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

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Speaker 2: This is the Bloomberg Surveillance Podcast. Catch us live weekdays

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Speaker 2: at seven am Eastern on Apple car Player, Android Auto

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Speaker 2: with the Bloomberg Business app. Listen on demand wherever you

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Speaker 2: get your podcasts, or watch us live on YouTube.

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Speaker 3: Now, are Michael McKee an important conversation with the President

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Speaker 3: of the Boston Fed, Susan Collins.

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Speaker 4: I'm Michael McKee, the international economics and Policy correspondent for Bloomberg,

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Speaker 4: and we are at the Boston Federal Reserve Bank today.

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Speaker 5: They're holding their sixty eighth.

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Speaker 4: Annual Economics conference, and we're joined by the President of

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Speaker 4: the Boston Fed, Susan Collins. Thank you very much for

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Speaker 4: being with us today.

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Speaker 6: It's delighted to be here. Thanks for being at the

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Speaker 6: Boston Fed.

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Speaker 5: It's great. We tell everybody. It's a beautiful day. But

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Speaker 5: it's thirty nine degrees so winter is finally hit here.

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Speaker 6: The seasons are wonderful.

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Speaker 4: Oh, speaking of winter, December eighteenth, you have another FED meeting,

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Speaker 4: and at this point there seem to be some questions

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Speaker 4: about whether or not the Fed will be cutting rates again,

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Speaker 4: because this week we got some firm inflation news retail

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Speaker 4: sales were okay, but not particularly strong at this point.

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Speaker 5: Are you thinking that.

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Speaker 4: We should see a cut or is it better to

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Speaker 4: pause and wait?

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Speaker 6: So I think it's important to say there's no preset path.

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Speaker 6: I do see rates as still in the restrictive range,

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Speaker 6: which means that over time some amount of easing will

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Speaker 6: be appropriate. But you know, the economy is in a

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Speaker 6: very good place right now, and inflation's coming back down

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Speaker 6: to target. The labor markets are healthy, we're seeing solid growth.

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Speaker 5: The goal of policies.

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Speaker 6: Really to sustain that healthy set of conditions, recognizing you know,

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Speaker 6: there are risks on both sides, and so I think

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Speaker 6: we're well positioned. I certainly wouldn't take another ease in

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Speaker 6: December off the table, But again we're not in a

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Speaker 6: preset path, and so we'll have to look carefully at

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Speaker 6: the data and see what makes sense when we get

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Speaker 6: to December eighteenth.

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Speaker 4: Well, the data this week showed inflation a little bit

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Speaker 4: stronger than it had been in the CPI and the PPI,

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Speaker 4: and those who do the nerdy calculations for the PCE

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Speaker 4: say we're going to see.

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Speaker 5: The same thing.

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Speaker 4: Should you keep your foot on the brake a little

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Speaker 4: more then because inflation is not back down to your target.

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Speaker 6: So you know, I don't focus too much on any

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Speaker 6: one data point. I think it's really important to look holistically.

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Speaker 6: And when I do that, what I see is that,

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Speaker 6: first of all, inflation has come down significantly. I focus

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Speaker 6: on the you know, a couple of month averages, and

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Speaker 6: if you take food, energy, and in particular shelter out,

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Speaker 6: the rest of inflation has actually been in the range

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Speaker 6: consistent with the two percent, exactly what we'd like to see.

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Speaker 6: What's really still elevated as shelter and that is taking

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Speaker 6: time to come back down, and a lot of that

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Speaker 6: really reflects shocks from the past. I'm not seeing evidence

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Speaker 6: of new price pressures, and so I think it's important

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Speaker 6: to stay the course. But that analysis of the data

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Speaker 6: is part of why I thought it was really appropriate

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Speaker 6: for us to begin easing in September, and to be

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Speaker 6: in an environment where we are really looking over time methodically,

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Speaker 6: perhaps patiently, to be normalizing policy, to maintain those healthy

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Speaker 6: conditions that I talked about a moment ago.

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Speaker 5: Well, let's look at the other side of the mandate. Employment.

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Speaker 4: We had a very strong employment report, and then we

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Speaker 4: had a very weak employment report, granted, affected by hurricanes

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Speaker 4: and strikes.

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Speaker 5: So what's your judgment of.

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Speaker 4: Where the labor market is when you look holistically at

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Speaker 4: all of the labor data and when I.

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Speaker 6: Look at all of the data, and you're absolutely right,

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Speaker 6: there have been some stronger readings, there have been some

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Speaker 6: readings over time that were a bit weaker, and there are.

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Speaker 5: A lot of special factors.

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Speaker 6: So looking at averages over time, looking at the range

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Speaker 6: of information, what I see is a labor market that

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Speaker 6: looks similar to conditions that we've considered full employment, so

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Speaker 6: in terms of job openings and quit rates, and the

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Speaker 6: fact that wage growth has been coming down and given

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Speaker 6: the high productivity we've seen is consistent with the move

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Speaker 6: back down to two percent inflation and staying there. And

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Speaker 6: unemployment has stayed in a range that is near four

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Speaker 6: percent low by historical standards, so yes, higher than a

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Speaker 6: year ago. So all of that, to me says healthy

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Speaker 6: labor market conditions. Things to watch carefully and don't focus

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Speaker 6: too much on any one piece of data.

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Speaker 7: Have to look at the whole picture.

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Speaker 4: All right, healthy labor market. Inflation's coming down, even if

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Speaker 4: it's stalled a little bit, but it's in the twos

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Speaker 4: and the economy is stronger than people had forecasts. So

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Speaker 4: do you agree with Chairman Powell and saying there is

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Speaker 4: nothing telling you you have to cut rates very quickly?

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Speaker 6: So I think that I don't. I agree that I

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Speaker 6: don't see a big urgency. At the same time, I

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Speaker 6: do think that preserving those healthy conditions, right, I mean,

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Speaker 6: that's what our mandate really is from Congress. Price stability

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Speaker 6: and maximum employment sustained over time, not just at some

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Speaker 6: point in time. And so as I said before, I

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Speaker 6: do see financial the policy stances being in a restrictive

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Speaker 6: place and over time, normalizing that I think is going

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Speaker 6: to be important. But we're well positioned to be really

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Speaker 6: careful in assessing the data and making decisions about the pace,

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Speaker 6: about the timing, and so that you know, that's how

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Speaker 6: I think about that.

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Speaker 4: Let me ask about the elephant in the room, and

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Speaker 4: that is the new President elect of the United States.

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Speaker 4: His policies have not been fleshed out. Tremen Pol's made

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Speaker 4: it clear you don't know exactly what's going to happen.

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Speaker 4: But do you expect that something in whatever his fiscal

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Speaker 4: plans are will affect the economy and you will have

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Speaker 4: to take another look, say at what your economic projections

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Speaker 4: are and what the plot projections are for twenty twenty five.

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Speaker 6: Now, as we get information about the economy, certainly that

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Speaker 6: includes about fiscal policy. Of course, it's really important to

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Speaker 6: factor that in. And there are lots of things we

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Speaker 6: look at. Fiscal policies certainly one of them, but I

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Speaker 6: don't want to speculate on what policies that haven't been

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Speaker 6: enacted or implemented might look like.

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Speaker 4: Well, do you think that tariffs as an economic concept

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Speaker 4: add to inflation?

