480 --> 00:49:27,920
Speaker 6: The llms themselves are not different from what others might

995
00:49:27,960 --> 00:49:29,880
Speaker 6: be using, but how we're using them is very different.

996
00:49:30,040 --> 00:49:33,960
Speaker 3: How do you you mentioned, as an example, giving an

997
00:49:34,040 --> 00:49:37,520
Speaker 3: LM a prompt that you would in the past maybe

998
00:49:37,600 --> 00:49:41,040
Speaker 3: ask an analyst to do. Who are the winners and

999
00:49:41,080 --> 00:49:45,000
Speaker 3: losers when it comes to jobs here? What's the adjustment here?

1000
00:49:45,200 --> 00:49:47,960
Speaker 3: Does the hedge fund not need that analyst anymore because

1001
00:49:48,000 --> 00:49:49,160
Speaker 3: it has this tool?

1002
00:49:49,640 --> 00:49:52,279
Speaker 6: So in this sort of we're currently only opening this

1003
00:49:52,440 --> 00:49:54,319
Speaker 6: up to a small number of hedgephons to kind of

1004
00:49:54,920 --> 00:49:57,640
Speaker 6: test the constant. But what we have seen immediately is

1005
00:49:57,680 --> 00:50:01,959
Speaker 6: that actually the analysts benefit because this is the sort

1006
00:50:01,960 --> 00:50:04,160
Speaker 6: of thing that they would have had to do. But

1007
00:50:04,239 --> 00:50:07,440
Speaker 6: where they really shine is trying to be forward thinking.

1008
00:50:07,440 --> 00:50:09,879
Speaker 6: So yes, you have to do the analysis to understand

1009
00:50:10,360 --> 00:50:13,200
Speaker 6: what has happened in the past and have some understanding

1010
00:50:13,239 --> 00:50:16,799
Speaker 6: of the sensitivities. But building a spreadsheet would have taken

1011
00:50:16,840 --> 00:50:19,439
Speaker 6: two or three hours of your time during which maybe

1012
00:50:19,440 --> 00:50:21,759
Speaker 6: you would have been able to do other things. So

1013
00:50:21,880 --> 00:50:24,920
Speaker 6: I think of this as us giving the superpowers to

1014
00:50:25,040 --> 00:50:27,520
Speaker 6: these users rather than replacing.

1015
00:50:27,080 --> 00:50:29,919
Speaker 3: Them, so augmenting their work one percent.

1016
00:50:30,280 --> 00:50:33,160
Speaker 1: But you know, I who's think about headphone guys, men

1017
00:50:33,520 --> 00:50:38,120
Speaker 1: or women or whomever are looking at things differently and

1018
00:50:38,280 --> 00:50:42,800
Speaker 1: have their own special algorithms that lets them find pockets

1019
00:50:42,800 --> 00:50:47,759
Speaker 1: of opportunity. In financial markets, if you're creating I'm just

1020
00:50:47,840 --> 00:50:51,480
Speaker 1: curious if that ultimately you're going to be creating data

1021
00:50:52,320 --> 00:50:56,560
Speaker 1: for more people to find those opportunities. Do those opportunities

1022
00:50:56,640 --> 00:50:58,600
Speaker 1: kind of go away? You know what I'm saying that

1023
00:50:58,640 --> 00:51:01,520
Speaker 1: if a lot of people are chasing and getting access,

1024
00:51:01,640 --> 00:51:07,160
Speaker 1: yay democratization of really sophisticated investing strategies, But the more

1025
00:51:07,200 --> 00:51:09,440
Speaker 1: they do it and get that advantage, does that advantage

1026
00:51:09,520 --> 00:51:10,120
Speaker 1: kind of go away?

1027
00:51:10,719 --> 00:51:13,680
Speaker 6: I think this is a great question and one that

1028
00:51:13,760 --> 00:51:18,080
Speaker 6: we have to think through very carefully. But I would

1029
00:51:18,160 --> 00:51:20,920
Speaker 6: give you a comparison, for example, with the Bloomberg terminal.