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Speaker 6: They can, And again we would have to see if

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Speaker 6: there are tariffs that are implemented, more about the specifics

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Speaker 6: and the dynamics for those.

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Speaker 4: Now, if there's a fiscal impulse in whatever the President

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Speaker 4: elect chooses to do, is the economy growing too fast

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Speaker 4: for that right now? Would that be a danger a worry?

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Speaker 6: So again, I think there are lots of things that

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Speaker 6: determine how the economy evolves and grows over time. Fiscal

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Speaker 6: policy is certainly one of them and certainly does have

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Speaker 6: an impact on that, and we'd have to factor that

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Speaker 6: in and look through that. You know, I do think,

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Speaker 6: and Chair Powell has also said this that you know,

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Speaker 6: fiscal policy at the moment is on a path that's

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Speaker 6: not sustainable. But again, we when we make our policy

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Speaker 6: decisions to focus on our mandate from Congress, it's really

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Speaker 6: based on the data that we have available and the

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Speaker 6: analysis and the assessments that we can do on that basis.

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Speaker 4: As far as I know, President like Trump has never

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Speaker 4: threatened to fire you.

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Speaker 5: But I wanted to ask.

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Speaker 4: What is in your mind the relationship between the Federal

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Speaker 4: Reserve and the executive branch.

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Speaker 6: So what I would say is that the FED is

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Speaker 6: structured by Congress as an independent body, and that that

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Speaker 6: is important in terms of the ability for us to

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Speaker 6: do our job well. There's a lot of analysis that

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Speaker 6: shows that independent central banks are more effective at keeping

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Speaker 6: inflation low and stable, and we have really seen how

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Speaker 6: important it is to keep inflation low and stable in

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Speaker 6: terms of the impact the higher prices past inflation have had.

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Speaker 6: And so I think that is a very good structure

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Speaker 6: to enable us to do our jobs well.

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Speaker 4: So the things that come across social media basically just

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Speaker 4: noise in the background to you as a policymaker.

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Speaker 6: I am very focused on doing my job and there

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Speaker 6: is more than enough to keep me very focused and

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Speaker 6: very busy now.

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Speaker 5: The people, well you can't see them.

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Speaker 4: I could see them out there, all the traders on

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Speaker 4: their knees going tell us when you're going to.

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Speaker 5: Do this sort of thing.

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Speaker 4: Can we basically say, because of the potential changes that

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Speaker 4: are coming and the data that we have seen, that

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Speaker 4: the dot plot for twenty twenty five and the SEP

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Speaker 4: for twenty twenty five, those are kind of out the

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Speaker 4: window now and we should really wait until December or

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Speaker 4: even later to get a good idea of where you

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Speaker 4: think you're going to be and the economy is going

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Speaker 4: to be.

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Speaker 6: All things I think always evolve, and so in about

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Speaker 6: a month or so, we will have a new SEP

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Speaker 6: and information from all of the policymakers about what they think.

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Speaker 6: And so I think it's always true that you know,

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Speaker 6: in the middle of the SEP information you don't want

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Speaker 6: to take too much from what might have been written down,

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Speaker 6: penciled in. I would say a number of weeks earlier,

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Speaker 6: a lot of the data evolves.

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Speaker 4: What are the people in these tall buildings around us,

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Speaker 4: all the corporate leaders in your district telling you about

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Speaker 4: how they see the economy going forward and their plans.

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Speaker 6: Yeah, and I appreciate you asking about that. I think

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Speaker 6: one of the really important things that I do, that

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Speaker 6: my colleagues do is talk to people across the economy

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Speaker 6: in lots of different sectors. So being out and about

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Speaker 6: throughout New England and what I'm hearing is pretty consistent

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Speaker 6: with what I said at the outset that people are

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Speaker 6: cautiously optimistic. They see an economy that seems resilient. Labor

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Speaker 6: markets have moved into much more normal conditions relative to

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Speaker 6: the unsustainable, more overheated conditions from a year or more ago,

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Speaker 6: and the price pressures really have abated considerably. So that's

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Speaker 6: all very consistent. But of course, you know, the aggregate

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Speaker 6: data masks a range of different specifis across individual firms

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Speaker 6: and sectors and regions, and it's really I think helpful

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Speaker 6: to hear all of that and pull the qualitative information

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Speaker 6: together with the statistics.

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Speaker 4: One last question at your conference. It's on financial technology

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Speaker 4: this year and the Boston Fed's been in the middle

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Speaker 4: of financial technology and just coincidentally, coming up at the

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Speaker 4: top of the hour, we have our Bloomberg Technology Show.

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Speaker 5: So let me ask you.

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Speaker 4: A lot of tech talk over the last five, six,

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Speaker 4: seven years has been tech talk.

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Speaker 5: How fast are we going to see some.

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Speaker 4: Sort of impact on the average person from new payment systems.

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Speaker 4: I realize you have fed now in place, but how

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Speaker 4: fast are people going to say, hey, this is something different?

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Speaker 5: And are we going to.

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Speaker 4: See any kind of digital currency adoption, whether it's private

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Speaker 4: or government in the next few years.

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Speaker 6: So, you know, the impacts of technology have many, many dimensions,

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Speaker 6: and I think we're already seeing some impacts in terms

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Speaker 6: of the roles that fintech are playing across our economy

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Speaker 6: in different ways. And the conference today and tomorrow is

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Speaker 6: intended to really bring experts together who have knowledge and

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Speaker 6: done analysis from different vantage points to see as we

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Speaker 6: put together the things we know, what are some of

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Speaker 6: the things we don't know and need to know better.

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Speaker 6: And so what we're really focusing on is a number

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Speaker 6: of different themes, including financial inclusion, what are some of

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Speaker 6: the implications of the innovations for access to financial services?

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Speaker 6: And then also what are some of the implications of

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Speaker 6: technological innovation for the transmission of monetary policy, for our

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Speaker 6: supervision and regulation of financial institutions, and also for financial stability.

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Speaker 6: So thinking about both the opportunities and the risks, and

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Speaker 6: I think we are already seeing some of those implications,

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Speaker 6: but it's still unfolding. It's complicated, and it's moving pretty quickly.

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Speaker 6: So that's what we're trying to better understand. And again

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Speaker 6: we're delighted that you're all here while we're in the

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Speaker 6: midst of a conference on an important topic.

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Speaker 5: Well, thank you for having us.

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Speaker 4: Susan Collins, the President of the Federal Reserve Bank of Boston.

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Speaker 5: We'll send it back to you now.

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Speaker 8: Hi, Michael McKee, thank you very much. Bloomberg's Michael McKey

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Speaker 8: speaking with Boston FED President Susan Collins here in Boston.

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Speaker 2: You're listening to the Bloomberg Surveillance podcast. Catch us Live

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Speaker 2: weekday afternoons from seven to ten am. Easter Listen on

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Speaker 2: Apple car Play and Android Auto with a Bloomberg Business app,

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Speaker 2: or watch us live on YouTube.

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Speaker 9: And commute this morning.

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Speaker 3: It's very important on radio on Apple car Play and

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Speaker 3: Andrew Auto to talk to the number one chart guy

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Speaker 3: in the world.

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Speaker 9: It really works on radio, maybe works better.

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Speaker 3: On YouTube, but we're going to be chart free this

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Speaker 3: morn with Uri and Timmer. The title is Director Global

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Speaker 3: Macro at Fidelity Management, and you can see the miracle

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Speaker 3: of this work on LinkedIn, where he's very very visible.

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Speaker 3: Urine Timmer joins us now as he gets ready for

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Speaker 3: the charts of the weekend. How do you amend your charts?

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Speaker 3: When there was a news bulletin yesterday, the Bloomberg headline

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Speaker 3: that there's seven trillion dollars in cash laying out there,

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Speaker 3: how does that affect Will dan Off? How does that

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Speaker 3: affect Uri and Timmer?

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Speaker 10: Well, so that's that's cash shitting in money market accounts.

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Speaker 10: Of course, we are the leading provider of money market

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Speaker 10: so we're intimately involved with that with that cash. But

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Speaker 10: my sense always has been that a lot of that

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Speaker 10: cash did not flee the stock market in a flight

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Speaker 10: to quality, which is kind of typically what you would expect,

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Speaker 10: and then when when people feel more comfortable, they bring

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Speaker 10: the cash back in. In this case, the cash came

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Speaker 10: out of the banks during the regional bank crisis, you

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Speaker 10: know a few years ago, when banks, you know, still

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Speaker 10: are paying half a percent on deposits, and they were then,

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Speaker 10: and the Fed was raising rates and providing alternatives. So

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Speaker 10: I don't quite see this as a powder keg of

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Speaker 10: cash waiting to chase stocks. And the metric I look

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Speaker 10: at is money market fund access assets as a percentage

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Speaker 10: of the market cap in the stock market, and there

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Speaker 10: it's more it's more normal, it's more normal. So that

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Speaker 10: money came out of the banks, my senses eventually will

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Speaker 10: go back into banks, but not necessarily stock.

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Speaker 3: We need to go into the crown jewel secrets of fidelity.

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Speaker 3: And Abby, thank you so much for listening this morning

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Speaker 3: and giving us access to mister Timmer.

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Speaker 9: I'm going to cut to the chase.

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Speaker 3: What's the elasticity I've yield in money market flows? If

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Speaker 3: yields come down two decimal points or one decimal point

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Speaker 3: or a big figure, when does Paul get out of

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Speaker 3: his money market funds?

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Speaker 10: Well, I think the FED is not going to cut

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Speaker 10: as much as many people have thought and continue to think.

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Speaker 10: So maybe it goes to four, it's at four and

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Speaker 10: five eighths. If that's the case, money market yields stay

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Speaker 10: around four or so, and I don't think that is

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Speaker 10: going to create a tsunami, you know, out of cash

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Speaker 10: into other assets, whether it's bombs or equities. So I

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Speaker 10: don't think we're going to have another sort of zerb

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Speaker 10: era where we go to zero or one and risk

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Speaker 10: premia in the bond market. Gets suppressed and then that

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Speaker 10: money flees into the risk market. So I don't think

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Speaker 10: we're going to get there, but it would be, it

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Speaker 10: would It would take you know, more than what we've

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Speaker 10: seen so far, for sure.

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Speaker 8: You're in What do you make of the move we've

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Speaker 8: seen in financial markets since the election, big move of stocks,

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Speaker 8: yields pushing higher, dollars, stronger, Bitcoin at ninety thousand per token,

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Speaker 8: What do you make of all that?

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Speaker 10: Yeah, so the markets are always in price discovery mode. Right,

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Speaker 10: Sometimes the new information comes in gradually, slowly, a company

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Speaker 10: reports earnings, the stock price adjusts, and sometimes the information

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Speaker 10: comes in all at once, as we had with the election. Right,

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Speaker 10: you can tell that people were sitting on their hands,

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Speaker 10: you know, it was supposedly too close to call, so

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Speaker 10: nothing really got done. And the market's brutally efficient in

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Speaker 10: discounting new information, and so on November sixth, it had

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Speaker 10: a lot of new information to discount, and that's what

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Speaker 10: it did. And that's what price discovery is. And so

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Speaker 10: the red wave trade right, small caps, less fed cuts

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Speaker 10: return of the term premium rotation into financials, energy industrial,

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Speaker 10: so broadening.

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Speaker 9: That is the trade.

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Speaker 10: And I think in twenty sixteen that trade was pretty

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Speaker 10: much sort of done by December, right, So it happens

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Speaker 10: very quickly. It's not like this is like the first

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Speaker 10: bat of the first inning, like it's done instantaneously.

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Speaker 3: You have portfolios of Fidelity that are over fifty percent

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Speaker 3: in their top ten stocks and they're very mag seventy.

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Speaker 9: Et cetera, et cetera. What do you see in your

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Speaker 9: chart work on the flows?

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Speaker 3: Luisja Motto would say, the distributions in and out of

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Speaker 3: MEG seven right now? Are we selling? Are we buying?

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Speaker 3: What are we doing well?

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Speaker 10: I think the good news for the MAG seven is

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Speaker 10: that they're not that expensive, right. I think I take

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Speaker 10: a broader brush. I look at the nifty to fifty,

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Speaker 10: the top fifty stocks, just because I have a data

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Speaker 10: sets all the way back to the sixties and eighteen

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Speaker 10: sixty and during the early seventies the original nifty to

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Speaker 10: fifty and the late nineties, the dot com era, those

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Speaker 10: fifty stocks were trading at twice the valuation multiple as

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Speaker 10: the bottom four fifty. Today, the top fifty are trading

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Speaker 10: at a twenty five percent premium. So you can't call

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Speaker 10: it a bubble if the valuation is not extreme. The

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Speaker 10: price movements have been extreme, but not the valuation. So

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Speaker 10: what we've seen over the past few months really since

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Speaker 10: the FED started cutting rates, is that this has been

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Speaker 10: a bullish broadening. So the market has broadened eighty percent

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Speaker 10: of stocks are above the two hundred moving average, but

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Speaker 10: it's not happening at the expense of the mega growers,

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Speaker 10: and in that sense, this cycle in a way has

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Speaker 10: kind of gone in reverse. It's like a Benjamin Button cycle,

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Speaker 10: where you know, usually a bull market starts very broad right,

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Speaker 10: because the junkie low price stocks that are obliterated in

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Speaker 10: the bear market come bouncing back, and then as the

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Speaker 10: cycle matures, it gets more narrow and the blue chips

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Speaker 10: are left standing at the end and you get those

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Speaker 10: breath divergences. This time, it's been the opposite. It started

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Speaker 10: with the Mac seven and then even during the rate

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Speaker 10: hiking cycle, those were the safe stocks to be in

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Speaker 10: because they were immune to the FED because they had

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Speaker 10: so much cash. And now they're holding their absolute performance.

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Speaker 10: But the market has broadened really since a year ago,

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Speaker 10: and so it's kind of the best that you can

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Speaker 10: hope for. Like are a more disorderly version of that

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Speaker 10: would be that the mag seven or the nifty to

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Speaker 10: fifty decline because money is moving from those stocks to

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Speaker 10: the broader market. Then the index, the S and P

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Speaker 10: would have trouble staying up just because of the weight

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Speaker 10: of those seven stocks. But we're not seeing that yet.

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Speaker 8: Your titles director of Global Macro. Where do you see

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Speaker 8: the US versus non US right now?