1030
00:51:21,960 --> 00:51:25,440
Speaker 6: By now, everybody in investing industry has one right and

1031
00:51:25,520 --> 00:51:28,160
Speaker 6: yet there are still opportunities that exist, and you would think, okay,

1032
00:51:28,160 --> 00:51:31,200
Speaker 6: but if everybody has access to the same data. Why

1033
00:51:31,239 --> 00:51:34,040
Speaker 6: do we still find these dislocations? Why do people still

1034
00:51:34,040 --> 00:51:41,280
Speaker 6: disagree similarly with the Togo terminal. We are looking specifically

1035
00:51:41,840 --> 00:51:45,120
Speaker 6: at how questions are being asked, and not everybody has

1036
00:51:45,160 --> 00:51:48,120
Speaker 6: the same risk capitite, Not everybody has the same investment

1037
00:51:48,160 --> 00:51:51,279
Speaker 6: horizon not asked the same questions, so the conclusions they

1038
00:51:51,360 --> 00:51:52,960
Speaker 6: arrive at could be very different.

1039
00:51:53,120 --> 00:51:54,719
Speaker 1: All right, got to ask you all and got about

1040
00:51:54,719 --> 00:51:57,080
Speaker 1: thirty seconds. You hold the record for the fastest Harvard

1041
00:51:57,080 --> 00:51:59,640
Speaker 1: Economics PhD. How quickly did you get it? Two and

1042
00:51:59,680 --> 00:52:01,400
Speaker 1: a half two and a half years? What does it

1043
00:52:01,440 --> 00:52:02,080
Speaker 1: normally take?

1044
00:52:02,480 --> 00:52:03,759
Speaker 6: I think it's about five.

1045
00:52:04,280 --> 00:52:06,320
Speaker 1: How'd you do that? Did you use chat EPT?

1046
00:52:07,320 --> 00:52:09,000
Speaker 6: I wish I had it, probably could have cut that

1047
00:52:09,040 --> 00:52:10,600
Speaker 6: down even more to the.

1048
00:52:10,480 --> 00:52:16,239
Speaker 1: Thesis or I believe it's chat PhD for very very

1049
00:52:16,239 --> 00:52:19,719
Speaker 1: cool stuff. Fun let us know how things go, I

1050
00:52:19,719 --> 00:52:22,759
Speaker 1: mean performance wise. I mean you guys have back tested it.

1051
00:52:22,880 --> 00:52:25,120
Speaker 1: You did doing the strategy, doesn't we tested.

1052
00:52:25,200 --> 00:52:27,600
Speaker 6: We back tested every day basically for all the insights

1053
00:52:27,600 --> 00:52:29,680
Speaker 6: and so on, and so there's a lot of curation

1054
00:52:29,760 --> 00:52:31,600
Speaker 6: that goes into that very.

1055
00:52:31,320 --> 00:52:31,879
Speaker 9: Very cool stuff.

1056
00:52:31,960 --> 00:52:34,520
Speaker 1: Yeah, thank you so much. Look forward to continuing this conversation.

1057
00:52:35,000 --> 00:52:37,879
Speaker 1: Jan Solgi He is chief executive officer of Toggle Ai.

1058
00:52:38,000 --> 00:52:40,360
Speaker 1: Joining us right here in our Bloomberg Interactive Broker Studio.

1059
00:52:40,640 --> 00:52:45,239
Speaker 2: This is the Bloomberg Business Week podcast of a Little Apple, Spotify,

1060
00:52:45,400 --> 00:52:49,120
Speaker 2: and anywhere else you get your podcast. Listen live weekday

1061
00:52:49,160 --> 00:52:52,760
Speaker 2: afternoons from two to five pm Eastern on Bloomberg dot com,

1062
00:52:52,800 --> 00:52:56,120
Speaker 2: the iHeartRadio app tune In, and the Bloomberg Business App.

1063
00:52:56,160 --> 00:52:59,120
Speaker 2: You can also watch us live every weekday on YouTube

1064
00:52:59,360 --> 00:53:01,360
Speaker 2: and oh my is on the Bloomberg terminal.

1065
00:53:08,040 --> 00:53:08,480
Speaker 8: Mm hmm