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Speaker 10: Well, so US, you know, excellence, you know has been

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Speaker 10: in place for a decade. And it's interesting, you know,

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Speaker 10: because we're always debating, you know, do we go outside

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Speaker 10: the US? I mean the US trading at twenty two x,

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Speaker 10: US is trading at fifteen PE, So the US is

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Speaker 10: sixty percent more expensive than the rest of the world,

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Speaker 10: whether it's em or EFA, you know, Japan, Europe. But

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Speaker 10: you need a catalyst, right, Evaluation is not alone. You

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Speaker 10: need relative earnings. Like if you go across the street

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Speaker 10: and you're in the halls of fidelity, there is price

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Speaker 10: follows earnings and relative price or relative performance follows relative earnings.

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Speaker 10: And so that is missing with between the US and

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Speaker 10: the rest of the world because US earnings continue to dominate.

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Speaker 3: Price follows earning. Sounds like Bettina Dalton nineteen eighty. Okay,

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Speaker 3: let's go there. I got an election. I got a

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Speaker 3: belief nominal GDP's gonna pop. Do you, within the research

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Speaker 3: of the fundamental animals at Fidelity and what you're doing

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Speaker 3: with charts, agree that nominal is gonna pop. Revenues are

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Speaker 3: gonna pop, and that's earnings will sustain.

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Speaker 10: Yes, So we are one year into an earning cycle.

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Speaker 10: Trailer earnings are up eight percent this year. Forward earnings

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Speaker 10: are off about twelve or so. The expectation is that

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Speaker 10: earnings will continue to grow next year. And if we

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Speaker 10: do get this nominal GDP pop and earnings are a

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Speaker 10: nominal thing, the earning cycle can continue. But the thing

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Speaker 10: I worry about is the return of the of the

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Speaker 10: fed model, you know, back in the green span in

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Speaker 10: days of the eighties. Bond yields kind of you know,

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Speaker 10: are causing indigestion again the rockvigilantes, And we've seen that

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Speaker 10: now repeatedly over the last few years, and I think

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Speaker 10: that is a risk for twenty twenty five.

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Speaker 9: You gonna be in New York soon, I will be please.

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Speaker 3: You got to come in because we got to talk

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Speaker 3: about Babson the global ranking.

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Speaker 10: They just got yes, And while while you have me

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Speaker 10: on the air, I don't know what your schedule is,

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Speaker 10: but I would be like to invite you to come

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Speaker 10: look at our chart room after the show.

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Speaker 9: Or it's like the Vatican, folks. You've got the golf

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Speaker 9: stream right, Oh, sure you can go over you're in.

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Speaker 9: I don't think I can do it. I think I

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Speaker 9: have to race to the airport.

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Speaker 3: Okay, I'm sorry, but Paul's got the golf stone, so

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Speaker 3: we'll do it next time.

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Speaker 11: You're in.

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Speaker 3: Timmor, thank you so much with Fidelity there, and we're

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Speaker 3: efforting a number of their managers as well to talk

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Speaker 3: about this spectacular year we've seen in the markets world.

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Speaker 2: This is the Bloomberg Surveillance Podcast. Listen live each weekday

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Speaker 2: starting at seven am Eastern on applecar Play and Android

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Speaker 2: Auto with the Bloomberg Business app. You can all so

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Speaker 2: listen live on Amazon Alexa from our flagship New York

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Speaker 2: station just Say Alexa playing Bloomberg eleven.

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Speaker 3: Joining us now Gotamcunda Yale University with other parchment along

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Speaker 3: the Charles River as well. He's given us such good

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Speaker 3: help here with the election. I want to talk and

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Speaker 3: this is a fancy technology seminary. Everybody here knows how

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Speaker 3: to use a cell phone a computer.

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Speaker 9: They're back. They're using Fortran here at the boss.

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Speaker 3: Okah, gout them as simple as this is a ludite

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Speaker 3: America where there's a huge in our polarization. There's a

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Speaker 3: huge body of people that just aren't in taking advantage

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Speaker 3: of technology and are almost anti technology.

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Speaker 11: I mean, I think certainly there's a huge anti technology push,

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Speaker 11: but it's striking that some of the most deleite protect

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Speaker 11: people in the world.

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Speaker 3: That's my second question. I got Elon Musk. As part

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Speaker 3: of the new administration, he defines entrepreneurship in technology, and

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Speaker 3: you got it. I got partially a ludeied America.

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Speaker 9: Yeah.

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Speaker 11: So you have sort of this icon of entrepretion technology

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Speaker 11: on the Musk, and you also have RFA Junior, possibly

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Speaker 11: the most anti science person in America, and apparently they're

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Speaker 11: going to be serving in the same administration. So it's

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Speaker 11: quite a contrast. And technology shocks, like China shocks, have

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Speaker 11: had big impacts on the labor market. We know that

441
00:24:22,560 --> 00:24:24,880
Speaker 11: they seem to be affecting people in lots of different ways.

442
00:24:24,920 --> 00:24:28,320
Speaker 11: We've seen the decrease in manufacturing employment at times, and

443
00:24:28,440 --> 00:24:30,320
Speaker 11: all of these things added up to create a level

444
00:24:30,320 --> 00:24:33,639
Speaker 11: of social ferment in this country that we're just starting

445
00:24:33,640 --> 00:24:36,439
Speaker 11: to see the implications of. But the flip side of

446
00:24:36,440 --> 00:24:39,640
Speaker 11: that is a lot of parties that have won landslide

447
00:24:39,760 --> 00:24:42,240
Speaker 11: that will won big election. So this was a decisive

448
00:24:42,280 --> 00:24:44,280
Speaker 11: victory by the Repobulmans, but not a landslide. I didn't

449
00:24:44,280 --> 00:24:45,960
Speaker 11: look anything on like two thousand and eight, for example,

450
00:24:47,520 --> 00:24:50,359
Speaker 11: had interpreted that as gigantic sweeping mandates for all of

451
00:24:50,400 --> 00:24:53,040
Speaker 11: their policies and found out that actually people were voting

452
00:24:53,040 --> 00:24:55,119
Speaker 11: on one issue and that was and in this case

453
00:24:55,200 --> 00:24:59,600
Speaker 11: almost certainly inflation. And they've then sort of leaned into

454
00:24:59,680 --> 00:25:03,760
Speaker 11: the overinterpreted their victory. How much of this anti technology

455
00:25:03,760 --> 00:25:05,640
Speaker 11: thing that you're talking about is a product of people

456
00:25:05,680 --> 00:25:09,840
Speaker 11: starting to overinterpret the victory And we don't know yet,

457
00:25:09,880 --> 00:25:12,960
Speaker 11: but it's pretty striking when we see this, and Paul.

458
00:25:12,840 --> 00:25:15,560
Speaker 3: You see this with the technology reports we see like

459
00:25:15,640 --> 00:25:19,320
Speaker 3: Apple or Microsoft or in the video, it's like two

460
00:25:19,359 --> 00:25:20,280
Speaker 3: planets it is.

461
00:25:20,480 --> 00:25:24,800
Speaker 8: It's just extraordinary. Galtem, I mean, we're these cabinet picture

462
00:25:24,800 --> 00:25:28,880
Speaker 8: coming fast and furious from President elect Trump and his campaign.

463
00:25:29,440 --> 00:25:30,600
Speaker 8: What's your takeaway so far?

464
00:25:30,920 --> 00:25:34,439
Speaker 11: So in the first century AD, the mad Roman emperor

465
00:25:34,480 --> 00:25:39,320
Speaker 11: Caligulam decided to make his horse encinitatis a console of Rome.

466
00:25:40,520 --> 00:25:42,680
Speaker 11: That horse was still a better pick than Matt Gates

467
00:25:42,720 --> 00:25:48,000
Speaker 11: Attorney general. You know, you sort of see Republicans Democrats

468
00:25:48,040 --> 00:25:50,960
Speaker 11: we expect recall, but you can see Republicans recoiling that

469
00:25:51,400 --> 00:25:54,080
Speaker 11: in general. What does not expect that the Attorney general's

470
00:25:54,119 --> 00:25:56,840
Speaker 11: closest contact with the Justice Department before they get the

471
00:25:56,920 --> 00:25:59,960
Speaker 11: job is being investigated by the Justice Department for sexual

472
00:26:00,480 --> 00:26:02,080
Speaker 11: like that. That seems out of the ordinary.

473
00:26:02,200 --> 00:26:04,640
Speaker 8: So it talk to us the role that Congress will

474
00:26:04,680 --> 00:26:09,760
Speaker 8: play in the confirmation process for some of these appointees.

475
00:26:09,560 --> 00:26:12,440
Speaker 11: As large as they choose it to be. I think

476
00:26:12,800 --> 00:26:14,840
Speaker 11: of the set of appointees, they're the sort of the

477
00:26:14,840 --> 00:26:18,479
Speaker 11: normal appointees Burtom, Marco Rubio, who are going to get

478
00:26:18,760 --> 00:26:20,680
Speaker 11: the most Democrats will vote just you know, say thank

479
00:26:20,720 --> 00:26:24,920
Speaker 11: god we got him in fine. Stephanic at the UN

480
00:26:24,960 --> 00:26:27,760
Speaker 11: will probably get something some a little like that. But

481
00:26:27,920 --> 00:26:30,520
Speaker 11: the flip side is clearly Democrats are going to go

482
00:26:30,560 --> 00:26:35,320
Speaker 11: insane over the idea of the sort of Hegseth Tulsie

483
00:26:35,320 --> 00:26:37,280
Speaker 11: Gabbard at d and I that's the one that people

484
00:26:37,280 --> 00:26:39,320
Speaker 11: in the internet, and I'll say in the in the

485
00:26:39,400 --> 00:26:41,720
Speaker 11: in the National security community. People are simply going in

486
00:26:41,960 --> 00:26:44,080
Speaker 11: like that. They don't even know how to process that prospect,

487
00:26:45,600 --> 00:26:48,840
Speaker 11: you know, or if k Junior or at HHS, I don't.

488
00:26:48,840 --> 00:26:51,760
Speaker 11: I think people don't quite realize you. HHS has a

489
00:26:51,960 --> 00:26:55,000
Speaker 11: budget of almost two trillion dollars. So the scale of

490
00:26:55,000 --> 00:26:58,159
Speaker 11: what we're talking about wowing him there is just unimaginable.

491
00:26:59,000 --> 00:27:00,879
Speaker 11: And this is someone who, when he is running for president,

492
00:27:00,960 --> 00:27:02,840
Speaker 11: proposed that one of the things he wanted to do

493
00:27:02,960 --> 00:27:05,919
Speaker 11: was just stop all research and development on you drugs. So,

494
00:27:05,960 --> 00:27:07,840
Speaker 11: I mean, you know, four years no R and D

495
00:27:07,920 --> 00:27:11,800
Speaker 11: in the life sciences. I think people could probably object

496
00:27:11,800 --> 00:27:12,000
Speaker 11: to that.

497
00:27:12,080 --> 00:27:15,919
Speaker 8: So what's the realistic of you in Washington? Just about

498
00:27:16,080 --> 00:27:19,600
Speaker 8: to what extent was some of these Republican senators in

499
00:27:19,640 --> 00:27:23,400
Speaker 8: effect go against their president by blocking some of these appointees.

500
00:27:25,040 --> 00:27:26,840
Speaker 11: I think people are starting to think not that much.

501
00:27:28,440 --> 00:27:31,639
Speaker 11: It wouldn't shock me if Gates doesn't get confirmed, just

502
00:27:31,680 --> 00:27:34,520
Speaker 11: because he's made so many enemies in the Congress that

503
00:27:34,560 --> 00:27:39,000
Speaker 11: they might there'll be a reaction. But the others, I

504
00:27:39,000 --> 00:27:42,160
Speaker 11: think the betting is that the level of patronage that

505
00:27:42,400 --> 00:27:44,440
Speaker 11: Trump has and the level of sway he has over

506
00:27:44,440 --> 00:27:46,919
Speaker 11: the party he ran ahead of all of these people, right,

507
00:27:46,960 --> 00:27:51,520
Speaker 11: he got more votes than most of these people. In Michigan,

508
00:27:51,760 --> 00:27:55,480
Speaker 11: the Democrats held the Senate seat because tens of thousands

509
00:27:55,520 --> 00:27:58,080
Speaker 11: of people came in voted for Donald Trump and left

510
00:27:58,080 --> 00:28:02,840
Speaker 11: the rest of the ballot blank, yep, and so and so.

511
00:28:03,280 --> 00:28:05,320
Speaker 8: Maybe if you're a Trump supporter, I think a lot

512
00:28:05,320 --> 00:28:08,760
Speaker 8: of those folks felt like he's doing exactly what we

513
00:28:08,800 --> 00:28:12,760
Speaker 8: wanted him to do, which is to be unconventional, you know,

514
00:28:12,880 --> 00:28:14,679
Speaker 8: kind of drain the swamp, to use a term from

515
00:28:14,680 --> 00:28:19,159
Speaker 8: the past cycle. Maybe there is public support for this.

516
00:28:19,359 --> 00:28:22,280
Speaker 11: So I think there is certainly a base of Trump

517
00:28:22,280 --> 00:28:24,800
Speaker 11: supporters for whom this is exactly what they wanted, and

518
00:28:24,840 --> 00:28:27,600
Speaker 11: this is this is sort of they're extremely enthusiastic about that.

519
00:28:28,359 --> 00:28:30,879
Speaker 11: But my strong suspicion is that base is not fifty

520
00:28:30,920 --> 00:28:32,399
Speaker 11: percent of the country, and it's not anything close to

521
00:28:32,480 --> 00:28:34,160
Speaker 11: fifty percent of the country, And there are a lot

522
00:28:34,160 --> 00:28:36,800
Speaker 11: of people who probably did not vote to find out that,

523
00:28:37,240 --> 00:28:39,440
Speaker 11: you know, we're not going to be inventing new vaccines

524
00:28:39,480 --> 00:28:42,440
Speaker 11: for the next few years. And note it's not just

525
00:28:42,480 --> 00:28:43,320
Speaker 11: a four year problem.

526
00:28:43,440 --> 00:28:43,520
Speaker 3: Right.

527
00:28:43,560 --> 00:28:46,440
Speaker 11: When you eliminate these capabilities, you can't wave a magic

528
00:28:46,480 --> 00:28:48,960
Speaker 11: want and bring them back. It takes generations to build

529
00:28:49,000 --> 00:28:51,440
Speaker 11: the sort of scientific establishment that we have, and that

530
00:28:51,520 --> 00:28:52,200
Speaker 11: is now at RESK.

531
00:28:52,240 --> 00:28:54,000
Speaker 9: This is going to be in your lectures.

532
00:28:54,280 --> 00:28:57,960
Speaker 3: Yeah, it is Tom Nichols of Naval War College, all

533
00:28:58,000 --> 00:29:01,080
Speaker 3: of his work. Three times in the last two days

534
00:29:01,120 --> 00:29:05,040
Speaker 3: people have sent me Death of Expertise Tom Nichols book

535
00:29:05,040 --> 00:29:08,640
Speaker 3: that I interviewed him four years ago, The Death of Expertise?

536
00:29:09,320 --> 00:29:11,160
Speaker 3: Is it as grim now as it's ever been?

537
00:29:11,560 --> 00:29:14,200
Speaker 11: Tom's great and I would say it might be grimmer

538
00:29:14,400 --> 00:29:17,400
Speaker 11: than even then. I think that even he expected. You know,

539
00:29:17,880 --> 00:29:20,520
Speaker 11: going into the election, you would talk to Democrats and Republicans,

540
00:29:20,880 --> 00:29:23,640
Speaker 11: and Democrats, even the ones, the ones who are really scared,

541
00:29:23,840 --> 00:29:26,520
Speaker 11: would talk about these sorts of appointments and you know,

542
00:29:26,560 --> 00:29:27,600
Speaker 11: you'd say, like, what.

543
00:29:27,600 --> 00:29:28,000
Speaker 9: Did it not do?

544
00:29:28,160 --> 00:29:30,880
Speaker 11: Get the worst case? Yeah, And Republicans would say, well,

545
00:29:30,960 --> 00:29:32,920
Speaker 11: you know, we'll just have a normal Trump, a normal

546
00:29:32,960 --> 00:29:35,160
Speaker 11: Republican administration with a guy who makes sense mean tweets.

547
00:29:35,640 --> 00:29:38,640
Speaker 11: So when you think about the spectrum of where people

548
00:29:38,680 --> 00:29:40,840
Speaker 11: thought they were going to end up, these appointments are

549
00:29:40,880 --> 00:29:44,240
Speaker 11: sort of the worst fears of Democrats, except not even that.

550
00:29:44,320 --> 00:29:47,880
Speaker 11: Nobody was saw that Matt Gate's coming. So yeah, there's

551
00:29:47,920 --> 00:29:52,120
Speaker 11: a profound death of expertise problem. But let's back out

552
00:29:52,120 --> 00:29:54,880
Speaker 11: from that. I think we we all as a society,

553
00:29:55,000 --> 00:29:58,040
Speaker 11: just haven't thought through. Let's back up for a second.

554
00:29:58,200 --> 00:30:00,320
Speaker 11: A lot of this is about new communication technology. We

555
00:30:00,320 --> 00:30:02,440
Speaker 11: talk about social media, things like that which make the

556
00:30:02,440 --> 00:30:04,920
Speaker 11: world more transparent. So we see that the experts were

557
00:30:04,920 --> 00:30:06,520
Speaker 11: never as great as they thought they as we thought

558
00:30:06,560 --> 00:30:08,600
Speaker 11: they were in their failures. That doesn't mean they're useless.

559
00:30:08,640 --> 00:30:13,720
Speaker 11: There's there're convenience stakes. When the printing press was invented, right,

560
00:30:14,600 --> 00:30:16,880
Speaker 11: the big foreign part, the big consequence of that that

561
00:30:17,000 --> 00:30:19,320
Speaker 11: historians go back is they say the Thirty Years War,

562
00:30:19,520 --> 00:30:22,320
Speaker 11: the worst war of europe history, that killed a third

563
00:30:22,320 --> 00:30:26,760
Speaker 11: of the population of Germany, was directly driven by the

564
00:30:26,800 --> 00:30:27,960
Speaker 11: event of the printing We're.

565
00:30:27,800 --> 00:30:30,040
Speaker 3: Going to continue this discussion in New York, as I'm

566
00:30:30,040 --> 00:30:33,640
Speaker 3: sure we will with got him Conda. He's with Yale University.

567
00:30:33,640 --> 00:30:39,960
Speaker 10: Here.

568
00:30:41,040 --> 00:30:45,320
Speaker 2: This is the Bloomberg Surveillance Podcast. Listen live each weekday

569
00:30:45,400 --> 00:30:48,600
Speaker 2: starting at seven am Eastern on applecar Play and Android

570
00:30:48,640 --> 00:30:51,560
Speaker 2: Auto with the Bloomberg Business app. You can also watch

571
00:30:51,640 --> 00:30:54,880
Speaker 2: us live every weekday on YouTube and always on the

572
00:30:54,920 --> 00:30:55,880
Speaker 2: Bloomberg terminal.

573
00:30:56,160 --> 00:31:00,520
Speaker 8: Dearn Morse Conference keynote speaker and professor at UC Berkeley's

574
00:31:00,640 --> 00:31:02,520
Speaker 8: up Business School out there. Thanks so much for joining

575
00:31:02,600 --> 00:31:05,760
Speaker 8: us here. You're a keynote speaker here today. What are

576
00:31:05,760 --> 00:31:06,720
Speaker 8: you going to be talking about.

577
00:31:07,560 --> 00:31:11,240
Speaker 1: I'm gonna be talking about AI and the use in credit.

578
00:31:11,440 --> 00:31:16,000
Speaker 1: So thinking about not just in understanding AI, in terms

579
00:31:16,040 --> 00:31:20,200
Speaker 1: of deploying and getting better, better precise underwriting and.

580
00:31:20,120 --> 00:31:22,240
Speaker 7: Knowing people's risk and this sort of stuff.

581
00:31:22,240 --> 00:31:25,840
Speaker 1: That there's a general landscape of using AI for all

582
00:31:25,920 --> 00:31:29,360
Speaker 1: kinds of provision of lending to people, and then there's

583
00:31:29,360 --> 00:31:32,640
Speaker 1: some red flags, right, and so kind of putting the

584
00:31:32,720 --> 00:31:35,440
Speaker 1: landscape out there, we need to think about both sides.

585
00:31:35,520 --> 00:31:38,520
Speaker 1: We need to inform not just policy makers we're here

586
00:31:38,560 --> 00:31:41,840
Speaker 1: at the FED, but also the providers, right, so they

587
00:31:41,880 --> 00:31:44,400
Speaker 1: can think about things that they need to step up

588
00:31:44,440 --> 00:31:45,440
Speaker 1: and understand.

589
00:31:45,720 --> 00:31:47,360
Speaker 8: We were back last time I was up here in Boston.

590
00:31:47,400 --> 00:31:49,360
Speaker 8: We were doing some work with the Boston Consulting Group

591
00:31:49,360 --> 00:31:51,400
Speaker 8: and they were saying, is they talked to their clients

592
00:31:51,440 --> 00:31:54,840
Speaker 8: across all industries about the use of AI. They want

593
00:31:54,880 --> 00:31:56,800
Speaker 8: to get a commitment that they will do it in

594
00:31:57,400 --> 00:32:00,840
Speaker 8: the correct way, you know, and really, because AI is

595
00:32:00,880 --> 00:32:03,120
Speaker 8: so powerful that you want to make sure that whoever

596
00:32:03,160 --> 00:32:05,440
Speaker 8: they're talking to, that they make a commitment to doing

597
00:32:05,440 --> 00:32:08,000
Speaker 8: it in the right way, the sustainable way. How does

598
00:32:08,040 --> 00:32:09,880
Speaker 8: that apply to finance?

599
00:32:10,440 --> 00:32:10,720
Speaker 7: Right?

600
00:32:10,800 --> 00:32:13,080
Speaker 1: So the problem is there's so many different aspects of

601
00:32:13,080 --> 00:32:14,000
Speaker 1: what correct means.

602
00:32:14,200 --> 00:32:14,400
Speaker 7: Right.

603
00:32:14,480 --> 00:32:18,640
Speaker 1: So if we think about AI, where where it's going

604
00:32:18,680 --> 00:32:21,000
Speaker 1: to be deployed, right, it's not just you know, it's

605
00:32:21,320 --> 00:32:24,120
Speaker 1: understanding credit risk and reaching more of the population in

606
00:32:24,200 --> 00:32:26,760
Speaker 1: ways that biases are hindering.

607
00:32:26,800 --> 00:32:27,480
Speaker 7: So that's great.

608
00:32:27,800 --> 00:32:33,160
Speaker 1: There's also targeting and marketing, there's monitoring, there's chatbots to

609
00:32:33,240 --> 00:32:37,120
Speaker 1: get information, authenticating fraud, right, all these sort of uses.

610
00:32:37,800 --> 00:32:41,040
Speaker 1: Along the other side, though, we have to think about

611
00:32:41,120 --> 00:32:45,120
Speaker 1: things like there's been research that finds it AI teaches

612
00:32:45,160 --> 00:32:50,120
Speaker 1: itself itself how to collude, right, which has all kinds

613
00:32:50,160 --> 00:32:53,400
Speaker 1: of your mind goes crazy when you start thinking about that.

614
00:32:53,480 --> 00:32:56,920
Speaker 1: And then AI can be deceptive and conveyance to get

615
00:32:56,960 --> 00:32:57,960
Speaker 1: an outcome at once.

616
00:32:58,040 --> 00:33:00,640
Speaker 9: So this is a sequel to the matrix too or something.

617
00:33:00,680 --> 00:33:03,880
Speaker 3: I mean, really, that's what reef shows up, right, right,

618
00:33:04,080 --> 00:33:06,520
Speaker 3: Paul can play Reeves. I can't play Reeves. Come on,

619
00:33:06,680 --> 00:33:09,800
Speaker 3: most of the public is scared stiff of this. Let's

620
00:33:09,880 --> 00:33:12,800
Speaker 3: begin with the timeline. Are you looking out to two

621
00:33:12,840 --> 00:33:15,320
Speaker 3: thousand and thirty or are you looking out to two

622
00:33:15,320 --> 00:33:16,560
Speaker 3: thousand and forty.

623
00:33:16,760 --> 00:33:19,040
Speaker 7: Or twenty twenty five? Right?

624
00:33:19,080 --> 00:33:21,440
Speaker 1: I mean AI is already being you Are we ready

625
00:33:21,440 --> 00:33:25,320
Speaker 1: for this? We're We're not ready, nor are the providers ready?

626
00:33:25,400 --> 00:33:25,560
Speaker 9: Right?

627
00:33:25,600 --> 00:33:29,440
Speaker 1: The providers themselves are concerned about the You know, there's

628
00:33:29,440 --> 00:33:32,320
Speaker 1: startups left and right right, and whether the startup's doing

629
00:33:32,360 --> 00:33:34,960
Speaker 1: and what inputs are we using? Do we let AI

630
00:33:35,440 --> 00:33:39,240
Speaker 1: go on all the data that's available about someone to profile?

631
00:33:39,280 --> 00:33:39,440
Speaker 9: Heay?

632
00:33:39,480 --> 00:33:41,440
Speaker 3: But do you flying to Park Avenue and you're gonna

633
00:33:41,440 --> 00:33:43,800
Speaker 3: talk to James Diamond and his team at JP Morgan.

634
00:33:43,920 --> 00:33:45,360
Speaker 9: You're gonna bring Keen Green.

635
00:33:45,640 --> 00:33:48,479
Speaker 3: Of Berkeley along just to impress everybody, and they're going

636
00:33:48,520 --> 00:33:51,760
Speaker 3: to go we need to trust this process. What's the

637
00:33:51,800 --> 00:33:56,080
Speaker 3: trust factor at this conference in Boston? To people trust AI?

638
00:33:56,680 --> 00:33:59,840
Speaker 1: I think I don't. I don't know the answer to

639
00:33:59,840 --> 00:34:01,680
Speaker 1: do they trust? I don't think they know one way

640
00:34:01,760 --> 00:34:04,320
Speaker 1: or the other. I think where we are is that

641
00:34:05,000 --> 00:34:09,120
Speaker 1: right now? If we understand what inputs AI is using,

642
00:34:09,160 --> 00:34:11,480
Speaker 1: so you can control that right where where it's going,

643
00:34:11,640 --> 00:34:16,200
Speaker 1: what information it's using to make decisions. If you control

644
00:34:16,320 --> 00:34:19,560
Speaker 1: the inputs, you're able to put a bound on what

645
00:34:19,680 --> 00:34:22,120
Speaker 1: it can do. Right, You can tell it not to lie,

646
00:34:22,200 --> 00:34:24,680
Speaker 1: you can tell it only to use these days these

647
00:34:24,680 --> 00:34:29,280
Speaker 1: certain inputs that are non discriminatory and other things. Right now,

648
00:34:29,360 --> 00:34:34,040
Speaker 1: what the how that input use is getting deployed is

649
00:34:34,040 --> 00:34:34,800
Speaker 1: a black box?

650
00:34:35,840 --> 00:34:41,200
Speaker 8: How from your research talking to providers of credit, how

651
00:34:41,239 --> 00:34:43,279
Speaker 8: are they using AI yet? Are they using AI? Or

652
00:34:43,320 --> 00:34:44,840
Speaker 8: is it still the loan officer that I got to

653
00:34:45,000 --> 00:34:46,320
Speaker 8: convince I'm a good credit.

654
00:34:46,440 --> 00:34:46,600
Speaker 7: No?

655
00:34:46,600 --> 00:34:49,120
Speaker 1: No, definitely they are using AI, right, I mean from

656
00:34:49,320 --> 00:34:52,400
Speaker 1: the you know, the simple way the chatbots right getting

657
00:34:52,400 --> 00:34:56,080
Speaker 1: information in right that that is, those are AI driven,

658
00:34:56,160 --> 00:35:02,680
Speaker 1: but also the processing and understanding under general parameters right

659
00:35:02,800 --> 00:35:06,320
Speaker 1: under you know, how would you maximize for for profitably

660
00:35:06,440 --> 00:35:11,000
Speaker 1: lending or how would you for profitably marketing new new.

661
00:35:10,880 --> 00:35:11,920
Speaker 7: Products to people?

662
00:35:12,239 --> 00:35:12,439
Speaker 8: Right?

663
00:35:12,480 --> 00:35:16,920
Speaker 1: They are using there? Are they deploying underwriting at a

664
00:35:16,920 --> 00:35:18,080
Speaker 1: full scale using AI?

665
00:35:18,440 --> 00:35:18,520
Speaker 8: No?

666
00:35:18,719 --> 00:35:21,160
Speaker 7: Probably not. There are some exceptions to that.

667
00:35:21,680 --> 00:35:24,839
Speaker 1: But but we're using it little bits here and there,

668
00:35:24,960 --> 00:35:27,920
Speaker 1: and it pretty soon the whole arena changes.

669
00:35:28,280 --> 00:35:33,040
Speaker 3: Is America behind on this or are we leading the way? Ah?

670
00:35:33,360 --> 00:35:35,920
Speaker 7: Probably leading? Leading?

671
00:35:36,000 --> 00:35:39,160
Speaker 1: Is not you know the the there are other countries

672
00:35:39,239 --> 00:35:41,480
Speaker 1: that are also deploying.

673
00:35:41,680 --> 00:35:42,680
Speaker 7: China's deploying.

674
00:35:43,080 --> 00:35:46,200
Speaker 1: There's there's a whole movement in Europe and a law

675
00:35:46,239 --> 00:35:49,000
Speaker 1: in Europe regarding some of these things, and so it's

676
00:35:49,719 --> 00:35:52,800
Speaker 1: we are ahead, but we are with others in that

677
00:35:52,800 --> 00:35:53,239
Speaker 1: that lead.

678
00:35:53,520 --> 00:35:55,600
Speaker 3: So Paul I got a credit rating of one hundred,

679
00:35:55,640 --> 00:35:58,480
Speaker 3: it's like so low. You know, they don't even talk

680
00:35:58,520 --> 00:36:00,799
Speaker 3: to me and I get email all the time. We'll

681
00:36:00,800 --> 00:36:03,200
Speaker 3: give you forty five thousand, We'll give you ten thousand.

682
00:36:03,560 --> 00:36:05,440
Speaker 3: Is that AI driven? Is it your fault?

683
00:36:06,880 --> 00:36:08,520
Speaker 7: I don't know the answer to that question.

684
00:36:08,680 --> 00:36:11,359
Speaker 1: But but that's where we are, right, that's where we

685
00:36:11,840 --> 00:36:15,040
Speaker 1: you know, there there are people that are you know,

686
00:36:15,080 --> 00:36:17,600
Speaker 1: they have credits course that maybe those credits course are

687
00:36:17,640 --> 00:36:21,239
Speaker 1: not right, and maybe AI helps right and and so,

688
00:36:21,440 --> 00:36:23,200
Speaker 1: and then there are other people that you know.

689
00:36:23,719 --> 00:36:27,080
Speaker 8: Well, here's my concern is that small community banks, they're

690
00:36:27,120 --> 00:36:29,360
Speaker 8: not going to be able to make the technology investments.

691
00:36:29,440 --> 00:36:31,880
Speaker 8: Therefore that whatever benefits may be out there, and I

692
00:36:31,880 --> 00:36:33,520
Speaker 8: don't know if there are, bye by the way, you

693
00:36:33,520 --> 00:36:36,120
Speaker 8: have to convince me. I'm afraid that they're not going

694
00:36:36,160 --> 00:36:38,480
Speaker 8: to have the same capabilities to say a JP Morgan Chase.

695
00:36:38,920 --> 00:36:41,799
Speaker 1: So I'm I'm a big fan of having a community

696
00:36:41,840 --> 00:36:45,759
Speaker 1: financial architecture. Banks, the lenders the CDFI's right, and the

697
00:36:45,840 --> 00:36:50,160
Speaker 1: reason it's so important is because in downturns, these the

698
00:36:50,160 --> 00:36:55,560
Speaker 1: research shows that these these local community facing lenders and banks,

699
00:36:55,920 --> 00:36:59,719
Speaker 1: they they're able to stick with their customers and when

700
00:36:59,760 --> 00:37:03,920
Speaker 1: they them the most. So so far, technology has not

701
00:37:04,040 --> 00:37:08,080
Speaker 1: replicated that. Now, whether AI becomes a localized lender because

702
00:37:08,120 --> 00:37:10,680
Speaker 1: its ability to act local is a question.

703
00:37:10,520 --> 00:37:11,080
Speaker 7: We don't know.

704
00:37:12,120 --> 00:37:15,160
Speaker 1: And how what does that mean for the financial architecture

705
00:37:15,160 --> 00:37:17,080
Speaker 1: of the United States is a complete unknown.

706
00:37:17,360 --> 00:37:18,480
Speaker 8: So I mean, at the end of the day, if

707
00:37:18,480 --> 00:37:21,600
Speaker 8: I'm providing credit, I want to just I want a

708
00:37:21,640 --> 00:37:24,239
Speaker 8: good credit environment where I get paid back, where I'm

709
00:37:24,280 --> 00:37:28,000
Speaker 8: able to diverse, you know, distribute you know, debt into

710
00:37:28,040 --> 00:37:30,200
Speaker 8: my marketplace. But I want to get paid back. I

711
00:37:30,239 --> 00:37:33,520
Speaker 8: don't want to take undw credit risk. Ideally AI will

712
00:37:33,560 --> 00:37:34,600
Speaker 8: help me do that better.

713
00:37:35,239 --> 00:37:38,120
Speaker 1: Ideally it will help you do that better, and also

714
00:37:37,920 --> 00:37:41,200
Speaker 1: to manage new products and new services for customers to

715
00:37:41,239 --> 00:37:44,719
Speaker 1: figure out where they might be exposed to risk or

716
00:37:44,760 --> 00:37:47,320
Speaker 1: where they might be evolving in their business or household.

717
00:37:47,560 --> 00:37:50,960
Speaker 3: Thirty second question, We don't enough time. It's unfair Type

718
00:37:50,960 --> 00:37:55,000
Speaker 3: one Type two construct. Is AI in banking going to

719
00:37:55,080 --> 00:37:57,680
Speaker 3: help banks or help them not lose money.

720
00:37:58,600 --> 00:38:00,799
Speaker 7: It's going to help them grow their business.

721
00:38:01,640 --> 00:38:03,160
Speaker 8: That's kind of what they want to that's what they

722
00:38:03,160 --> 00:38:04,560
Speaker 8: want to hear at. Dear Morse, thank you so much

723
00:38:04,640 --> 00:38:05,200
Speaker 8: for joining us.

724
00:38:05,440 --> 00:38:05,960
Speaker 5: A deare Moorese.

725
00:38:05,960 --> 00:38:09,640
Speaker 8: She's professor of finance at the University of California at Berkeley.

726
00:38:10,000 --> 00:38:14,440
Speaker 2: This is the Bloomberg Surveillance podcast, available on Apple, Spotify,

727
00:38:14,600 --> 00:38:18,720
Speaker 2: and anywhere else you get your podcasts. Listen live each weekday,

728
00:38:18,840 --> 00:38:21,880
Speaker 2: seven to ten am Eastern on Bloomberg dot com, the

729
00:38:22,000 --> 00:38:25,759
Speaker 2: iHeartRadio app, tune In, and the Bloomberg Business app. You

730
00:38:25,840 --> 00:38:29,120
Speaker 2: can also watch us live every weekday on YouTube and

731
00:38:29,280 --> 00:38:30,840
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